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ILCYM 2.5 USER MANUAL

1. riv BSR SCoOPRLAQROEL i ILCYM s Projects Explorer 23 Gd dem 23 20 U metadata lt 2 Palette b projectRegistry ac LE project udig 4 d Ee U PTM project Info ul Hi Mosaic Info i Info s Distance 81 4I4 a Dem Shape BB Dem_Reclass m dem cortad E m Eri reclass world adm00 BI ERI dem terrain aspect a ERI Qzeom 11 Wildca cunit Selection B Version f Ilcym borrar Phenologynew bem_Reclass asc B Version f Ileym borrar Phenolaogynew Dem_Shape_ shp ILCYN File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help Fir Di RQ SCOSORXKAQRHE 11 ILCYM s Projects Explorer 23 m i Information Table 53 fal ill metadata P Palette p aR AlN projectRegistry IQ C w search A project udig Son Bir Ey A m LE PTM project Info Features i Info FID SP ID value un Disbwice dem poly 3 1 1 10 Layers X F 4 S AR TH dem poly 3 2 2 20 ZC dem poly 3 dem poly 3 3 3 3 0 mask dem poly 3 4 4 40 A WeatherStations dem poly 3 5 5 5 0 m dem FB Dem Reclass m dem cortad m Eri reclass rd world adm00 Nm ERI m dem terrain aspect Da ERI _ Wildca c unit n 75 6734 11 923 ILCYM 3 0 User Ma
2. eg ET in fi gU Tua l e 7 Janish 2 r T Wang Lan Ding r T k Stinner 3 C k k T r T 1 12e Ty ith ToT Stinner 4 Logan 3 Logan 4 j Ht I k e Uus 20 4 1 ET T T nin ILCYM 3 0 User Manual Y UY UI UA e h UI gt UI UY Kontodimas 2004 Kontodimas 2004 Kontodimas 2004 Ratkowsky et al 1982 Tanigoshi and Browne 2004 Wang et al 1982 160 Hilber amp logan 2 Hilber amp logan 3 Taylor Lactin 3 Sigmoid or Logistic T temperature in Degree Celcius r T development rate at temperature 7 R 1987 cal degree 1 mol 1 ILCYM 3 0 User Manual 161 Table 3 Sub models fitted to mortality in ILCYM software Linear root m T c b T al JT NES Negative Linear root Quadratic negative exponent Linear negative exponent 3P 3P 3P 3P 3P 3P T gt 0 4P 4P 4P 4P 4P Gaussian denominator Gaussian Simple gaussian Gaussian 1 with log Polynomial d ZoT R model 1 dez gt T gt 0 Polynomial model 2 N 3P m poi poi O ILCYM 3 0 User Manual 162 13 Polynomial b model 3 1 b AC eb T b T Polynomial model 5 Polynomial model 6 Polynomial model 7 ar 40 l b e Polynomial model 8 Polynomial US Polynomial 4 i ey odd 10 m T ea Taer d Z gt TER dEZz gt T gt 0 21 Polynomial model 11 Polynomial SP model 13 AZ REN T gt
3. ii Output graph Click on view graphic button to plot the population development and growth curves As a result of this operation the figure below displays the evolution of number of individuals for each life stage over time The simulation started with 100 eggs Over time these eggs passed through different life stages or dies When the population contains adult female in this example first females emerges after about 80 days new eggs are added to the population through ILCYM 3 0 User Manual 80 oviposition reproduction First generations can be differentiated by the waves described by lines however with each new generation the overlap of generations increases Once the population s structure stabilized the figure would show straight lines for each life stages Deterministic Simulation Output m Life Table Graphics Age specific survival Individuals y Larva A Pupa Female Expon Female 0 50 100 150 200 250 300 350 400 Age days iv Modifying the scale of the graphs The user could right click on the image and the following window will appear Image Properties Chart Title bge specific survival Chart X Age days Chart Y Individuals Legend Leg X Leg Y Here the user can modify the legend s coordinates scales and the graph title ILCYM 3 0 User Manual 81 As in stochastic simulation this simulation can also be conducted at several constant temperatures at
4. Gamma 06 lt Defaults j Revert Apply Import Export Cancel l OK Opacity allows the transparency of an image to be set often useful to allow artificial boundaries to show through the raster Scale Control the scale at which the raster is shown RGB Channel Selection Provides control over mapping raster channels to Red Green and Blue channels for display The gamma of each band can be controlled allowing you to adjust how much of a contribution each band makes to the final display Band Allow the selection of a data band Most processed images are already defied in terms of Red Green and Blue If you are working with raw satellite information you will need to carefully select the correct radar visual light or infrared band for the analysis being performed Gamma Allows fine grain control over the contribution being made 0 1 Multiplies the contribution brightening the channel accordingly 1 0 Direct 1 to 1 ratio 1 Minimises the contribution dimming the channel accordingly ILCYM 3 0 User Manual 131 e Single Band Rasters Used to handle single band rasters such as digital elevation models where you can map value ranges to artificial type filter text he v Single Band Rasters Cache Simple Raster Single Band Rasters 100 0 920 0 an MI 9200 19400 1940 0 o EE 20600 3080 MA 20800 WER 50000 alpha i y e XML This page
5. 3 1 2 Importing project If you have created a project and you want to display or work on it in another computer the complete project should be imported into the ILCYM workspace ILCYM 3 0 User Manual 30 This is because when a project is created the file paths are saved and should be updated when transferring the project in a new computer To import the project right click in Project Explorer view and click on Import existing project into workspace ILCYM File Edit Operations Layer Model Builder window H 2 2 BL BN Deb be ILCYM s Projects Explorer 3 u Upload Data Import existing project into workspace Refresh Delete Properties Import existing project into workspace A window will appear click on Browse button to look for the project to import and then click on mport button 3 1 3 Deleting project During project creation if the user has forgotten some life stages and desire to delete the project and create a new project s he must right click on the project and select Delete option ILCYM 3 0 User Manual 31 f ILCYM File Edit Operations Laver Model Builder Window Help fm 2 Hb BW DER bien iL ILCYM s Projects Ex 8 O e E IS SOPiEE Upload ata Import existing project into workspace Refresh Delete Properties 3 1 4 Project Properties To view a project summarize of life stage path and rate just right click on the project
6. A software package for developing temperature based insect phenology models with applications to regional and global analysis of insect population and mapping Henri E Z Tonnang Henry S Juarez Pablo Carhuapoma Juan C Gonzales Diego Medoza Marc Sporleder Reinhard Simon J rgen Kroschel ILCYM 3 0 User Manual Insect Life Cycle Modeling ILCYM Version 3 0 A software package for developing temperature based insect phenology models with applications for local regional and global analysis of insect population and mapping ILCYM Version 3 0 International Potato Center CIP 2013 Integrated Crop Management Division Agroecology IPM ISBN 978 92 9060 380 1 CIP publications contribute important development information to the public arena Readers are encouraged to quote or reproduce material from them in their own publications As copyright holder CIP requests acknowledgement and a copy of the publication where the citation or material appears Please send a copy to the Communication and Public Awareness Department at the address below International Potato Center 2013 La Molina Ave 1895 La Molina Apartado 1558 Lima 12 Peru cip cqgiar org e www cipotato org Correct citation Tonnang E Z H Juarez H Carhuapoma P Gonzales J C Mendoza D Sporleder M Simon R Kroschel J 2013 ILCYM Insect Life Cycle Modeling A software package for developing temperature based insect phenology models with appl
7. Cancel ILCYM 3 0 User Manual 97 Biological parameters of several generations at fluctuating temperatures This analysis allow you to visualize the host population after several generation within a time frame ar Biological Parameters of several generations at fluctuating temperature lolx Biological Parameters of several generations at fluctuating temperature Simulate two species Temperature file Load temps C Documents and Settings Henri Desktop Daily new Hyo008s txt View file Minimum temperature min z Maximum temperature 2 m Number female parasitoids 15 Host number 100 Done Age specific survival 1st year Egg Larva Pupa Female Expon Female Individuals 0510 20 a u 2 a 70 a 2 a 110 120 1430 40 150 160 m 180 190 20 210 20 20 240 20 260 20 a 20 300 310 30 30 340 230 xo 3 Age days Finish Cancel ILCYM 3 0 User Manual 98 3 3 Potential Population Distribution and Mapping Populations are spatially simulated through grid based within a defined area according to grid specific daily temperatures interpolated from available databases If the study insect is a pest the tool can plot indices based on simulation results for visualizing the establishment risk the spread and damage potential of that pest species on a map 3 3 1 Climate data ILCYM can simulate maps at different resolution p e 10 minutes which is equivalent of 18 x 18 Km
8. Development Rate Stage Egg Model Sharpe de Michelle 10 Parameters p 0 77 T1 297 9 Ha 5276 7 Hl 30462 9 Formula y p x 298 16 exp Ha 1 987 1 2 Stage Larva Model Sharpe de Michelle 10 Parameters p 0 32 T1 303 32 Ha 6126 33 H1 24557 73 v gt ILCYM 3 0 User Manual 64 Note It required once you complete the development of a complete phenology model for a your species click on summary to cross check that all life stages of your insect were well saved and are included in the summary file Additionally verify that the name number and the mathematical expression of the equation that you have accepted when clicking on finish bottom is identical to what was saved for each life stage If these details are not made you may not be able to proceed to model validation and simulations 3 1 9 Compiling project Click on Compile simulation button the project will be compiled all functions and parameters will be organized in a special format to be read in R and used for simulations This process takes few seconds and when it is finished the progress windows will disappears and 2 files will be created PhenologySims Rdata and PhenologySims r inside the project folder in workspace These files will be used for subsequent analysis in ILCYM ud File Edit View Favorites Tools Help Q Back 4 2 ya Search Er Folders ies Address C ILCYM 2 1 workspace PTM project File and Folder Task
9. The release of natural enemies parasitoids against pests hosts is common in several integrated pest management systems and the basic precept of such practice is that parasitoid will contribute to reduce and stabilize the pest host population density The proposed simulation can assist with the interpretation of the potential parasitoid efficiency in reducing and or stabilizing the host density for utilization in classical biological control Such analysis can also assist in estimating the number of parasitoids that can be release for classical biological control purpose af Several generation at constant or fluctuating temperature E ni xj Several generation at constant or fluctuating temperature Simulate two species Temperature file Load temps C Documents and Settings Henri Desktop Daily new Hyo008 txt View file Minimum temperature min E Maximum temperature max El Number females parasitoids 20 Host number so Done Age specific survival One year Host IV Eag v Larva v Pupa V Female v Male Individuals Parasitoid V Eag VW Larva v Pupa v Female V Male 10 20 30 w 50 60 70 a 90 100 110 120 130 0 1580 160 170 180 190 200 210 20 20 240 250 a 20 a 20 W 3100 m 3x0 340 30 30 Age days ILCYM 3 0 User Manual 94 Biological parameters of one generation at constant temperature The biological parameters are referred to the life table parameters Below is a
10. how these data should be used Therefore ILCYM allows the use of different types of experimental data as input information for developing a pest phenology model however the data should allow modeling of the species whole life cycle and should be arranged in a manner to meet certain criteria employed within the software Data collection and its arrangement to be used as input data in ILCYM is the topic of this section 1 4 Life table data Studying insect population ecology is often based on life table studies A life table is conducted by following a population of n x individuals from its birth up to the birth of all progeny of these individuals Events like death or reproduction are monitored in equal time interval hours days years etc depending on the organism under study This methodology is used for populations of many organisms including humans and other animal populations to describe the life ILCYM 3 0 User Manual 9 expectancy of individuals life insurance companies use this to estimate the probability of death of a person of certain age and their reproduction capacity Specific statistics were developed to calculated life table parameters describing the population growth according to the Malthusian law of population increase Life table analysis is broadly employed in studying populations however since the life cycle is more complicate in insects due to different immature life stages than in other animals se
11. 197 213 Govindasamy B P B Duffy and J Coquard 2003 High resolution simulations of global climate part 2 effects of increased greenhouse cases Climate Dynamics 21 391 404 Hijmans R J S E Cameron J L Parra P G Jones and A Jarvis 2005 Very high resolution interpolated climate surfaces for global land areas International Journal of Climatology 25 1965 1978 Hilbert D W and J A Logan 1983 Non linear models and temperature dependent development in arthropods a reply to Dr Jerome A Onsager Environmental Entomology 12 4 Ikemoto T 2005 Intrinsic optimum temperature for development of insects and mites Environmental Entomology 34 1377 1387 IPCC 2007a Climate Change 2007 Impacts Adaptation and Vulnerability Contribution of Working Group Il to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ntergovernmental Panel on Climate Change Cambridge University Press Cambridge UK IPCC 2007b Fourth assessment report AR4 Climate change 2007 Syntesis report pp 104 ntergovernmental Panel on Climate Change Geneva Switwerland Janisch E 1932 The influence of temperature on the life history of insects Trans Entomol Soc Lond 80 137 168 Jarvis C H and R H A Baker 2001a Risk assessment for nonindigenous pests 2 Accounting for interyear climate variability Diversity and Distributions 7 237 248 Jarvis C H and R H A Baker 2001b Ris
12. 30 30 30 30 30 30 30 30 30 30 30 30 30 Lo cO Ch 0 s Lo Ri je Chi square test for a fixed rate of oviposition X2 P value 1 81 50781 0 Chi square test for a adjusted rate of oviposition Using model 1 HZ P value Post Oviposition Selected Project Copidosoma koehleri 1 2 1 4 Female rate oviposition normalized 1 0 00 02 04 06 08 Normalized age ILCYM 3 0 User Manual 61 This is a nonlinear model used to describe the relationship between the cumulative proportions of P operculella eggs and parasitized per female and normalized female age Post Oviposition a m E Selected Project Copidosoma koehleri Oviposition ratio 1 5 Normalized age Post Oviposition Selected Project Copidosoma koehleri considering males females Paratization eggs day fernale age days ILCYM 3 0 User Manual 62 Post Oviposition Selected Project Copidosoma koehleri Quality control output O Quality control oviposition rate normalized Oviposition ratio Paratization considering females Reproduction females female female age days Markers observed data means solid lines fitted models Note ILCYM only provide a visual display of post oviposition analysis These outputs are not included on the overall phenology of the species under investigation 3 1 7 Project progress To know about the progress of yo
13. At this stage you might finalize the report about your model developed 9 Employ the model for your purposes You might apply the new model for pest risk mapping which is the third module of ILCYM i e produce maps indicating spatially the potential population growth of a particular pest within a region of interest Bear in mind that the data collection might take a long time At cold temperatures development times of test individuals might be quite long the whole lifecycle might last more than one year In such conditions the cohort approach for collecting the data might be better than establishing a life table where a whole life cycle of one generation need to be monitored As a role developing an IPhM should not take longer than one year 1 2 The conceptual basis of ILCYM Phenology models predict time of events in an organism s development Development of many organisms that cannot internally regulate their own ILCYM 3 0 User Manual 7 temperature poikilothermic organisms ectothermic organisms is dependent on temperatures to which they are exposed in the environment Plants and invertebrates including insects and nematodes require a certain amount of heat to develop from one point in their life cycle to another e g from eggs to larvae Because of yearly variations in weather calendar dates are not a good basis for making management decisions Measuring the amount of heat accumulated over time provides a physiological time
14. Davidson r T 2P 1 bT e Davidson UY 0 1942 1944 ILCYM 3 0 User Manual 158 Oo Oo O2 Uo N UI LR UY UA W W W W O oo ON A O BS p lt Angilletta Jr Anlytis 2 Anlytis 3 Allahyar Anlytis 1 ILCYM 3 0 User Manual k kT T MaxDev Rate e 1 por T T oo T T D T T r T P l 0 T T rT P 0 6 l 6 max min r T a T T_T T rT P 1 0 6 1 r T a T TT TY 1 r T a T T XT T T lt T ET mim T u bu imb T T max min ne Z TER mE Z gt TER n Z gt T gt Tmin m Z T lt Tmx ne Z TER mE Z gt TER n Z gt T gt Tmin m Z T lt Tmx 5P ne Z TER mE Z gt TER n Z T gt Tmin m Z T lt Tmx 5P ne Z TER mE Z gt TER n Z gt T gt Tmin m Z gt T gt Tmin Pradham 1945 Angilletta Jr 2006 Stinner 1974 Hilbert and Logan 1983 Lactin et al 1995 Analysis 1977 Analysis 1980 Analysis 1977 Allahyar 2005 Briere et al 1999 Briere et al 1999 159 2 A UI N Em A ON DO A 48 A O Nn 0 uA 52 UA W Kontodimas 1 r T a T T T T 2 KE 4 r T Kontodimas 2 ME To re Kontodimas 3 Ratkowsky 2 nT a r T Ke J 2 K T T r T D e K T T 1 e i Janish 1 2C g C709 y ps Tanigoshi r T a aT aT ar
15. DeMichele 3 Sharpe amp DeMichele 4 Sharpe amp DeMichele 5 Sharpe amp DeMichele 6 Sharpe amp DeMichele 7 ILCYM 3 0 User Manual Comment Reference Sharpe and DeMichele 1977 u 156 ge ga N gr UA UA UI Y Sharpe amp DeMichele 8 A as Sharpe amp DeMichele 9 A ga Sharpe amp DeMichele 10 A ge Sporleder er al 2004 u Sharpe amp DeMichele 11 Sharpe amp DeMichele 12 Sharpe amp DeMichele 13 A ga Sharpe amp DeMichele 14 A y ILCYM 3 0 User Manual 157 Dallwits and Higgins r T b T e T gt Tmax 5 Deva 1 Y r T 0 T Tmax 1992 Dallwits and Higgins 1992 4P Longan 1976 Longan 1976 Briere et al 1999 d Z TER Briere et al d Z T lt Tmax 1999 1 ON Deva 2 a 1l1028b 4072In e B a i E 2 0 28b 0 72In 1 b 1 1 5b 0 39b 17 Logan 1 18 Y Logan 2 Nn Nn N Y 19 Briere 1 20 Briere 2 Stinner et al 1974 21 A ye Stinner 1 Hilber and logan N N A Be Hilber amp logan 1 Lactin et al bo UI A ze Lactin 1 24 Linear rT a b T 2P Exponential 2 simple HT b e 26 Tb model Exponential model r T sy gr NO 2P 4P 3P 2P 28 Exponential r T e T Tw 1 29 Ratkowsky 1 r T b T T y op m etal k
16. Development gt LA Rate and temperature effect Oviposition gt Time and its variation gt Exponential Models ee KE Dichotomic models Once an option is chosen the user is requested to the select the insect life stage and the analysis will start automatically and this will provide a statistical outputs a mathematical expression of your selected distribution function and a figure showing the data points entered in the analysis and the resulting development frequency curves for each temperature After the calculation this window will appears to proceed click Ok button if you agree to the selected function or Cancel and select another function based on the model selection criteria Y Development Time Do you want to save the model Probit For Egg Cancel If the user wants to change a model already selected just click on Reset button and click the life stage for reevaluation ILCYM 3 0 User Manual 43 HE Development Time using dichotomic models fo S Es Project PTM project Life stages 9 Egg O Larva O Pupa Female Male Binary models Logit Probit Cloglog Polyembryony 1 Apply Reset model x PY 1 Y Yy ESTIMATION OF PARAMETERS diis bLA eta Family binomial Link function logit Estimate Std Error z value Pr zl Temperature10 6 112 732 1 641 68 683 1e 04 Temperaturel5 87 707 1 280 68 512 le 04 Temperaturel6 1 77 726 1 133 68 572 1e 04 TU Temperature20 3 64 395
17. Simulation and Analysis of mapping population To access the outlook of a perspective go to menu Window Open Perspective and Select the perspective you want to use File Edit Operations Layer Model Builder Window Help oc O m New Window 3 a La Es 3 l AT iso lA Open Perspective k 77 Model Builder ILCYM s Projects Explorer pj Show View FA Population analysis amp mapping Reset Perspective H validation and Simulations Close Perspective Clase All Perspectives Other Freferences 3 1 Model Builder mur AEX File Edit Operations Layer Model Builder Window Help i AEDEM Bla la ID o I o Post oviposition vR ILCYM s Projects Explorer 22 H F ILCYM s model builder is a complete modeling interface that helps the software users to develop insect phenology model IPhM Some of its key features include e The wizard that automates the creation of new life stage processes or the editing of existing processes e The property sheets that let the user to quickly modify the properties of input data sub model and produce the overall phenology model e The model window where user build and save the developed models ILCYM 3 0 User Manual 24 e Layout tool that help the user to neatly arrange the IPhM e The entire IPhM is saved in HTML file to enable user to easily share or export for reports and publications write up The model builder in ILCYM helps the user to build mana
18. Temperatures Repetitions 10 15 20 25 30 55555 Estimate J Existing data Browse Plot points Save data Models aL Lambda Dt View Results Status Prablems e New data select for simulating life table parameters at constant temperatures then fit the points with curve e WN Insect number of insect to be simulated e Days number of days for the simulation usually 365 representing one year ILCYM 3 0 User Manual 73 Temperatures text box here the user must enter the temperatures separated by comma for each temperatures corresponds a number of repetitions Repetitions text box for inputting the number of repetitions separated by comma Estimate button this button runs the simulation Existing data option select this option to conduct simulation with existing data Browse button use this button to check for data simulated Plot points button for plotting data points recently simulated or the data loaded Models list list different type of model for fitting life table parameters simulated points Save button save the data View results button display the results i Displaying life table parameters The window below will appears when you click on plot point s button Parameters LJ eg ILCYM 3 0 User Manual 74 ii Fitting life table parameters to non linear functions The following window appears when the
19. They should be in meters when the coordinate reference system CRS is longitude latitude B Version flleym Climate bEM dem asc Terrain option Slope Output raster BA WersionOfllcymi borrar 5lope asc Aspect is measured in degrees similar to a compass bearing clockwise from magnetic north A surface with O degrees aspect would represent a north direction an east facing slope would be 90 degrees a south facing slope would be 180 degrees and a west facing slope would be 270 degrees The aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors Aspect can be thought of as the slope direction The values of the output raster will be the compass direction of the aspect The slope identifies the gradient or rate of maximum change in z value from each cell of a raster surface i Displaying terrain faces by aspect S ILCYM x File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help F3 b ar qe e Pe a A 9 d i ILCYM s Projects Explorer 22 O dem 22 metadata AQUINO ee AN TAS MEUM ONE v IT y 5 Palette b projectRegistry ES TT b N 1i N RN ben Bec a Rh ia tcl e i project udig MAT 2 j AS I NIU Ez V NTE 21 j 2 gt u PTM project iN XA RS NS 4 SUED j i ea y Info NA Nye Ar CA eS q Selection URL VS USE 4 2 NICA Y E 4 a y A BB dem terrain aspect EFE AZoom 1 9 Wildca c uni
20. inductive modelling approach has made considerable advances and a great number of computer programs including BioMOD GARP HABITAT etc have been developed reviewed by Venette et al 2010 This modelling approach showed advantages where detailed information about insect species is not available however critical limitations are the failure to consider the species biological characteristics in the modelling framework Venette et al 2010 Hence resulting risk maps may inform about potential establishment but they do not provide information on the species population growth and damage potential or temporal population change within a cropping season or year in a given region By contrast the deductive approach uses a process based climatic response model i e phenology model for a particular insect species of interest Phenology models are analytical tools for the evaluation understanding and prediction of the dynamics of insect populations in ecosystems under a variety of environmental conditions and management practices and more recently they are also being used in phytosanitary risk assessments Baker 1991 Jarvis and Baker 2001a b Ihe development of insects as in other ectothermic organisms depends on the ambient temperature This temperature dependency can be applied in a process oriented framework forecasting the potential distribution of insect species is completely independent of observed occurrences and this approach
21. t 02 T 14 799 46 731 6 ambda 082 0 008 7 t 846 t 9 144 0 1200 e ceo Development time days Simulated Observed P Egg 6 192 0 073 6 670 0 0000 Larva 22 887 t 54 952 0 372 o I ae Pupa 6 X 0 346 11 818 0 0000 T 3 a Mortality 2 Simulated Percent P Egg 155 0 059 12 0156 Larva 353 0 075 0 296 0040 u 145 E 072 0 113 528 Pupa 2 2 2 Fitting dicator fo a state Euclidian Dist Egg 39 17509 Larva 63 92916 Pupa 24 ee H Age days Male 21 46800 The dots are experimental results and the lines are phenology model outputs ILCYM 3 0 User Manual 78 3 2 3 Deterministic simulation The deterministic simulation simulates population using a rate summation and cohort up dating approach throughout a long term period one or more years with multiple overlapping generations for a specific location based on minimum and maximum daily temperatures and visually output the potential population increase At the present stage of ILCYM development this simulation considers only the growth process as an unbounded process in which the population grows without limit if uncontrolled Such simulation can be conducted under constant or fluctuating temperature within a period of a generation one year and several years f ILCYM File Edit Species Interaction Window Help Fir Om m Deterministic Constant Temperature 2L ILC M s P ES EMI stochastic Fluctuating Temperature 00 me
