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User Manual for the Small Lakes

1. I Korman Joshua 1962 II British Columbia Ministry of Environment Lands and Parks III BC Environment Fisheries Branch IV Series SH224 B7U83 1994 333 91 6316 02855369 C94 960139 X 1 ABSTRACT Korman J C Walters J C Sawada and E A Parkinson 1994 User Manual for the Small Lakes Integrated Management Model Version 2 0 Province of British Columbia Fisheries Technical Circular No 95 This manual provides instructions on how to operate and parameterize a model SLIMM designed for use by managers of small lakes in B C A detailed description of model structure is also provided The model focuses on rainbow trout and is designed to assist managers in anticipating the results of a variety of management actions in three main categories regulations stocking and habitat alterations The model is based on a variety of empirical relationships that link factors such as fish growth to density These relationships form dynamic links to lower trophic levels which are not explicitly modeled Output is in the form of time series graphs or equilibrium values for 49 indicators ACKNOWLEDGMENTS We would like to thank all of those involved in the process of developing a management system for rainbow trout in small lakes Chris Bull and Tom Johnston for early discussions on ideas Al Martin for his work in putting the project together the Small Lakes Management Biologists for their participation in the workshop process Steve Matthew
2. double the mean CPUE measured in the field i e the Regional Base CPUE parameter estimated from field catch effort statistics over whole fishing seasons is likely to be only about half the CPUE at the start of each season the back extrapolation from field average to early season CPUE is needed in the Total Effort calculation We chose the 2 factor for early season average CPUE after trying the model for various lakes where total effort and catch data were available and seeing what the factor needed to be in order to match these data Effort predictions from the equations above can be replaced by fixed effort levels chosen by the model user This is useful if empirical effort data is available An alternative modelling approach would be to make predictions of total fishing effort over a region or group of lakes then predict the proportion of this effort allocated by anglers to each lake This more complex approach will be needed if the system eventually is used to deal with many lakes at a time but when dealing with a few lakes at a time it is probably better to just assume that local effort is essentially unlimited i e proportional to local quality of fishing and regional parameters 30 5 5 Recruitment The recruitment sub model predicts the number of yearling recruits entering a lake based on an estimate of the amount and quality of available spawning and rearing habitat in the streams Survival to age 2 in the lake is a function
3. User Manual for the Small Lakes Integrated Management Model Version 2 0 by JOSH KORMAN CARL WALTERS JOEL SAWADA ERIC PARKINSON FISHERIES TECHNICAL CIRCULAR NO 95 1994 Province of British Columbia Ministry of Environment Land and Parks Fisheries Branch User Manual for the Small Lakes Integrated Management Model Version 2 0 Manual by Josh Korman Car Walters Joel Sawada and Eric Parkinson Model Code by Carl Walters Josh Korman and Tim Webb ESSA Environmental and Social Systems Analysts Ltd Suite 300 1765 W 8th Ave Vancouver B C V6J 5C6 5U B C Fisheries Center 2204 Main Mall U B C Vancouver B C V6T 1Z4 B C Fisheries Branch Research and Development Section 2204 Main Mall U B C Vancouver B C V6T 1Z4 BRARY B C ENVIRONMENT LI 4st FLOOR 810 BLANSHARD ST VICTORIA BC B C Ministry of Environment CANADA VV 1X4 Fisheries Technical Circular No 95 1994 Canadian Cataloguing in Publication Data Main entry under title User manual for the small lakes integrated management model Fisheries technical circular ISSN 0229 1150 no 95 Issued by the Fisheries Branch ISBN 0 7726 2099 7 1 Lakes British Columbia Management Computer programs 2 Trout British Columbia Habitat British Columbia Computer programs 3 Fishery management British Columbia Computer programs
4. through the Stock Dependent Parameters Window Survivals of juveniles are adjusted down from this maximum to take into account size and density dependent survival by using the parameters below Size at which survival is zero 1 cm 1 10 size at which survival is 50 7 cm 7 10 curve shape parameter 2 2 99 These parameters determine the relationship see view option between the size at lake entry and the annual survival to age 2 The default values are designed for productive monoculture lakes in the southern interior are a result of a combination of data from Stringer et al 1980 Hepworth and Duffield 1991 and Parkinson and Johnston data on file In mixed species lakes survival of fry can approach zero and survival of larger juveniles 5 15 cm can be much lower 10 20 than that in monoculture lakes Burrows 1993 Piscivorous fish including cannibalistic rainbow trout seem to be a major factor in the mortality of small juvenile rainbow Parkinson and Johnston data on file Since the maximum prey size for a salmonid piscivore is about 35 of the predator s body length Parkinson et al 1989 survival of fish larger than 35 of the body length of the largest piscivorous fish should approach that of monoculture situations Proportion of size dependent mortality applied to summer fry emigrants 0 75 fall fingerling emigrants 0 5 These parameters adjust annual size based mortality experienced by these emigrants down t
5. will inevitably cause substantial effort responses and possibly shifts in fishing effort among lakes Higher quality lakes will attract more effort until the quality of fishing is driven down to some point where other lakes look equally attractive This process of homogenization via angler choices and reaction should be a matter of basic concern to managers since it will tend to negate attempts to maintain much diversity of opportunity SLIMM attempts to model at least the more dramatic responses in fishing effort that are likely to result from major regulation and stocking policy changes It assumes that these changes are due to a combination of 1 time invariant effects of lake ABD and 2 time varying effects of fish abundance size and regulation It is assumed that effort on any lake responds very rapidly to changing abundance within each fishing season so that overall effort for any fishing season must be found by integrating over rapidly changing effort levels within the season To model these rapid changes we assume that abundance N decreases during the season according to the rate equation dN dt gNE where q is catchability and E is instantaneous e g daily effort this rate ignores natural mortality during the fishing season We then assume that instantaneous effort is roughly proportional to abundance remaining at any time i e E kN where k is a response constant that depends on the region and accessibility this assumpti
6. 2 8 3 2 2 4 Age Pane The Age Pane displays a histogram of the annual age structure of the populations being simulated It is useful for tracking cohorts from pulse stocking policies as they move through the population over time Double clicking on the pane brings up the Age Structure Y Axis Dialogue Box which allows you to adjust the Y axis maximum of the histogram Unlike the other panes the Age Pane is updated each year of the 8 simulation a green bar running horizontally along the top of the pane provides a visual reference for the current year of the simulation Because this graph is updated every year the model runs considerably slower when the Age Pane is used 3 3 Main Menu Bar The Main Menu Bar is located at the top of the Parent Window see figure 3 1 The Main Menu Bar provides the user access to the other windows which contain the data base Lake Info the parameters which control the behaviour of the model Parameters management actions Policy choice of indicators View and output options Report 3 3 1 View 3 3 1 1 Age Structure An alternative method to see the age structure for the last year of a simulation is to select the View option of the Main Menu Bar Select the sub menu View Age Structure This will display a window containing a line graph of density versus age Use this option if you want to see the final age structure of the equilibrium population without slowing SLIMM down by updating the Age Pane each
7. Although extremes do occur most streams with normal temperature regimes flows and gravel quality should have egg to fry survivals in the 10 30 range Summer fry survival 50 30 80 This parameter should reflect the survival of fry at relatively low densities since surplus fry at high densities are presumed to emigrate involuntarily in the fall ie emigration not survival is assumed to be density dependent Oversummer mortality rates in steelhead and Atlantic salmon at low densities ranged from 18 28 per month see references in Hume and Parkinson 1987 suggesting that oversummer survival should range from 40 60 for a 2 5 to 3 month period Fraser 1969 studied survival of steelhead and coho fry in stream channels where emigration and immigration was prevented Under these conditions survival at low density was of 90 95 for steelhead and 75 87 for coho fry Survival at high density was 1 4 1 5 for steelhead and 6 2 10 7 for coho N Pe 3 EE AE E 22 Migration Mortality 3 Migration mortality is potentially higher than most managers might anticipate Coho salmon smolt survival during downstream migration Sawada 1993 was 5 per km 40 per 10 km A lower value is recommended for smaller streams with fewer predators and for fish not undergoing the stress of smoltification Note that migration mortality applies to fry and fall fingerlings only Fingerling overwinter survival 60 30 80 Winter can be a t
8. For average winter habitat For monoculture lakes For productive monoculture lakes All fish over this age mature 25 5 0 MODEL STRUCTURE 5 1 Density Dependent Growth Density dependent growth is both a fundamental driving force in the model and a phenomena which is familiar to any biologist who has observed stunted populations of trout Our method of accounting for the dynamic response of growth follows the method of Walters and Post 1993 This method is attractive because the biological load on the lake is neither driven by biomass where the effect of large fish is weighted too heavily or numbers where the effect of small fish is too great The model uses the Y length which is a compromise between the two We feel this is the best choice given the lack of empirical data on the effects of density on growth The major assumptions of this method are as follows 1 Growth is represented as a linear plot of length at age t versus length at age t 1 Walford plot 2 The intercept of this plot changes with density but the slope does not 3 The relationship between the intercept and the Y length is linear The most common non linear Walford growth curve is one where a shift in diet produces an acceleration in growth at a threshold size eg piscivorous Bull trout Parkinson et al 1977 Walford plots are linear eg lines a b and c in Figure 5 1 and can be represented by the following equation i Length aget A
9. all undersized fish will be released 3 3 3 3 Effort Related Parameters Window The base or average effort level for each lake is calculated by taking into account the Regional Base Effort Travel Time Parameter which uses lake accessibility as input and Regional Base CPUE Regional Base Effort and Regional Base CPUE are parameters that have been estimated separately for each management region 1 8 from empirical data on effort CPUE and accessibility The Regional Base Effort parameter essentially represents effort that would occur in a lake having typical fishing quality CPUE size if that lake had zero travel time from the nearest town in the region The model takes the base effort level and makes it dynamic with changes in CPUE This relationship can be viewed by clicking on the box next to the View Effort CPUE label at the bottom right corner of the window If the actual effort value for a lake is known the default effort values can be replaced by fixed effort levels This is done in the Effort Parameters Window by clicking on a box near the bottom of the window labeled Hold angler days ha at Constant Level typing the effort value in the box which appears and hitting the enter key to register the value i The proportion of effort lost in catch and release situations parameter allows the user to specify a proportion of fishing effort that is lost in catch and release situations i e when bag limit is zero 3 3 3 4 Season Dependen
10. can be used to observe the effects on size at age of alterations in the Walford intercept and slope parameters Two features alter the overall height of the growth curves the competition index and the growth curve calibration density The competition index is the density of fish in the lake total ha at which the length of age 1 fish is reduced to 1 2 the maximum length observed at very low fish densities Section 5 1 The growth curve calibration density value in the View Length at Age Setup establishes the density for which the viewed growth curve is calculated This density is in numbers ha and is compared directly to the competition index The density value for the most recent simulation is given in the yellow box labeled Current total ha in Lake 1 This value can be used when calibrating the growth curve see Section 4 2 1 The weight and time at lake entry are also entered in the View Length at Age Setup Box These values are needed to provide a starting point for the growth curve in the lake and to account for the reduced growing season in the lake which fish experience during their first year of life The weight at stocking is entered in the Entry Weight Box The time and life stage at entry are controlled with radio buttons the user can adjust the proportion of a year s growth which emigrant fish will experience 3 3 3 2 Catch Parameters Window The three middle parameters in the Catch Parameters Window minimum recruitabl
11. increased mortality of hatchery fish The default value is 0 1 meaning hatchery fish suffer 10 greater mortality than wild fish of the same size A saturating relationship is used to set the maximum yearling capacity in ha The survival of fry fall fingerlings and yearlings entering the lake is assumed to follow the same survival versus size curve Section 4 2 8 The survival to yearling of fry and fall fingerlings is taken from this curve and adjusted to reflect a partial year in the lake Yearling sizes of fry and fall fingerling are 14 calculated using growth increments read off the Walford plot Section 4 2 1 Fig 5 1 which are adjusted down to reflect a partial year in the lake The survival of fry and fall fingerlings as yearlings is read off the survival versus size curve 3 3 3 8 Spawning and Fecundity The spawning fecundity relationship is a regression line which can be altered by the user All fish greater than the minimum spawning lengths will mature and spawn Spawning mortality is applied maturing fish in addition to annual natural mortality Section 3 3 3 1 If the fish are stunted and do not reach normal spawning size all fish are assumed to mature at a specified minimum spawner age with a specified fecundity The proportion of hatchery fish returning to spawn in the natural habitat determines the proportion of fish actually reproduce after maturing 3 3 3 9 Saving and Restoring Parameter Files Parameter files HY
12. p gt Length age t where a the Walford intercept points 1 and 2 in Figure 5 1 and p the Walford slope The intercept is the size a one year old fish would be after having spent an entire year growing in the lake The Walford slope parameter represents the rate of fish growth The point where the linear relationship intercepts the 1 1 line represents the asymptotic length points A and B An analysis of growth data across all regions gave a mean value of 59 for the Walford slope parameter This slope is represented by line a in Figure 5 1 A steeper slope starting at the same _ intercept indicates a higher growth rate of older fish relative to age O fish and produces higher asymptotic size line b SLIMM assumes that the Walford slope does not change with density Fish in two lakes with similar Walford slopes but different Walford intercepts also approach different asymptotic lengths lines a and c The empirically derived value of 59 for the Walford slope is probably biased downward Faster growing fish within a cohort are caught at a younger age leaving disproportionately more of the slower growing fish in older age classes Adjusting the Walford slope parameter to change the asymptotic size lines a and b affects the size of smaller fish less than larger fish Adjusting the Walford intercept affects the length at age of all fish lines a and c For each year of the simulation the Walford intercept is adjusted to account for growth r
13. to measure fish stock abundance Trans Amer Fish Soc 112 608 617 Bley P W 1987 Age growth and mortality of juvenile Atlantic salmon in streams a review U S Fish Wildl Serv Biol Rep 87 4 Briggs J C 1953 The behaviour and reproduction of salmonid fishes in a small coastal stream Calif Dept of Fish and Game Fish Bull 94 Burrows J A 1993 Size related survival of resident salmonids from time of lake or river entry evidence from the literature and scale evaluations Prov of B C Fish Manage Rep No 100 Bustard D R and D W Narver 1975 Aspects of the winter ecology of juvenile coho salmon Oncorhynchus nerka and steelhead trout Salmo gairdneri J Fish Res Board Can 32 667 680 Donald D B and R S Anderson 1982 Importance of environment and stocking density for growth of rainbow trout in mountain lakes Trans Amer Fish Soc 111 675 680 Dotson T 1982 Mortalities on trout caused by gear type and angler induces stress N Am J Fish Manage 2 60 65 Elliott J M 1987 Population regulation in contrasting populations of trout Salmo trutta in tow lake district streams J Anim Ecol 56 83 98 Elliott J M 1993 A 25 year study of production of juvenile sea trout Salmo trutta in an English Lake _ District stream p 109 122 In R J Gibson and R E Cutting ed Production of juvenile Atlantic salmon Salmo salar in natural waters Can Spec Publ Fish Aquat Sci 118 Fleck J
14. total stream area m Kg ha gt 2 yrs old Biomass of fish wild and hatchery gt 2 years old kg ha Yield kg ha Weight of fish retained per hectare of Hatchery yearling equivalents harvested 3 3 2 Lake info of kill that is Age X hatchery or wild Lake Info is used to access the SLIMM data bases and for selecting lakes to simulate There are two types of data bases DBASE IV DBF and ORACLE The DBASE IV files are compatible with a variety of applications including EXCEL For users on the B C MOE Provincial wide area network SLIMM will also be able to access a centralized ORACLE data base Fisheries Aquatic Data Base which contains inventory data on B C lakes and streams and is maintained in Victoria The connection to the ORACLE data base will not be incorporated into SLIMM until the Fisheries Aquatic Data Base is released o i a ERM 10 To access the DBF data bases select the Lake Info menu on the Main Menu Bar as well as the sub menu titled Lake Info and the Lakes Data base Window will appear A default DBF data base SLIMM DBF was loaded as you entered SLIMM This default data base contains a selected set of small lakes that have more complete data sets These represent a variety of types of systems such as coastal versus interior wild versus hatchery and monoculture versus mixed species Use the radio buttons to display lakes in this file that are situated in the v
15. year of the simulation 3 3 1 2 Plot Setup To assign indicators to Graph Panes 1 3 select the View option of the Main Menu Bar and select the submenu Plot Setup The 49 available indicators are grouped into the following five broad classifications 1 Catch per unit effort CPUE in numbers of fish angler day 2 Fork length in cm 3 Percentage of age X in catch 4 Fishing mortality 5 Assorted The Time Series Graph Setup Window contains a list of the 49 indicators in the above table The user can assign up to 3 indicator numbers to each pane by typing the indicator number in one of 3 boxes for each pane or clicking on the desired box followed by clicking on the desired indicator to place in the box The indicators assigned to each pane should be selected from the groups represented by the four boxes in order to keep the y axes consistent The default setting includes CPUE in Pane 1 mean length in catch length at age 2 and length at age 4 in catch in Pane 2 and age 2 and 4 in catch in pane 3 To adjust the Y axis scale of each Graph Pane insert the appropriate value in the y axis minimum and maximum boxes for each graph pane You can also alter the y axis title and the display of x labels and the number of ticks which appear on the y axis Click on the Colors Button in the Time Series Graph Setup Window to adjust the color and line thickness for the time series graphs To change the color of any of the graph elements g
16. 0 lakes in British Columbia which is used to provide input information required by the population model for lake specific simulations and 3 a Graphical User Interface GUI which allows SLIMM users to implement different management actions alter assumptions and or structural relations within the population sub model access the data base and view the results of model simulations This User s Guide describes how to install SLIMM on your computer Section 2 how to operate the model access the data base and how to adjust the model parameters Section 3 The choice of parameters is discussed in Section 4 and Section 5 provides details of the model structure Version 2 0 of the guide and model will constitute the official published version As modifications to the model are made these will be available in an electronic form with the manual in Wordperfect format from the Research and Development Section at U B C For non Government of B C users the E mail address is EParkins ubc env gov bc ca 2 2 0 INSTALLING SLIMM ON YOUR COMPUTER 2 1 Computer Requirements The minimum usable hardware configuration for SLIMM is an IBM or compatible 80386DX 25 MHz system with an 80387 math coprocessor 4 MBytes of memory and a VGA graphics card SLIMM must be run under enhanced mode from Microsoft Windows version 3 1 The model will run on machines with less power but it will run slowly it may take up to 90 seconds to run a simulation The id
17. 4 2 8 In Lake Juvenile Survival ii cw ey ww wb ls he eee be e RR ie RR Be ES 22 4 2 9 Spawning Fecundity Parameters ee se see eee ees 23 4 2 10 Hooking Mortalities Ls win Ee k Es WA ae Re ie sl RR eh a ee Res 23 5 0 MODEL STRUCTURE L 51 aas sl ou ok ot ORM EEE Ce WE RARR ED ed 25 5 1 Density Dependent Growth si sede RE RR a 8 oe ee Bee a ee EER 25 5 2 Density Dependent Survival ais 26 5 3 Calculation af Catch 4 442 RE Bonk RAU RE Dog ie RO EE de Me IE eee ie OR DAE 27 5 4 Calculation of Effort nea ana GE DO a ER ee N N DARA ee ve 28 S S Recruitment os ER OE NAAA 30 5 5 1 Habitat Capability of Streams o ss ee N ee ees se 30 5 5 2 Stream Recruitment Pe RR a ra ee EE Ee EES EER es 30 5 5 3 Lake Recruitment 55 ak k kan RE A BAe ie e bood os 31 REFERENCES cda Rade ce ee BOS Sk OS Spe ea he Se hea EA ee ese 32 EE N N r N oud VEER GEKAP AN m Mere i Ne NE S EE Ee 1 ve A A A RR RR RR i 1 0 INTRODUCTION The Small Lakes Integrated Management Model SLIMM was created to provide a tool for regional management biologists to assess alternative management actions such as harvesting and stocking policies SLIMM contains 3 basic elements 1 a dynamic age structured salmonid population sub model which simulates the response of wild and hatchery populations to management actions influencing density and age specific mortality 2 data bases of physical limnological and chemical information for up to 3 00
18. Enter the stocking rates in numbers per hectare or numbers 5 per lake and the stocking weights Use the mouse to move the icon to one of the cells in the spreadsheet Enter a stocking value and click the mouse button to register the change The adjacent cell will fill in with the converted figures To use the copy and paste attributes to fill the spreadsheet click the mouse on a cell or group of cells and then click on the copy button Drag the mouse across the cells to be filled in Release the mouse and click on the paste button The highlighted cells will now have values 3 Constant rate Click on the Constant Rate Radio Button Change the stocking rate by highlighting the current value with the mouse and entering a new value yearlings ha yr in the box hit the enter key to register the change 4 Historic Stocking Rate Bring up the historic stocking rate from 1980 to 1992 by clicking on the radio button next to the Historic Stocking Rate label To compare results between runs click on the box beside the Overlay label in the bottom left corner of the parent window When an x is inside the box the overlay option is enabled Each subsequent simulation will be represented by different lines on the graph panes Try doubling the stocking rate to 600 ha yr using the constant stocking rate option and with the overlay function on start another simulation by clicking on the Start Run Button To clear the graph panes click the Overla