22. the table below will be display This new table contains the same information as the input parasitation table however with an added column displaying median actual oviposition time The values of median development time were internally estimated Parasitation Table x Parasited number Of 2010924 11 40 7527241 6 91568326 6 0445163451 140 360026 0 7 605018161 ILCYM 3 0 User Manual 88 After inputting all information you can click on multiple or single selection and then choose the best fitted model for parasitism following the same procedure as in previous sections e g developmental rate The window also provides space for additional values Once the fitting process completed a window such as below will appear displaying the selected sub models for parasitism ar Parasitation Rate inl xl Model selected Life stage selected Larva Graphic Output text Model selected SharpeDeMichelle 13 Parameters estimated P 40 54687 TI 302 12268 Ha 83545 62522 HI 128549 45506 development rate 1 day c2 0 5 10 15 20 25 30 35 40 45 50 55 Reset Model temperature degree celsius Next gt Finish Cancel Note The steps described above are only necessary for variable parasitation rate Under species interaction go to graph a menu will appear with distinct analysis aX ILCYM File Edit Simulations Species Interaction Window Help ris Actual oviposi
23. 0 945 68 128 1e 04 Temperature23 55 715 0 828 67 302 1e 04 30 Temperature24 51 133 0 750 68 143 le 04 Temperature26 1 45 819 0 654 70 065 1e 04 80 Temperature26 7 48 348 0 753 64 216 le 04 E Temperature3l 36 279 0 512 70 836 le 04 3 70 Slope 31 722 0 461 68 776 le 04 E 60 5 SELECTION CRITERIA 2 50 Deviance AIC MSC R Squared Adj R squared E probit 812 4738 974 366 0 404 0 996 0 995 T 4096 logit 617 0965 778 988 0 387 0 997 0 996 3 cloglog 1544 4569 1706 349 0 471 0 984 0 981 E 30 3 20 ESTIMATED Temperature Log median Log lower Log upper Days Lower Upg 10 1 10 6 3 554 3 534 3 574 34 943 34 248 35 2 15 0 2 765 2 745 2 785 15 876 15 560 16 1 3 16 1 2 450 2 430 2 470 11 591 11 360 11 gis 4 20 3 2 03 2 050 7 614 7 462 5 23 56 736 1 776 5 791 5 676 5 0 0 0 5 1 0 1 5 20 25 3 0 3 5 4 0 45 5 0 6 24 0 1 612 1 592 1 632 5 012 4 913 5 1 In development timer n deys ncs ER MH man a sen one Status Done Problems Line 1 indicates adjusted model family Line 2 indicates the best fitted model Block of lines below line 2 Intercepts for each temperature standard error SE z values and the probability The last line of the block shows the common estimated slope these parameters all describe variation of the development time at different temperatures Selection criteria Lists of criteria used to select one out of many provided functions or models The most important are the AIC that demonstrates the d
24. 000818907225038856 000959070690441877 00961405783891678 0271490067243576 0504707619547844 0745519399642944 0848972722887993 ILCYM 3 0 User Manual H BH HH 3H BOOOOOOOOOmmBmHmHBHBHHHHHHHHHHHHHrH 63946676254272 64732682704926 65360009670258 65826642513275 66294312477112 66763579845428 66413927078247 65901613235474 65716683864594 65369343757629 65184795856476 65325486660004 64816904067993 64633011817932 63482570648193 62021553516388 60415863990784 58826661109924 5756276845932 pl pd po pol po pol feat po pol p fk pul feat fk feat fet pol pul pu 915068507194519 830137014389038 830137014389038 830137014389038 830137014389038 830137014389038 830137014389038 830137014389038 830137014389038 1 60952234268188 1 65583121776581 1 70684242248535 1 75253129005432 1 76787889003754 H pa pl pl ERE RE RE jt 56155431270599 54305970668793 5307856798172 51712381839752 50654125213623 50339293479919 51355886459351 523796916008 53862988948822 126 3 3 4 Managing layers a Navigation Tools The Navigation Menu allows you to control what the current Map editor is displaying Zoom Tool The Zoom tool allows the user to zoom towards or away from the map The Zoom tool provides the following options e Ifthe left mouse button is clicked then the zoom is towards the map The point clicked is the new center of the display after the zoom e lf the right mouse button is
25. 10 2 3 Lu 0 10 20 x Lu Tempe rata re C Tempe rata re C Using the cubic model Using the cubic model GRR 27 045634 9 376208T 0 099589257 0 0046558921 GL 708 0449 73 49996T 2 758667T 0 03567794T ge E 3 150 a a g a 8 8 go B w E E 20 D 0 0 10 2 x Lu 0 10 2 x Lu Temperature C Tempe rat re C Using the cubic model Using the cubic model 150 ia Dt 1209 996 158 7624T 6 862552T 0 09690732T 4 pB5 1 1761914 0 03411259T 0 002088872T 4 3 513316e 05T 115 Er e 5 w m E z 2 105 so m Biological parameters of one generation at fluctuating temperature Just as for biological parameters of one generation at constant temperature some analysis can be conducted under fluctuating temperature ar Biological parameters of one generation at fluctuating temperature Biological parameters of one generation at fluctuating temperature Simulate two species r Temperature file Load temps C Documents and Settings Henri Desktop Daily new Hyo00s txt View file Minimum temperature min zi Maximum temperature max Y Number females parasitoids 15 Host number 100 M See males Done Age specific survival 1st generation Edg Larva Pupa Female Expon Female Individuals 0 510 aa 3 40 a a n a DO 10 110 10 130 10 150 10 170 180 190 20 210 20 20 240 20 260 20 20 20 300 310 30 30 340 230 x0 30 Age days lt Back Next gt Finish
26. 3 0 User Manual 134 deterministic simulation tool If the population increases during a year the establishment risk can be considered hign lt should be also noted that the index is based on data and information describing the temperature dependent phenology of the species and the temperature variability observed within a restricted area generally the simulation is based on temperature interpolated from historical data Therefore the index does not indicate the risk of introduction which depends on many other factors potential pathways of introduction Effects of other biotic or abiotic factors that might depress pest populations are not included in the calculation for example the availability of host species The latter can be addressed by simulating the index only for areas in which pest specific host plants are produced i e by using the area of production as a filter in GIS modeling as applied in the potato pest atlas For pest antagonists similarly areas can be filtered by using only areas where the target pest prevails today ii Generation index GI The generation index estimates the mean number of generations that may be produced within a given year The index is computed by averaging the sum of estimated generation lengths calculated for each Julian day iii Activity index A This index is explicitly related to the finite rate of population increase which takes the whole life history of the pest into considerati
27. all eggs need to be further reared in batches the eggs from each individual female during one evaluation interval can be reared ILCYM 3 0 User Manual 13 jointly to adult stage and their sex determined The data can be arranged as shown in Figure 3 for analysis in ILCYM P Oviposition Male Notepad File Edit Format View Help Dead Dead H o e oHom uy n e oO o o Oom oOPrn pP nrmOOPrnr OPnPOO AdoOfN OQ OON IpPIpPnr P amp mNOnPHNODIOpPpPuDuue e amp oOopPpo fp p a m fu a p a ib m o Qu un OFF pP unu m OD O0 Dn pPOuu iD DIODuUuIpPDoDuoDodoudcDuoru mD pu o ORARDAJDOORA Yin un PI UJ JJ OY Cn UJ FS P on on UJ asno AAA Un cJ UJ CO CO IU I D UJ EP IP 0 na H QD Qo iO Un NJ OO OO UJ qo Js NJ Js 9 4s s INI UJ o on NJ Js UJ J UJ NJ Js Q9 CO Ch OO Cn 49 JL NJ HS o on C C H9 o D UJ IJ IS UJ HIS OO Qo UJ JF s NI EP UJ UJ NI on BUN C CO IP CO PO IO UJ NO NJ UJ on T UJ NI on UJ P s 5 O IP IP OO D o UJ C PO SUD I3 S Oo amp NJ UJ on sun NJ JB ud Oo 49 UJ un Js CO IP MEE H9 on UJ JS UJ NJ NJ JS UJ NO UJ GJ D INT oJ XO O9 On cJ a WOOO UJ Q oO PH 0 HP O OQ i uJ uJ nJ OO Oi con 4 IS on on c PS n 4 OO n P OO P pP u JO P 0 wi C PI ANOO QD OQ OO OO O OQ IO P J 9 hJ OH UJ RI NO ISO UJ NJ NI ES ES 4 4 UJ C SJ EOS UJ Fs a on Fs on D 0 8 al 0 4 T 8 4 0 0 2 0 2 3 0 2 3 2 4 0 5 5 2 0 0 6 0 0 5 2 0 4 2 0 D 4 5 1 ie D 0 3 0 2 1 P Oviposi
28. and select Properties option T ILCYM File Edit Operations Layer Model Builder Window Hel Est md EE LA A Wl IS lo le ILCYM s Projects Ex 53 Ol Upload Data Import existing project into workspace Refresh Delete Properties If the user has entered a wrong spelling for a life stage different on how it is writing in input data file i e LarvA instead of Larva this can be changed by ILCYM 3 0 User Manual 32 enabling the option Modify and then clicking on the button Apply This is because ILCYM objects are case sensitive Project Properties m Mame PTM project Location D ZILCYM 2011 product ILOYM 2 1 workspace Life stages Inmatures Egg Larva Pupa Adults Female Male Madify Rate 0 5 Note Within this window you can change the spelling of the any immature stages but you can not increased or reduce the number of stages In case the number of stages in your created project is not conforming to the number of stage in your data we recommend you to create a new project 3 1 5 Uploading data For demonstrating how to manipulate data in ILCYM we used the collected data of the potato tuber moth Phthorimaea operculella Zeller as an example in this manual The data is described in Sporleder et al 2004 The phenology model for this specific pest is already established and it can be use for modeling studies including risk mapping for spatial simulation of P operculella for a
29. and the risk of establishment and expansion these can be described as a inductive and b deductive The inductive approach combines through statistical or machine learning methods the known occurrence records of insect species with digital layers of environmental variables It uses minimal data sets and simple functions to describe the species response to temperature and other climatic factors Generally presence absence data or occurrence data only from different locations are sufficient for creating risk maps The combination of occurrence records and environmental variables can be performed through the application of climate match functions that seek out the establishment potential of an invasive species to new areas by comparing the long term meteorological data for each selected location where the species is absent with the same data for the location of origin or locations where the species prevails Sutherst et al 2000 Sutherst and Maywald 1991 For applying this approach computer aided tools such as CLIMEX Peacock and Worner 2006 Vanhanen et al 2008a Vanhanen et al 2008b Wilmot Senaratne et al 2006 and BIOCLIM Kohlmann et al 1988 Steinbauer et al 2002 have been developed and used ILCYM 3 0 User Manual 1 to predict insect species demography for pest risk analysis Rafoss 2003 Sutherst 1991 Zalucki and Furlong 2005 and possible climate change effects Sutherst and Maywald 1990 The
30. at a determined temperature ILCYM 3 0 User Manual 55 The statistical analysis shows the estimation of the parameters of the best model used to quantify the effect of the temperature on the total oviposition of the females per day File Edit F dl Mortality senescence Window Help fw Comparison Post Ovipositian WR Development j Ovipoasition les Relative Frequency A Fernale ratio in the oviposition Model already selected Life stage selected Female 1793 O B exp AF me o e Model selected Gamma Parameters estimated 1 15784 4 3879 Fa a Cumulative oviposition rate 96 Reset Model 0 6 0 9 Normalized female age days median survival time Note While conducting this analysis it will be preferable to choose only one model at time no multiple models selection is recommended here ILCYM 3 0 User Manual 56 Model 2 m a im L E a e a a a 0 6 0 9 la 1 5 1 8 4 1 2 4 Normalized female age days median survival time iii Variable rate of oviposition It believed that insect fecundity may be limited by temperature in different levels either during period of eggs maturation or through the time requisite for strategic ovipositing of the eggs Hence insect females cannot foresee the number of oviposition opportunity that she may encountered on a given day the optimal rate of egg maturation may theref
31. clicked then the zoom is away from the map The point clicked is the new center of the display after the zoom e lf the left mouse button is dragged to form a box then the box indicates the new area that will be displayed on the screen a zoom in e lf the right mouse button is dragged to form a box then the area on the screen during the drag will be fit into the box a zoom out e Rotating the mouse wheel will zoom towards or away from the map keeping the center of the display the same An alternative to using the mouse wheel is holding alt and moving the mouse left or right Pan Tool A drag with the left mouse button down will move the map across the display Navigation Commands May of these commands may also be found in the Navigation Menu 22 Show All Zoom In El Zoom Out p Back Alt Left e Back Displays the previous view The back button is active only after the view has been changed and is not saved between sessions ILCYM 3 0 User Manual 127 e Forward Displays the next view The forward button is active only after the back button has been pressed e Refresh Redraw the screen e Stop Drawing Stop the current rendering process e Show All Sets the zoom so that all available data is displayed e Zoom In The Zoom In button zooms towards the data by a set amount The center of the zoom is the center of the map e Zoom Out The Zoom Out button zooms away from the data by a set amount The center of
32. data sets with their respective geographical coordinates from the database The extracted temperature data are organized in either in 365x2 for daily data or 12x2 for monthly matrices using the longitude as column and latitude as rows representing 365 or 12 matrices each for the minimum and maximum temperatures Thereafter a point object is created for each geographical coordinate longitude and latitude in the form of a table with two columns the first column includes the minimum temperatures and the second the maximum temperatures that is directly used for spatial phenological simulation With these temperatures and the phenology model of the species the generation length the net reproduction rate the intrinsic rate of population increase the finite rate of increase and the doubling time are estimated Kroschel et al 2013 Temperature inclusion in the phenology model Using cosines approximation of temperature the indices can be mapped under present and projected SRES emission scenarios for predicting responses to present and future climates Calculation of Indices From life table parameters formulations yielding to three indices are conducted Kroschel et al 2013 i Establishment survival index EHI The establishment risk maps visualize the capacity of invasive pest species to establish permanent populations based on spatial and temporal variability in temperature They assist identifying the regions where a species h
33. file by writing Dead This allows differencing between zero oviposition when the insect is alive and zero oviposition due the dead of the insect ILCYM 3 0 User Manual 40 3 1 6 Developing the overall phenology To obtain a full phenology of a particular species the six 6 evaluations below are performed in subsequent order 1 Development time Fits the development curve in a parallel line assay to and its variation accumulated development frequencies of each constant temperature tested The application delivers an estimate of the median development time days with the standard error SE at 95 Confidence Limits for each temperature and a parameter describing the variation in development times between individuals 2 Development rate This parameter is obtained through fitting of various functions that describe the relationship between temperature and the development rate 3 Senescence The fitting of various functions that describe the relationship between temperature and the adults senescence 4 Mortality The fitting of functions that describe temperature dependent mortality is done and with the help of some statistical criteria best model is selected 5 Total oviposition The total oviposition is obtained by fitting functions that describe temperature dependent total oviposition per female 6 Relative oviposition Fit a function to describe the age dependent relative frequency oviposition frequency c
34. in this example you have 5 temparatures which stipulate that 5 values of development rate are needed Reset Model Reset model button allows resetting the model selected To select new model just click on the life stage button and choose another model Below is ILCYM s display of a single sub model selection Anl xl Model selected Life stage selected Egg Graphic Output text ans T 1 0 as r T Y e Tj 0 9 0 8 0 7 Model selected Logan 1 0 6 Parameters estimated 0 5 y 0 02876 Tmax 39 20374 p 0 15697 v 5 96136 0 4 development rate 1 day 0 3 0 2 0 5 10 15 20 25 30 35 40 45 50 55 Reset Model temperature degree celsius Finish Cancel On the right side of the window a graph is displayed with the observed data point i e the median development rates and the 9596 CL determined from the previous analysis experimental data points are provided in blue and the resulting curve using these parameters is shown in the graph red line Note To ensure that your choosing model was correctly saved in the worspace of the program you must press next next and the finish button a window will appear displying the selected model his parameters and the graph ILCYM 3 0 User Manual 51 Changing scale on the graph To change the scales axis or input title you need to right click on the model window window which only contains your chosen mo
35. is therefore referred to as deductive The difference between the inductive and deductive modelling approach is the level of abstraction which is higher in the inductive or climate match approach in which the mathematical methods employed lead to a greater generality Instead process based phenology models are either detailed or simplified mathematical models which describe the basic physiological principles of the insect species growth namely its development survival and reproduction the complexity of these models can range from simple models with no age structure and limited environmental inputs to age stage structured ILCYM 3 0 User Manual 2 or multi species models with complex environmental drivers The two approaches do not necessarily compete but may also be used to complement each other Degree day models are often used to describe the linear development of insects using the accumulation of temperature above the minimum temperature threshold Allen 1976 see Nietschke et al 2007 However due to the non linearity of the development curve especially when temperature deviates from the intrinsic optimal temperature of a species degree day models are poor predictors of insect development This method works well for intermediate temperatures but produces errors i e significant deviations from the real development when daily temperature fluctuates to extremes Stinner et al 1974 Worner 1992 Modern more p
36. is used to allow raw access to the xml used to express style information The XML format used is the Stlye Layer Descritor type filter text he v Cache Raster Color Mask lt sld Opacity gt Simple Raster lt ogc Literal gt 0 7 lt ogc Literal gt lt sld Opacity gt Single Band Rasters lt sid ColorMap gt XML lt sld ColorMapEntry color 00BFBF opacity 1 0 quantity lt sld ColorMapEntry color 00FF00 opacity 1 0 quantity lt sld ColorMapEntry color 00FF00 opacity 1 0 quantity lt sld ColorMapEntry color FFFFOO opacity 1 0 quantity lt sld ColorMapEntry color FFFFO0 opacity 1 0 quantity 7 lt sld ColorMapEntry color FF7F00 opacity 1 0 quantity lt sld ColorMapEntry color FF7F00 opacity 1 0 quantity lt sld ColorMapEntry color BF7F3F opacity 1 0 quantity sld ColorMapEntry color BF7F3F opacity 1 0 quantity sld ColorMapEntry color 141514 opacity 1 0 quantity lt sld ColorMap gt lt sld RasterSymbolizer gt lt sld Rule gt lt sld FeatureTypeStyle gt lt sld UserStyle gt 4 l Document requires validation iens Validate Press this button to check that your XML is valid ILCYM 3 0 User Manual 132 3 3 5 Spatial simulations and mapping a Estimating life table population parameters ILCYM simultaneously extracts for a selected region the daily or monthly maximum and minimum temperature data for one year 365 days or 12 months
37. name evaluated temperature and optionally a number that indicates the replication at a given temperature and the interval p e Phthorimaea operculella 28 1 1d that is Phthorimaea operculella was the species used in this experiment incubation temperature was constantly 28 C and it was the first life table constructed at this temperature and the evaluation interval between rows in the document is one day P Species name temperatue Notepad File Edit Format View Help T rr rr rr r Fr Im im rr rm uuuuuurrrrrrr rr Imirm im im uuuuurrrrrr rr r Fr Im irm m iT ZEZEZZEZ ZE ZE ZE ZE ZE ZE DUDO DOS TP rr gg or mp m m m zzzzzzzzxz Xuuuuurmrrrcrrrrrmmrmrm RR LY AAA UO Ur rrmrmrmrrrrmmmm RIDER INN O A RR A UU UCU C Ur PCr II FE TI EC mrmmrm zzzzzzzzzzuuuuuur mrnrmrrcrrrrrnrmnmmmrm COH BHRIROPHBDBNJTUUUTUOUrrrrrrrrrrmmmm QOd HHBBITOHNRNNICOTTUUOTOOrrrrmrrrrrrmmrmmm oo fb D u u oo OB fb b u u oo e MD u oa E E E E L L A L L L L L L L P P P P P P M M M M M M M M M M M d Figure 2 Life table data text file generated by saving the above spreadsheet as txt file tab delimited for use in ILCYM The file name should indicate the species name studied and the temperature at which the life table was constructed If the female rate in the progeny is expected to be variable In case that the female rate in the progeny is not constant but possibly affected by temperature or female age
38. rate of increase A Doubling time Dti Fi 0 1553145 0 05104357 0 001 9254937 3 255843e 05T B 28s R Adi 0 467 AIC 34 538 Deviance U Ro 10 55335 3 68353T 0 3407498T 0 00741629T R 0 993 R Adj 0 872 AIC 15252 Deviance 0 837 GRR 27 04563 9 376208 0 09958925T 0 004655892T R 0 822 R Adj 0 686 AIC 39161 Deviance 89 862 GL 708 0449 4 73 49996T 2 758667T 4 0 03567794T R 0 997 R Adj 0 888 AIC 33887 Deviance 35 335 Acide 1 175191 0 0341 12597 0 002089872T 3 5133168 057T R 088 R Adj 0365 AIC 33 896 Deviance Dt 1209 996 158 7624T 5 862552T 0 09690732T R 0 871 R Adj 0 303 AIC 4404 Deviance 310 927 fmm Io a em ja E is O 5 30 0 00534326 0019649279 0 0430 206 0 0794 3092 0 06024276 ILCYM 3 0 User Manual 2 5096 7 4733 13 2514 15 924 6 5954 OO 7449525 1635 032563 100535756 129 725556 51 311117715 1 2 3617638 1019043614 32799202 r6 83149128 SF 9957590 1 044013966 16 09243181 r6 40 4829 34 0422053 106267943 872555555 37 9207232 2312 66571 l 8355008 gs 63812632 lalx 96 XA nd Using the cubic model Using the cubic model Fa 0 1582148 4 0 0210425T 4 0 001925403T 74 3 255943e D5T Ro 10 55335 3 68353T 0 3407498T 4 0 00741629T 005 15 3 2 0065 ft 3 10 004 E E i 1 z 5 om 00 0 0
39. scale that is biologically more accurate than calendar days Phenology models for insect species based on temperature are important analytical tools for predicting evaluating and understanding the dynamics of populations in ecosystems under a variety of environmental conditions The International Potato center CIP initially developed a temperature driven phenology model for the potato tuber moth Phthorimaea operculella Zeller Lepidoptera Gelechidae which well predicted the life table parameters in different agro ecological zones This model was validated through field and laboratory data It was used to predict the establishment risk and potential pest activity in specific agro ecologies according to temperature records Linked with geographic information systems GIS and atmospheric temperature the model allowed simulation of three risk indices on a worldwide scale and was also used to predict potential future changes in these indices that may be caused by global warming The success of the approach used on developing and implementing the P operculella model stimulated the extension to other insect species CIP therefore developed the Insect Life Cycle Software ILCYM version 3 0 presented in this manual The main goal of the software is to facilitate the development of insect phenology models and provide analytical tools for studying insect population ecology The authors are aware that a single modeling approach does not fit to e
40. selected the project once on the simulation window e Life stages Egg Larva Pupa Female Male here you have all the life stages of your species as defined during project creation e Ratio ratio between males and females this parameter was defined during project creation e Load temps this button allows the user to load temperature data e N Insect number of insect to be simulated e View input temp This button allows user to view their input temperature e Simulate button start the simulation process e Cancel button help user to cancel the operation i Output life table Once you click on the simulation button the application will simulate a life table with all the data for your selected phenology and the temperature that was inputted in the previous step ILCYM 3 0 User Manual 69 T Stochastic Simulation Output miax N Egg Egg Egg Egg Egg dead Egg Egg dead Egg Egg dead Egg Egg dead Larva Egg dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Larva dead dead Pupa dead dead Pupa dead dead Pupa dead dead M a gt 11 Statistical summary The screen below shows the summary of the
41. selecting best sub model will appear as below and best model will be mark in red color Models SSR AIC Sharpe amp amp DeMichelle 1 amp D E 4 56 601 Sharpe 44 DeMichelle 2 6 DE 4 54 917 Sharpe 44 beMichelle 3 6 0E 4 53 015 Sharpe 44 DeMichelle 4 0 0019 50 555 Sharpe 4 DeMichelle 5 amp DE 4 57 015 Sharpe amp amp DeMichalle 6 6 DE 4 57 015 Sharpe 44 DeMichelle 7 0 0019 B2 555 Sharpe 4 beMichelle 8 6 0E 4 58 917 Sharpe 44 DeMichelle 9 6 0E 4 b 464 Sharpe 4 DeMichelle 10 0 0019 48 555 Single sub model selection When this option is selected you will use a single sub model at a time adjust its parameters until a good fitting is obtained Multiple Selection Single selection Manually changing sub model initial parameters only for single selection ILCYM s user can manually change sub model initial parameters using the window below You just need to enter your desire value of parameter in the allocated space Porameters TI 285 11 Th 302 15 Ha 19737 511 HI 100000 Hh 200000 ILCYM 3 0 User Manual 49 Automatically changing sub model initial parameters only for single selection This option is used to automatically modify the initial parameters of a sub model to ease the convergence of the fitting algorithm The user adjusts the parameters by clicking on Readjust button to obtain adequate parameter values that can easily converge and provide best fit of the curve Several clicks on the Readjust button
42. simulated life table on the left and related life table statistics e g sex ratio fecundity development time etc and calculated life table parameters e g fm intrinsic rate of natural increase Ro net reproduction rate on the right L Stochastic Simulation Output Life Table Statistical Analysis Graphics Simulated Life Table Summary Statistic Life Table Summary A Egg Larva Pupa Female Male new Egg 3 Observations 100 0 0 0 0 Number of insect 100 0000000 99 Sex ratio 0 4545455 92 Males 24 0000000 90 Females 20 0000000 Immature death 56 0000000 44 0000000 Eggs Females 145 3000000 JPOoo6o Time Insect Percent Accumm Egg 6 049 82 82 3 82 Larva 22 755 53 64 63 52 997 Pima 13 44 23 02 43 998 o J J J CD o I Parameters Life Table Summary WO 0 0 04 1000 p Parameters 0 07482309 Ro 29 06000000 GRR 65 35587378 T 45 03105335 lambda 1 07769348 Dt 9 26381361 Oe cn OQ Y OQ O O o o b qo MN OIN Q O01 0 000000000000000000000000 co eoo Y Y Y 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 55 5 5 5 5 59 amp Gun GM 0000 0000 0000 0 00 00 0000 0 O0000 0000 0 0 0 00 0 0 0 0 0 0 0 0 0 0 40 08 oooooooooooooooooo0ooo Din in ke m in m e ILCYM 3 0 User Manual 70 iii Graphics Age stage specific distribution rate Stable age stage distribution O Age specific survival rate Individuals iv Age stage distribution Life Ta
43. temperature experiments The reproduction model might include functions for different processes depending on the insect specie under study i e changing sex ratio in adults due to temperature age dependent reproduction frequencies temperature dependent reproduction frequencies etc The overall approach is factor process based while temperature is the principle driver factor of these processes Insects species that show seasonality generally have an over wintering stage in which the insect hibernates or diapauses The factors which are responsible to reactivate hibernating insects is often not temperature alone temperature might ILCYM 3 0 User Manual 4 be an indirect factor but for modeling considering temperature alone might not explain this process in its totality ILCYM s approach is more adequate for insect species that do not hibernate and hence do not show seasonality in its development However many components of ILCYM might be used for such species as well ILCYM s compiles the established function into a general generic phenology model that uses rate summation and a cohort up dating approach for simulating populations The cohort up dating algorithm is based on scheme proposed by Curry et al 1978a that was further described by Wagner et al 1985 and Logan 1988 In published articles there is not so much discussion on including temperature induced mortality in immature life stages and recruitment Both are necessary
44. the difference that the user simply input the number of temperatures with no repetitions because the result will always be the same Constant Temperature Fluctuating Temperature Y ILCYM File Edit Species Interaction Window Help Fir Or Deterministic A m stochastic L metadata Validation PTM project Constant Temperatures Deterministic o amp Es New data N Insects Days Temperatures Repetitions Estimate Existing data Browse Plot points ares Save data Cubic Rm E Quadratic Ro Logarithmic GRR E Exponential gt GL Lambda 4 n N View Results Status Problems Note In these simulations always remember to input temperature values within the range that the insect under investigation can properly develop if you input a temperature that is not suitable for development ILCYM will output NA for life table parameter values ILCYM 3 0 User Manual 82 3 2 4 Species interactions This section explains how two phenology models for distinct species a host and a parasitoid can simultaneously be simulated The process here is deterministic and the algorithm used is the same as in single species simulation explain earlier For demonstration of the simulation steps and outputs the phenology models of the potato tuber moth Phthorimaea operculella Zeller Lepidoptera Gelechiidae and its larva parasitoid Apanteles subandinus Blanchard Hymenoptera Bra
45. user utilizes this tool for updating the path in the geographic simulation window after reloading a new data base Climate DaraBase DABO Climas 10minutes 2000 This tool allows creating a new map or creating more maps from an existing phenology 9 Create new map Regenerate map Existing Parameters e Create new map option create a new map e Regenerate map option create a map with an existing phenology e Load file button load the existing phenology file this button is enabled when Regenerate map is selected e View file button open the existing phenology to verify the intensity or for possible modification this button in enabled when Regenerate map is selected __ View Phenology File 2AR DEVE MOD DEVE SLOPE MOD DIS MOD MOR lt PAR_MOR lt MOD_TAZ lt PAR_TAZ lt M OD OWVIZc PAR OWVIz listil Egg development time 3 MOD DIS 1 e probit SLOPE 1 lt 15 4261400434957 development_rate PAR DEVE 1 lt clp 0 787 Tl2297 3 Ha 5276 7 Hl 30462 23 MOD DEVE 1 z v tp lt ff298 16 expl Hajl 987 1 298 16 1500 11 expi HI 987 01 11 Lam expir11 3875 1 11 1511 mortality Mop moR 1 z y a x z b xc PAR MOR 1 c a 0 0003258 b 0 0176809 c 0 3598379 HHH Larva FHF development_time MoD DIS 2 probit SLOPE 2 10 13643 development rate PAR DEVE 2 lt c p 0 3168 T1 303 3187 Ha 6126 333 Hl 4557 72
46. want to test plan for precision etc Use dummy data you may use the dummy data provided in ILCYM to get familiar with the analysis and learn about the approach You might also create own dummy data with different numbers of temperatures and with different numbers of insects in the experiment for learning about the statistical precision of your planed experiment Decide about the type of data you want to collect see chapter on data collection life table data ILCYM 3 0 User Manual 6 versus cohort studies or mixed advantages and disadvantages for your purposes you should know before designing the experiment 5 Collect the data 6 Use the model builder of ILCYM defining all sub models for the overall phenology model at this stage you might start writing a report on results obtained What to report 7 Once all sub models are selected they are compiled to obtain the overall phonology model ILCYM compiles the overall model automatically according to your initial interactive statements writing when starting a new project It recommended that user s have some level of familiarity with the structure of the overall phenology model and the modeling approach of ILCYM before starting serious analysis with the software 8 Conduct sensitivity analysis and validate the model through comparing simulation results with the data from fluctuating temperature experiments or data published in the literature ILCYM provides tools for that
47. window will appear ar Several generations at constant temperature i iol x Several generations at constant temperature Simulate two species Constant temperatures 12 14 16 20 25 Number female parasitoids 100 Host number 200 Days 365 Parameters calculation ILCYM 3 0 User Manual 90 Constant temperature input the temperatures for which you desire to simulate your species make sure these temperature are within the developmental ranges for both the host and its parasitoid Number female parasitoids input the number of female parasitoids for the simulate Host number input the number of host that for the simulate Days number of day for the simulation generally one year 365 days you can also simulate just for the growing period of a particular plant Below is the sample output for the simulation results displaying the age specific survival for each stage of the insect host at each inputted temperature Several generations at constant temperature Simulate two species Constant temperatures 12 14 16 20 25 Number female parasitoids 100 Host number 200 Done I Days 365 Parameters calculation Age specific survival 1st year Host IV Egg v Larva IV Pupa IV Female v Male im 8 Individuals 8 Temperatures v 12 IM Iv 14 Wl v 16 Iv 20 MIB 1 17 07 0 2 72 20 7 v 25 210 mM 20 20 20 20 m a 20 W 3100 30 a 34
48. with the current settings Revert Reset the style pages to their previous settings Close Dismiss the style editor Import Import style settings from an sld file Export Export style settings to an sld file ILCYM 3 0 User Manual 129 Feature Style Pages When the Style Editor dialog is opened on a feature layer the following pages are available e Cache e Filter e Simple Feature e Simple Lines e Simple Points e Simple Polygons e Theme e XML Raster Style Pages When the Style Editor dialog is opened on a raster layer the following pages are available e Raster Color Mask The Raster Color Mask makes a single color of a coverage transparent Often used in satellite images to indicate areas where no information was recorded type filter text he v Raster Color Mask Cache Raster Color Mask Simple Raster V Enable color mask PERI Single Band Rasters XML Color mask Apply Import Export Cancel OK ILCYM 3 0 User Manual 130 e Simple Raster Allows simple control over the rendering of a raster image G Lay Style Editor type filter text he v Simple Raster Cache Raster Color Mask Opacity 100 Simple Raster Single Band Rasters me XML Min scale 2 94680158 Max scale j V RGB Channel Selection Red Band 1 REDBAND y Gamma 10 Green Band 2 GREEN BAND Gamma 10 lt Blue Band 3 BLUEBAND
49. 0 l b e 6P ILCYM 3 0 User Manual 163 25 1 8 Ne 0 m 2 ON Taylor 1 Taylor 2 ILCYM 3 0 User Manual E in r im r mT l rm T h y y qe 9 N Y Nn gr o gt gt I VS T me Jg E Y vr ac 33 34 9 UN 36 37 o 4 42 Wang 8 Shape arc DeMoivre Gompertz Gompertz Makeham Weibull Briere Briere 2 Analytis m T ILCYM 3 0 User Manual 7 D T T To a 5P ZT gt Tmi n m Z gt T lt Tmax 165 Janisch amp Analytis T temperature in Celcius m T mortality function at temperature ILCYM 3 0 User Manual 166 Table 4 Sub models fitted to adult senescence in ILCYM software The sub models fitted to adult senescence in ILCYM software are the same they also maintain their respective ID as shown in Table 2 excluding the sub models listed below Name IER EE NN 4 Sharpe amp DeMichele 4 EN MN Sharpe amp DeMichele 6 7 Sharpe amp DeMichele 7 Sharpe amp DeMichele 8 Sharpe amp DeMichele 9 Sharpe amp DeMichele 10 10 j e I Logan 1 s f 36 Anlytis 1 27 Anlytis 2 ILCYM 3 0 User Manual 167 Anlytis 3 Allahyari a A Kontodimas 2 Ratkowsky 2 Table 5 Sub models fitted to total oviposition in ILCYM software The sub models fitted to adult total oviposition in ILCYM software are the same the
50. 0 a 30 m 30 ILCYM 3 0 User Manual 91 Several generations at fluctuating temperature Once this option is selected the window below will appear titled several generations at constant or fluctuating temperature meaning the analysis is conducted both under constant and fluctuating temperature ar Several generation at constant or fluctuating temperature B x Several generation at constant or fluctuating temperature Select one parasitism percentage option Host Parasitaid projects Hast pr Project Parasitoid Apanteles Project Attack Stage Percentage parasitism calculation PPj Variable parasitism rate NH j j Constant parasitism rate A C Daily simulated oviposition Back Next gt Cancel Three options are display under percentage parasitism calculation Variable parasitism rate refer to the variable parasitation rate this option will be selected if you wish to consider that the parasitation rate is a function of temperature Prior to its selection you must make sure that a function representing parasitation rate has already been fitted and save Constant parasitism rate refer to parasitation rate which is constant in value that does not depend to any variable ILCYM 3 0 User Manual 92 Daily simulated oviposition this option allows of linking both phenology model through the number of female parasitoid oviposited egg Click next and the window
51. 011 2011 104 10 99 15 62 25 9 13 2011 2011 105 11 38 17 52 26 9 14 2011 2011 106 10 99 16 38 27 9 15 2011 2011 107 11 38 16 38 28 9 16 2011 2011 108 11 38 17 14 29 9 17 2011 2011 109 12 16 16 76 30 9 18 2011 2011 110 11 77 14 47 31 9 19 2011 2011 111 10 6 15 23 32 9 20 2011 2011 112 10 99 14 09 33 9 21 2011 2011 113 8 63 14 47 34 9 22 2011 2011 114 10 99 14 47 35 9 23 2011 2011 115 9 42 16 36 9 24 2011 2011 116 10 99 16 37 9 25 2011 2011 117 10 99 15 23 38 9 26 2011 2011 118 10 99 15 62 39 9 27 2011 2011 119 11 38 14 47 40 9 28 2011 2011 120 11 77 15 23 41 9 29 2011 2011 121 11 38 16 76 42 9 30 2011 2011 122 12 16 17 52 43 10 1 2011 2011 123 11 77 18 28 44 10 2 2011 2011 124 11 77 17 9 45 10 3 2011 2011 125 10 99 17 52 46 10 4 2011 2011 126 11 38 22 09 47 10 5 2011 2011 127 9 03 22 86 ILCYM 3 0 User Manual 93 When your climate station is loaded you need to go to minimum temperature and maximum temperature to select tmin and tmax for your analysis Minimum temperature click on the combo box and select tmin Maximum temperature click on the combo box and select tmax Number of female parasitods input the number of female parasitoids Host number input the number of host Calculate for starting the simulation The graph below is an example of a simulation of a PTM Apanteles interacting system with a constant parasitation rate The number of Apanteles is 20 with a fecundity of 8 for each individual the total number of PTM is 50
52. 4 Day 5 Day amp Day Day amp Day 3 Day 10 Day 11 Day 12 Day 13 Day 14 Day 15 Day 16 Day if Day 18 Day 1 Day 20 Day 21 Day 22 ILCYM 3 0 User Manual 37 i Upload Data Data Type Cohort studies of single LIFE STAGES 9 Life Table Interval evaluation 1 Life Table Type Incomplete 9 Complete Female Male Only Female Remove All Data Files Data Path Data Name Temperature D ULCYM 2011 tablas de vidalTrisleurodes vapo tabla 10 txt 10 D MLCYM 2011 tablas de vidalTrialeurodes wapo tabla 15 txt 15 D MILCYM 201 1 tablas de vidal Trialeurodes wapo tbabla 18 bxt 18 D JILCYM 2011 tablas de vidal Trialeurodes wapo tabla 20 bxt 20 D ILCYM 201 1 tablas de vidalTrialeurodes vapo tabla 25 bxt 25 D MLCYM 201 1 tablas de vidalTrialeurodes vapo tabla 28 txt 28 D ILCYM 2011 tablas de vidalTrialeurodes vapo tabla 32 txt Je Rate 0 5 Oviposition Data Here is an example of a file that includes the data for all insects at one specific temperature It is recommended to include the temperature in which the life table was established in the file name for easy identification p e PTM 20 txt PTM for potato tuber moth and 20 indicates the temperature 20 C in which the life table was established The user must indicate the interval of evaluation of S He project if the evaluation is daily write 1 one if is twice per day write 0 5 For co
53. 45 ERI 393074 749727700341653 11 79145335671764 0 9150685071945 ERL393075 74 97193670086502 11 79145335671764 0 9150685071945 ERI 393076 7497110336756474 11 79145335671764 0 8301370143890 ERI 393077 74 97027003426446 11 79145335671764 0 759632229804 ERI 393078 74 96943670096418 11 79145335671764 0 6041958928108 ERI 393079 74 96860336766392 11 79145335671764 0 3457008898258 ERI 393080 74 96777003436364 11 79145335671764 0 2774704396724 ERI 393081 74 96693670106336 11 79145335671764 0 2206943184137 ERI 393082 74 96610336776308 11 79145335671764 0 2774704396724 ERI 393083 74 9652700344628 11 79145335671764 0 2774704396724 ERI 293084 74 96443670116254 11 79145335671764 0 2774704396724 ERI 393085 74 96360336786226 11 79145335671764 0 2797448039054 ERI 393086 74 96277003456198 11 79145335671764 0 3457008898258 ERI 293087 74 9619367012617 11 79145335671764 0 2797448039054 ERI 393088 7496110336796143 11 79145335671764 0 2774704396724 z Create ERL393089 74 96027003466115 11 79145335671764 0 2019504159688 i Info 13 Feature Editing t mo c Text file to shape file With this menu you can create a shape file of points from a text file that contains fields with latitude and longitude both in decimal degrees First you must indicate the filename of your txt file and you have to provide the output fi
54. 5 4 1000 126 i 20 3 10 1000 1 20 3 11 1000 0 k 23 5 500 0 E 23 amp 300 525 gt 23 7 300 162 8 2 5 Fa LIN 1 a 23 3 300 n 24 400 0 5 24 5 400 174 6 24 6 400 130 f 24 T 400 0 A 26 1 3 TOO a 1 5 26 1 4 700 32 4 26 1 5 700 513 1 7 26 1 amp TOO ii o 4 26 1 T TOO i aw 5 26 1 4 400 ad amp 26 7 5 400 323 of f 26 1 5 400 13 jr E 26 1 1 400 2 3 26 7 8 400 n 4 31 2 1400 0 5 51 3 M00 246 6 1 4 1400 332 51 5 1400 5 3 amp 1400 0 Figure 5 Example for recording cohort study data in a spreadsheet A and the same data saved as a txt file tab delimited B for use in ILCYM The first column indicates the temperature evaluated here a total of 7 temperatures were tested second column indicates the evaluation intervals number here measured as days after experiment set up third column indicates the number of insects used in each temperature and the forth column indicates the number of individual that had developed to the next stage on each evaluation date For further explication see the text ILCYM 3 0 User Manual 17 ILCYM handle such data as interval censored data and retrieves the interval limits from the previous row i e development between day 3 and day 5 after experiment set up The recording should be continued until the last individual of the cohort developed to the next stage or died ILCYM retrieves the mortality rate in each life stage for each temp
55. 5 minutes equal to 9 x 9 km 2 5 minutes equal to 4 5 x 4 5 Km and 30 seconds that is equal to 0 9 x 0 9km The lower resolution for example 10 minutes are used to map larger areas such as the whole word highest resolutions give detailed information in the map ILCYM s climate input data are in fltformat a Current temperature data The temperature data used for spatial simulations present scenario were obtained from WorldClim available at http www worldclim org The database is a set of global climate layers grids with different spatial resolutions that contains monthly average minimum maximum and mean temperatures that were interpolated from historical temperature records worldwide NOAA data between 1950 and 2000 The data are well documented in Hijmans et al 2005 For spatial population simulations and model output validations at different locations point by point temperature data directly obtained from local weather stations can be used b Future temperature change data For simulating population parameters for P operculella tor the year 2050 climate change scenario ILCYM s input downscaled data to project temperature changes The predictions based on the WorldClim database are described by Govindasamy et al 2003 The downscaling of data which was conducted by Ramirez and Jarvis 2010 is freely accessible at http gisweb ciat cgiar org GCMPage ILCYM s was also adapted to input ILCYM 3 0 User Manual 99 temperat
56. 89 MOD DEVE 2 amp v tp x 288 163 exp Haj1 887 1 298 16 1500 11 exptiHlj1 987 LTD LI expir1 1 3875 111 11511 mortality 3 MOD_MOR 2 lt Y a x 2 b x4 c PAR_MOR 2 lt cla 0 0027 b 0 1402 c 2 0457 HHH Pupa FHF development_time MOD_DIS 3 probit SLOPE 3 lt 5 798806 development rate PAR DEVE 3 amp c p 0 664287 T 304 8939 Ha 133 803 HI 27966 14 MOD DEVE 3 amp v tp x 288 163 exp Haj1 987 1 298 16 1500 11 exptiHlj1 987 CITI La expi 1101 987 1 11 1511 YW racnwebalibss 3 ILCYM 3 0 User Manual 137 Select one or more checks of the indices Indices al AI ERI Input the geographic coordinates of the region to simulate Coordinates Mins 160 Miny 760 Read from layer Get Rectangle no 100 Max x Read from layer button read the coordinates of the layer selected Adjust button adjusts the coordinates to the closer point taking the database as reference Get Rectangle button automatically generated the coordinates of the selected area by clicking in the ILCYM box Selection tool located in the toolbar E Maximum extent button get the coordinates of data base Temperature filter optional this option allows the user to filter temperatures in the climate database and the user is required to enter the lower and upper limits of temperatures range of temperature that your species are adapt
57. CD Requirements or may be downloaded from the website below https research cip cgiar org confluence display ilcym Downloads In case you have downloaded the software make sure you unzip the requirements file and place the full package on your desktop before starting the installation 2 2 Installing ILCYM 2 2 1 Window XP operating system for computer with 32 bytes To install ILCYM software in this operating system the following steps need to be executed 1 Double click in the INSTALL icon 2 Select the route where the software will be installed generally in C Program files 3 Follow the instructions Once the application has been installed you will see the following window ILCYM 3 0 User Manual 20 f ILCYM Jo File Edit Operations Layer Model Builder Window Help im Die Lif L lu la Les lA li l Post Oviposition VR ILCYM s Projects Explorer 3 m EFT ILCYM Insectillife Cycle Modeling International Potato Genter 2 2 2 Windows Vista 7 and above for computer with 32 bytes 1 Right click on the installer icon 2 Click on properties 3 Click on compatibility 4 Select the box compatibility mode 5 Select window XP you may jump this step the computer will automatically locate the appropriate window pack compatible for ILCYM 6 Click ok Select the route where the software will be installed 8 Follow the instructions When th
58. CYM File Edit Operations Layer Spatial Analysis ILCYM Tools Window Help il m ee New Window T ILCYM s Projects Ex 2 ma 0O Open Perspective Ld Shaw View E Y FTM project Reset Perspective Close Perspective Close All Perspectives Preferences TS Layers E3 m Select the path by clicking on Browse click on Apply button and the Ok button Make sure you select the master folder that contains the Tmin and Tmax folders f Preferences ES type filter text Climate DataBase Path Catalog Climate DataBase Path General Climate Path D 1BD_Clirmas110minutes 2000 Browse i InstalllUpdate Project Rendering Tool ubig LIT Define the path of the environ Restore Defaults Apply ILCYM 3 0 User Manual 101 Below is the structure of the data It has to be in two separate folders one for Tmin and the other for Tmax File Edit View Favorites Tools Help Q Back gt 27 Search Ke Folders E Address D BD_Climas 10minutes 2000 v File and Folder Tasks A Tmax Tmin Make a new folder 3 Publish this Folder to the Web E3 Share this folder Other Places z 5 gt z File Edit View Favorites Tools Help Q Back P 27 yo Search ig Folders faa Address D 1BD_Climas 1Ominutes 20001Tmin iv Go Details 10minutes 2000 File Folder Date Modified Todd E tmin_01 Fle 2011 10 34 AM File and Folder T
59. Ca SCS Facto Add to map e Cut Cut allows you to make a new grid consisting of a selected part of the area of an existing grid You can define the area to be cut and placed in the new grid by coordinates These parameters can also be selected by drawing a rectangle on the map They can also be copied from an existing grid Mi Cut EDER po Dimension af the output file IA AM A ac imum Longitude latitude Add to map f Merge The Merge function can be used to spatially append or mosaic Raster of different map extents however they must be in the same coordinate system The Raster can be totally overlapping partially overlapping adjacent or entirely separated If the inout Raster overlaps the order of precedence is defined by the order of the raster in the argument list ILCYM 3 0 User Manual 118 When the input Raster overlaps it is viewed as a set of layers where NoData is transparent The output Raster receives the first value at each cell that is not NoData For a set of overlapping raster a number can be entered as valid input but this input should be the last one in the list since it will populate the remainder of the Raster MM Merge O 0 Add to map g Reclass Reclassifying your data means replacing input cell values with new output cell values The most common reasons for reclassifying data are to e Replace values based on new information e Group certain values together e He
60. Da eee tette 35 b Uploading life table data ooccccccccccnconcconconconncononncnnnnnnnancnnnnnnnnncnnnnnnnas 37 CY OVIDOSITOBD TIO curii dote a idoneam a ated a emen ined edes ceutEdis 40 3 1 6 Developing the overall phenology ooococcccoconcccoconcocnncononnacononnannnnonanons 41 ILCYM 3 0 User Manual i a Development time and its variations oooncnccconnnnccnonnncononennnnonennnonanonnnnanenoss 42 b Devel pmentrfale unmuns ee DT 45 A A re NEE ee 52 Mortal e ia Deo 53 eN Reproducir Doo 54 o Po cups E Pas Dolat lavo a Um cups UR a din 63 3 158 PIOJECL Summaz are 64 els COMPING POSC ee ee 65 3 TO Trelect COMP APIS OM are el 66 3 2 Validation and Simulations sse nnne 68 9 2 otochiastic SIMULATION ia ses asien Ss be A 68 a Stochastic Simulation at fluctuating temperature sss 68 b Stochastic simulation at constant temperatures sseesssssse 73 3 2 2 Model validation validation of established model is done using stochastic SIMIO cc D 77 3 2 3 DCLCMTINISHC SIMIO energie 79 3 2 4 Species IMTS ACTON S anita 83 3 3 Potential Population Distribution and Mapping occcccccncccccnnccncncccncnccnanoconacnnnnnos 99 3 3 1 Olimate dala nee 99 a Current temperature data cccccoonncnncccconncnncncnnnnnnncnnnnnnnnnnnonancnnnnnonancnnnononanenns 99 b Fut
61. For incomplete life table where two oviposition files for male and female oviposition respectively are loaded as input data a function representing female ration in the oviposition is selected through fitting and added into the overall phenology model Note The variable oviposition rate analysis is only includes on the overall phenology for species with variable rate usually defined when creating a project v Complement analysis Post Oviposition VR The post oviposition in ILCYM stands for age specific survival rate that described the proportion of number of eggs alive at any given age time This evaluation is only made when the oviposition rate is variable that means when the female oviposition rate depends on age or temperature ILCYM 3 0 User Manual 59 File Edit Model Builder Window Help HE DEN e La BU Comparison hhhbhhh BEEEEE hbhhbhhh EEEELEE Post Oviposition v R ikkk Lk kb LELLE ikE EEE Post oviposition metadata gt PTM project Selected Project Copidosoma koehleri Quality control output oO un a a o a m oF 20 Temperature in C ILCYM 3 0 User Manual 60 By clicking on Quality control output button the user can view the statistical quality control output Quality control output These insects are killed on different days and being the same insect Position Temperature 6 20 8 20 10 20 68 25 28
62. Id Date Year nday tmin tmax 1 6 1 2011 2011 1 0 88 16 38 2 6 2 2011 2011 2 0 059 20 722 3 6 3 2011 2011 3 0 934 20 365 4 6 4 2011 2011 4 2 797 17 463 5 6 5 2011 2011 5 4 194 17 748 6 6 6 2011 2011 6 1 425 19 936 i 6 7 2011 2011 f 2 074 17 558 8 6 8 2011 2011 8 3 142 19 389 9 6 9 2011 2011 9 1 913 21 056 10 6 10 2011 2011 10 0 495 19 817 11 6 11 2011 2011 11 2 744 17 653 12 6 12 2011 2011 12 1 643 19 008 13 6 13 2011 2011 13 2 717 16 082 14 6 14 2011 2011 14 4 999 17 677 15 6 15 2011 2011 15 2 61 18 509 116 A 16 9011 2011 15 2 PR 1712 ILCYM 3 0 User Manual iii Define the co variables for calculating the indices Define the co variables Define co variables Z value field Load altitude 8 VersionOfIlcym Climate DEM Volt fit Minimum X 75 834021 Maximum X 74 926521 Minimum Y 12 379371 Maximun Y 11 491038 Cellsize 10 000833 Latitude Note It is mandatory to have the digital elevation model DEM as co variables latitude and longitude are optional iv Define the function use by thin plate cubic quadratic Method of interpolation amp output file Method of interpolation Thin plate smoothing spline Functions Cubic y Include estimated indexes with other temperatures Browse temperatures B VersionOfllcym Climate Indexinterpolator Inden EM rees ILCYM 3 0 User Manual 113 v Outputs FF ILCYM pu pu p File Edit Navigation Modeling ILCYM