19. Kamploops area lakes unless there is good lake specific data available After setting the winter proportion to 10 winter was not covered by Naito s data these values are spring 42 summer 28 fall 20 Summer CPUE in three Okanagan Lakes was about 70 of that in the spring E A Parkinson data on file This and anecdotal evidence from experienced anglers suggests that catchability is highest in the spring 1 0 lower in the summer 0 70 and higher again in the fall 0 90 and winter 1 0 4 2 6 Rearing Habitat Parameters The parameters in this pane establish the capability of the stream to produce juvenile outmigrants The actual number of emigrants produced depends on the habitat capability combined with various parameters defined in the Stream Recruitment Window Many of the parameters in this window are lake specific Average Stream Width Average Stream Length Rearing Area Spawning Area and the Inlet and Outlet Streams Currently the Provincial lakes data base lists only the number of inlet and outlet streams instead of the size or quality of habitat Specific habitat quantity and quality information is only currently available from regional reports data on file and personal knowledge Development of the SLIMM has identified rearing habitat parameters as a major data gap in the Provincial Lakes Data base Sensitivity of Rearing Area to Changes in Discharge Disabled The use of the sensitivity to discharge featur
20. L and H Andrusak 1977 Adult enumeration and fry production at Redfish Creek British Columbia 1975 76 Prov of B C Fish Tech Circ 28 Fraser F J 1969 Population density effects on survival and growth of juvenile coho salmon and steelhead trout in experimental stream channels p 253 268 In T G Northcote ed Symposium on Salmon and Trout in Streams Institute of Fisheries University of British Columbia Havens A C and S Sonnichsen 1992 Evaluation of enhancement efforts for rainbow trout in south central Alaska 1991 Fishery data series no 92 37 Alaska Department of Fish and Game Division 33 of Sport Fish Anchorage Alaska Hilborn R 1985 Fleet dynamics and individual variation why some people catch more fish than others Can J Fish Aquat Sci 42 2 13 Hepworth D K and D J Duffield 1991 Supplemental stocking for estimating population size and first year survival of fingerling rainbow trout stocked in a Utah reservoir N Am J Fish Manage 121 11 19 Hume J M B and E A Parkinson 1987 Effects of stocking density on the survival growth and dispersal of steelhead trout fry Salmo gairdneri Can J Fish Aquat Sci 44 271 281 Hume J M B and E A Parkinson 1988 Effects of size and time of release on the survival and growth of steelhead fry stocked in streams N Am J Fish Manage 8 50 57 Hume J M B and K Tsumura 1992 Field evaluation of two rainbow trout strains introduced in
21. P are ASCII text files which contain all the information displayed in the parameter windows as well as the regulation code assigned to all lakes which are being simulated These files also store information on lake selection lake data access stock structure and graphics configuration The file SLIMM HYP is the file which is brought up at the start of the SLIMM model To save the model in its altered form use the Files Save Option from the Parameters Main Menu Bar item to save all the changes to the file This file can then be retrieved during a later session using the Files Restore Option to restore SLIMM to the exact state that it was in when the parameter file was last saved If the user wishes to bring up a particular set of lakes at the beginning of each session name that file SLIMM HYP HYP files from previous versions of SLIMM are not compatible with the current version If you try to load old HYP files the model will crash 3 3 4 Policy 3 3 4 1 Regulations Harvesting policies can be viewed and manipulated through the Fishing Regulations Window accessed from the Policy Main Menu Bar item In the model one of five different regulatory policies regulatory types can be assigned to each lake being simulated The policy choices can be modified as can the assignment of regulatory types to specific lakes The user can change the regulation code by altering the number in the box located in the lower right corner of the window To save ch
22. Rep No 64 Nelson W C 1987 Survival and growth of EE trout peers in oe lakes of Colorado Colo Div Wildl Tech Pub No 36 34 Northcote T G 1969 Patterns and mechanisms in the lakeward migratory behaviour of juvenile trout p 183 204 In T G Northcote ed Symposium on Salmon and Trout in Streams Institute of Fisheries University of British Columbia Parkinson E A 1990 An Evaluation of adaptive management and minimal sampling as techniques for optimizing rainbow trout stocking rates Prov of B C Fish Manage Rep No 96 Parkinson E A J Berkowitz and C J Bull 1988 Sample size requirements for detecting changes in some fisheries statistics from small trout lakes N Amer J Fish Manage 8 181 190 Parkinson E A J M B Hume and R Dolighan 1989 Size selective predation by rainbow trout on two lacustrine Oncorhynchus nerka populations Prov of B C Fish Manage Rep No 94 Parkinson E A and P A Slaney 1975 A review of enhancement techniques applicable to anadromous gamefishes Prov of B C Fish Manage Rep No 66 Parkinson E A P A Slaney and T G Halsey 1977 Some effects of forest harvesting on a central interior lake in British Columbia Prov of B C Fish Manage Pub 17 Porch C E and W W Fox 1990 Simulating the dynamic trends of fisheries regulated by small daily bag limits Trans Am Fish Soc 119 836 849 Rawstron R R 1973 Harvest mortality and cost o
23. ability ceases to increase exponentially at 28 cm an empirical fit of the Rieman and Myers data gives a minimum recruitable length of 14 cm a length at 1 2 maximum vulnerability of 28 cm and a vulnerability length curve shape of 4 Catchability 004 004 04 Catchability can be thought of as the proportion of the fish removed from one hectare by one angler day of effort A value between 0 004 and 0 015 is a reasonable starting point Data in Alexander and Shetter 1969 give a catchability of 024 Catchability is the key link between fish density and CPUE but it is lake and season specific and is therefore difficult to specify precisely Similar CPUEs can be generated by high fish densities combined with low catchability or low densities combined with high catchability The biological parameters within the model should be used to establish the density of fish in the lake and then the catchability can be used to as a final adjustment to fit model CPUE to observed CPUE for a lake Lake area associated with q 6 5 This parameter is the area of the lake on which q was measured The default value is from Alexander and Shetter 1969 It is used to scale the catchability for simulated lakes which have different surface areas See section 3 3 3 2 for more details Percent Voluntarily Released 10 This parameter is lake specific and depends on factors such as the 20 attitudes of anglers size of fish and CPUE The default value comes fro
24. age Rep 35 No 75 Thorp G N 1987 Hill Creek spawning channel kokanee fry enumeration spring 1987 B C Fish Br Fish Proj Rep No KO22 Tredger C D 1991 Estimation of angler effort using index boat counts Prov of B C Fish Tech Circ No 94 Trojnar J R and R J Behnke 1974 Management implications of ecological segregation between two introduced populations of cutthroat trout in a small Colorado Lake Trans Am Fish Soc 103 423 430 Walters C J and J R Post 1993 Density dependent growth and competitive asymmetries in size structured fish populations Trans Am Fish Soc 122 34 45 Ward B R and P A Slaney 1988 Life history and smolt to adult survival of Keogh River steelhead trout Salmo gairdneri and the relationship to smolt size Can J Fish Aquat Sci 45 1110 1122 Ward B R and P A Slaney 1993 Egg to smolt survival and fry to smolt density dependence of Keogh River steelhead trout p 209 217 In R J Gibson and R E Cutting ed Production of juvenile Atlantic salmon Salmo salar in natural waters Can Spec Pub Fish Aquat Sci Wiley R W R A Whaley J B Satake and M Fowden 1993 An evaluation of the potential for training trout in hatcheries to increase poststocking survival in streams N Am J Fish Manage 13 171 177 Wydoski R S 1980 Relation of hooking mortality and sublethal hooking stress to quality fishery management p 43 85 In R A Barnhart an
25. ameter values for SLIMM Recommended range in brackets a Natural survival 80 50 90 at gt Age 2 0 Zion 1530 Walford Slope 65 60 80 600 400 1000 fish ha 0 107 Var mean ratio of Catch angler 2 3 Vulnerability vs Size 14 cm 28 cm 4 parameters catchability 0 016 value is based on proportion 0 004 0 04 removed in i ha by 1 angler day Seasonal Effort Distribution Spring 42 Summer 28 Kamloops area lakes Fall 20 Winter 10 Seasonal Vulnerability Spring 1 0 summer 0 7 fall Kamloops area lakes 0 9 winter 1 0 Maximum Egg Capacity 300 000 m spawning area may be very low Rearing Capacity parameters 1 58 0 97 0 45 Egg to fry survival 15 5 40 gt 50 G0 80 downstream migration survival 3 km Fingerling Overwinter Survival 60 30 80 N Annual Parr Survival 60 25 85 Survival vs Size Curve 1 0 cm 7 0 cm 2 Parameters Mortality adjustment for Fry 0 75 Fingerlings 0 5 Hatchery 0 1 Maximum Yearling Capacity 1500 200 2000 fish ha Fecundity Slope Intercept 60 eggs cm 17 cm Minimum Spawner Lengths Males 20 cm Ali fish over these lengths mature Females 23 cm Spawning Mortality Males Females 50 Minimum Spawner Age 5 150 Fecundity Hooking Mortality 5 2 20 For unbaited barbed hooks Productive monoculture lakes Productive monoculture lakes extrapolated from kokanee Applied to fry and fall fingerlings only
26. and brown trout see references in from Burrows 1993 but also with rainbow trout Ayles 1975 These studies provide little stock specific information for this model since the majority of hatchery rainbow trout in British Columbia are produced from wild parents The differences between genders has been partially examined in studies of precocious maturation and a few studies have examined differences in stock type but information concerning growth rates mortality schedules and vulnerability to harvest does not appear to be available 17 Natural Survival 80 yr 50 90 This is the annual survival of immature fish after their second scale check in April and is assumed not to change until the fish reach sexual maturity Measured survivals of 50 80 for age 1 rainbow Stringer et al 1980 Johnston et al 1991 Havens and Sonnichsen 1992 are presumed to establish a lower limit Stringer et al 1980 lists the survival rates of yearlings to be 48 Rawston 1973 documented a mean natural survival of 45 range 33 51 5 for hatchery fish planted at catchable size in California Natural survival was 65 from age 0 to 2 in Marion Lake B C Sandercock 1969 Over winter survival of rainbow trout in a small Michigan lake was close to 100 with 86 total recovery over two years Alexander and Shetter 1969 Survival is assumed to be independent of density at this stage but juvenile survival can be density dependent see Section 4 2 8 Walford Gr
27. anges to the regulatory policies to an ASCII text file REG use the Save on File Option from the File Options menu bar item on the Regulations Window The default file name is SLIMM REG To restore a previously saved regulations file select the Restore File Option The hooking mortality rates associated with the different fishing regulations can be viewed by clicking on the Hooking Mortality menu bar item on the Regulations Window 3 3 4 2 Stocking This menu bar item allows the user to bring up the stocking window The outcome is identical to double clicking the mouse on the Stocking Graph Pane 15 3 3 5 Run This main menu bar item allows the user to start overlay and stop the model The outcome is identical to using the Start Run Stop Run and Overlay boxes located at the bottom left hand corner of the Parent Window 3 3 6 Report 3 3 6 1 Send results to file After selecting this item either enter a new file name or select an old file name where results will be stored A set of results will to be stored each time the Run Button is pressed until the check mark in front of this sub menu item is removed by selecting it again Values in the model are either Parameters input by the user eg stocking rate constant effort level survival or Indicators output by the model eg CPUE fish size length at age This menu item writes up to 3 selected Parameters and all the Indicators see View Plot Setup for a list to a data base