63. Larva2 Larva3 Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larva4 Larvad Larvad Egg Egg Larval Larval Larval Larval Larval Larval Larval Larva2 Larva2 Larwa Larva2 Larva2 Larva3 Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larvad Larvad Larvad Q Q Q Q 12 35 21 7 Q Dead Dead Dead Dead Dead Dead Dead Dead Dead Dead Dead Larvad Larvad Larvad 0 0 0 0 0 28 29 18 34 27 23 19 27 34 23 26 20 14 Egg Egg Larval Larval Larval Larval Larval Larval Larva Larva2 Larva2 Larva2 Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larvad Larvad Larvad Q 0 0 Q 0 18 38 37 46 26 28 11 23 26 13 14 22 26 52 22 35 8 0 Figure 6 Life table data file dealing with only female The main difference here to Figure 3 is that all individual that evolved and became male are not accounted for the file only shows immature life stage egg larva or pupa is recorded for each evaluation time ILCYM 3 0 User Manual 19 ll ILCYM APPLICATIONS 2 1 System requirements To be able to run ILCYM software you need to have Java and R programs installed in your computer all these programs are embedded in ILCYM CD Usually JAVA is automatically installed at the same time with ILCYM root platform R and its libraries are installed manually All packages and programs required by ILCYM are included in the CD or in zipfile on the following routes
64. M s common errors 4 1 ILCYM s crashing or hanging Due to overload of tasks ILCYM software sometimes crash or hang In such condition the software will run in stand by and there is disconnection with Rserve In order to reverse the situation it is recommended that you terminate all the processes of including Rserve and lunch the software afresh To stop all ILCYM s processes simultaneously click on Ctrl Alt Delete bottoms of your keyboard then click on task manager under process select Hserve exe and click on EndProcess bottom g Windows Task Manager Colle es E Windows Task Manager o File Options View Windows Help File Options View Help Applications Processes Services Performance Networking Users Applications Processes Services Performance Networking Users Task Status Image Mame N User Name CPU Memory Descriptio te Documentol Microsoft Word Running WkCalRem exe juancarlos 00 336K Microsoft EI E Running WINWORD EXE juancarlos 00 57 560K Microsoft Plug in Development org cgiar cip ilcym 4 prod Running winlogon exe 00 880 K E S ILCYM Running WiFiMsg exe juancarlos 00 660K Module to Macromedia Fireworks 8 Untitled 2 png 66 Running unsecapp exe juancarlos 00 780K Sinkto rec Running taskmgr exe juancarlos 02 2 124K windows taskeng exe juancarlos 00 2 116K Task Sche SynTPHelper exe juancarlos 00 292K Synaptic
65. Menu below File Edit Species Interaction Simulations Window Help me Deterministic diles Pau ca E Stochastic S metadata Validation PIM project 3 2 1 Stochastic Simulation ILCYM stochastically simulates a user defined number of life tables each with a user defined number of individuals through rate summation and random determination for each individual s survival development to the next stage and sex under constant or fluctuating temparature Stochasticity in reproduction is calculated according to the variance observed in the data on total oviposition per female used for developing the model a Stochastic Simulation at fluctuating temperature T LCYM File Edit Species Interaction Window Help Fir Deterministic ees LI Stochastic i Constant temperatures o metadata XE ERE Fluctuating ternperature v PTM project ILCYM 3 0 User Manual 68 The following window will appear The menu has two sub menus Click on simulation button for life stage simulation E Fluctuating Temperatures Stochastic a C k Project PT ak project Temperatures Day Tmin Tmax Life stages Egg Larva Pupa Female Mole 13 4 Patio 0 5 on ce d d w mM E IA Load tempz GL CY Mi temp fluctuante txt View input temp M Insects 100 0 B im 10 16 7 A AA 1 14 3 Simulate Cancel 12 137 Status Problems e Project PTM project user most
66. O Add to map ILCYM 3 0 User Manual 116 c Aggregate The aggregate tool is used to create a new Raster layer with a lower resolution The Aggregate procedure allows indiscriminately grouping of cells in a grid file to an output file with a lower resolution larger grid size You must specify the aggregation factor which determines how many cells will be merged into one and thus how big will the new cells For example by a factor of 2 the new cells will have twice the length and two times the width of the original cell In other words four cells are merged into one Aggregation starts at the upper left end of a raster If a division of the number of columns or rows with factor does not return an integer the extent of the resulting Raster object will either be somewhat smaller or somewhat larger than the original Raster Layer The values in the aggregate grid cells depend on the procedure chosen maximum mean median minimum or sum fashion lll Apprepate L3 E fie L o Output File Jl End Mean v Factor 9 Expand Q Truncate Add to map d Disaggregate This feature is frequently used to create a new Raster layer with a higher resolution smaller cells The values in the new Raster Layer are the same as in the larger original cells The tool divides the grid cells into smaller cells The ILCYM 3 0 User Manual 117 values of the original cells are assigned to the smaller cells MM Disaperepate Hes TTS
67. To create a project follows the steps below Go to File Menu gt New gt ILCYM Project Note The first two options in the New sub menu refer to creating a new uDig project which should not be confused with an ILCYM project ILCYM 3 0 User Manual 26 Edit Operations Layer Model Builder Window Help a New Layer 144 Open Project G New Map Close Project SP ILCYM project 0 Close Chrl r3 other Close All Cerl ShiFk Vy Create Feature Type Ctrl s Ctri ShiFt 5 Select a wizard Wizards type Filter text eS Project Se ee ILCYM 3 0 User Manual 27 Creating new project Fill the fields to create 3 new project Registry Info Project Name PTM project Species Name Phthorimaea operculella Author Date 41412011 Obs Ae Immature life stages Egg Larva Pupa Adults life stages Female Male Means that the fields must be obligatorily filled user should input all the development stages of the insect that S He will be evaluating separated by a comma For complete life table data the user must write the life stages as it is written in the data files note that ILCYM s is character case sensitive For Adults stages by convention you need to write start with Female and then Male stages If you forgot one or more life stages after creating the project you need to delete that project and create a new project The functions used in creating a pr
68. a number of models that might describe well the development curve of insects ILCYM does not check the appropriateness of each model automatically You might test several models and select the best one according to the selection criteria AIC and MSC ILCYM 3 0 User Manual 45 f ILCYM File Edit Operations Layer Model Builder LTE D 4 LJ Mortality S Bia ww l A senescence u ILCYM s Projects Ex 3 77 Species Interaction gt u PTMproject Window Help Development 2 LA Rate and temperature effect Oviposition gt Time and its variation gt ILCYM contains different models for development rate these models are represented by the name of the first authors who first developed the equation i e Shape De Michelle Deva Logan etc Under the name of each author there are series of models developed from the original equations marked by number i e Shape De Michelle 1 Logan 2 etc af Development Rate 15 xl Development rate Select the life stage and then the selection model type Selected Project PTM Project Life stages C Egg C Larva Pupa Multiple Selection C Single selection Models Sub models Selected models ID Submodels Select all 59 models ES SharpeDeMichelle Stinner Other models Additional values Temp Value M Limits M The first page of the wizard displays the name of the project and the stages that the user created du
69. ages of the pest survive throughout the year Finite Rate of Increase Lambda A measure of the rate of growth of a population The amount that the population must be multiplied by to give the population size in the next time unit assuming the population is in stable age distribution Generation Index This index is used in risk mapping and estimates the mean number of generations that may be produced within a year Generation Time The average age at which a female gives birth to her offspring This is equivalent to the time that it takes for a population to increase by a factor equal to the Net Reproductive Rate Intrinsic Rate of Increase rm A measure of the rate of growth of a population This is the instantaneous rate of change per individual per time interval assuming the population is in stable age distribution It is equal to the natural log In of the Finite Rate of Increase Mean Life Expectancy How long an individual can be expected to live on average This is influenced only by the age specific mortality graph ILCYM 3 0 User Manual 148 Net Reproductive Rate Ro The average number of offspring an individual in a population will produce in his her lifetime Unlike the Total Fertility Rate Ro depends on age specific mortality rates Sex Ratio The fraction of the population that is female Technically this value is not a ratio but this has become a common way of representing the gender distribution of a population Th
70. and ignore the raisons mentioned earlier For complete life table data arrangement are similar as explained above where life stage of each individual is traced in one column and the state of each individual is noted in rows for each evaluation time until the last individual of the group has died The main difference here is the absence of male progeny ILCYM 3 0 User Manual 18 For cohort study the data arrangement is identical to the description provided above only that male file is omitted Below is an example file for complete life table at a given temperature ite Table at 25 Degree Only Female Notepad File Edit Format View Help Egg Larvad Larvad Larvad O 0 0 0 0 28 29 18 34 27 23 19 27 34 23 26 20 14 Egg Egg Larval Larval Larval Larval Larval Larval Larvae Larva2 Larva2 Larvae Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larvad Larvad Larvad Q 0 Q 0 0 18 38 37 46 26 28 11 23 26 13 14 22 26 32 22 35 16 11 Egg Egg Larval Larval Larval Larval Larval Larva2 Larvae Larva2 Larva2 Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larvad Larvad Larvad Larvad Egg Egg Larval Larval Larval Larval Larval Larval Larvae Larva2 Larva2 Larva2 Larva3 Larva3 Larva3 Larva3 Larvad Larvad Larva4 Larvad Larvad Larvad Larvad Larvad Egg Egg Egg Larval Larval Larval Larval Larval Larval Larva2 Larva2 Larwa Larva2
71. as the potential to pose an invasive threat after the pest s introduction The map plots an index establishment risk index ERI which is the ratio between periods time intervals in which population are expected to increase and total periods within a year The index is defined as the number of time ILCYM 3 0 User Manual 133 intervals with a net reproduction rate RO above 1 li21 divided by the total number of time intervals within a year li By default the maps as presented in the atlas are generated by using a 1 month time scale however the calculation can be also based on other time scales for example 1 day intervals The formula for using monthly intervals is as follows cI 1iz EC I ERI in which is the interval of the month with 1 2 3 12 and its value is 1 if the population is expected to increase within this interval I 1 if RO 2 1 and the value is O if the population is expected to decrease l O if RO lt 1 according to the established temperature driven phenology model and the total number of intervals is 12 If the index is calculated on a daily time scale the formula becomes yi 36s 1 ERI D I where l than is the interval of day i with i 1 2 3 365 and the total number of intervals becomes 365 The EHI takes values between O and 1 A ERI 1 represents areas where the specie s population is expected to grow throughout the year An ERI lt 1 characte
72. asks A la tmin_01 hdr E tmin_o2 Fit Make a new Folder 112 tmin_02 hdr 3 Publish this folder to the E tmin_03 flt US a tmin_03 hd E Share this Folder a Is tmin_04 Fle 112 tmin 04 hdr Other Places Y sl tmin_OS Flt tmin_05 hdr x ES tmin 06 Flt M 12 tmin_06 hdr Tmin E tmin _07 flt File Folder 14 tmin_07 hdr Date Modified Today April 08 ec tmin_08 Flt 2011 10 34 AM tmin_08 hdr E tmin 09 Flt lia tmin_09 hdr ES tmin_10 Flt 14 tmin_10 hdr E tmin_14 Flt 4 tmin_11 hdr E tmin 12 flt 112 tmin_12 hdr 3 3 2 ILCYM Tools A number of practical and analytical functions are available in ILCYM Tools menu These functions allow you to create a shape file of points from a text file extract data from climate database and convert grid from and to different GIS formats File Edit Navigation Modeling ILCYM tools Spatial Analysis Window Help DE amp Rasterto polygon a H ILCYM s Projects Explorer 23 4 Raster to points lll metadata Text to shapefile projectRegistry Extract by points u project udig u PTM project Export ascii files Import ascii file x4 q Index interpolator Ta Layers 25 0 e4z V world adm00 rm ER FIR dem terrain aspect Wildca c unit Zoom 1 2013 O 157 102 6778 ILCYM 3 0 User Manual 102 a Raster to polygons Convert a raster dataset to polygon The inpu
73. below will appear ar Several generation at constant or fluctuating temperature B x Several generation at constant or fluctuating temperature Simulate two species Temperature file Load temps C Documents and Settings Henri Desktop Daily new Hyo00 tx Minimum temperature min E Maximum temperature Number females parasitoids 15 Host number 100 Calculate Host Load temps Allow you t load the temperature file View file for viewing the load temperature file ILCYM S input standard climate station file as shown below File Edit Format View Help codigo Hyo008 Locality chicche Lat 11 810161 Long 75 284856 Type Daily Alt 4125 Id Date Year nday tmin tmax 1 8 20 2011 2011 81 7 83 16 38 2 8 21 2011 2011 82 9 82 16 38 3 8 22 2011 2011 83 8 63 16 38 4 8 23 2011 2011 84 8 23 15 62 5 8 24 2011 2011 85 10 6 17 52 6 8 25 2011 2011 86 9 82 16 38 7 8 26 2011 2011 87 7 83 16 8 8 27 2011 2011 88 7 83 16 38 9 8 28 2011 2011 89 9 03 16 76 10 8 29 2011 2011 30 10 99 17 52 11 8 30 2011 2011 91 10 6 16 12 8 31 2011 2011 92 9 82 16 13 9 1 2011 2011 93 9 82 16 14 9 2 2011 2011 94 7 83 15 23 15 9 3 2011 2011 95 7 43 16 16 9 4 2011 2011 96 7 43 16 38 17 9 5 2011 2011 97 7 43 16 18 9 6 2011 2011 98 9 42 16 19 9 7 2011 2011 99 10 21 17 9 20 9 8 2011 2011 100 8 63 17 9 21 9 9 2011 2011 101 10 99 18 28 22 9 10 2011 2011 102 12 55 17 52 23 9 11 2011 2011 103 10 6 14 85 24 9 12 2
74. ble Statistical Analysis Graphics What graph do you want to see O Age stage specific survival rate O Age specific survival rate a 5 c 2 41i 2 2 i 4 2 e ILCYM 3 0 User Manual Age stage specific distribution rate Egg Larva Pupa Female Male Adults 40 60 80 Age days 40 60 80 Age days 71 v Age specific survival rate Stochastic Simulation Output BAX Life Table Statistical Analysis Graphics What graph do you want to see Age stage specific survival rate Stable age stage distribution Age specific survival rate vi Modifying the scale of the graphs In some cases the graph does not show the plot correctly to see it well right click on the graph and select Properties Modify the scales legend or chart and click on Accept button Image Properties Chart Title Age specific survival Chart X Age days Chart Y Age specific survival rate Legend Scale Leg X Ainx n MinY 0 Leg Y Maroc 100 Maxy 0 ILCYM 3 0 User Manual 72 b Stochastic simulation at constant temperatures This tool allows the simulations under constant temparatures f LEYI File Edit Species Interaction Window Help Fir Deterministic S IevyM sB ES O Stochastic Constant temperatures MW metadata Validation Fluctuating ternperature PTM project Constant Temperatures Stochastic ee RES amp New data N Insects 100 Days 365
75. classify values to a common scale for example for use in a suitability analysis or for creating a cost raster for use in the Cost Distance function e Set specific values to NoData or to set NoData cells to a value The function re classifies groups of values to other values For example all values between 1 and 10 become 1 and all values between 11 and 15 become 2 Reclassifies data from a grid according to class limits specified by the user These limits can be adjusted manually Add the button can be inserted extra classes and with the Remove button these can be eliminated ILCYM automatically displays the minimum and maximum file ILCYM 3 0 User Manual 119 Po Mew value Add class Remove class on In Add to map h Overlay This tool is applied to two files with the same dimensions and location number of columns and rows resolution and location min and max X and Y coordinates Overlay can stand one on the other so to speak and make some arithmetic operations on corresponding grid cells and place the results into a new file Arithmetic operations covered include addition subtraction multiplication division and calculation of minimum and maximum values Overlay allows you to place them on top of each other as it were carry out some arithmetic on corresponding grid cells i e cells directly above each other and place the results in a new third grid The arithmetical operations included are addition subtrac
76. conidae were chosen i Actual oviposition time In ILCYM the first step in conducting simulation of interacting species is to evaluate the actual oviposition time of the female parasitoid which is defined as the exact length of the oviposition period This quantity is different to the time span between first oviposition and oviposition of the last egg To estimate this parameter go to window then open perspective select validation and simulation Under species interaction select actual oviposition time as shown below Fie Edit Simulations Species Interaction Window Help P D m Actual oviposition time Parasitation rate ILCYM s Projects E Graphs il metadata ME Apenteles Project He PTM Project In this example ILCYM s projects window contains two projects PTM project standing for Phthorimaea operculella Apanteles project designating Apanteles subandinus project ILCYM 3 0 User Manual 83 After clicking on actual oviposition time the window below will appear select female represented by Fand proceed with the analysis by clicking the next bottom Actual Oviposition time Actual Oviposition time Select the life stage and then the selection model type Selected project Apanteles project Life stages select the life stage F means female Type selection models You should select one of the options Evaluate the life stage using several models Evaluate the life sta
77. d a bioclimatic based modelling approach nsect Science 12 45 56 ILCYM 3 0 User Manual 153 Appendix Table 1 Functions fitted to development time in ILCYM software aD Function Expression Reference Dichotomy functions l PY 21 X X 2 t Z 1 Pru arena 1x xj he r Z B X t DX F X E Y X D Z D B X B X 7 D P Y 1 X X B X By A 2 Probit tee ie 3 Cloglog In In P Y 1 B X B X Exponential functions Exponential F X E m b X c X modified 1 Exponential modified 2 14 26 Exponential e X 4 modified 3 Exponential modified 4 4 Weibull ILCYM 3 0 User Manual 154 For dichotomy functions A natural logar thm of the days observed X ith temperature considered as a categorical variable so that the value to replace in the linear part of formula is either 0 or 1 m success 1 The statelasted until the individual turn to next state failure 0 Thestatedoes not change or theindividual died before adulthood For exponential functions F l X cumulated relative frequency of the days observed for the ith temperature X normalized age days median survival time of each temperature F X cumulated relative frequency of the days observed of each temperature ILCYM 3 0 User Manual 155 Table 2 Sub models fitted to development rate in ILCYM software Sharpe amp DeMichele 1 Sharpe amp DeMichele 2 3 Sharpe amp
78. data from the same temperature replications can be pooled or used separately when fitting models for describing temperature effects on insect development or fecundity however if the temperature for repeated life tables deviated by more than 1 C the data should not be pooled but submitted separately to the analysis ILCYM 3 0 User Manual 10 1 4 1 Data records for complete life tables Data for each life table can be arranged in an ordinary spreadsheet The life stage of each individual of the cohort is traced in one column i e number of columns n The state of each individual is noted in rows for each evaluation time generally one day until the last individual of the cohort has died An example is given in Figure 1 For each individual surviving the development stage which is in the example given egg larva or pupa is recorded for each evaluation time indicated in the spreadsheet as E egg L larva and P pupa The evaluation interval is generally one day however the evaluation time might be changed for example 12 h 8 h etc however the interval should be constant throughout the experiment and always the same in all life tables constructed at different constant temperatures that enter the analysis for developing the phenology model in ILCYM The number of life stages to be evaluated depends on the species under study and needs to be chosen by the investigator Letters for indicating each lif
79. dead M M dead 21 13 M 17 22 20 M M dead M M dead 11 4 M 4 13 20 M M dead i M M dead 5 5 M 5 10 12 M M coco dead M M dead 3 2 M 3 6 6 M M dead M M dead 2 1 M 2 4 4 M M dead i M M dead 1 2 M 1 1 1 M M dead M M dead 1 1 M 1 1 1 M M dead M M dead dead dead M dead 0 1 M M dead M dead dead dead dead dead dead dead 0 M M dead dead dead dead dead dead dead dead dead dead M M dead i dead dead dead dead dead dead dead dead dead dead dead dead life table data in a spreadsheet Each column For subjects that remained in the same stage as in the 2 day earlier evaluation the state of subject is clear for the missing time intervals and can be filled however if a subject developed within this 2 day interval into the next stage then the state is unclear for the missing intervals In that case missing values need to be filled ILCYM will handle these data as interval censored data i e ILCYM 3 0 User Manual 12 development between interval x and interval x 2 days For further information on this merit see section data analysis Transforming spreadsheet data into the format for analysis in ILCYM ILCYM software only run data in text formats Data organized in a spreadsheet need to transform in test format with the extension txt see Figure For easy identification of the data it is recommended to include the following identifiers in the document s name Species
80. del and go to Properties then write the number or text in the Scale Area c Senescence At adult stages males and female because the insects die instead of developing to a next stage the sub model in this section describes the temperature dependent senescence rate ILCYM software provides a number of sub models just as for development rate that can be used for describing temperature dependent senescence rate The process of model selection is identical as on develomental rate Y ILCYM File Edit Model Builder Window Help Fir HE Mortality Mm Compariso HH A E SENESCENCE OLLCYM Development d a mie Ovipositian PTM project LA Senescence nee Model already selected Life stage selected Female Graphic Output text r r amp r 5y Model selected Rawtosky 1 Parameters estimated b 0 00391 Tb 21 32343 senescence 1 day Reset Model Er C 1 f T T T T T 1 0 5 10 15 20 25 30 35 40 45 50 55 temperature degree celsius ILCYM 3 0 User Manual 52 d Mortality Mortality is another important process in an insect life cycle that is affected by temperature ILCYM s quantify the effect of temperature on the immature stages egg larva and pupa of the insect life cycle Many non lineal models that can best describe the mortality induced by temperature low mortality near an optimal temperature and mortality increase with the deviation from the optimal temperature are g
81. e Stage Order number and changing the value manually ILCYM before uploading files check for possible problems and highlight them When Clicking on View button ILCYM shows the file with errors that can be solve through ILCYM interface Y Warning N There are some observaciones in your Files ILCYM 3 0 User Manual 36 Warnings in the files m b Uploading life table data A life table tracks the history of an insect cohort generally starting from eggs i e it shows the life history of each individual of the cohort in columns The state of all individuals is noted daily rows until the last individual of the cohort has died For each individual surviving the development stage in the sample below Egg Larva and Pupa is noted non survivors are marked i e Dead In the case of male adults Male is entered while for living female adults the number of oviposited eggs is noted This is a complete life table The life table would be incomplete if the cohorts history was followed up until the insects have reached the adult stage i e incomplete life table In this case additional data about oviposition from additional experiments p e another group of adult insects are required called oviposition file see below When using uploading complete life tables the oviposition file is generated automatically from the data Insect insect insert3 insect d insect 5 ninina il Day 1 Day 2 Day 3 Day
82. e models are manifold The approach used to develop and implement the potato tuber moth model can be principally used for other insect species The strong collaboration between CIPs ILCYM 3 0 User Manual i Agroecology IPM team and the Research Informatics Unit made it possible to develop the software program Insect Life Cycle Modeling ILCYM version 3 0 with the objective of facilitating the development of further insect phenology models and to provide analytical tools for studying insects population ecology It is hoped that the ILCYM software will benefit researchers from national and international agricultural research institutes and universities who either intend to start with insect modeling or want to apply advanced modeling techniques without having the requisite mathematical knowledge or being experts in the field Ultimately the application of ILCYM software and modeling results should provide a better understanding of insect s biology and ecology and in the long term should support a rational decision making process in pest management and improving farmers food security and daily lives ILCYM 3 0 User Manual ii Acknowledgment The Insect Life Cycle Modeling software described here has been jointly developed by staff members of the Integrated Crop Management Division ICM Division and the Research Informatics Unit RIU of the International Potato Center CIP We are grateful for the financial support received by th