28. arious Regions To access other DBF files including complete files for each Region select Files Choose DBF file from the list The current version of the SLIMM data base only contains DBF files from regions 1 5 and 8 e g REGION1 DBF as well as the default lake file SLIMM DBF After retrieving a DBF file click on a specific lake name to view the available inventory and stocking data Double click on the lake name to include it in subsequent simulation runs Lakes selected for a simulation will be highlighted by an x beside the lake name Double clicking on a selected lake name will de select that lake and remove the x The stocking information contains a history of the number and weight of fish stocked since 1980 by different stocking types The yearling equivalent stocking type combines all stocking types into a single number and weight per year Fish stocked as fry and fall fingerlings are converted into numbers of yearlings of a given weight based on growth and survival calculations explained in Sections 3 3 3 7 and 4 2 8 of this guide The yearling equivalent numbers and weights displayed in this window and in the manual stocking rate window historic option are based on densities which are low relative to the competition index and therefore the size as yearlings of fish stocked as fry or fall fingerlings may be overestimated During a simulation the current simulated density and the competition index are used to calculat
29. ate reductions associated with intraspecific competition A Au 1 Size Weighted Density Competition Index 750 where 0 is baseline Walford Intercept input parameter the size Weighted Density is calculated from the current density in the lake Y density X length summed over all age classes see Walters and 26 Length at Age t 1 Length at Age t Figure 5 1 A generalized Walford plot of length at age t 1 versus length at age t Numbers and letters refer to points and lines mentioned in the text Post 1993 for rationale and the Competition Index input parameter is the density which reduces the Walford intercept to 50 of pase Note the Competition Index fish ha and the Size Weighted Density fish cm ha are in different units Ideally the Competition Index input parameter should actually be expressed as fish cm ha Fish cm ha is however an unusual unit of density which would have little meaning to most biologists To avoid requiring the user to provide the Competition Index in unfamiliar units this parameter is entered in units of fish ha and then SLIMM makes an approximate conversion to put the Competition Index into units of fish cm ha This conversion consists of multiplying the Competition Index input parameter by 750 If an estimate of the competition index is available in units of fish cm ha divide it by 750 and input this value to SLEMM as the Competition Index 5 2 Density Depend
30. d T D Rodof ed Catch and Release Fishing as a Management Tool a National Sportfish Symposium Humbolt State University Wydoski R S G A Wedemeyer and N C Nelson 1976 Physiological response to hooking stress in hatchery and wild rainbow trout Salmo gairdneri Trans Am Fish Soc 105 601 606
31. e depends on accessing a discharge data base which is currently not available Maximum Egg Capacity 3 000 m Experiences with spawning channel management suggest that the maximum number of eggs that can be deposited per unit area of spawning gravel is in the order of 3 000 21 5 000 eggs m In a pink and chum salmon stream where most of the area was used for spawning maximum numbers of fry were produced at egg deposition rates of 3 000 to 4 000 eggs m McNeil 1969 Elliot 1987 1993 found that maximum fry production in a brown trout stream occurred at much lower densities 40 eggs m when averaged over the entire area of the stream suggesting that in at least some streams much of the area is unsuitable for spawning Alkalinity Based Rearing Habitat Parameters Constant 1 58 A 0 97 B 0 45 These values are taken from an analysis by R A Ptolemy see Section 5 5 1 and represent predicted densities in optimal quality habitat Lake specific information on habitat capability perhaps generated by methods other than the alkalinity based equation can be entered using the User Defined Rearing Capacity Option at the bottom of the window Four life history stages are listed with the opportunity to enter the weight and carrying capacity at each life history stage The weights are used to determine the average annual weight across different emigrant ages of wild emigrants to the lake 4 2 7 Stream Recruitment The parameters in th
32. e length length at 1 2 max vulnerability vulnerability curve shape parameter define a relationship which predicts the vulnerability of each age class to fishing based on the predicted size To view the vulnerability as a function of size and the effect of these three parameters click on the View option from the spreadsheet The catchability q parameter is used in the model to predict fishing mortality and catch and is equivalent to CPUE N where N is the number of vulnerable fish available at the beginning of the fishing season An alternative definition of catchability is the proportion of the fully vulnerable population in 1 hectare caught by one unit of effort angler day i In SLIMM effort and stocking rates are specified on an aerial basis angler days ha based on the entire surface area of the lake Since most of the fish and fishing action are concentrated around the lake 12 perimeter the density of fish seen by the angler is higher than the model would predict using the entire lake surface The lake area associated with the q parameter can be used if the area of the lake where catchability was measured is known To account for this the model automatically adjusts the catchability This adjustment can be turned off by removing the x in the Lake area adjustment for q is enabled Box The proportion of fish voluntarily released can be altered by use of the slider This rate of release is for legal sized fish it is assumed that
33. e the number of yearlings produced and the yearling size of various types of stocked fish in order to more accurately reflect the true situation While you cannot edit the lake data base files from SLIMM it is easy to do it from EXCEL Simply load the file into EXCEL and edit the record You can add records to a file say SLIMM DBF from another file say REGION1 DBF by copying the entire row from one file to the other To add a new lake which currently isn t in the data base supplied with SLIMM 2 0 insert a blank row in an existing file at least one record below the field names and enter the new information When you are done making changes to the file highlight all records including the field names and select the Data Set Data base menu items in EXCEL Then save the file as a DBASE IV file by selecting the Save As item from the Files main menu item in EXCEL 3 3 3 Parameters Variables controlling the dynamics of the modeled populations and their response to management actions can be viewed and or modified through a series of windows accessed from the Parameters Main Menu Bar item Some parameter windows are more complex than others Additional details on the function of the more complex ones are located in Setting Parameters Section 4 The effects of many parameters can be visualized using the View options which are present in many windows The values of many parameters can be varied with slider controls which can be either dragged
34. eal configuration is an 80486DX 33MHz or 50 MHz system with 8 MBytes memory a SUPERVGA graphics card and monitor running in 600 x 800 mode the SVGA driver in MS Windows 3 1 2 2 Installation The SLIMM distribution diskette contains a readme file which also documents this installation procedure If you have a previous version of SLIMM on your computer delete the contents of the directory where it resides or install the new version of SLIMM in a different directory To install the model a Insert the distribution diskette into your floppy drive b Using the FILE MANAGER in WINDOWS create a subdirectory on your hard disk where you want to install the model e g H WORK SLIM 3 Copy pkunzip exe from the distribution diskette to the subdirectory that you have just created d To unarchive the model files type pkunzip a slim if a is the 3 5 drive where you inserted the distribution diskette e Using a file editor such as WINDOWS NOTEPAD modify slim ini so that the drive and path specified in the file correspond with the drive and path where you have just installed SLIM e g change C SLIMM xbs110 dll to H1WORKASLIMixbs110 dll An error message Can t find Installable ISAM when starting the model indicates that this file hasn t been modified correctly f Using a file editor include the following line in your autoexec bat file located in your root directory 4 share exe AUTOEXEC BAT is located on your boot disc or i
35. ec rr o s 11 3 3 3 3 Effort Related Parameters Window 00 0 cece eee tenet 12 3 3 3 4 Season Dependent Parameters Window ks 12 3 3 3 5 Rearing Habitat Parameters se sesse ee ee ee EE EN N se se 12 3 33 6 Stream Recruitm nt se Es org S a RAN A 13 3 3 3 7 In Lake Juvenile Survival ua Seed God Rd ed ae ie Re ee We RED ta ig 13 3 3 3 8 Spawning and Fecundity es see se ss eks 14 3 3 3 9 Saving and Restoring Parameter Files ee eee eee ees 14 SIE Policy Maia daa o y add a AA E 14 33 41 Renato EAS k AEREA RATA AA a A A 14 ad SOEKE id O AAA PA bo E er aes 14 33 5 A II E eee AS 15 3 36 Repon 605k eS he E EA A IE E 15 3 3 6 1 Seng results to fle Sa iia is nea RR A IA ARA 15 3362 Print GODE 45 eee VR AA A dd E 15 4 0 SETTING PARAMETERS 2 0000 ks ee es mm aS eric EE eee 16 ALE Introducton AA OE Cede A OT EO RE EE TOIT 16 4 2 Setting Model Parameters ss ees sae ence ee ee teen eee se AR RE es 16 4 2 1 Stock sex Dependent ses sae ss Ee dade aaa corso rssa na 16 4 2 2 Regional Growth Parameters o se ss eee EN Ee ee ee Ee Ee 18 4 2 3 Catch parameters ss see is RE k ED Ge ie ERK SES ARE Ne Es 19 4 2 4 Effort Parameters eie se Re ow sig Mere WE Sk ie ig oue i EN Re OE NR 20 4 2 5 Seasonal Parameters aaa ss lt aa 20 4 2 6 Rearing Habitat Parameters lt ec ee eee ee wees 20 4 2 7 Stream Recruitment ima KU eee DR ed T ee ee ew VS DEE Dk 21
36. ed in the system by providing an access time index ATI for each lake from the lake data base then predicting a base or average effort level for the lake from this ATI The ATI is calculated as a sum over travel types paved road dirt road 4wd road trail airplane boat of distance for each type times an estimated time per unit distance hrs km for that type Effort when fishing quality is average at regional average CPUE is then predicted as an inverse function of ATI using the equation Base Effort eresional base form 1 ATI 0 7 7sord power 2 regional base CPUE Here Regional Base Effort Regional Base CPUE and Regional Power are empirical parameters that have been estimated separately for each management region 1 8 from empirical data on effort CPUE and ATI The Regional Base Effort parameter essentially represents effort that would occur in a lake having typical fishing quality CPUE size if that lake had zero travel time from the nearest town in the region The Regional Power parameter is estimated very simply as the slope of a plot of the log of effort versus log of ATI for a region The peculiar exponential way that Regional Base Effort appears in the function arises from the mathematics of predicting integrated effort over a fishing season from the instantaneous effort response model above The 2 in the denominator of the function arises from assuming that CPUE near the start of the fishing season is likely to be about
37. ent Survival Density dependant survival is a well established phenomena that has been documented in the stream phase of trout life history but is difficult to measure in the lake stage The model accounts for density dependent survival in 3 stages During the egg stage there is a eigen relationship of maximum egg density in E gravel The tormi of this ie is 27 EggDep EggDen i EggDen MaxEggDen EmergeFry EggDep EggSurv where EggSurv is Egg Survival input parameter MaxEggDen is the Maximum Egg Capacity input parameter of the spawning area EggDen m is the density of eggs prior to deposition EggDep m is the density of eggs deposited and EmergeFry is the density of emerging fry m with all densities expressed as a function of spawning area only Note that the half saturation value of this curve EggDep 1 2 MaxEggDep is when EggDen MaxEggDen Egg Survival is the density independent survival of eggs that have been successfully deposited Limits to rearing habitat in streams see Sections 5 5 1 5 5 2 may also effectively induce density dependent survival Although excess juveniles are assumed to migrate involuntarily rather than die they may suffer much higher mortality in the lake after being forced to emigrate at a smaller than preferred size This effect results from the size dependent survival of juveniles entering the lake see Section 4 2 8 The model also includes density dependent survival of
38. eps Control and display the results of the simulation The maximum number of years which can be simulated is 50 In many instances you will want to compare the results of one simulation with those of another 6 completed under a different management scenario To do this turn on the Overlay feature click on the empty box an x denotes Overlay is turned on When Overlay is active the results of each simulation are maintained on the graph panes Each new simulation run is distinguished on the graph panes by a different line 3 2 1 Stock Structure Control Within each selected lake SLIMM simulates the dynamics of up to two populations If you select the Wild Hatchery option on the Stock Structure Frame located on the bottom right of the Parent Window SLIMM will simulate the dynamics of both wild and hatchery fish To model the dynamics of a hatchery stock there must either be a stocking history in the data base for the lake being simulated or you must specify a stocking history from the Stocking Pane or Stocking History Dialogue Box see section 3 2 2 immediately below If you want to eliminate the wild stock from your simulations select the Hatchery Only option if you want to eliminate the hatchery stock from your simulations select the Wild Only option There is no need to select the Hatchery Only option if a stocking history is not specified or present in the data base The Male Female option forces SLIMM to eliminate any hatchery
39. f three domestic strains of tagged rainbow trout stocked in large California impoundments Calif Fish and Game 59 4 245 265 Rieman B E and D Myers 1990 Status and analysis of salmonid fisheries kokanee population dynamics Idaho Dept Fish and Game Job Perf Rep Proj F 73 R 12 65 p Ricker W E 1975 Computation and Interpretation of Biological Statistics of Fish Populations Bull Fish Res Board Can 191 Sandercock F K 1969 Bioenergetics of the rainbow trout Salmo gairdneri and the kokanee Oncorhynchus nerka populations of Marion Lake British Columbia Ph D thesis Department of Zoology the Univ of B C Vancouver B C 165 pp Sawada J O 1993 An examination of differential survival in downstream migrating coho salmon smolts M Sc thesis Department of Zoology the Univ of B C Vancouver B C 100 pp Shapavalov L and A C Taft 1954 The life histories of the steelhead rainbow trout and silver salmon with special reference to Waddell Creek California Calif Dept Fish and Game Fish Bull 98 Smith S B 1957 Survival and Growth of Wild and Hatchery Rainbow Trout Salmo gairdneri in Corbett Lake British Columbia Can Fish Cult 20 7 12 Southwood T R E 1978 Ecological Methods John Wiley and Sons New York Stringer G E A F Tautz T G Halsey and C Houston 1980 Further development and testing of a lake stocking formula for Rainbow trout in British Columbia Prov of B C Fish Man
40. file that can be read by EXCEL If the Save Results for Year 30 Only Box is checked one line representing the values of the indicators in the last model year will be printed each time the model is run If this Box is not checked one line per year is written to the file each time the Run Button is pressed This option can generate large files if not turned off when saving of output is not required This function is useful for storing and summarizing the results of many runs For example CPUE or other indicators can be examined as a function of stocking rate Check the Constant Stocking Rate Box in the Stocking Rate Window select Constant Stocking Rate as one of the three parameters and save 30 year results only Now run the Model a number of times with different stocking rates Load the data base file saved as a OUT file into EXCEL and use EXCEL to plot the results The output file is a dBase file but uses a OUT extension rather than the usual DBF extension When opening the OUT ODAsE Tle DUL USCS d A DELER ERG Ee EA EE IE EE file in EXCEL Go to the SLIMM directory type OUT in the file name box of the Open File Dialogue Box and Choose the file that you wish to open l o 3 3 6 2 Print Graph To send the graphic output of the model to a printer select the Report Main Menu Bar and the Print Graph option If the printing is not successful you will need to install a printer from the Control Panel within the MS Windows Program Manage
41. habitat These can be reduced in a simple multiplicative way using a subjective index of habitat quality 5 5 2 Stream Recruitment Actual egg deposition declines with potential egg deposition Section 5 2 Fry numbers are equal to egg deposition times egg survival Following emergence fry may either choose to remain in the stream or migrate Juveniles can migrate at a variety of sizes and ages 1 Fry can migrate immediately to the lake 2 Fall fingerlings migrate to the lake after rearing for the summer 3 Age 1 juveniles migrate in April after over wintering and 4 Age 2 juveniles migrate in April after over wintering a second time At each migration time emigrants includes both voluntary emigrants plus the involuntary emigrants Involuntary emigrants are excess fish forced out of the stream once the habitat capability of the stream for a particular life history stage is reached At each migration time voluntary emigrants are removed from the stream and the remaining number of juveniles minus the habitat capability of the stream is equal to the number of involuntary emigrants Note that involuntary migration increases with density but mortality rates in the stream are constant Emigrants that reach 31 the lake are subject to size dependant mortality Migration mortality is applied to fry and fall fingerlings migrating to the lake If migration mortality is 3 per kilometre then juveniles in a 6 km long stream will migrate an a
42. hes and Science Citation Index Some of the best data available was not in the primary literature For example Stringer et al 1980 provides the best information on rainbow trout survival rates planted at different life history stages in the Kamloops area Nelson 1987 1988 are other examples of secondary literature which was difficult to find We expect that useful unpublished information will continue to surface as the data requirements for the model are more widely circulated 4 2 Setting Model Parameters This section contains the recommended values and ranges in brackets for all parameters in the model along with the background information that these recommendations are based on Parameters are listed in the order top to bottom that they are encountered under the Parameter menu item in the model Default parameter values are summarized in Table 4 1 and are included in the SLIMM HYP file supplied with the model 4 2 1 Stock sex Dependent Stocks can be designated as hatchery and wild or male and female Stock or gender specific parameters are therefore needed for natural survival growth and angler vulnerability of these groups Although generic values for each parameter are available the literature search and discussions with the Provincial fisheries biologists suggest that stock specific parameters are not currently available Some studies examine differences between domestic and wild salmonids primarily with brook trout
43. ime of high stress for salmonids in streams see references in Bustard and Narver 1975 Overwinter survival of coho in a small coastal stream was only 35 but was twice as high for fish that moved into more favourable winter habitat consisting of old beaver ponds Bustard and Narver 1975 Survival should be set to the low end of the range in systems with severe winter conditions drying very cold temperatures without snow Cover high flows and to the high end of the range for systems with good winter habitat deep pools backchannels Annual parr survival 60 25 85 This parameter should reflect the survival of parr at relatively low densities since surplus parr at high densities are presumed to emigrate in the spring ie emigration not survival is assumed to be density dependent Parr survival is probably size dependent Annual fry to parr survival for steelhead in the Keogh River was only 25 whereas annual survival of older parr was at least 85 Ward and Slaney 1993 voluntarily migrating 0 0 90 100 The preferred migration age and time can be very stream specific Northcote 1969 The default values reflect a situation where fry and fall fingerlings prefer to remain in the stream most fish prefer to leave in the spring at age 1 and the remainder prefer to leave at age 2 the following spring 4 2 8 In Lake Juvenile Survival Maximum survival of juveniles is assumed to be equal to the survival of older fish in the input
44. ing of the fishing season expressed as fully vulnerable equivalents ha The baseline q shown in the Catch Parameters Window is adjusted based on stock sex specific and seasonal multiplication factors 0 1 defined in the Stock Sex Dependent Parameters Window and Seasonal Parameters Window respectively In larger lakes the fish and angling effort are concentrated in a donut around the perimeter of the 28 lake but in SLIMM fish density fish ha is specified on the basis of total area To account for this the baseline value of q can be adjusted using a correction factor Q e dT A 2 A 7 005 A 2 A 7 005 where A is the area of the lake to be simulated and A is the Lake area associated with q in the Catch Parameters Window This adjustment assumes a roughly circular lake with a donut width of 50 m Catchability of is also adjusted for vulnerability The vulnerability of each age class is depends on the current length FL and 3 parameters minimum recruitable length FL length at 1 2 max vulnerability VulHalf vulnerability curve shape parameter b Section 3 3 3 2 Vulnerability FL FL VulHalf FL FL EL 5 4 Calculation of Effort Any attempt to develop management policies for providing a diversity of fishing opportunities over a set of lakes within a large region for example by managing some lakes for quantity production and others for quality or catch and release or fly fishing only
45. is pane establish the number of juvenile emigrants to the lake given the number of eggs deposited and the habitat capability for eggs fry and parr Predictions in the SLIMM can be very sensitive to survival at any life history stage For example when populations are limited by the amount of spawning habitat population densities in the lake can be directly proportional to egg fry survival Egg survival 15 5 40 This parameter should reflect the egg to fry survival at relatively low densities since egg survival at high densities is limited by the spawning ground capacity Egg saturation of spawning gravel has been reported in various species of salmonids Elliott 1993 McNeil 1969 Maximum egg survival of rainbow trout is close to 100 under hatchery conditions but survival under natural conditions is often much lower because of a variety of factors including egg deposition density mechanical disturbance low water levels temperature extremes dissolved oxygen levels and a suite of predators pathogens and parasites McNeil 1969 Reported survivals in the literature cover a wide range steelhead 75 86 Briggs 1953 Shapavolov and Taft 1954 kokanee spawning channels 10 70 Hutchinson 1987 Thorp 1987 rainbow trout 11 20 Allen 1951 Atlantic salmon 15 30 Bley 1987 steelhead 2 12 Ward and Slaney 1993 kokanee 4 2 Fleck and Andrusak 1977 rainbow trout 1 9 Sandercock 1969 rainbow trout 0 5 Martin 1987
46. juveniles during their first year of life in the lake Survival of fish age 2 and older is assumed to be density independent The density of age 2 fish Age2Den is determined by a saturating relationship involving current yearling density YrigDen and maximum yearling density ha MaxYrigDen set by the user Age2Den YrigDen 1 YrlgDen MaxYrlgDen 5 3 Calculation of Catch Catch rates are determined by a number of factors and parameters in the model The density of fish combined with the length vulnerability parameters and seasonally adjusted catchability all influence the number of fish caught The distribution of catch rates among anglers is assumed to follow a negative binomial distribution Bannerot and Austin 1983 Hilborn 1985 Porch and Fox 1990 The distribution of catch rates is used to calculate the proportion of the total catch that falls within legal bag limits and can be retained Illegal retention is assumed to be zero These calculations assume that individuals within a party do not pool their catch and their bag limits and that the variance to mean ratio is based on catch rate data for individual anglers The catchability q parameter is used in the model to predict fishing mortality and catch Catchability is the instantaneous fishing mortality rate see Ricker 1975 p 8 from one unit of effort rod hour in one hectare and is equivalent to CPUE N where N is the number of vulnerable fish available at the beginn