83. e German Federal Ministry for Economic Cooperation and Development BMZ Germany and the Regional Fund for Agricultural Technology FONTAGRO Washington D C without which this software could have not been developed ILCYM 3 0 User Manual i Table of Contents nn o I Ye dele izs de mr a i l INTRODUCTION ze ee ee 1 1 1 The modeling approach applied in ILCYM 22uus00400nennennnennnnnennennnnne nn 4 1 2 The conceptual basis or ILC YM a u a aa aa 7 A Shaun cT xod 9 rA Lite table dani lio 9 1 4 1 Data records for complete life tables ooccconccconncccnnccccnnnanononnnononos 11 1 4 2 Data records for incomplete life tables oocccconcncccnnccconnnoconncocnnnnnnnns 15 ik IESYNEAPPEIGATIONS sie 20 2 1 System FEQUIFEMEMIS cccccceseccccsscecceeseeccseeeecceuscesseuseeecsaeeeeseeeeessaneeessageees 20 22 NAS ANNA ECY M een 20 2 2 1 Window XP operating system for computer with 32 bytes 20 2 2 3 Windows Vista 7 and above for computer with 64 bytes 22 Hl IESKM S PERSPEETIVES he eisen 24 ome Builder T einen 24 3 1s 1 Creating ACW Project na 26 3 lec ln aea ad two cula hoo onte dc ta x 30 3 1 3 Deleting project ccc oooonccnccccconconcccononcnncnononncnnnonnnnnnnnnnnnnnnnnnnnonnncnnnnononncannnnnnas 31 3 1 4 Project Properties A A a a a a E a 32 So UDI AIN dad E OO E E T 33 a Uploading CONOM data nee toten Oc hi ide
84. e application is launch click on the R symbol in the toolbar as shown below CA a window will appear indicating that the requirements have not been installed ILCYM 3 0 User Manual 21 To load the applications select the path where the installers are located it maybe on the CD or in your desktop select the items one by one and click the button Install to start your installation MM System Requirements Installation Software to install EG Requirements Software Is R 2 15 1 installed in CH Installed Are Bserve and R libraries installed Installed Caution Install R in C X as shown below CNR 2 15 1 2 2 3 Windows Vista 7 and above for computer with 64 bytes To install ILCYM software in this operating system you must follow the instructions listed below 1 Double click in the INSTALL icon 2 Instal ILCYM directly in C as shown below ILCYM 3 0 Setup Choose Install Location Choose the Folder in which to install ILCYM 3 0 gt Setup will install ILCYM 3 0 in the following folder To install in a different Folder click Browse and select another folder Click Install to start the installation Destination Folder CAILCYM Browse Space required 232 6MB Space available 11 5GB ILCYM 3 0 User Manual 22 3 Follow the instructions and continue your installation until the end Note For all window operating systems 1 Make sure the R 2 15 1 software is installed directl
85. e primary sex ratio is the proportion of births that are female Stable Age Distribution The age distribution which the population will reach if allowed to progress until there is no longer a change in the distribution Survivorship The probability that an individual survives from age zero to a given age Total Fertility Rate TFR The total number of offspring a female would have on average if she were to live to the maximum age Compare with Net Reproductive Rate ILCYM 3 0 User Manual 149 VI References Allen J C 1976 A modified sine wave method for calculating degree days Environmental Entomology 5 388 396 Andrewartha H and L Birch 1955 The distribution and abundance of animals University of Chicago Press Chicago Baker R H A 1996 Developing a European pest risk mapping system 1 EPPO Bulletin 26 485 494 Baker R H A C E Sansford C H Jarvis R J C Cannon A MacLeod and K F A Walters 2000 The role of climatic mapping in predicting the potential geographical distribution of non indigenous pests under current and future climates Agriculture Ecosystems amp Environment 82 57 71 Braasch H U Wittchen and J G Unger 1996 Establishment potential and damage probability of Meloidogyne chitwoodi in Germany 1 EPPO Bulletin 26 495 509 Curry G L R M Feldman and K C Smith 1978 A stochastic model for a temperature dependent population Theoretical Population Biology 13
86. e stage can be freely chosen Non survivors are marked always as dead Emergence of male adults will be recorded as M while for living female adults the number of eggs laid per female during the evaluation interval is noted Excurse Notes on the evaluation interval Since at high temperatures the development is faster than at low temperatures it could be that the interval of one day might be too broad for determining well the variation in insect development to the next stage In the example given Figure 1 all eggs remained egg at the 4 evaluation and had developed into larvae at the 5 evaluation Therefore these data would not provide good information to assess the distribution curve for the development from eggs into larva The median development time would be expected to be between 4 and 5 days but its real value and the slope of the distribution curve cannot be assessed In this case it would be helpful to reduce the interval time to 8 or 12 hours for obtaining at least one data point in which the proportion of subjects developed into larvae is higher than 0 and lower than 100 For lower temperatures such a shallow evaluation interval probably would be not necessary because the development time increases significantly and the development time distribution curve could ILCYM 3 0 User Manual 11 be well established even when a broader evaluation interval would have been used for example of 2 days The evaluation interval could be dif
87. ed to this will speed up the process of estimating indices and will only produced values within your chosen range Such option may guide the user to not estimate indices on zone of extreme temperature like the desert or pole O Temperature filter Tmin max Select the path and name of the output map Clicking on Apply button the simulation will run this process take some minutes or hours depending on the size of area and the resolution Summary on how to create a map in ILCYM Load climate data base Add shape file Select the project ILCYM 3 0 User Manual 138 Goto geographic simulation Select the region to simulation by clicking on the ILCYM box selection tool Click on get rectangle Designate or write the name of the output file Click on apply button and wait for several minutes or hours or days depending on the size and resolution of the maps Load the map the steps are identical to adding shape file c Simulation Point Under ILCYM population analysis and mapping perspective menu go to Modeling and click on point The following window will appear where you can simulate the life table parameters and or indices in a location Also the user can upload its own temperature data file to calculate these indices B8 By Point Calculate Life table parameters graphics Life table temperatures Climate database B GIS_Training August232012 Climate 2000_World l Calculate Parameters Plot Parame
88. el linked with geographic informations systems In J Kroschel and L Lacey eds Integrated Pest Management for the Potato tuber moth Phthorimaea operculella Zeller A potato pest of global importance Tropical Agriculture 20 Advances in Crop Research 10 Margraf Verlag Weikersheim Germany Sporleder M J Kroschel and R Simon 2007 Potential changes in the distributions of the potato tuber moth Phthorimaea operculella Zeller in response to climate change by using a temperature driven phenology model linked with geographic information systems GIS pp 360 361 XVI International Plant Protection Congress BCPC Hampshire UK Glacow UK Sporleder M R Simon J Gonzales P Carhuapoma H Juarez F De Mendiburu and J Kroschel 2009 ILCYM Insect Life Cycle Modeling A software package for developing temperature based insect phenology models with applications for regional and global pest risk assessments and mapping user manual nternational Potato Center Lima Peru Steinbauer M J T Yonow I A Reid and R Cant 2002 Ecological biogeography of species of Gelonus Acantholybas and Amorbus in Australia Austral Ecology 27 1 25 Stinner R E A P Gutierrez and G D Butler Jr 1974 An algorithm for temperature dependent growth rate simulation Canadian Entomologist 106 519 524 Stinner R E J Butler G D J S Bacheler and C Tuttle 1975 Simulation of temperature dependent development in p
89. emperature and the subsequent columns represent number of egg laid by the individual in a constant time interval generally one day until its death ILCYM 3 0 User Manual 14 If only temperature is expected to affect the female rate in the progeny but not female age the eggs obtained from each temperature tested can be reared together pooled because then the effect of female age will not be analyzed in that case the female rate is considered to be constant throughout the life span of female adult 1 4 2 Data records for incomplete life tables The life tables would be incomplete if the cohorts history were followed up until insects have reached the adult stage Data recording would be the same as for complete life tables but only the event of male and female emergences would be indicated After adult emergence the survival time of adults would not be further monitored and hence the columns can indicated as dead in the subsequent cells of the row Data for a single life table would look as shown in Figure 2 Reproduction would be assessed with other subjects in additional experiments at the same temperatures and adult survival time would be retrieved from these experiments LH 20 G Notepad File Edit Format View Help IT T TI T ITI ITI ITI ITI ITI IT T TI TI ITI ITI ITI ITI ITI mnmmmmTnmTmmnTmiiUuuuuuuuuuuuurrrrz i5r mmm mnmmmmmnmmnmTnmnmuuuuuuuuuuuurrrrzTcrtr Imi Ir mE EEEEzzuuuuuuuuuuuuu
90. er moth is today reported in more than 90 countries and is considered the most damaging potato pest in the developing world The leafminer fly which is highly polyphagous is reported in 66 countries In its global pest management research effort CIPs Agroecology IPM team is interested in better understanding pest biology and ecology in order to find out why some species are more invasive than others We also aim to predict the potential pest population development in different agroecological zones as well as to determine critical infestation periods for better targeting pests during the cropping season Phenology models for potato pests based on temperature have become important analytical tools in CIP s research program for predicting evaluating and understanding their population dynamics in agroecosystems under a variety of environmental conditions At the beginning a temperature driven phenology model for the potato tuber moth was developed and validated through field and laboratory data which successfully predicted life table parameters for different agroecological zones It was then used to predict the establishment risk and potential pest activity in specific agroecologies according to temperature records It has also been used to estimate the population structure under given temperatures and allows for performance simulations of field applications and to determine field application rates and frequencies Further possible applications of thes
91. erature from the number of individuals used and the number of individuals that developed to the next stage Survivors Evaluations that resulted in zero observations change of stage need to be included in the record otherwise ILCYM would not determine well the time span in which the individuals developed to the next stage Data for adults survival time of males and females are recorded in the same manner The difference is that adults do not develop into another stage but die Hence the number of insects tested column 3 should be equal to the sum of individuals that were recorded as dead over all evaluation for a single temperature No additional mortality rate is calculated as for the immature life stages The oviposition data are recorded as described above for life table data The number of eggs oviposited should be retrieved for the cohort of females included in this experiments Data type dealing with only female population ILCYM authors recommend two sex life table to be used as input data to the software as described above This is because most insect species Lepidoptera Coleoptera Orthoptera and Diptera are bisexual having both males and females and both sexes may cause economical loss or be vectors of disease In addition there is variation in developmental rate among individual and between sexes in natural population However traditional way of collecting life table Lotka 1907 only deal with female population
92. etadata E Gpenteles Project He PTM Project The window below will appear go to the right of the host project combo and select the host project then select the attack stage and input the parasitation table DI x Parasitation rate Select the life stage and then the selection model type Host Project PTM Project Attack stage Hala Parasitoid Project Apenteles Project Parasitation table summary View table Multiple Selection C Single selection Models Sub models Selected madels lyti Io Sub models Select all 59 models SharpebeMichelle Stinner Other models Additional values Temp Value NI BE gt E Host project PTM project for this example Attack stage Larva Apanteles subandinus is a larva parasitoid for Phthorimaea operculella ILCYM 3 0 User Manual 87 Parasitation table summary Click on this bottom to select the location on your computer where the table summary of parasitation is store A sample table format is display below E Apanteles Parasitation Table IE X File Edit Format wiew Help r5 15810534 92 95 778924 62 91568326 140 2368098 r a In this table the first column designate the temperature the second column is the number of female parasitoid and the third column is the number of host parasitized View table Once the table is loaded you can view the table by clicking on view table bottom
93. eviations between observed and predicted data MRC is an extension of AIC and the R that explains how the model captures the variability within the data To modify the scales legend s coordinates titles of the graphs click on the properties in the popup menu of the image ILCYM 3 0 User Manual 44 To visualize the changes click on the Accept button If you want to restore change only right click on Restore option in the menu 100 90 Image Properties 50 d Chart e 9 70 Title D t 6096 Chart X In development time Ln days E Copy E Properties Chart Y accumulated development frequency p 50 m Restore T 40 Legend Scale 5 gt E 30 Leg X 0 MinX 0 MinY 0 eo le 100 45 O 20 Leg Y Maxx MaxY 1096 Gray Scale 0 0 0 0 5 1 0 1 5 2 0 25 3 0 35 4 0 45 5 0 In development time Ln days Copy option allows copying the image to the clipboard and then pasting in any other document Restore option restore the image to the original design b Development rate The inverse of the median time 1 median time calculated by the estimated function of the development distribution is the development rate due to temperature This evaluation in ILCYM complements the evaluation of the development time here you fit a model that describes the temperature dependent development rate for each particular life stage ILCYM provides
94. f the weather stations defining co variables for the calculation and choice of the function used by thin plate algorithm ILCYM 3 0 User Manual 110 i Geo reference your inputs data and use a digital elevation model that fit in your region as shown below ILCYM File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help r3 Y al Y e qe lt P gt e pos a a ER gt amp E ILCYM s Projects Explorer 22 O Gd dem X Ziele E cm u metadata Palette b IN V projectRegistry AER E c I project udig Riche i PTM project Info s E Mosaic Info To display i Info information i select the info Distance 2 42 Wla mask v 3 WeatherStations vmm dem rm Dem Reclass rpm dem cortad 3B Ei reclass 7 world adm00 rm ER EA dem terrain aspect Da ERI tool and click on a Map Q Zoom 14 v C Wildea c unit Selection Select climate data type or the risk indices data type Type of data Climate Daily weather data Project ILCYM 3 0 User Manual 111 iii Select the data for each weather station v Index Interpolator B VersionOflIleym Climate Daily Note Make sure each of your data file has the following structure Deren nme OE i a Eile Edit Format View Help Codigo Hyo010 Locality Huancas Lat 11 805694 Long 5 505406 Type Daily Alt 3596
95. ferently chosen specifically to each temperature evaluated however in the data spreadsheets used for developing IPhM in ILCYM the interval rows needs to be the same in all life tables temperatures Therefore even if the interval used for one life table was for example 2 days and the interval used for the life table at the highest temperature was 12 hours all soreadsheets need to be filled using an 8 hour interval Figure 1 Example for recording represents an individual and its state life stage is recorded in a constant time interval generally one day until its death Different development stages of the species are recorded by using stage specific letters Adult males are marked as M and for surviving females the number of eggs laid per evaluation interval is recorded For further explanations see the text A B c D E F AAA AN E E E E E E E E E E E o E i E E E E E E E E E E E E i E E E E E E E E E E E E E E E E E E E E E E E E L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L E L L L L L L L L L L L L E L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L P P dead P F P P L L L L L P P dead P P P P P P P P P P P dead P P P P P P P P P P P dead P P P P P P P P P P P dead P P P P P P P P P P M dead P P P P P P P P q P M M dead 18 13 M 14 P P P P dead M M dead 25 17 M 18 4 5 M M
96. for more realistic simulation and both are included in ILCYM Development reproduction and survival in insect species describe primarily insect demography for understanding population dynamics additional Knowledge about dispersal and migration as well as the influence of other biotic or abiotic factors affecting the insects survival are necessary The cohort up dating algorithm calculates population number it also provides information about the quantitative biology of the insect species under study Let note that the resulting population increase only represents the potential population growth of the species at a given temperature regime Real population increase depends on the additional biotic and abiotic factors affecting populations in a given environment Including such factors would introduce much more complexity into the algorithm which is not provided in this version of ILCYM However when a model for a given species is developed it can directly be applied in ILCYM GIS environment for spatial analysis based on real or simulated daily temperature data ILCYM s simulates the potential population increase over time and pest distribution as well as host plan land covers data for analyzing climate change impact etc The steps of developing a model with ILCYM are principally four 1 Collect the data through conducting temperature experiments or if available from the literature ILCYM 3 0 User Manual 5 2 Define the functions describ
97. from file view Allows you to upload temperatures values from a file and the view button allows you to view the value Insect number number of insects Steps combo time step for your simulation which is the multiple of 4 which represents de number of hours within a day For example choosing 48 means your time steps is half an hour Ratio text box ratio between females and males appears automatically from your phenology model Degrees text box for analyzing the effect of temperature increase Calculate allow the calculation Export to Excel for exporting the output to an excel file cting Get parameters option we can calculate the life table parameters and the indices If the user select Get graphics option this image will appear showing the variation of the parameter in the year a By Point Calculate Life table parameters graphics Life table temperatures d a m in Em tn E m E 5 51510 T L m o 46 905 Minim Y axis scale 2 Rm Lambda br Save value Julian day um 423 Maximum 60 72 oelect each life table parameter to plot its variation in the year ILCYM 3 0 Us er Manual 141 B8 By Point e Save value button this button saves the value of the parameter Y axis scale Minimum 42 3 Maximum 60 72 e Change modify the Y axis scale e Gray scale check displays the graph in gray scale ILCYM can jointly display
98. ge and automate phenology models Without model builder the management of models and the data supporting them can be difficult Phenology models contain a number of interrelated life stage processes and with the model builder ILCYM user can at any time add replace or delete sub models In addition users can replace old data with new information change assumptions as well as model parameters and consider alternatives sub model combinations In summary ILCYM model builder is a flexible interface for creating visualizing running modifying documenting and sharing models e Create phenology Model The user creates a phenology model by adding sub models Each life stage has a wizard a sequence of dialog boxes that prompts user s for the information needed to define the stage process and then add the process to the over all phenology model e Visualize phenology model Data life stage sub models and their choosing parameters are symbolized in the window named summarize In this window user can visualize the flow of processing in the phenology model building and see which life stage are included in an analysis and which possible output is created from which input e Run model The Model Builder runs the sub models that make up the over all phenology model It creates the output data sets saves them to the software workspace and loads them as object for simulations and mapping e Modify phenology model Every section in a life stage pr
99. ge using one model ILCYM 3 0 User Manual 84 For example if the option evaluate the life stage using several models is choose the window below shows different functions that can be fitted for estimating actual oviposition time The process of selecting the best fitted model is identical as in the previous section of this manual Actual Oviposition time Several models Modeling using several models Life stage selected F stands for female Models display name of the mathematical expressions available Estimation method stand for minimization algorithm ILCYM 3 0 User Manual 85 The windows below display the statistical values and the graph of the selected model representing actual oviposition time Actual Oviposition time Model selected 54 3103 Accumulated frequency 96 ILCYM 3 0 User Manual 86 ii Parasitation rate Parasitation rate is used in ILCYM to designate parasitism rate this quantity maybe constant or temperature dependent In case you choose to consider it as a factor of temperature you will need to fit a nonlinear function to represent its variation with temperature Under species interaction click on parasitation rate as shown below File Edit Simulations Species Interaction Window Help Actual oviposition time Parasitation rate ri e ILCYM s Projects E Graphs BI m
100. he specific life stage for easy identification p e PTM_egg PTM for potato tuber moth and egg for the egg life stage The files should be saved as txt files The data are sorted according to 1 The temperature first column 2 The number of days observed for the development time or senescence in adult second column 3 Number of insects in the sample cohort third column 4 The number of individuals that developed to the next stage on this particular day fourth column ILCYM 3 0 User Manual 35 Temperatura Days Sample Dev Sen Upload Data a Data Type 9 Cohort studies of single LIFE STAGES Life Table gt gt hj Ee cH C Data Path Data Mame Stage Order D APTM datas FTM_egg txt CPTM datas PTM Female txt DAPTM datas PTM larva Ext DAPTM datas PTM male txt D APTM datas PTM pupa Ext bata Files Pate 0 5 Owvipasitian Data D PTM datas PTM_owo Ext c Co h h JS y e MOL U eu han 00 Chosen data files appear in the data file list file location file name and order of particular stage that the data are representing is indicated The Stage Order is placed automatically in the sequence in which the data were uploaded however you are requested to rearrange the order following the order in which you have declared your life stages If you started with egg following by larva the file with egg should be 1 and larva 2 You change the order by clicking th
101. he route while naming your map file to correct the error you need to rewrite the name of the above error as E New_Maps mapa_Gl asc or also E New Maps map Gl asc or E NewMaps ILCYM 3 0 User Manual 146 Y Add Data c Fer Resource Selection ch Please select a resource E mapabl Resources Selected 1 When you have correct the error click finish and the map will appear on ILCYM s output window as shown below 3 countries 3 23 ILCYM 3 0 User Manual 147 V Glossary Activity Index This index is used in risk mapping and is explicitly related to the finite rate of population increase Actual oviposition time This tern is use to represent the female parasitoid exact length of the oviposition period Age Distribution The proportion of individuals in a population of same age in each class Age Specific Fertility Rate The number of progenies per individual within a specific age interval during a specified time Age Specific Mortality Rate The fraction of individuals in a population that die during a given age interval Doubling Time The time it would take a population to double given no changes in age specific mortality or fertility rates Any change in the fertility or the mortality graphs changes doubling time Establishment Risk Index This index is used in risk mapping and identifies those areas in which an insect pest may survive The index is 1 when a certain proportion of all immature life st
102. i los subelongatus and Scolytus morawitzi a CLIMEX analysis EPPO Bulletin 38 249 258 Venette R C D J Kriticos R D Magarey F H Koch R H A Baker S P Worner N N G mez Raboteaux D W McKenney E J Dobesberger D Yemshanov P J De Barro W D Hutchison G Fowler T M Kalaris and J Pedlar 2010 Pest risk maps for invasive alien species a roadmap for improvement BioScience 60 349 362 Wagner T L H I Wu P J H Sharpe and R N Coulson 1984 Modeling distributions of insect development time a literature review and application of the Weibull function Annals of the Entomological Society of America 77 475 487 Wagner T L H I Wu R M Feldman P J H Sharpe and R N Coulson 1985 Multiple cohort approach for simulating development of insect populations under variable temperatures Annals of the Entomological Society of America 78 691 704 Wilmot Senaratne K A D W A Palmer and R W Sutherst 2006 Use of CLIMEX modelling to identify prospective areas for exploration to find new biological control agents for prickly acacia Australian Journal of Entomology 45 298 302 Worner S P 1992 Performance of phenological models under variable temperature regimes consequences of the Kaufmann or rate summation effect Environmental Entomology 21 689 699 Zalucki M P and M J Furlong 2005 Forecasting Helicoverpa populations in Australia a comparison of regression based models an
103. ications for local regional and global analysis of insect population and mapping International Potato Center Lima Peru pp 175 Press run 50 March 2013 This document can be downloaded from the internet webpage www cipotato org ilcym Check the webpage for updated versions of the present document ILCYM 3 0 User Manual il Preface The International Potato Center CIP seeks to reduce poverty and achieve food security on a sustained basis in developing countries through scientific research and related activities on potato Solanum tuberosum L sweetpotato Ipomoea batatas L Poir and other root and tuber crops and on the improved management of natural resources in the Andes and other mountain areas The origin of the potato is the High Andes in South America Its global distribution began about three hundred years ago first to Europe and then to other parts of the world Many potato pests have evolved in the center of origin of the potato Andean potato weevils of the genus Premnotrypes Coleoptera Curculionidae are major problems for potato growers in the Andean region from Venezuela to Bolivia but have fortunately not spread to other potato growing regions outside the Andes Instead the potato tuber moth Phthorimaea operculella Zeller Lepidoptera Gelechidae or the leafminer fly Liriomyza huidobrensis Blanchard Diptera Agromyzidae have become invasive in many tropical subtropical or temperate regions The potato tub