47. lope intercept 60 egg cm 17 cm The default parameters for the regression line were obtained from Allen 1951 Minimum spawner length males females 20 23 cm Age and size at maturity are not dynamic and therefore all fish mature the following spring once they reach these lengths These lengths can be adjusted to match lake specific data Spawning mortality males females 50 50 Mature fish probably experience additional mortality due to increased susceptibility to disease Johnstone et al 1978 or increased vulnerability to predation Alexander and Shetter 1969 Data from Hume and Tsumura 1992 on the relative proportion of males at age 1 and 2 suggests that male maturation mortality was 80 More recent data from a variety of lakes suggest values close to 50 Tsumura data on file Much of this data is from lakes without streams and spawning mortality may be higher in situations where maturing fish have to endure the rigours of actual spawning o Minimum spawner age fecundity at minimum spawner age 5 150 These values set a minimum age by which fish are forced to mature even if they have not reached the minimum sizes above The minimum fecundity for small fish is to ensure that maturing females do not have negative fecundities 4 2 10 Hooking Mortalities Hooking Mortality 5 2 20 listed in Regulations Window Hooking mortalities are reviewed by Wydoski 1980 N EE TE ge MA 24 Table 4 1 Summary of the recommended par
48. lues can be used if no other information is available with respect to a length at age curve Other physical and chemical parameters such as TDS and or mean depth can be used to predict growth TDS and shoal area are the basis of the current B C stocking formula Stringer et al 1980 Donald and Anderson 1982 using stepwise multiple regression attributed 42 of the inter lake variation in weight at age 2 to variation in total dissolved solids 30 to stocking density and 3 to mean depth The low correlation between growth and these physical and chemical factors suggests that regional growth parameters are a poor substitute for lake specific data 19 4 2 3 Catch parameters coefficient of variation in lengths 107 This parameter represents the standard deviation mean of fish length of a given age and is assumed to be constant over all ages The value of 0 107 came from age 2 and 3 rainbow trout from Kentucky and Alleyne lakes in the Kamloops Region of British Columbia Parkinson et al 1988 var mean ratio of catch angler 2 3 This parameter describes the relationship of catch rates among the anglers in terms of variance over the mean for a negative binomial distribution It is used to model the fraction of the catch above the specified bag limit by assuming a negative binomial distribution of catch rates lots of anglers with low catch rates a few anglers who know what they are doing Mathematically the binomial distribution has
49. m data on Alleyne and Kentucky lakes where almost all fish over 25 cm are retained 4 2 4 Effort Parameters Our ability to set parameters for a dynamic effort response is currently limited In SLIMM effort is assumed to respond to rapid changes in abundance within each fishing season see Section 5 4 however a sharp decline in CPUE in Alleyne Lake in 1987 failed to produce a significant effort response Parkinson 1990 In simulating a single lake the dynamic effort response should be disabled by setting the effort to a constant based on empirical data Standardized weekend boat counts can be converted to estimates of angler days using Tredger 1991 In the absence of empirical data the model will use the regional average listed in this window SLIMM has identified the factors that control the movement of effort as a major data gap in our understanding of the dynamics of the small lake fishery While the recommendation at present is to ignore the dynamics of effort a dynamic effort response is unquestionably an important factor which will tend to frustrate attempts to improve angling quality on individual lakes 4 2 5 Seasonal Parameters The allocation of effort between the seasons and seasonal catchability will vary from lake to lake and region to region however the results generated by the SLIMM are typically not very sensitive to changes in these parameters We suggest using the effort distribution calculated by Naito 1992 for four
50. n 4 Restart Windows as directed Or 1 Exit windows 2 Type SET at the prompt 3 You will see a list of settings Type SET WINVIDEO SUPERVGA at the prompt 4 Logout and reboot your machine To create a distribution diskette go to the DOS prompt in the SLIMM directory and run MOEDIST BAT after inserting a formatted 1 4 MB diskette in your floppy drive 4 3 0 OPERATING INSTRUCTIONS We assume that the user has a basic working knowledge of the Microsoft Windows environment If you are not familiar with the use of a mouse or unfamiliar with the MS Windows 3 1 interface please consult the MS Windows operating system User s guide The interface of SLIMM is composed of multiple windows The Parent Window or main screen is the first window you see after the title banner The Parent Window has three elements 1 A set of controls at the bottom of the window that affects the operation of the model and the graph panes which are displayed 2 A set of panes which display model results and historical future stocking rates 3 A Main Menu Bar at the top of the window which allows you to access other windows which contain the data base and parameters controlling the behaviour of the model and management actions 3 1 Quick Start Use this section to get the model running and change stocking rates and fishing regulations before looking at the detailed descriptions More details for running the model are provided in Section 3 2 Ins
51. n your root directory on your hard disc An error message Couldn t lock file SHARE EXE hasn t been loaded when trying to save results to an EXCEL file while running SLIMM indicates that the SHARE EXE line hasn t been added to your AUTOEXEC BAT file 3 g Create a SLIMM program item in the MS Windows Program Manager using the File New Item menu choice as described in the Windows user documentation An example of what to type in the File New Item dialogue box is given below SLIMM should now be ready to run Parameter files HYP files for version 2 0 are not compatible with the files created or distributed with previous versions of the model Do not attempt to load old parameter files using the new version of SLIMM or the model will crash Description Command Line Working Directory Shortcut Key If you normally operate Windows in VGA mode 600 x 480 pixel resolution you will not be able to see all of the information displayed in some of the larger windows in SLIMM We recommend that you use SUPERVGA mode 800 x 600 pixels To use this resolution you will need to change the screen resolution before starting SLIMM This can be done through the following steps 1 From the Main program group within the Windows Program Manager open the Windows Setup Program 2 Select the Options Change System Settings menu choice 3 Under the Display drop down list box select the SUPERVGA 800 x600 screen resolutio
52. ndex is complicated by the interaction between growth and survival Havens and Sonnichsen 1992 working in Alaskan lakes found a density dependent effect on survival ha se alia ai ai paneer arnt ER award sedan code 18 but the relationship between density and growth was less clear Over the range of densities 20 360 fish ha the reduction in first year growth was smali This failure to see a reduction in growth may be linked to the effect of density on survival A reduction in growth may not be observed because effective densities at higher stocking rates were much lower than expected because of low survival at high density When some growth data are available the values of the growth parameters and competition index can be fitted to the available data Ideally two growth curves would be available one at very low densities and one at a higher specified density The steps are 1 Set the Growth Curve Calibration Density to a low value 5 and the competition index to a high value 500 2 Using the view option adjust the Walford intercept and slope so that the growth curve in the View Option approximates the empirical growth curve measured at low density 3 Set the Growth Curve Calibration Density to a higher density yrlgs ha for which an empirical growth curve is available 4 Adjust the competition index without adjusting the Walford growth parameters so that the growth curve in the view option approximates the empirical gr
53. nning of the model year April 1 Using the copy and paste attributes of the spreadsheet the user can quickly fill the spreadsheet with the desired values To enter values in the spreadsheet click on the desired position Enter a stocking value and click the mouse button to register the change The adjacent cell will fill in To use the copy and paste attributes to fill in the spreadsheet Click on a cell Then click on the copy button and release the mouse Highlight the cells to be filled in by dragging the mouse across them Release the mouse and Click on the paste button The highlighted cells will fill in with the appropriate values These manually entered spreadsheet values are saved when the HYP file is saved see section 3 3 3 6 below 3 Constant Stocking Rate Constant stocking rates are set by clicking the radio button beside the title Constant Stocking Rate and entering a stocking rate in the box Press enter after changing a number in order to register the change Note that stocking rates are given in yearling equivalents using a default value of 100 mm for yearling length about 10 g if the constant stocking rate option is used weights are not specified on the manual entry option for stocking past 1992 unless specified under the manual option or if a stocking policy is drawn in The length of emigrants to the lake determines their survival to age 2 in the lake Sections 3 3 3 7 based on parameters given in Section 4
54. o account for the reduced time in the lake The default parameters of 0 75 for summer fry and 0 5 for fall fingerlings reflect the portion of a year spent in the lake before the start of the model year in April 23 Proportion increase in size dependent mortality applied to hatchery fish 0 1 This parameter provides the option of setting higher rates of juvenile mortality for hatchery versus wild fish in order to reflect the belief that hatchery fish survive poorly compared to wild fish The default value of 0 1 indicates that mortality rates for hatchery fish are 10 higher than similar sized wild fish eg if survival of wild fish is 60 survival of hatchery fish will be 56 This relatively small effect reflects our belief that the relative survival of hatchery fish for lacustrine rainbow is not nearly as low as for steelhead where 0 5 would not be unrealistic Ward and Slaney 1988 Parkinson and Sianey 1975 Maximum yearling capacity 1500 yearlings ha 200 2000 Lower values of this parameter induce higher density dependent mortality by limiting the maximum number of yearlings that can survive via a saturating curve In productive monoculture lakes maximum yearling densities can approach 2000 ha Johnston and Parkinson data on file The default value is for a productive monoculture lake Values for unproductive or mixed species lakes are considerably lower Havens and Sonnichsen 1992 4 2 9 Spawning Fecundity Parameters Fecundity s
55. of size 5 5 1 Habitat Capability of Streams The number of inlet and outlet streams available from the data base is multiplied by an estimate of the average width and length of each stream and the proportion of the stream consisting of rearing and spawning habitat to estimate the amount of available production area Once the amount of habitat is defined maximum capacities for the various life history stages are used to translate amount of habitat into habitat capability at a given life history stage The numbers of emerging fry are limited by a saturating relationship see Section 5 2 involving maximum egg capacity of the spawning area Stream habitat capability for juvenile rainbow trout is determined by either a regression model or user defined habitat capabilities perhaps involving an alternative regression model The SLIMM regression sub model predicts fish numbers per 100 m unit FPU using a regression developed by R A Ptolemy B C Fisheries Branch 780 Blanshard St Victoria B C FPU 100m Constant K x Log Juvenile Weight K x Log p Alkalinity Alkalinity is used as a measure of stream productivity To use this with the data from the small lakes data base we estimate stream alkalinity from Ph measured in the lake using a standard titration curve Juvenile sizes can be entered as parameters by the user The numbers predicted by the Ptolemy relation represent the maximum values that would be expected in high quality
56. older fish only whereas changing the intercept lowers the size of all age classes Competition Index 600 fish ha 400 1000 for productive monoculture much higher for mixed species lake lower for less productive lakes The competition index is the density total numbers ha which reduces the dynamic Walford intercept by 1 2 see Section 5 1 A close approximation is the density that induces a 50 reduction in the 1st year growth Walford intercept The effect of competition varies among lakes and depends on factors such as productivity and the presence of coarse fish The SLIMM modelling exercise has identified information on the competition index as the most critical data gap in small lakes management Explicit values of the competition index are not available in the literature but estimates can be inferred in some cases Preliminary analysis of data from a series of small productive monoculture lakes Johnston and Parkinson in prep suggests that for these systems the competition index is in the range of 600 1000 fish ha Mixed species lakes are likely to have very high competition indices since growth of trout is suppressed even at very low trout densities by a large non _ salmonid fish biomass Less productive lakes would have lower competition indices since growth of trout at very low densities is similar to that of more productive lakes but growth declines more rapidly with increases in trout density Estimation of the competition i
57. on ignores any limit on effort that might arise over larger spatial scales e g whole region due to the size of the potential angler population for any small set of lakes such limits can safely be ignored Combining these assumptions for dN dt and E results in the rate model dN dt qN for within season abundance change Integrating this rate equation over the fishing season then results 1 in the fouowing prediction models for total effort and catch given initial ul spring abundance No 111 29 Total Effort 1 q log 1 qkN Total Catch qkN i qkN The key prediction here is that total effort should vary logarithmically with abundance at the start of the season In the model we calculate N as the sum over all fish ages of the product of density relative vulnerabilities and body lengths including length here means that we in effect assume that a lake with fish twice the size as in another nearby lake will attract double the effort if CPUE is similar in both places Further for the each annual calculation we replace the k constant in the Total Effort prediction equation above with a Base Effort constant that can be calculated easily from available field data the Base Effort constant is calculated see below so that Total Effort will equal a regional average for any lake that has relative abundance gN that is near the regional average and also has typical accessibility The effect of lake accessibility is model
58. or clicked over with a mouse The size of the jumps when the slider is clicked is fixed in the model code as a proportion of the maximum value displayed when a window is opened To increase the maximum value of a slider move the slider to the right hand edge of the slider track and type a new maximum in the adjacent text box don t forget to hit return after you ve entered the number 11 3 3 3 1 Stock Sex Dependent Parameter Window The majority of parameters in the Stock Sex Dependent Parameters Window affect how the model predicts changes in growth in response to fish density Parameters in this window are either stock hatchery wild or sex specific male female depending on which option is selected from the Stock Structure Frame on the Parent Window Natural survival is the annual survival of age 2 and older fish Clicking on the View option brings up a graph which displays length at age on April 1 The Walford slope and intercept parameters control the shape of the length at age graph The Walford intercept can be determined by one of three options controlled by the radio buttons in the Growth Prediction Method Frame of the Stock Sex Dependent Parameter Window The three options are the slider controls prediction by lake pH and use of the Regional average The regional averages of the Walford intercept and pH are listed in the Parameters Regional Growth Window With the use Walford intercept above option selected the View option
59. owth Intercept Productive monoculture lakes 28 cm 24 30 Productive mixed species lakes 20 cm 15 25 The Walford intercept represents the intercept of a plot of length at age t 1 versus length at age t see Figure 5 1 under low density conditions A close approximation is the length of a fry which has spent one entire year July to July in the lake at very low trout densities In contrast to the slope the Walford intercept is dynamic varies through time as a function of fish density in the lake High elevation monoculture lakes in Colorado 2400 4000 m have walford intercept values of 15 17 Nelson 1987 Based on experience with productive barren lakes maximum values for this growth parameter for B C lakes approach 30 cm Walford Growth Slope 66 60 80 The Walford slope represents slope of a plot of length at age t 1 versus length at age t The structure of the model assumes that the slope is not dynamic that is it does not change with density over the coarse of a simulation The mean value for approximately 100 lakes across B C is 59 see Run Growth Analysis in SLIMM This value is probably biased downward because of size selective harvest of fast growing fish but this analysis suggests that slopes are similar across a wide variety of lakes If some estimate of density and a length at age is available the intercept and slope parameters can be adjusted to mimic the empirical curve Decreasing the slope lowers the size of
60. owth curve measured at higher density In many cases a low density growth curve will be not be available for the lake in question Maximum growth rates for yearlings stocked at 10 cm in productive monoculture lakes in the southern interior of B C are 35 and 60 cm at age 2 and 4 respectively with an asymptotic length of about 70 cm at older ages data on file For monoculture rainbow growth at very low densities is probably limited by physical conditions rather than food availability This suggests that maximum growth rates should be only adjusted to reflect the growing season and temperature regime and not lake productivity relative to mid elevation 1000 1500m lakes in the southern interior In the presence of other fish such as redside shiner asymptotic lengths at low trout density are limited by food availability and are rarely greater than 45 cm Vulnerability 1 Differences in angling vulnerability among stocks do occur eg Trojnar and Behnke 1974 and this parameter is included to allow for situations where vulnerability differences are known or suspected to occur 4 2 2 Regional Growth Parameters This window provides a reference for the Walford intercept and pH values for the Parameters Stock Sex Dependent Window The values in this window were obtained from empirical growth rate data and represent mean values for Walford intercepts and pH for each region Predicting the Walford intercept by regional or lake specific pH va
61. r Instructions for installing a printer are given in the MS Windows user s guide 7 3 z 16 4 0 SETTING PARAMETERS 4 1 Introduction Setting model parameters involves supplying a range for each parameter value either from the literature or local knowledge Recent literature was examined with a computerized search of Aquatic Sciences and Fishery abstracts This procedure reviewed abstracts from the recent primary literature The list of abstracts resulting from this search was large gt 300 however the number of references containing usable data was small 20 suggesting that with the exception of hooking mortality Dotson 1982 Wydoski et al 1976 Marnell and Hunsaker 1970 the specific information needed for this model was not common in the recent primary literature Important data such as differences between hatchery and wild growth rates survival rates and vulnerabilities to angling were not available although the need for this information has been documented Wiley et al 1993 Information was available on different species of salmonids For example Hume and Parkinson 1987 summarized data on stream survival of age 0 to age 1 salmonids but only one of ten studies examined rainbow trout In many cases data from other salmonid species will be useful in quantifying values for parameters such as egg mortality or juvenile stream survival Older and grey literature publications were searched using a combination of citation searc
62. raph background lines 1 3 click on the element in the Colors Dialogue Box and then click on the desired color on the 9 color palette If you select the Thick Lines no line patterns check box an x will appear when this option is selected the lines on the graphs will be thicker but if you use the overlay option you will not see different patterns for each successive overlay The thick line option is useful for presentation purposes Table 3 1 Summary of 49 indicators used in the SLIMM Indicator Name CPUE Hatchery or Wild Total fish caught including released angler day Hatchery or Wild CPUE Kept Total wild and hatchery fish retained angler day Mean Length Hatchery or Average fork length of all hatchery or wild fish caught Wild including released in cm over the year Mean Length Kept Average fork length cm of wild and hatchery fish retained over the year Length Age X Hatchery Fork length cm of Age X hatchery or wild fish in spring Wild Age X in Catch retained Hatchery Wild Percentage of age class X mortality attributed to fishing Ratio of wild to hatchery yearlings in the lake Density of wild yearlings in the lake ha in spring Density of fish wild and hatchery gt 2 Years old ha Numbers of fish retained per hectare Average age of all fish in the lake Average age of all fish in the catch 4 angler days ha lake surface l Eggs Deposited Eggs deposited m of spawning not
63. s and Maurice Lirrette for help in testing the model and Vicki Blann and Marc LaBelle for ensuring the completion of the process and distributing the model B C Hydro generously provided funding for the model construction and testing E 11 Table of Contents 1 0 INTRODUCTION dust A e eo a al oe EE dad AA AR 1 2 0 INSTALLING SLIMM ON YOUR COMPUTER o ocoooooooo oro 2 2 1 Computer Requirements sesse see ses SEE ee ais Ds N Ee Ee eed 2 2 2 IMSCAMADON AE EE ID RE EA ee PINS ELE 2 3 0 OPERATING INSTRUCTIONS 4 vis dE AAA Me oe N Re oe eS 4 3 1 Quick Start EE Sa EER EE See RE SE a DE Bee ER LS Re AN 4 PA ei 415655 DA EL A EE 5 3 2 1 Stock Structure Control ER ee EE We EER AAA BES 6 3 2 2 Pane Selection Control 3 2 2 1 Graph Panes 3 2 2 2 Indicator Summary Pane ooo o oo o o oooo o ss 6 4923 Stocking Panes L a IG Se ae Caraga ee See eRe a N AR 7 3224 Age Pane onic Aloe Fane AA EGS SAAS ERTS SE BORN BP G 7 33 Mein Menu Bar oo a kun id ee Rea Sees eae eS tee 8 IIA ENS Se faa ad Sat Be se at a OE L r ae SA SA DEE 8 SIT AGE SUCESO 54 EER A LLA S EE AOI ade dead ee A 8 3 3 1 2 Plot SQUID LA 55 e ae Aa do a E AA OS 6 A REA 8 34 DT ak ld seses KS Aad DA ARE Era ae ee VA Eee a Mi 9 34 3 PATA MEtETS Li oe oa Sa a SEER SS ARE DON RASS Ae 10 3 3 3 1 Stock Sex Dependent Parameter Window ss ss ee EE eee eee eee 11 3 3 3 2 Catch Parameters Window oe ves eg n
64. s by surviving the eggs and later life history stages by the rates given by the sliders in the upper section of the window The column labeled Voluntarily Migrating can be altered by the user The Displaced Column is the difference between the Habitat Capacity Column and the Predicted Abundance Column The Total Migrating Column is the sum of the Voluntarily Migrating and the Displaced Column This value is the total number of migrating fish in each life history stage The box labeled Total Yearling Equivalents is calculated using the size survival rates given in the In Lake Juvenile Survival Window 3 3 3 7 In Lake Juvenile Survival This window establishes a size survival relationship for fish in the lake until age 2 The maximum survival rates displayed in the top two boxes can be altered by the user in the Stock Sex Dependent Parameter Window Section 3 3 3 1 The three sliders control the survival versus size relationship of the fish until age 2 This relationship can be seen by clicking the mouse with the icon on the box labelled View function for wild yearlings The parameters in the lower section of the window account for the decreased amount of time spent in the lake for summer fry and fall fingerlings and the subsequent effect this has on annual survival rates Hatchery fish are assumed to survive poorly compared to wild fish the box labelled Proportion increase in size dependent mortality applied to hatchery fish establishes the