104. ing the temperature driven processes in insect development using the model builder and compile the over all model the latter step is done by ILCYM interactively 3 Validate the model using additional data that were not included for developing the model generally this data are from experiments conducted under fluctuating temperatures and conduct sensitivity analysis 4 Use the model p e for spatial analysis and mapping in the third module of ILCYM Before and during the development of IPhM be aware of the following steps 1 What is the species you are interested in think first is the ILCYM approach the right one How you want to use the model Modeling is not the purpose itself there should be another aim why you want to have a model the purpose might be to learn about the insect biology alone In any case researchers who start with the experiments described here will learn something about the species population biology The knowledge gained can be applied latter for many different purposes 2 Collect literature on the species for which you want to make a model what has been done so far Are literature data available that you can be used for modeling or model validation 3 Define hypothesis Finally you are working on a piece of science and science requires hypothesis 4 Design and plan your experiment what do you need insect rearing facilities incubators thermometers or loggers what are the temperatures you
105. iven in this program The best sub model can be selected based on the available statistic just like in the previous life stages ILCYM File Edit Operations Layer Model Builder Window Help E37 ED GL Mortality Bla A WU Kl Senescence ILCYM s Projects Ex 23 LA Species Interaction E _ PTM project Development gt Oviposition Here you can modify the functions and their respective initial parameters depending on how they fit to the data Adjusting the parameters as described in the section Modifying initial parameters ILCYM 3 0 User Manual 53 Mortality og Model already selected Life stage selected Pupa Graphic Output text m T a xo eT a xo T c 80 70 Z 60 Model selected Model 38 gt 50 Parameters estimated E al 8 9909e 11 40 bi 0 6776 a2 88 0882 b2 0 5189 1 0 0745 7 10 0 0 5 10 15 20 25 30 35 40 temperature degree celsius ur The above window displays the results of an evaluation of mortality The left side of the screen shows the statistics of the analysis and on the right side a graph displays model results At optimum temperature for development the mortality is lowest but increases at high and low temperature depending on the insect species The statistical analysis shows the estimation of the parameters of the best model used to quantify the effect of the temperature on the mortality e Reproduction The oviposition o
106. izard developmental rate display Models Sub models Selected models Sharpe Dewi che lle SharpeDeMichelle 1 SharpeDeMichelle 2 Deva SharpebeMichelle 2 Sharpe bemichelle 8 Logan SharpebeMichelle 3 Sharpebe Michelle 12 Eriere SharpebeMichelle 4 SharpebeMichelle 7 Stinner SharpebeMichelle 5 Sharpebe Michelle 14 Lactin SharpebeMichelle 6 Kantadirmes SharpebeMichelle 7 Janish SharpebeMichelle 8 SharpebeMichelle 9 SharpebeMichelle 10 Hilbert amp Logan SharpebeMichelle 11 Other SharpebeMichelle 12 SharpebeMichelle 13 SharpeDeMichelle 14 Below is ILCYM s window for two sub model selections l pmentRat Jog Select one ond save it or try with others Life stage selected Egg Models R2 R2_Adj SSR AIC MEC Sharpe 88 DeMichelle 1 0 991 0 982 606 4 66601 112 Sharpe 48 Desichelle 2 0991 0 977 606 4 54917 LAI Sharpe amp amp DeMichell C3 Sharpe 88 DeMichelle 2 a Graph Output Statistical Output Graph Output Statistical Output ate day development rate 1 day o mn z E a 2 i o 0 5 10 15 20 25 30 35 40 45 50 55 0 5 10 15 20 25 30 35 40 45 50 55 temperature degree celsius temperature degree celsius Le comal ILCYM 3 0 User Manual 48 You can choose all models and compare is such case several windows will appear with the result in figures and a unique window for parameters estimates comparison When you click on indicate best model button the statistical criteria for
107. k assessment for nonindigenous pests 1 Mapping the outputs of phenology models to assess the likelihood of establishment Diversity and Distributions 7 223 235 Keller S 2003 Integrated pest management of the potato tuber moth in cropping systems of different agro ecological zones n J Kroschel ed Tropical Agriculture 11 Advances in Crop Research 1 Margraf Verlag Weikersheim Germany ILCYM 3 0 User Manual 150 Kohlmann B H Nix and D D Shaw 1988 Environmental predictions and distributional limits of chromosomal taxa in the Australian grasshopper Caledia captiva F Oecologia 75 483 493 Kriticos D J J R Brown G F Maywald I D Radford D M Nicholas R W Sutherst and S W Adkins 2003 SPAnDX a process based population dynamics model to explore management and climate change impacts on an invasive alien plant Acacia nilotica Ecological Modelling 163 187 208 Kroschel J Sporleder M Henri E Z Tonnang Juarez H Carhuapoma P Gonzales J C Simon R 2013 Predicting climate change caused changes in global temperature on potato tuber moth Phthorimaea operculella Zeller distribution and abundance using phenology modeling and GIS mapping Agricultural and Forest Meteorology 170 228 241 Kroschel J and M Sporleder 2006 Ecological approaches to integrated pest management of the potato tuber moth Phthorimaea operculella Zeller Lepidoptera Gelechiidae pp 85 94 Proceedings of the 45th Ann
108. lename of the new shape file The txt file must have a header row containing the variable names It is preferable if the columns are separated by commas or tabs The importation ILCYM 3 0 User Manual 106 wizard will read your data when you tick the box which specifies the separator you are using ILCYM will figure out what type of data is present in each column of the database text integer whole or real decimal numbers But if you wish you can change this automatically generated setting The same goes for the maximum number of spaces that a value of the variable will need If you indicate fewer spaces than they are actually used the data will be truncated cut off at the position that you indicated not rounded The program then reads the input file and allows you to select the fields that have the X longitude and Y latitude coordinate data By default only numerical fields are listed for you to choose from When you do click on accept a new shape file of points is created Text file to Shape file u Output file Choose the delimiter that separates your fields Tab Semicolon Comma C Space C First row contains field names x Longitude y Latitude Field Options Field name Data type ILCYM 3 0 User Manual 107 d Extract by points The Extract tool assigns values to the locations specified in the active point s shape file You can extract values from a grid file or climate data In all cases
109. llows users to compare same stages of different insect species On model builder window clik comparison the window below will appear select the stage you want to compare and load their completed phenolonogy model succesively and click on get graph to visualize the overlapping outputs ILCYM 3 0 User Manual 66 a Phenology comparison Evaluations Development rate Load phenologies Remove project Phenology Mame in graph life tagez Stage order EA ILEYA rurrtime june3 0 produet PTA project FTAA Egg EA ILEYA runtime june 3 0 product Ek Copidosoma Egg Output directory En ILEYA runtime june3 0producteomparisson gt Evaluation Development rate Life stage Egg Egg 0 60 0 56 0 52 0 48 9 0 44 0 40 2 0 36 0 32 S 0 28 802 0 20 2016 oO 0 12 0 08 0 04 0 00 7 PTM Copidosoma 0 5 10 15 20 25 30 35 40 45 50 temperature degree celsius Note The comparison can only be operated on the same life stage and ILCYM allows a maximum of three species to be compared at the same time lt is also preferable to compared species with identical number of immature life stages in their aver phenology model ILCYM 3 0 User Manual 67 3 2 Validation and Simulations ILCYM users can conduct two 2 type of simulations stochastic and deterministic For each simulation the life table parameters of your species are estimated The simulations are found in the Simulations
110. lls in all Ascii in a stack B VersionOfIIcym borrar Phenologynew PTM_Stack stk Cols 1089 9 Vertical bars Horizontal bars Points Lo Al ERI GI ILCYM 3 0 User Manual 125 iv Using Calculate you can produce a single raster from the multiple Greate stack Check tack Load stack rasters in a specified stack the value in each cell of the output grid being the sum mean minimum or maximum of the values rasters in the stack Operations Sum Mean Min Plot Calculate Export stack B WersionOfIlcym borrar Phenologynew TM Stack stk Max B VersionOfllcym borrar Phenologynew Sum asc v a Export Notepad ERI n Finally you can Export the all the raster files in the stack together to a single TXT file Such a file can be used to make comparisons on a cell by cell basis e g in a spreadsheet program 0 0257228631526232 OOOOOOOOOOOOooooooooooooooooooooo 0284171216189861 0307321473956108 0324024856090546 0340657643973827 0357283502817154 034208707511425 0320454090833664 0311583057045937 0296302419155836 0287373345345259 0291289407759905 026954498142004 0260581504553556 0215958170592785 0159818790853024 00991304032504559 00747529696673155 00547554949298501 00323560414835811 00105397601146251 000925424217712134 000781398557592183 000666781212203205 000627230736427009 000723182456567883
111. metadata 2 Palette ERL421058 projectRegistry 2 cK m ERI i project udig i PTM project j Property Value zu Attributes gt EREM A o SEE EEEEEEEERITEISITEIFSSESISIESSSSSSISSESES SS S Distance Eri 0 223441541194916 3 Layers 23 B X 75 2477700232562 SIIDAR Y 11 81312002252252 Ma ERI Feature world adm00 Bounds 75 247770 11 8131 NB ERI ID ERLA21058 m dem terrain aspect Geometries Default Geometry Point Selection Editing Create i Info 1 1 v Wildca c unit Feature Editing m me iv Output tables LM ENERO UNS ON FE UB US P 2 File Edit Navigation Window Help Dir e o SE TESORO b 5 Ta Layers 3 Old dem amp m E ES i uu E B O T D a d oy Palette Any search VIA y ERI oy ck e Features Selected 0 i a world adm00 Info FID x y ERI i E i Info ERI 393065 74 98027003386777 11 79145335671764 0 3457008898258 a pet ERI 393066 74 97943670056749 11 79145335671764 0 3457008898258 ERI 393067 74 97860336726721 11 79145335671764 0 3810696303844 ERI 393068 74 97777003396695 11 79145335671764 0 3810696303844 ERI 393069 74 97693670066667 11 79145335671764 0 5339988470077 ERI 393070 74 97610336736639 11 79145335671764 0 7596322298049 ERI 393071 74 97527003406611 11 79145335671764 0 8301370143890 ERI 393072 74 97443670076584 11 79145335671764 0 9150685071945 ERL393073 74 97360336746556 11 79145335671764 0 91506850719
112. milar characteristics The Index Interpolator tools create a continuous or prediction surface from sampled point values You can measure hourly temperature at strategically dispersed sample locations and predicted values can be assigned to all other locations Input points can be either randomly or regularly spaced or based on a sampling scheme The interpolation tool makes predictions from sample measurements for all locations in an output raster dataset whether or not a measurement has been taken at the location There are a variety of ways to derive a prediction for each location each method is referred to as a model With each model there are different assumptions made of the data and certain models are more applicable for specific data In the Index Interpolation we have implemented the Thin plate algorithm for interpolation This sub module module allows for the analysis of regional to local climate change patterns on pest establishment and abundance This module inputs daily montly minimum and maximum temperatures data it calculate some index see page 141 location by location and then applies the Thin plate algorithm for interpolation and output regional assessment When using the index interpolator some important steps are required geo referencing the input data and then use digital elevation model for fitting your selected region selection of your phenology model and the input data type selection of the data o
113. model uus02222400000n00nnnnnennnnnnnnnnnnnennnnn nennen 136 ILCYM 3 0 User Manual iii c Simulation POINK 0 ccccecceeeecceescceeeecescceseeeeestoesereeesccsseeeesseceeneeessceeaeess 139 d Simulallom Points sau een EM ETR FOIE D PEUT sou uEE Rev oS Ra 142 No HEC VINES COMMON SOS vice nein 145 Me HGIOSSANY E olaaa ios 148 Vie REIELONCES surco lied aid 150 ILCYM 3 0 User Manual iv I INTRODUCTION Interest in models to predict the environmental suitability for invasive insect pest species has grown radically in the last two decades In particular the need to understand the impact of climate change on the potential distribution of pests has accelerated the demand for tools to estimate the potential risk of their invading new environments and agricultural regions For this purpose maps are becoming important means of communication using different spatial scales from local regional to worldwide to visualize the potential risk of pest distribution and the economic damage it may inflict on crops Thus maps are used to inform policy and management in this field to aid in strategic pest management decisions such as restrictions on the importation of certain crops in international trade implementation of quarantine measures the design of pest surveys etc Baker 1996 Baker et al 2000 Braasch et al 1996 McKenney et al 2003 Two distinct approaches prevalent in the modelling of insect pests
114. models equations Parameters Joy Intrinsic rate rm m Netreproduction rate Ro Gross reproduction rate GRR Generation length in days GL Finite rate of increase A Doubling time Dt ILCYM 3 0 User Manual tm 0 298531 1 0 06292499T 0 004231378T 8 40322e 05T R 0521 R Adj 0 453 AIC 82 065 Deviance 0 037 Ro 43 0688 7 015443T 0 2745886T 0 002972T R 0 823 R Adj 0 797 AIC 114474 Deviance 95 578 GRR 73 95044 8 576513T 0 243155T 0 01371297T R 0538 R Adj 0 473 AIC 237 811 Deviance 13272 53 GL 376 9916 33 57452T 1 118309T 0 01326557T R 0998 R Adj 0 998 AIC 109 731 Deviance 79 062 A 1 20526 0 04452988T 0 003098806T 6 193123e 05T R 0 891 R Adj 0 875 AIC 148 511 Deviance 0 003 Dt 1089 617 178 0731T 9 676994T 0 1716693T R 0 661 R Adj 0613 AIC 281 065 Deviance 111701 9 There is the possibility for the user to fit parameter of his her choice with any model _ Parameters la Graphic Statistical summary Using the quadratic model 150 GL 293 4185 18 78341T 0 3223743T 100 Generation length in days GL Temperature C Using the cubic model 150 Dt 1089 617 178 0731T 9 676994T 0 1716693T Doubling time Dt Temperature C 3 2 2 Model validation validation of established model is done using stochastic simulation The validation tool in ILCYM allow
115. mplete life table distinction has been made for data that contain both female and male or only female information The user only needs to choose complete and select one of the options Life Table Type Incomplete Complete ILCYM 3 0 User Manual 38 For variable rate it is require that the user uploads 2 oviposition files one for female and the second for male These files must also include the Dead label p e Dead Death depending on how it appeared on the data file Rate Age and Temperature Oyiposition bata Cyviposition Female Ovipasition Male Dead label If the files contain life stages with different names from what were initially registered ILCYM recognizes the errors and displays appropriate names for replacement Dead label marks the difference between the stages that the insect dies which is writing in the data Dead or Death from the stage that the insect still alive KL but did not lay egg replace by zero e Replace wrong life table F ile tabla 18 txt Life stages Egg Larva LarvalI Pupa Female ale Life Stages with Errors Replace Life Staqge Larvael Replace with Larval In this example the user wrote Egg Larva Larvall Pupa Female Male and ILCYM evaluates each files and look for different names which are Larvael Larvaell Larvaelll LarvaelV Prepupae Pupae the user must select one by one and write the correct name in Re
116. n the grids Rather it points to the existing files with the data Therefore if you delete rename or move one its constituent grids to a different directory the stack will become invalid You can make a stack by adding ASCII files to a list and then naming the output STK file You can remove raster from the list individually or all at once if you make a mistake or change your mind ILCYM tells you about the dimensions and location of each grid you add to the stack These must be identical for all grids in the stack i Add raster to the stack E Stack Create stack Check stack Plot Calculate Export stack Add raster Remove raster Remove all File name Rows Cols Res MinX MaxX MinY MaxY B WersionOfI Icy 1066 1089 0 00 75 74 12 11 B Version fllcy 1066 1089 0 00 75 74 12 11 B WersionOfTIcy 1066 1089 0 00 75 74 12 11 B WersionOfTIcymiborrarPhenologynewWPTM_Stack stk Generate stack ILCYM 3 0 User Manual 124 li If you forget which grids are in a stack or wonder whether it is still valid use the Check Stack tab to obtain a list of the grids included in a specified stack B VersionOfIIcym borrar Phenologynew PTM_Stack stk B VersionOfllcym borrar Phenologynew ALasc B VersionOfllcym borrar Phenologynew ERLasc B VersionOfllcym borrar Phenologynew GLasc ii You can plot and make a histogram of the values of corresponding ce
117. n example where a constant parasitism rate was selected with a fecundity of 8 ar Biological parameters of one generation at constant temperature aj xj Biological parameters of one generation at constant temperature Select one parasitism percentage option Host amp Parasitoid projects Host Pra Project d Parasitoid Apanteles Project Attack Stage Larva Percentage parasitism calculation PPj C Variable parasitism rate XNE iv Constant parasitism rate PF Fecundity B Back Next gt Cancel ar Biological parameters of one generation at constant temperature oO x Biological parameters of one generation at constant temperature Simulate two species Constant temperatures 12 15 20 25 30 Number female parasitoids 15 Done Ill Host number 100 Days 365 Models Cubic Ro Cubic GRR Cubic GL Cubic Lambda Cubic Dt Cubic Modify models lt Back Next Finish Cancel ILCYM 3 0 User Manual 95 When the window below appear just click ok AA i For cubic models the number of temperatures is 5 Far the other ones is 4 ILCYM uses cubic model by default but you can change it below Below are ILCYM s outputs for different life table parameters of the insect host PTM Parameters Intrinsic rate tp Met reproduction rate Ro Gross reproduction rate GRR Generation length in days GL Finite
118. nual 104 b Raster to points Converts a raster dataset to points For each cell of the input raster dataset a point will be created in the output feature class The points will be positioned at the center of cells that they represent The NoData cells will not be transformed into points The input raster can have any cell size and may be any valid raster dataset i Load the raster that you wish to convert to points um c y 5 A T US a a A File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help Ar Di SCOPRKNAQAHG U ILCYM s Projects Explorer 22 EJ H dem X Fa metadata 22 Palette projectRegistry zd CH e la project udig F Info la PTM project Selection E Editing TS Layers 3 zm Create F L DAR Feature Editing ER 7 world adm00 Y F m dem terrain aspect AZoom 1 1 Wildca cunit m Coordinate Reference System of data is unknown Unexpected behaviour may result if it is not set ii Use the function raster to points u Raster to points Ay Raster file B WersionOfLlcym borrar PhenologynewtERT asc Shape Points B VersionOf Ileym borrar Phenologynew ERI shp ILCYM 3 0 User Manual 105 ii Output points f ILCYM c ER vn TR E AE es File Edit Navigation Modeling ILCYM tools Spatial Analysis Window Help Fir D Q o SRA amp i ILCYM s Projects Explorer 2 7 Ga dem 33 Hig mation 25 i i
119. ocess has property sheets that contain all information about the sub model mathematical expression parameters reset bottom etc For example by resetting a selected sub model user can modify component of their over phenology model and explore alternative outcomes ILCYM 3 0 User Manual 25 e Document phenology model ILCYM s model builder provides text boxes in which user can documents the methods of data collection different assumptions made during model development The documentation informs other users on how the model was built what assumption was made and what result was obtained Share produced phenology model ILCYM s user can share phenology model by sharing the model files created in the model builder All created file during model development are automatically store in the workspace In doing that it let users to open the methodology for wide scrutiny and helps refine and standardize modeling techniques Sub models can be imported allowing users to incorporate components that have been developed by others into their own models 3 1 1 Creating new project Before you start with an evaluation of data for developing an insect phenology model you need to create and register a new project All data evaluation outputs maps etc that are used or created during project development are managed within a single ILCYM s project Therefore for each insect species you want to develop a model a new project needs to be created
120. oject are well described below e Project Name Name of the Project e Species Name Write the name of the species e Author Name of the person creating the project e Date Date of the project creation it appears automatically e Observations Here author might include some experimental observations temperature RH other conditions problems etc or general note that s he is important to the project under creation e Immature life stages Stages need to include all life stages describing the whole life cycle of the insect species i e Egg Larva Prepupa Pupa or E L PP P Larval stages might be separated in different instars if required p e L1 L2 L3 etc or nymph1 nymphae etc e Adult s life stages Insect matures life stages F for Females M for Males ILCYM 3 0 User Manual 28 Female rate and project location Select the female rate Female rate O Variable rate LI Age Temperature Fixed rate 0 5 Project location ILCYA s workspace Age Check this button if you know that the female rate in the progeny is changing with adult female age In this case a new function will be included in the overall model that determined the age depended oviposition curve Temperature Check this option if you are dealing with species that the temperature has an influence on the female rate in the progeny In this case a new function will be included in the overall phenology model that de
121. on for example an index value of 4 would illustrate a potential population increase by a factor of 10 000 within one year all other population limiting factors including food availability etc are neglected To access the Population analysis and mapping perspective go to menu Window gt Open Perspective and Select Population analysis amp mapping ILCYM File Edit Operations Layer Model Builder Window Help a P E m New Window la La lO be li ebene A model Builder Show view ILCYM s Projects Ex 2 Population analysis amp mapping PTM project Reset Perspective H validation and Simulations Close Perspective Close All Perspectives Other Preferences ILCYM 3 0 User Manual 135 b Mapping phenology model Go to Modeling menu gt Mapping A mn EE n a Mapping P 2a Point d ILCYM s Projects Expl Points E metadata x Tecia2 This window below will appearfor geographic simulation Climate database B GIS_Training August232012 Climate 2000_World Create new map Regenerate map Existing parameters N Indices GI AI ERI Limit difference Coordinates MaxX 75 94352 MaxY 11 51165 Maximum extend a Temperature filter Tmin Tmax Output The climate database tool is for viewing and refreshing the climate data base path When a data base is change i e replacing 10 minutes 2000 to 10 minutes ILCYM 3 0 User Manual 136 2050 the
122. opulation dynamics models Canadian Entomologist 107 1167 1174 Sutherst R W and G F Maywald 1991 Climate modelling and pest establishment Climate matching for quarantine using CLIMEX Plant Protection Quarterly 6 3 7 Sutherst R W B S Collyer and T Yonow 2000 The vulnerability of Australian horticulture to the Queensland fruit fly Bactrocera Dacus tryoni under climate change Australian Journal of Agricultural Research 51 467 480 Sutherst R W 1991 Pest risk analysis and the greenhouse effect Review of Agricultural Entomology 79 1177 1187 Sutherst R W and G F Maywald 1985 A computerised system for matching climates in ecology Agriculture Ecosystems amp Environment 13 281 299 Sutherst R W and G F Maywald 1990 Impact of climate change on pests and diseases in Australasia Search Sydney 21 230 232 ILCYM 3 0 User Manual 152 Trnka M F MuSka D Semeradova M Dubrovsky E Kocmankova and Z Zalud 2007 European corn borer life stage model regional estimates of pest development and spatial distribution under present and future climate Ecological Modelling 207 61 84 Vanhanen H T O Veteli and P Niemela 2008a Potential distribution ranges in Europe for Aeolesthes sarta Tetropium gracilicorne and Xylotrechus altaicus a CLIMEX analysis EPPO Bulletin 38 239 248 Vanhanen H T Veteli and P Niemela 2008b Potential distribution ranges in Europe for Ips hauser
123. ore depend on the trade off associated with egg and time limited oviposition Owing to the fact that females of different size may as well have different amount of body reserves size dependent allocation trade offs between the females condition and their eggs production may be expected ILCYM 3 0 User Manual 57 ILCYM s allows the user to fit a nonlinear function describing oviposition frequency of female as show on the following windows Mortality Comparison Post Oviposition VR Senescence Development Oviposition d u PTM project Lo Relative Frequency A Female ratio in the oviposition Life stage Functions O Modell DModel2 U Model 3 O All Deselect All grad Model 1 v bO bi x b2 x Parameters bo bl 017 b2 0 ILCYM 3 0 User Manual 58 Model 1 m Model 1 m Graph Output Statistical Output Graph Output Statistical Output Nonlinear regression model model y bO bi x b2 x 2 data parent frame 1 0 0 b2 1 594801 0 169146 0 003425 residual sum of squares 0 0003671 0 8 Number of iterations to convergence 9 Achieved convergence tolerance 8 984e 08 c 0 6 Chi square test for a adjusted rate of oviposition S X2 P value Eo S 1 4 348583 0 2262006 bu kd 04 a 0 2 0 0 n 5 10 15 20 25 30 temperature C iv Female ration in the oviposition