65. sing a model based on alkalinity and fish 13 size Section 5 5 1 The alkalinity model predicts juvenile densities in optimal habitats the rearing habitat quality multiplier 0 100 can be used to reduce these estimates and affects the predicted capacity displayed in the yellow boxes Fish weight and rearing density for each of four life history stages can be entered manually This option is enabled by clicking the mouse on the box beside the label User Defined Rearing Capacity For comparison this section displays the predicted rearing habitat based on fish weight and alkalinity of the stream for the currently selected lake 3 3 3 6 Stream Recruitment Factors which limit the production of fish in streams are modified through this window The top section of the window contains sliders which set survival rates of the various life history stages The bottom section of the window uses the slider values in conjunction with parameters from the Rearing Habitat Window to calculate and display the numbers of fish migrating at each life history stage from stream to lake The Habitat Capacity for adults is the fecundity eggs female The Predicted Abundance for adults is the number of adults needed to produce the capacity of eggs The Habitat Capacity Column values for eggs to 3 parr are obtained from the rearing habitat window The Predicted Abundance Column takes the number of eggs and obtains the values for the various life history stage
66. stock and model the sexes of the wild stock individually If this option is selected parameters from the Stock Sex Dependent Parameter Window which were previously stock specific Hatchery Wild are now sex specific hatchery parameters male wild stock parameters female 3 2 2 Pane Selection Control There are 5 different types of panes that can be displayed to summarize model results for each simulated lake see figure 3 1 The number of panes displayed on the Parent Window is adjusted through the Pane Selection control at the bottom middle of the window By default three graph panes and the Stocking Pane are displayed If you select a different combination of panes to be displayed on the Parent Window click on either the Refresh Button to update the window 7 3 2 2 1 Graph Panes Graph panes display line plots of the chosen indicators at the end of each simulation Up to three indicators can be plotted as separate lines on each graph and are distinguished by different colours for each line Lines can be made thicker for presentation purposes Section 3 3 1 2 3 2 2 2 Indicator Summary Pane The Indicator Summary Pane contains the average value of specific indicators for the entire simulation period The indicators displayed in this pane are CPUE Kept Number of fish wild and hatchery retained per hour of fishing effort averaged over one year in Fish Angler Day Total Catch Ha The total number of fish wild and hatchery re
67. t Parameters Window This window allows the user to modify the catchability q parameter and proportion of effort due to seasonal factors The effort proportion values must sum to one across all seasons The seasonal catchability factors are simply scalars and do not have to sum to one 3 3 3 5 Rearing Habitat Parameters The top section of this window allows the user to enter an estimate of the total area of stream available for rearing and spawning The average stream width average stream length rearing habitat and spawning habitat are lake specific values to be entered by the user The number of inlet and outlet streams are read from the data base and cannot be altered The total stream rearing area is calculated as the product of stream width stream length number of streams rearing area in terms of 100m units The maximum egg density parameter is used to model density dependent egg survival See Section 5 2 A slider to allow for the effect of annual variation in discharge has been disabled but will be activated when SLIMM is connected to a stream discharge data base The recruitment sub model predicts the number of yearling recruits entering a lake based on a simple stream production model and the above mentioned estimates of the amount of rearing habitat in the streams The Rearing Habitat Parameters section predicts the carrying capacity of naturally produced fish habitat capability for each stream life history stage by u
68. tained annually per hectare of lake Surface 12172 Hot E N 2 7 Angler Days Ha The predicted effort per hectare lake surface over one year Travel Time The predicted travel time hr km to reach the lake 3 2 2 3 Stocking Pane The Stocking Pane provides one of the three ways of setting stocking rates yearlings ha for each lake being simulated The choices can be accessed by double clicking on the Stocking Pane and include 1 Historical Stocking Rate The historical stocking rate time series is read from the SLIMM data base Change the displayed stocking rate by clicking on the Stocking Pane at a horizontal position close to the year to be altered Drag the mouse across the box and draw in the desired pattern Another method of changing stocking rates is to double click on the Stocking Pane to bring up the Stocking History Dialogue Box From this dialogue box the user can adjust the Y axis maxima of the Stocking Pane manually enter a stocking history specify a constant stocking rate for the entire simulation period or reset the historical stocking pattern 2 Manual Stocking Rate Manually enter a stocking history by clicking on the radio button beside the title Manually Enter Stocking Rate A spreadsheet will appear From this spreadsheet you can enter stocking values in either numbers per hectare or total number per lake Add the stocking weights in grams These values represent the number and weight of yearlings at the begi
69. tall the model using the procedure outlined in Section 2 2 and start the model by double clicking on the SLIMM icon Click on the Continue button By default the lake seen on the screen after startup is Alleyne Lake a hatchery monoculture lake located near Merritt in region 8 To run the model and display some results use the mouse to click on the Start Run Button located on the bottom left of the Parent Window By default graph pane 1 displays CPUE graph pane 2 displays length at age and graph pane 3 displays the age distribution in the catch but these can be changed to include any of the 49 indicators see section 3 2 2 available To view the actual numbers behind each graph double click on a graph pane after a simulation has been run The model can be manipulated in various ways A key thing to remember is that After any typed changes of values the enter key must be hit to register the change Four methods can be used to change the stocking rate 1 Drag the mouse across the Stocking Pane The stocking rate will be reset to the values represented by the red bars of the histogram Double click the mouse on the Stocking Pane and the Stocking Window will come up This window allows the user to change the graph pane maxima and to change the stocking rate with three other methods These are 2 2 Manual entry option Use the mouse to highlight the radio button next to the Manually Enter Stocking Rate label A spreadsheet will appear
70. to three British Columbia Lakes N Am J Fish Manage 12 465 473 Hutchinson J 1987 Meadow Creek spawning channel enumeration and subsequent fry production 1987 B C Fish Br Fish Proj Rep KO17 Johnston N T K I Ashley and K Tsumura 1991 Survival growth and yield of rainbow trout in the Twin Lakes southwestern British Columbia prior to lake fertilization B C Fish Br Fish Proj Rep RD29 Johnstone R T H Simpson and A F Youngson 1978 Sex reversal in salmonid culture Aquaculture 13 115 134 Marnell L F and D Hunsaker 1970 Hooking mortality of lure caught cutthroat trout Salmo clarkii in relation to water temperature fatigue and reproductive maturity of released fish Trans Am Fish Soc 99 684 688 Martin A D 1987 Rainbow trout production from the Whiteswan Lake spawning channel in 1986 B C Fisheries Branch Fish Proj Rep KO 18 McNeil W J 1969 Survival of pink and chum salmon eggs and alevins p 101 120 In T G Northcote ed Symposium on Salmon and Trout in Streams Institute of Fisheries University of British Columbia Naito G J 1992 Modelling the dynamics of fish and anglers for management of rainbow trout Oncorhynchus mykiss trophy lakes in British Columbia Report No 105 School of Resource and Environmental Management Simon Fraser University Burnaby B C 167 pp Nelson W C 1988 High Lake Research and Management in Colorado Colo Div Wildl oge
71. two parameters the mean and a k value representing skew In the model the mean catch rate fish day is obtained from the model simulations Skewness is an input parameter but k has been replaced by the related variance mean ratio Naito 1992 fitted a negative binomial distribution to data from Alleyne and Kentucky lakes and obtained a mean of 0 52 fish day and a k of 0 4 Using a formula from Southwood 1978 p 40 these values of give a variance mean ratio of 2 3 The variance mean ratio rather than k is used as the skewness parameter because variance mean ratios can easily be calculated from CPUE fish day data if a fit to the negative binomial is assumed Simply calculate the variance not standard deviation and the mean of the catch rates for individual anglers and take the ratio Higher variance mean ratios indicate that the catch frequency distribution is more skewed a few anglers are catching most of the fish Minimum recruitable length length at 1 2 maximum vulnerability and vulnerability length curve shape 14 cm 28 cm 4 These parameters describe the position and shape of the relation between length and vulnerability to angling There is no empirical information describing this curve for rainbow trout but Rieman and Myers 1990 provide data for kokanee Their curve suggests an exponential increase in vulnerability between initial recruitment at lt 20 cm and the maximum size in the population 28 cm Assuming that vulner
72. verage of 3 km and thus experience 8 7 mortality Migration mortality of age 1 and 2 emigrants is assumed to be minimal 5 5 3 Lake Recruitment Once stream emigrants reach the lake their maximum survival is equal to the annual survival of adults Section 3 3 3 1 A series of adjustments Sections 3 3 3 7 4 2 8 are made which decrease the survival of lake immigrants and stocked fish up to spring age 2 1 Annual survival of juveniles is size dependent 2 Annual survival fry and fall fingerlings is discounted because they spend less than a full year in the lake 3 Survival of hatchery fish is reduced 4 A saturating relationship determines the number of yearling fish surviving to age 2 as a function of the density of yearlings at the beginning of the year and a maximum yearling cape Section 5 2 Age 2 emigrants are treated as large age 1 emigrants 32 REFERENCES Alexander G R and D Shetter 1969 Trout production and angling success from matched plantings of brook trout and rainbow trout in East Fish Lake Michigan J of Wildl Manage 33 682 692 Allen K R 1951 The Horokiwi Stream a study of a trout population New Zealand Marine Dept Fish Bull 10 Ayles G B 1975 Influence of the genotype and the environment on growth and survival of rainbow trout in central Canadian aquaculture lakes Aquaculture 6 181 188 Bannerot S P and C B Austin 1983 Using frequency distributions of catch per unit effort
73. y Box so it is disabled and click on the Refresh Button located at the bottom center of the Parent Window Change the angling regulations by clicking on the Policy label on the Main Menu Bar Click on Regulations to bring up the Fishing Regulations Window To view the regulations imposed on the lake view the Regulation Codes x Lake Frame located at the bottom right corner of the Fishing Regulations Window This box displays the lake name and the current regulation code which by default is code 3 Standard Regs Change the regulation to code 2 Trophy Catch and hit the enter key to register the change Try running the model with a high bag limit and change the regulations to a low bag limit and overlay the result Go back to the Fishing Regulations Window again Try raising the minimum size to 30 cm Do this by using the left mouse button to highlight the current minimum size value of 20 cm in regulation code 2 Use the backspace or delete key to remove the current value type the number 30 and hit the enter key Run another simulation by clicking the mouse icon on the Start Run Button 3 2 Options Some of the information listed below was briefly mentioned in the Quick Start Section This section goes into greater detail about all model functions To begin a simulation click on the Start Run button located on the bottom left side of the Parent Window The model will perform a complete simulation of x time steps as indicated by the Time St

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