124. place with text box and click on Replace button only the word Death must be omitted ILCYM 3 0 User Manual 39 c Oviposition file If oviposition is not included in the life table files incomplete life table or if data were individually collected for specific life stages cohort studies then an oviposition file is required for modeling reproduction for the insect species An example of data is given below where each row in the file represents an individual female The first column indicates the temperature in degree C to which the female was exposed The following row represents the day post adult emergence and the values represent the number of eggs oviposited at a particular day If females were tested in groups average number of eggs per female should be recorded for each repetition Tenperature Dayl D Davi Dayd Davi D Dav Dayi D Davl Davll Davl Davli cc LLLI ILL Ill 3 A 3 iD iD ID ID 14 ex ex e e exi e e exi exi e e exi e ex ex e e exi e e exi exi e exi exi e e ex DOOOO XHP BOOCiunudogcPOn d amp OgypP ErPpeogsagoeagagago rt PucaodgmoOgaOPnucguguwupemagpaooogogomoz Aaa aaa A ND aaa aa aa A E a D rongoOcOcCOomnumpmogoogogomPpPaoacdccaogbogzoggotd rongagoco grcOoocOoOOOcOgOOOcOCCO0d4m mogdcuorni Imm OODODODO FODOCODOPOONERBOODO Im coOp g cOOucH Pucocdocon lrmPpPpPPOoOc d amp ocogrur c If the individual dies the user must indicate this status in the
125. r reproduction of insects can be described by three temperature dependent components the total oviposition the relative oviposition frequency and the age specific survival rate each of this component are directly linked to the option initially chosen by the user during the creation of a new project see page 35 1 Total oviposition In ILCYM total oviposition represents the expected total number of eggs laid per an insect female during her whole life span as a function of temperature ILCYM 3 0 User Manual 54 This relationship is modeled with a nonlinear function as shown in the graph below T LCYM File Edit Model Builder Window Help Ej gt ly Mortality m Comparison Post Cyiposition VR KH 5 SENESCENCE M ka HE Development d T me Oviposition d Total o PTM project Lo Relative Frequency Tic Female ratio in the oviposition Model already selected Life stage selected Female Graphic Output text mT 2S aT bT c M e Model selected Model 2 Parameters estimated a 511 87845 b 58 39573 c 956 69944 fecundity female co e Reset Model temperature degree celsius ii Relative oviposition frequency The relative oviposition frequency is the proportion of total lifetime reproductive potential that elapses during each time period This accumulated oviposition frequency of the females is evaluated in relation to the normalized age of the females time median time
126. r with the same dimensions number of columns and rows and resolution and location minimum and maximum X and Y coordinates which are handled together as a group ILCYM 3 0 User Manual 115 a Describe window Describe operates on the active layer when the window is open or else on any layer selected using the Grid button Some of the information e g the number of rows and columns can also be obtained by double clicking on a grid layer in the legend and choosing the Info tab You can use this function to obtain the following information about the contents of a grid lll Describe IAILCYAWmapsxafrica 2000 GI asc Variable Value Rows 48 Cols 39 Minx 8 666677 MaxX 15 166678 Ahin 4 33333 Max 3 666672 cellsize 0 166667 Cells with data 1609 Cells with nodata 263 Minimum value 1 8 Minimum value 11 7 Sum 15489 6 Mean 9 6 Median 10 2 Mode 10 Standard deviation 1 5 Variance 2 1 b Mask window Create a new Raster object where all cells that are NoData in a mask object are set to NoData and that has the same values as x in the other cells The mask can be either another Raster object of the same extent and resolution or a Spatial object e g Spatial Polygons in which case all cells that are not covered by the spatial object are set to NoData This is frequently used when you wish to limit the output to match a specific shape of an existing grid Hof Ee 0 E Output file
127. res contained in the ILCYM spatial analysis sub module To access these features go to window click on open perspective select population analysis and mapping and click on spatial analysis ILCYM 3 0 User Manual 114 T EN t s EN File Edit Navigation Modeling ILCYM tools Spatial Analysis Window Help ES y e 9 Describe i ILCYM s Projects Explorer 23 E Gd den Aggregate u metadata Disaggregate ee Palette pro Cut T ck La project udig ve La PTM project 3 Info Mask Selection a Layers x E Reclass 2 W AIR Overlay vim ERU Raster calculator F m dem terrain aspect E m dem terrain slope 14 poly2 dissolve r Stack 04 poly r 7 dem reclass poly U dem polygon2 ry m dem reclass Terrain j4RM dem to points F m dem cortad dem AZoom 1 4 wildca c unit 10 35AM fm 2m 3 e OSIO 570 Below is a list of functions grouped by theme implemented in ILCYM Name Description Describes the content and structure of a grid file Cut a grid file as a template using a shape file Overlay Performs arithmetic on the values of the grid file resulting from two joint files Raster Calculator For conducting algebra with grid files addition subtraction multiplication division and additional math functions include log square root sine etc For computing slope aspect Stack A stack is a set of raste
128. ring project development as well as options to select the ILCYM 3 0 User Manual 46 best model either by comparing various sub models or all at once or separately by choosing a sub model In this menu you can operate multiple selections or single selection Multiple selections i Select all sub models at once and choose the best fitted sub model using inbuilt selection criteria 9 Multiple selection Single selection Models Sub models Selected models SharpeDeMichelle Sharpebe Michelle 10 Deva SharpeDeMichelle 11 Logan SharpeDeMichelle 12 Briere SharpeDeMichelle 13 Stinner SharpeDeMichelle 14 Lactin Deva 1 Kontodimas Deva 2 Janish Logan 1 Rawtosky Logan 2 Anlytis Logan 3 Hilbert amp Logan Logan 4 Other Logan 5 Briere 1 Briere 2 vw ii Select a group of models i e Sharpe De Michelle will displays all sub models that originated from the original Sharpe De Michelle et al model Models Sub models Sharpe DeMichelle 1 SharpebeMichelle 2 SharpebeMichelle 3 Briere Sharpe beMichelle 4 Stinner SharpebeMichelle 5 Lactin SharpebeMichelle Kontodimas SharpebeMichelle 7 Janish SharpebeMichelle 8 Rawtosky SharpebeMichelle 9 Anlytis Sharpebesichelle 10 Hilbert amp Logan SharpebeMichelle 11 Other SharpebeMichelle 12 SharpebeMichelle 13 SharpebeMichelle 14 ILCYM 3 0 User Manual 47 Below is the table that guide ILCYM s users in choosing sub model Icon Description aan LE ILCYM s w
129. rizes areas in which population growth is restricted to certain periods of the year for example an ERI 0 25 indicates an area where populations are expected to grow only during 3 month 3 12 of the year and decrease during the other 9 month 9 12 If the index is used for a prospective antagonist species p e parasitoids considered for release as a non native biological control agent the index expresses the capacity or potential to establish in an area that might be considered for inundative or inoculative release of the species In these cases the index represents the establishment potential of the species which is desired for long term control of the target pest species Generally by default the maps are generated using a monthly time scale that is adequate for multivoltine species these species have generally a short generation time with overlapping generations However such a short time interval is not appropriate for univoltine species which produce a single generation within a year because single life stages only develop during certain periods of the year Hence life table parameters like the net reproduction rate calculated for a given period are not representative for the species population development For estimating the establishment risk of univoltine species the whole life cycle of the pest need to be simulated throughout the year at best for several years with real temperature records as input data using ILCYM s ILCYM
130. rogressive models use non linear functions of higher biological significance i e Logan et al 1976 Sharpe and DeMichele 1977 etc and include stochastic functions for variability in development times among individuals within a population Sharpe et al 1981 Wagner et al 1984 Computer aided modelling packages such as DYMEX Kriticos et al 2003 NAPPFAST Nietschke et al 2008 ECAMON Trnka et al 2007 or ILCYM Sporleder et al 2007 Sporleder et al 2009 support the development of process oriented temperature driven and age stage structured insect phenology population models The latter ILCYM Insect Life Cycle Modeling software version 3 0 has recently been developed by the International Potato Center CIP Lima Peru and IS freely available at http www cipotato org ilcym This book describes the application of ILCYM software which supports the development of process oriented temperature driven and age stage structured insect phenology population models ILCYM interactively leads the user through the steps for developing insect phenology models for conducting simulations and for producing potential population distribution and risk mapping under current or future temperature climate change scenarios The objective of the ILCYM 3 0 User Manual 3 present document is to explain how the developed modeling approach works what type of data need to be generated to develop an insect phenology model IPhM what t
131. rrrcrTcr rt InItII IT E EEEEzuuuuuuuuuuuuurrrTr Tr r Inritr IT mEzEEzzuuuuuuuuuuurrrrcrr rr Iritir nmmmmmmTuuuuuuuuuuurrrrrrTr Ir mmm E E E L L L L L L L P P P P P P P P P P P P P P P P P P P P P P d Q uuuuuuuuuuuuuuuuuuuuuuurrrrTr T t mmm oo m D u u oo Figure4 Example for recording incomplete life table data at constant temperature 20 C in a text file As for complete life table each column represents an individual and its state life stage is recorded in a constant time interval generally one day until its death or development into adult Survival time of adults is not monitored further because adult survival time will be assessed in an additional experiment for determining adult survival time and oviposition however emergence of males M and females F is recorded ILCYM 3 0 User Manual 15 The oviposition file for each temperature would look as shown in Figure 3 either a single file if the female rate is constant over all temperatures or two files one for reproduced males and one for reproduced females if the female rate is expected to be variable 1 4 3 Cohort studies In cohort studies the structure of data for the analysis in ILCYN is different Survival time and mortality is assessed in the same way as for life tables but for a single life stage only Data are arranged by specific life stages i e there is one data file for each life
132. s Risk Analysis 23 651 661 Ramirez J Jarvis A 2010 Downscaling Global Circulation Model Outputs The Delta Method Decision and Policy Analysis Working Paper 1 International Center for Tropical Agriculture CIAT Regniere J 1984 A method of describing and using variability in development rates for the simulation of insect phenology Canadian Entomologist 116 1367 1376 Sharpe J H and D W DeMichele 1977 Reaction kinetics of poikilotherm development Journal of Theoretical Biology 64 649 670 ILCYM 3 0 User Manual 151 Sharpe P J H G L Curry D W DeMichele and C L Coel 1977 Distribution model of organism development times Journal of Theoretical Biology 66 21 28 Sporleder M J Kroschel and R Simon 2007 Potential changes in the distributions of the potato tuber moth Phthorimaea operculella Zeller in response to climate change by using a temperature driven phenology model linked with geographic information systems GIS pp 360 361 XVI International Plant Protection Congress BCPC Hampshire UK Glasgow UK Sporleder M J Kroschel M R Gutierrez Quispe and A Lagnaoui 2004 A temperature based simulation model for the potato tuberworm Phthorimaea operculella Zeller Lepidoptera Gelechiidae Environmental Entomology 33 477 486 Sporleder M R Simon H Juarez and J Kroschel 2008 Regional and seasonal forecasting of the potato tuber moth using a temperature driven phenology mod
133. s SynTPEnh exe juancarlos 00 1 148K Synaptics RtHDYCpl exe juancarlos 00 1 140K HD Audio tRserve exe Mancarlos 00 114 416 K Rserve ex QPService exe juancarlos 00 4 420K HP QuickP QLBCTRL exe juancarlos 00 1 856K Quick Laur PlusService exe juancarlos 00 832K Messenge notepad exe juancarlos 00 944K Notepad msseces exe juancarlos 00 3 820K Microsoft E m mA Since nn onc ocow Ian TRAY 4 p End Task Switch To New Task Show processes from all users End Process Processes 91 CPU Usage 2 Physical Memory 72 Processes 91 CPU Usage 1 Physical Memory 72 4 2 Loading map In some cases an error message may occur when you are loading a map in ILCYM this happens because the engine of the ILC YM GIS component Udig does not allow paths containing spaces blank For example the route of the map that you want to upload may look like E New Maps map Gl asc in this case you have two blank spaces the first between New and Maps and the second blank space between map and Gl asc ILCYM 3 0 User Manual 145 In your screen the error will appear on the window as follow Add Data Files Open one or mare files Please wart loading the following resources Finish Cancel Y Add Data Files Open one or mare files Please wait loading the following resources Udig recognizes the blank spaces as 20 for not having this type of problems it is best to avoid the blank spaces on t
134. s A DevelopmentRate E Move the selected items D Copy the selected items 2 Publish the selected items O dl Ports to the Web I E mail the selected items x Delete the selected items n RelativeOviposition Senescence Ba Other Places Y LJ TotalOviposition Details A PhenologySims RData 2 items selected C i R Workspace Total File Size 3 54 KB corey Progress ilcym 1 KB 2 aControl r E Tinn R E 1 KB project PROJECT File 1 KB PTM project ileym ILCYM File Summarize html Chrome HTML Document 3 KB ILCYM 3 0 User Manual 65 Below is another file that automatically appears when your phenology model is completely developed The file displays the all parameters and functions selected for the overall phenology model Parameter Values of the Functions Used Species name Apenteles Project name Apenteles Project Author name Henri TONNANG Compilation date 03 11 2012 Development Rate _ COCOS ARCE MC Pvale 1 Y 0 00713 x 1e 05 p 0 17866 2 Tmax 3625724 1e 05 To 298 56779 x 0 01043 Th 303 88815 1e 05 Ha 19123 5526 0 09523 Hh 403257 43968 5 19914 3 p 0 18126 1e 05 4 v 5 11109 1e 05 5 Distribution function of development IL iabe value Plabel P value 1 slope 12 91 1e06 slope 17 38 1e 06 Mortality L labei Ll value P labei Pvalue 1 Topt 22 3148 1 01883 Topt 22 06037 0 92961 3 1 10 Project comparison This feature a
135. s the user to evaluate the ability of the developed phenology model to reproduce the insect species behavior under fluctuating temperature conditions This is achieved by comparing experimental life table data obtained from fluctuating temperature studies with model outputs produced by using the same temperature records as input data T ILCYM File Edit Species Interaction Window Help F3 Deterministic 2 ILC M s Pa cad Stochastic pe metadata Validation 1 PTM project ILCYM 3 0 User Manual 77 E Model Validation Project PTA project Life stages Egg Larva Pupa Female Male Ratio 0 5 Load temps GAILCYMtablas d vidasptm temp fuctuante txt View input temp Lite table GAILCYMtablas d vidasptrmtabla de vida 1 t t E Incomplete PP Insects 100 Ovipasition tile Validate Cancel Status Problems e Life table button for inputting life table files used to validate your develop model with fluctuating temperature e N insects number of insects used for the simulation e Load temps button this button allows loading the data this data must be in daily format e Validate button start the validation process e Cancel button close model validation window i Output graph for model validation 5 Model Validation Output eJ re Statistics life table summary Life table parameters Simulated Observed P x 07 0 008 0 076 0 09 Ro 719 t 55 GR 73 953
136. shows changes on the initial curve of the model when the curve begin converging click on Set parameters to set the range of initial values of parameters and then click on readjust until a best initial curve is found A Back button is also provided in case you want to return in a passed situation Auto Adjustment Adjustment 1 Back Set Inputting additional values Sometimes input data does not contain sufficient information that can properly guide the selected sub model to converge towards threshold temperatures If the user has any data points on how the species under investigation could behave near threshold temperature of development s he can include these values using the window below At first you will stick the Additional values button and then inter a temperature value lower or high proceed to the next window to inter the development value For example if you inter two values of temperature lower and high you will also enter two values of development corresponding to each temperature All values should be separated by a comma Additional values Temp Value Note This window for additional values can also be used to input additional data points that were not intitally include in the input data Always remember that for each temparature corresponds a value of developmental rate and all ILCYM 3 0 User Manual 50 are separated by a comma Example Temp 10 12 15 25 36 Value 0 1 0 2 0 4 0 8 0 1
137. specific region or zone of interest described later in this manual Once the project is registered you might upload the files containing the experimental data used to determine the model functions Right click on the project and click in Upload Data ILCYM 3 0 User Manual 33 f ILCYM File Edit Operations Laver Model Builder Window Help i wi m o e HELA la WY ID be lS i o i 5 i re iL ILCYM s Projects Ex Ld Upload Data Import existing project into workspace Refresh Delete Properties Indicate the type of data to be evaluated for example data Cohort studies need to be chosen and browse for the data files by clicking on Load data After a left mouse click on a file browse window right the file appears in the upload window Include only the data that are used to determine development times and mortalities in immature life stages and survival times of adults For modeling reproduction an extra oviposition file is required that need to be uploaded specifically ILCYM 3 0 User Manual 34 Upload Data x Data Type 22 Cohort studies of single LIFE STAGES O Life Table Data Files Data Path Data Name Stage Order Rate 0 5 Oviposition Data a Uploading cohort data Data for all temperature experiments from one life stage are included in one file i e one file for each specific life stage It is recommended to name the file according to the Species name and t
138. stage in the life history of the insect under study For example if the life history of the species comprises three immature life stages i e egg larva and pupa five files are required that is one for Egg one for Larva one for Pupa stages and one for Female adults and Male adults survival each In addition one or two oviposition files are required depending if the female rate in the species is constantly the same across temperatures or if the female rate needs to be evaluated from the data variable rate The latter is when temperature affects the female rate or if the female rate is expected to change with female adult age Data on oviposition need to be retrieved from the same cohort of adults female adults used to establish the overall model Researcher can freely chose the number of life stages according to their choices or requirement for example the larva stage can be separated by specific larval instars Li L Ls etc or a prepupal stage could be includes Important is that the whole life cycle is covered and that there is no overlapping when assessing the development survival time of individual life stages instars There should be a clear definition when each single life stage instar is completed and the insects used to initiate a cohort study should be of this specific physiological age An example for arranging the data is given in Figure 5 The data file contains 4 columns of
139. t ILCYM 3 0 User Manual 122 ii Index map 7 ILCYM File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help E ILCYM s Projects Explorer 22 O gd dem 8 m Lo metadata Palette b projectRegistry QU CE la project udig A ARA la PTM project Info Selection S0104R Ela QZeom 19 Widea cunt iii Displaying terrain faces by aspect combined with index map it help to visualize the index in 3d Y ILCYM lola x Eile Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help E o eH imi PAIR IA AAA B ILCYM s Projects Explorer 22 O Gj dem 33 EL metadata us 2 Palette b projectRegistry Qt CE E project udig re AS u PTM project Info Selection 9201042 El PA dem terrain aspect ILCYM 3 0 User Manual 123 k The Stack menu A stack is a set of raster with the same dimensions number of columns and rows and resolution and location minimum and maximum X and Y coordinates which are handled together as a group Grouping rasters in a stack makes it easier to process many files in the same way and allows a number of additional analytical procedures A stack is stored in a file with the extension STK A STK file is nothing but a list of the grids that the stack contains It does not contain any of the actual data i
140. t raster can have any cell size and must be a valid integer dataset The cell values of the input raster the VALUE field will become a column with the heading name of the raster in the attribute table of the output feature class To better use this function at first reclassify your raster see Reclass function This will allow you to change and classify the values in the raster The final output may be useful for estimating areas of changes i Load the raster file Y ILCYM lola x Eile Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help i e SCOSGRXKAQR OL i ILCYM s Projects Explorer 23 mui Gad dem amp im metadata lt 2 Palette b projectRegistry ICH cK ES ie project udig i PTM project Info 49 8i Mosaic Info i Info ow Distance TLIA 42 a Dem Shape m dem cortad TIP Eri_reclass 7 world adm00 ERI EJE dem terrain aspect Ms ERI QZeom 11 5 iL Wildca c unit Selection li Reclassify the raster file Input file B VersionOfllcym borrar dem cortad asc B VersionOfllcym borrar Phenologynew Dem_Reclass asc Minimum Maximum ILCYM 3 0 User Manual 103 iii Use the reclassify raster to convert to polygon acm e ee ee File Edit Navigation Modeling ILCYMtools Spatial Analysis Window Help
141. tadata Validation PTM project This window will appear E Fluctuating Temperatures Deterministic Project PT ad project Life stages Eqg Larva Pupa Female Male Ratio 0 5 Opations for simulations Several years Cine generation Load temps GAILC M tablas d vidaxptmstemp fluctuante bd View input temp MP Insects 300 Simulate Cancel Status Problems e One year option simulates a life table for one year e Several years option simulates life tables for the subsequent years ILCYM 3 0 User Manual 79 One generation option simulates a life table for one generation the life table parameters are calculated with this option e N insects number of insects used for the simulation e Load temps button allows the user to load its own temperature data e Simulate button simulates the life table e View input temp For viewing the input temperature for the simulation e Cancel button closes the window of deterministic simulation I Displaying life table parameters Click on the Create Life Table button the application will simulate a life table with the loaded data and the following window will appear Deterministic Simulation Output m T Life Table parameters 21m 00000000060600000060060000000000 000050000 0000 0000 O OOOO 00000006060 000000 0000000000 06000 00000006060 oo 000 000000600600 O OOOO Oo o 0 0 n 0 0 0 0 0 n 0 0 0 0 0 n 0 n 0 n 0 0 View Graphic
142. termined the temperature depended oviposition curve Fixed Rate If the female rate is expected to be constant i e independent of the temperature and female age or any other parameter mark this button and indicate the value of female rate usually 0 5 In all situations one button needs to be checked other wise the project registrations will fails i e the wizard does not switch to the next step If the female rate in the progeny is well known from previous studies to be constant over all temperatures and independent from the age of ovipositing females then it is recommended to check both buttons ILCYM 3 0 User Manual 29 All projects will be saved in the workspace created automatically during the software installation and will be displayed in ILCYM project explorer view ILCYM s Projects Explorer 30 O project a DevelopmentRate i DevelapmentTime a Mortality al Progress ilcym al PTM project ileym a RelativeOyviposition Senescence o TotalOwiposition If the ILCYM project view does not appear go to Window menu gt Show view gt Other then expand ILCYM views and click in ILCYM project explorer f Show View type Filter kext Window Help New Window 2 General Catalog Open Perspective k Cheat Sheets Show view 53 Catalog Reset Perspective Ta Layers E l l i Project Close Perspective WHA Projects Close All Perspectives A Search a Preferences a Style Other
143. ters LT parameters Indices Temperature Longitude 544639 Latitude Insect number 100 Ratio 0 532 Parameters Rm Ro GRR T Lambda With Calculate we estimate the life table parameters and the indices with Life table parameter graphics you visualize them and with Life table ILCYM 3 0 User Manual 139 temperatures you visualize the life table and the corresponding input temperature Climate database refresh This is for actualizing the climate database by clicking on refresh Calculate parameters Option for calculating life table parameters Parameters check calculates only the life table parameters Indices check calculates only the indices Plot parameters Option for plotting life table parameters Get from map Allows you to obtain the temperatures values of the point in the map by clicking in the Get temps button located in the toolbar then clicking on the map the location you want and finally clicking on the Get from map button from By point window Longitude text box is automatically filled when the user select one point of the map Latitude text box is automatically filled when the user select one point of the map File Edit Navigation Modeling ILCYM Tools Spatial Analysis Window wi q amp Po 8 Hy Box Selection ILCYM s Projects Exp ILCYM box selection no 2j T metadata z GC Get Temps J Tecia2 T j ILCYM 3 0 User Manual 140 By sele Get
144. the parameters variation with temperature for a year d Simulation Points Under ILCYM population analysis and mapping perspective menu go to Modeling and click on points The following window will appear where you can simulate the life table parameters and or indices in several locations maximum 4 142 ILCYM 3 0 User Manual 8 Points Climate database B 6IS_Training August232012 Climate 2000_WorldClimate Calculate Parameters LT parameters Y Indices Temperature Longitude Browse files 36 345 Get from map 80 5632 Remove temp Clear table 2 AANA Insect number Steps 48 Ratio Degrees The button on the window above has similar meaning as on point analysis window e Remove temp Allow you to remove a longitude amp latitude from selections e Clear table Allow you to clear all selections After calculating with Calculate option we will obtain the windows below ILCYM 3 0 User Manual 143 Parameters Pointi Point Point3 0 065 0 049 0 051 21 5 20 816 16 893 103 757 11 996 20 367 46 905 62 252 55 613 1068 105 1052 14 214 13 636 5 9 6 6 75 4 2 10 10 O Am Lambda Dt m Generation length amp i Julian day Julian day Generation length Generation length Julian day You can select any of the life table parameters and visualize the outputs ILCYM 3 0 User Manual 144 IV ILCY
145. the result is a text txt file containing the fields of the original shape file plus de value of the grid where each dot is located For example if you have a shape file for a particular location and you want to extract the values of the data points contained in your shape file you click on shape file button and load the file specify on output bottom where you want to save the new file In doing this make sure the climate data base is uploaded Shapefile B GIS_Training August232012 FieldObservationsGener_Conoc shp X Longitude Y Lotitude Jutput file BGE Training Augqust 32012 ILCyMOutputs 2000 borrar extraxt_byClimate bet gaug y p Climate database B 6I5_Training August232012 Climate 2000_WorldClimate Raster file ame A Extract by points Shapefile BAGIS Training August232012 XFieldObservationskGener Conoc shp X Longitude Y Latitude r Output file BAGIS Training amp ugust232012 AILCyMOutputs2000 s borrarextraxt_byClimate bd Climate database B WGIS Traimning August 32012 XCIlimateN2000 WorldClimate amp Raster file OEA TIS Erenn em ILCYM 3 0 User Manual WB Extract by points m 108 e Export Raster files With the Export Raster files function you can export ILCYM ascii files to a number of different formats bil Idrisi GRD Files i Export raster files File type amp BI IDRISI ASCO O GRD FILES Input file B GI _Training August232012 ILCyMOutpu
146. the zoom is the center of the map e Zoom to Selection Zoom to the selected features b Add shape file wizard This wizard helps you import spatial data and add it to a Map When the Add Data Wizard first comes up you must choose data source to work with Some simulations may request a map to be loaded in the software to load one or more maps go to File File gt New gt New Layer option or click in the next button L3 in the map where to Start Spatial simulation u Data Sources Open one or more files 2 Files E Map Graphic ILCYM 3 0 User Manual r Open Look gy CD shapes papas 4 T admo prueba shp My Recent Ecuador shp Documents f india bangladesh shp S mundo shp 3 3 nepal shp pe_departments shp Desktop Sil pe provinces sho world _adm00 shp My Documents 4a My Computer File name world_sdm00 shp a My Network Files of type shp gd O 2cm and the next window will appear This is useful to have a reference 128 Click on File and then Next and look for the path of one shape p e world Er l c Style Editor dialog The style editor dialog is used to modify layer s display on screen Mode Point in DI Polygon sar A EA A tree of style pages for the selected content is displayed Each page allowing the modification of one aspect of the visualization process Apply Press this button to update the Map
147. tion multiplication division and calculation of minimum and maximum LL X Operation Add O Multiply O Minimum O Substract Divide O Maximum omae Add to map ILCYM 3 0 User Manual 120 i Raster Calculator The Raster Calculator provides you a powerful tool for performing multiple tasks You can perform mathematical calculations to create new map themes In the Calculate window you can do mathematical calculations with one or more grids The advantage of Calculate is that you can do several steps at once The disadvantage is that the calculations are much slower so if you have very large grids you re better off using the Overlay function Use the Add raster file button to choose the files you want to use insert the operator s you require between grid names and provide a name for the output grid As with Overlay to be able to use Calculate with multiple grids these must all have the same dimensions number of columns and rows and resolution and location min and max X and Y coordinates ll Calculate x Map algebra expression OOOO o SIN TAN eJl asin acos atan LN f cos Ass E JL rung RouND pr exp SQRT Add to map J Terrain This menu allows computation of slope aspect characteristics from a raster with elevation data The elevation data should be in map units typically meter ILCYM 3 0 User Manual 121 for projected planar raster data
148. tion Female Notepad File Edit Format Yiew Help Dead Dead e H H H ns F e e ID m a D w oa iD m oa uj m a m p o mD w oe OOOOOOEFNSNDNDHFHFHROFODUGON N ADOOO MOOS HF OO N WO tb gp o Im w o oc ID ib t w oo FROWDENPWNN Un UJ EA UJ SI ST BEN SJ ONENEN OwA OP I I PO TPOpPlDDonJ J Oo m fu a OOnKNOORPuOooOOHPOBHnNUPI HOHIPpPOrnIpPIpPdOIpPups amp pPBNIODHNIOOO DIAIOupPO NJ UJ D PI O o un uJ IA DORPE OG Pn PS uu uJ IS OO Qu PB 4 Ou FI Un uon oO IR Oo I O0 O0 OO 5 S n us ES P P CO D OQ On pPOxDODpP Du T O0O oO u qODI amp p On OD IP u P IO 0 OD UJ TR C hI CO QD UND UJ IP Q UJ NI un uq C NJ SJ UJ CO CO CO CO Oo UJ o SOF OO un OPPORRE OD NND UN UuuununooDPPIOOOP POoPPPPLeOO OOHFOOOOONONOH OS WWOODOOO UN Oui unm4AOOOPOOO O amp 0O0F rn cDHPu ordbDrniouuo poocmoSODOODNODE US SCOP OD EODF ETRIADOR OUT NORNIRINIOXOGOFGSmMWE p o DODODODOODOWODOODPODODOONDONPONPNPNwWJNPODO0PPOOOPm JO DODODODODODODOPODODOOOPDOwWNDDwPOwWP uJ p INI Cn UJ UJ n UJ GJ on O IJ FO GJ An C7 ISO NI O P IP uu un c uj Js NI un on ud Js UJ EW Js on s UJ JS Oo JE CO INI CO XJ UJ CO INI CO OS NJ UJ CO on C on JS NJ JA ID pm a In m a Figure 3 Example for arranging the data in text file for differentiating between female and male individuals in the progeny above A data for males reproduced below B data for reproduced females In both data files the first column represents the t
149. tion time Parasitation rate ILCYM s Projects Exp m Several generations at constant temperature Several generation at Fluctuating temperature Biological parameters of one generation at constant temperature Biological parameters of one generation at Fluctuating temperature Biological parameters of several generations at Fluctuating temperature Note For these simulations users will be requested to input different number of hosts and parasitoids As the numeral proportion of these species varies you will study possible efficiency of a particular parasitoid in controlling a host pest ILCYM 3 0 User Manual 89 Several generations at constant temperature Choosing this option will allow you to simulate an insect host and its parasitoid within a designated time frame at constant temperature By clicking on the extreme right of the combo box you can select your insect host and parasitoid projects then specify the attack stage and click next Several generations at constant temperature E Several generations at constant temperature Select one parasitism percentage option r Host amp Parasitoid projects Host PTM Project M Parasitoid Apanteles Project d Attack Stage Percentage parasitism calculation PPj Variable parasitism rate 8 NH C Constant parasitism rate ON cmd After clicking next the following
150. to three binary distribution models Logit probit and complementary log CLL The user selects the best model based on added selection criterion such as the Akaikes selection criterion AIC On the basis of the selected function the median development times with 95 confidence limits are estimated output statistics are explained on subsequent pages ii Using exponential models From the input data a weighed median developmental time is estimated the obtained values are normalized and then plotted against the accumulated frequency at each temperature The plotted points are fitted to a series of 6 exponential functions that include the gamma and weibull distribution functions The user selects the best fitting function according to ILCYM s inbuilt selections criteria e g the Akaikes selection criterion AIC Using the best fitted function a global median developmental time is estimated and then used to calculate individual media developmental time at each temperature ILCYM 3 0 User Manual 42 The window below display how development time is estimated in ILCYM Under Model Builder go to development select Time and its variation and then choose one of the options Exponentia Models or Dichotomic Model and then proceed with your analysis f ILCYM File Edit Operations Layer Model Builder Window Help m ED QJ Mortality Ti i Habit i A Senescence L ILCYM s Projects Ex 23 7 Species Interaction PTM project
151. tools Spatial Analysis Window Help Fir B R EPHOKZAA RL E ILCYM s Projects Explorer 22 O Ga dem z SE A E Um i metadata m 52 Palette b i ds projectRegistry u project udig PTM project a To display s Distance information select the info tool and click on a Map im 228 IE 7 mask v A WeatherStations E dem E m Dem Reclass mm dem cortad nm Eri reclass 7 C world adm00 vm ERI v m dem terrain aspect Ma ERI Selection Editing Create Zoom 1 5 Wildca c unit Feature Editing 0 Coordinate Reference System of data is unknown Unexpected behaviour may result if it is not set 3 3 3 Spatial Analysis We used the Raster Package from R programming language to provide classes and functions for manipulating geographic spatial data in raster format Raster data divides space into cells rectangles pixels of equal size in units of the coordinate reference system Such data are also referred to as grid data A number of practical and analytical functions are available from the menu Spatial Analysis The functions are particularly useful when using very large datasets that cannot be loaded into the computer s memory Functions will work correctly because they process large files in chunks i e they read compute and write blocks of data without loading all values into computer memory at once The following window displays the overall featu
152. ts 2000 ERL2000 asc Out put file BAGIS Training August432012 ILCyMOutputs 2000 er1 bil Export When exporting ascii files to the generic binary format bil band interleaved by line a data file with the extension bil is produced as well as a header file with the extension HDR These files can be imported into a number of GIS programs including IDRISI Arc Info with the imagegrid command and ArcView where they can be opened as an image If you need a file in the similar formats BIP or BSQ you can rename the extension of the output file because these are the same when only one grid or band is stored in a file When exporting grids to IDRISI version 2 and earlier the result is a data file with extension IMG and documentation file with extension DOC f Import Raster files With the Import Raster feature you can import one raster into ILCYM from the IDRISI IMG or RST generic binary BIL BIP BSQ and ESRI binary export formats B Import raster files x Lo File type 9 BIL IDRISI ASCO GRD FILES Input file BAGIS Training August232012 ILCyM Outputs 2000 ERLasc B GIS_Training August232012 ILCyMOutputs 2000 a fit ILCYM 3 0 User Manual 109 g Index Interpolator The assumption that makes interpolation a viable option is that spatially distributed objects are spatially correlated in other words things that are close together tend to have si
153. ual Washington State Potato Conference Moses Lake Washington USA Logan J A 1988 Toward an expert system for development of pest simulation models Environmental Entomology 17 359 376 Logan J A D J Wollkind S C Hoyt and L K Tanigoshi 1976 An analytic model for description of temperature dependent rate phenomena in arthropods Environmental Entomology 5 1133 1140 Lotka A J 1907 Studies on the mode of growth of material aggregates American Journal of Science 24 199 216 McKenney D W A A Hopkin K L Campbell B G Mackey and R Foottit 2003 Opportunities for improved risk assessments of exotic species in Canada using bioclimatic modeling Environmental Monitoring and Assessment 88 445 461 Nietschke B S D M Borchert R D Magarey and M A Ciomperlik 2008 Climatological potential for Scirtothrips dorsalis Thysanoptera Thripidae establishment in the United States Florida Entomologist 91 79 86 Nietschke B S R D Magarey D M Borchert D D Calvin and E Jones 2007 A developmental database to support insect phenology models Crop Protection 26 1444 1448 Peacock L and S Worner 2006 Using analogous climates and global insect pest distribution data to identify potential sources of new invasive insect pests in New Zealand New Zealand Journal of Zoology 33 141 145 Rafoss T 2003 Spacial stochastic simulation offers potential as a quantitative method for pest risk analysi
154. ur project ILCYM has a tool that show at each moment the stage of the progress in developing a phenology this tool can be found in Progress menu File Edit Model Builder Window Help cir ik 2 LIES be l Om a Comparison Post Ovipasition VR 3 Bla BM be 77 000000000 ILC M s E x LI LI F ki a ee g ne i e u a fam i am gt Lo metadata PTM project gt ILCYM 3 0 User Manual 63 The screen below shows the insect developmental stages that have been evaluated the stages evaluated are check the ones that have not been marked are the ones that cannot be evaluated under the conditions the experiments have been made w Progress Evaluation Project PTA project Stages X Evaluations Dev Time Dev Rate Senes Mort Tot Ovi Rel Ovi Egg Larva Pupa Female Male s S s s s Frogress OR Compile to Simulations 3 1 8 Project summarize Click on Summarize button the window below will appear and display a project resume with the summary of each life stage and its parameters and functions selected in Model Builder during the development of your project Project Summary Development Time Stage Egg Model probit Slope 15 4261400434957 Stage Larva Model probit Slope 10 13643 Stage Pupa Model probit Slope 8 798806 Stage Female Model probit Slope 4 342945 Stage Male Model probit Slope 8 68589
155. ure datasets from other sources such as CliMond data base used in CLIMEX software c Temperature data format The data from WorldClim and majority of available data sources are in ascli bil or ArcGIS raster files However ILCYM uses flt data format float type When running the potential population mapping in ILCYM you need to convert your input data file in flt using the following steps i Converting ascii file in flt Step 1 Open ILCYM under Potential Population Distribution and Mapping perspective Step 2 Click on import raster files Step 3 A window will appear check ascii Step 4 Click on input button and you load your ascii file Step 5 Click on output button and write were you want to save your flt file Step 6 Click import ii Converting bil file in flt Step 1 Open ILCYM under Potential Population Distribution and Mapping perspective Step 2 Click on import raster files Step 3 A window will appear check bil Step 4 Click on input button and you load your bil file Step 5 Click on output button and write were you want to save your bil file Step 6 Click import ii Converting ArcGIS raster files in flt go to ArcGIS package and follow the instructions for converting ArcGIS Raster files to Float ILCYM 3 0 User Manual 100 d Setting the climate data base To set the climate data base in ILCYM go to Window menu gt Preferences gt Climate data base path option TIL
156. ure temperature change data ccccccccsssscceeceeeeeeeeeeeeeeeeeeeeeeeeeeeeeesaeeeees 99 GC Temperature datado Mali toi 100 d Setting the climate data Dase Pee eee 101 3 32 O A ei 102 a Raster to polygons occccooccnccconcnnccnononcononononnnncnnoonannnnononcnnnnnnrnnnnnnnnnnnnanennnnanens 103 D Raster 0 Done ae einen 105 C Textile 10 Snape Tle nennen 106 ILCYM 3 0 User Manual ii d Extract DY DOI Sais 108 e EXpOrt Raster files irssi a ta 109 D Import Fidstef 1M o5 arser a a N a 109 a Index TnterpolatOE aminas 110 3 9 9 9Pallal Analysis dd 114 a DESCHDE WINGOW ee 116 BD MASK WO RC c LI 116 CHAdaregalo taria idad 117 ANDisaggregale zelnen ee a Eee 117 A 118 A nee elemente 118 eM ce IMMER T UE We 119 DEO ILU m mr 120 D Raster calculator cts ee ee 121 EN deo RR TTE TUN oben Osa iio da 121 K The Stack men lancia e a 124 3 34 Managing a Saca 127 a Navigation Tool innata 127 b Add shape file WizZaro o ccccccccnnconcccconncnnnononanonnnononancnnnononanennnnnonanennnnss 128 NIE Edi Bene em 129 Feallfe Style Pagesas zielen 130 Raster SMe Pages ee 130 3 3 5 Spatial simulations and MAPPING cccccccccocncnncccconnnnnnonononnnnnononnnonononennnnnnnos 133 a Estimating life table population parameters ssesssssssss 133 Temperature inclusion in the phenology model 133 Galeulalion of Indices POT Lt 133 b Mapping phenology
157. urve Depending on the settings when a project is registered see page 33 optionally functions for describing temperature dependent and age dependent female rates will be evaluated Note For incomplete life table two oviposition files are loaded as input data a function representing female ration in the oviposition is estimated and added to the overall phenology model 7 Female ration in Fit a function to describe the female ration in the the oviposition oviposition ILCYM 3 0 User Manual 41 In case where the female rate of the species under investigation is variable a new evaluation Rate oviposition is conducted and included on the overall phenology model Such addition will make the overall phenology model for a species to contain 7 functions 8 Rate oviposition Nonlinear function describing oviposition frequency of female Post oviposition is also an additional evaluation that can be conducted for insect with variable rate this represents the age specific survival rate describing the proportion of the number of eggs alive at any given time 9 Post oviposition Stands for age specific survival rate that described the proportion of number of eggs alive at any given age time Note The post oviposition evaluation is not included in the overall phenology model a Development time and its variations For conducting this evaluation ILCYM s software offers two options i Using dichotomic models The data are fit
158. user selects the model to fit the life table parameters ILCYM has inbuilt nonlinear functions that can be used to fit the life table parameters The best fitted function can be compared to the output results from deterministic simulation for the same species In doing that ILCYM s users can confirm the performance of the simulation algorithms implemented in the software It is expected that the best fitting curve from stochastic simulation should be similar to the curve yielded by the deterministic simulation Parameters Graphic i Statistical summary Using the cubic model Using the cubic model f a 0 2985311 0 06292499T 0 004231378T 8 40322e 05T Ro 43 0683 7 015443T 0 2745886T 0 002972T 0 06 htc do rate fr Het reproduction rate Pos Temperature C Temperature C Using the cubic model Using the cubic model GRA 73 95044 8 576513T 0 243155T 0 01371297T 150 GL 376 9916 4 33 574527 1 118309T 0 01326557T E a u o o se ha wm 5 E 2 i m a 50 8 a o 0 o 10 20 30 Pu Temperature C Tempe ratre C Using the cubic model Using the cubic model 150 Dt 1089 617 178 0731T 9 676994T 0 1716693T A 1 20526 4 0 04452988T D 003098806T 6 193123e 05T Dowblhg tme Df Fihte rate of licreace 3 B B Temperature C Temperature C ILCYM 3 0 User Manual 75 iii Summary life table parameter and statistical outputs This windows display the parameter name and the
159. veral modifications were made in ILCYM software For constructing a life table for an insect cohort I e a group of individual of the same age the experiment generally starts from eggs that were all laid within the same time period p e within the last 12 or 24 hours The number of eggs used should be at least n gt 100 because during each life stage holds certain proportion of the insects that might die and the number of individuals entering each subsequent life stage will be hence reduced Then the number of individuals observed might be insufficient for the last life stages or the number of females might be insufficient to assess well fecundity It would be recommendable to have at least 30 surviving females in the experiment for assessment of fecundity Especially the number of insects used for constructing a life table at extreme high and low temperatures where mortality is generally high should be increased because of the expected increased mortality during immature life stages The number of individual used for life tables at different temperatures need not to be balanced Analysis of these data will include weights that account for differences in numbers of individuals that entered a certain life stage at a given temperature Life tables can be repeated at the same temperatures with another batch of individuals cohort from the population Also the number of life tables in each temperature does not need to be balanced In the analysis
160. very insect species of interest and that it cannot meet every purpose for which a model needs to be developed However it is believe that the approach presented here might be a model applicable to many insect species and in ILCYM 3 0 User Manual 8 many circumstances for which insect phenology models are developed An important issue in integrated pest management IPM research is to evaluate the potential effects of certain pest management strategies Parasitoid life tables can be analyzed in the ILCYM Model builder and parasitoid phenology models obtained then applied in ILCYM GIS component to identify regions in which the parasitoid can potentially establish and control its host an insect pest This is an important analysis for planning classical biological control and identifying potential parasitoid release sites For the development of a two species interaction model a parasitism rate function is used for linking the parasitoid and the host pest phenology models through a deterministic simulation procedure under constant and varying temperature conditions This process uses the predicted temperature dependent development times and parasitoid fertility rates for simulations of the host population growth and development 1 3 Data ILCYM analyzes data of different tyoes The question in modeling the effects of temperature on insect population development is not so much a question of what data should be collected but rather
161. which the first column represents the temperature used in the example a total of 7 temperatures were evaluated second column indicates the evaluation time after experiment set up records should start from the evaluation before first development was observed here measured as days ILCYM 3 0 User Manual 16 after experiment set up third column indicates the number of insects used in each temperature and the forth column indicates the number of individual that had developed to the next stage on each evaluation date In life table studies the evaluation interval needs to be always the same for calculating life table parameters Such condition is not needed with cohort study however missing evaluations should not be included in file for example if the cohort was evaluated after 3 days and again after 5 days and in day 4 no evaluation was conducted day 4 should not appear in the records LES 1000 E species name egg Notepad 10 6 33 1000 63 File Edit Format View Help 10 6 34 1000 165 10 6 35 1000 141 6 10 5 36 1000 153 E 10 6 37 1000 145 5 10 6 38 1000 74 6 10 6 38 1000 1 5 10 6 40 1000 amp 15 14 5600 5 15 15 300 50 6 15 16 300 51 15 1i 300 of 5 13 300 28 15 13 300 4 15 20 300 Lu 16 1 3 1000 16 1 10 1000 A wi 16 1 11 1000 tii ad 16 1 12 1000 334 al 16 1 13 1000 216 16 1 14 1000 21 1 16 1 15 10010 15 16 1 16 1000 n i 20 3 6 1000 0 J 20 3 7 1000 32 3 20 5 amp 1000 653 3 20
162. y also maintain their respective ID as shown in Table 3 without the sub models listed below ID Name ILCYM 3 0 User Manual 168 Table 6 Functions fitted to relative oviposition in ILCYM software Function Expression Reference Exponential aT T cer Ze w Exponential modified 4 Weibull Exponential l 3 modified 2 T temperature in Celcius KT relative oviposition function at temperature Table 7 Functions fitted to oviposition time in ILCYM software The functions fitted to parasitoid oviposition time in ILCYM software are the same they also maintain their respective ID as in shown Table 6 Table 8 Sub models fitted to temperature parasitoid rate in ILCYM software The sub models fitted to temperature dependent parasitoid rate in ILCYM software are the same they also maintain their respective ID as shown in Table 3 ILCYM 3 0 User Manual 169
163. y in C as shown in the window below MM System Requirements Installation Software to install CDMA Requirements Software Installed Are Rserve and E libraries installed Installed 0 6 6 A Caution Install R in C as shown below CXR 2 15 1 2 Sometimes ILCYM users miss to correctly follow some steps during the software installation process and when starting to run the software the following error message appears not connecting to R To remedy you should follow the instructions below Steps A Completely remove ILCYM and R in your computer 1 Goto control panel click on add remove program 2 Click on remove program and uninstall ILCYM 3 Still on add remove program click on R and uninstall R 4 Goto my computer click on C V then program file in case you are using window operating system with two program files one for 32 bites and the other for 64 bites check both folders and complete delete ILCYM ILCYM 3 0 User Manual 23 5 Goto my computer against click C and then delete the R folder Steps B Reinstall at fresh ILCYM by carefully following the instruction in section 2 2 of this manual lll ILCYM S PERSPECTIVES This latest version of ILCYM software is made of perspectives A perspective is a collection of views and actions which are useful for specific tasks for users ILCYM 3 0 contains 3 perspectives Model Builder Validation amp
164. ype of data need to be generated to validate a IPhM how IPhMs are implemented and what type of insect population analysis will be provided With the current version of ILCYM the authors intend to share the knowledge gained in insect pest population modeling research at the Agroecology IPM unit at CIP and provide an open source computer aided tool especially for researcher in developing countries that facilitates the development of own IPhMs using advanced modeling techniques without being experts in the field 1 1 The modeling approach applied in ILCYM Modeling of insect populations is for some reasons more complicate than modeling populations of other organisms Insects pass through different stage before reaching maturity within these immature stages they may die and when mature they reproduce Some species have seasonality i e different life stages of the insects are only found during specific seasons of the year others not I e populations are heterogeneous in their age stage structure because of continuous reproduction and overlapping generations but their development is always strongly temperature driven The approach used in ILCYM is to define sub models describing development and mortality in each immature life stage of the insect with its variation between individuals in a population and senescence time and reproduction frequencies of adults according to temperature These sub models are based on experimental data obtained through

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