Individual and Population Level Analysis and Validation of an Individual-based Trout Model

1. Individual and Population Level Analysis and Validation of an Individual-based Trout Model Roland H. Lamberson Humboldt State University

2. Individual and Population Level Analysis and Validation of an Individual-based Trout Model • Collaborators: • Steve Railsback Lang, Railsback, and Associates • Bret Harvey US Forest Service, Redwood Sciences Laboratory

3. Individual and Population Level Analysis and Validation of an Individual-based Trout Model • Products & publications: http://math.humboldt.edu/~simsys/

4. Individual and Population Level Analysis and Validation of an Individual-based Trout Model • Our Approach to Validation • Validation Experiments • Individual behavior • Population level behavior • Results • Conclusions

5. Individual-based Trout Model • Spatially Explicit with One Day Time Step

6. Individual-based Trout Model • Growth potential and mortality risk vary with: • Space (cell) • Depth, velocity, feeding & hiding cover, food availability • Fish • Length, weight, condition • Competition: Size-based hierarchy • Food consumed by larger fish in a cell is not available to smaller fish

7. Individual-based Trout Model • Movement (Habitat Selection) • Movement is the most important mechanism available to stream fish for adapting to changing conditions

8. Individual-based Trout Model • Movement Rules • Move to maximize fitness • Examine all habitat nearby each day • Move if fitness can be improved • Move to site with highest fitness measure

9. Individual-based Trout Model • Fitness measure • Expected Maturity (EM) • Probability of survival to fixed time horizon (usually 90 days) Times • Expected fraction of mature size at next spawning

10. Habitat Selection • Do realistic behaviors emerge? • Normal conditions: territory-like spacing • Short-term risk: fish ignore food and avoid the risk • Hungry fish take more chances to get food (and often get eaten) • Habitat conditions like temperature, food availability affect habitat choice

11. Validating Individual Behavior • Habitat Selection • Hierarchical feeding • Response to high flows • Response to interspecific competition • Response to predatory fish • Variation in velocity preference with season • Changes is habitat use with food availability and energy reserves • Railsback and Harvey, (2002) Ecology

12. Validating Individual Behavior • We compared 3 alternative theories: • EM: Our “expected survival and growth to maturity over a future time horizon” theory • MG: Fish select habitat to maximize today’s growth • MS: Fish select habitat to maximize today’s survival probability (minimize risk)

13. Validating Individual Behavior • Hierarchical Feeding • A consistent preference for specific feeding sites • Dominant fish displace others from preferred sites • Sub-dominant fish occupy preferred sites when dominant fish are removed

14. Validating Individual Behavior • Hierarchical Feeding • Simulation: • 10 adult trout in a small habitat • Five time steps to equilibrate • Largest fish are successively removed • Results: • Works for EM, MG (via food competition) • EM and MG result in different habitat preferences

15. Validating Individual Behavior • Habitat Selection • Maximize Survival • No hierarchical feeding • All fish use cell with highest daily survival probability

16. Validating Individual Behavior • Habitat Selection • Maximize Growth • Hierarchical feeding: • Clear preference for cell providing highest growth rate • Competition for food initially excludes most fish from the optimal cell

17. Validating Individual Behavior • Habitat Selection • Expected Maturity • Hierarchical feeding occursRisks in the preferred cell are much lower: mean survival times of 6900 days, vs. 180 days in cell that maximizes growth

18. Validating Individual Behavior • Response to High Flows • At flood flows, trout move to quieter water on the stream margin

19. Validating Individual Behavior • Response to High Flows • Simulation: Flow rises from 0.6 to 5 m/s, then recedes

20. Validating Individual Behavior • Response to High Flows • Results: • Works for EM, MG, MS • Moving to stream margin maximizes both growth and survival • (This experiment had no power to resolve the 3 competing fitness measures)

21. Validating Individual Behavior • Variation in Velocity Preference with Season • Adult trout use lower velocities in winter than in summer • Simulation: Four temperature scenarios 5, 10, 15, 20º C

22. Validating Individual Behavior • Variation in Velocity Preference with Season • Metabolism increases with temperatureMetabolism affects future starvation risk Only EM considers future starvation

23. Validating Individual Behavior • Response to reduced food availability • When food availability (or energy reserves) are reduced, trout take more risks to get more food

24. Validating Individual Behavior • Response to reduced food availability • Simulation: • Five adult trout in a small habitat • After 5 days, food availability was reduced by 2/3 • Results: • MG fish were already at the cell with highest intake • MS fish are not concerned with food • ...

25. Validating Individual Behavior • Response to reduced food availability • Results (continued): • EM produced movement to new habitat with higher (relative) food intake: the tradeoff between food and risk shifts • This requires fish to consider future consequences of food intake on starvation risk

26. Validating Individual Behavior:Overall Results

27. Population Level Analysis • Validation Experiments • Self-thinning - a negative power relation between weight and abundance • Critical period - density-dependent mortality in young-of-the-year • Age-specific interannual variability in abundance • Density dependence in growth • Fewer large trout when pools eliminated

28. Population Level Analysis • Self-thinning (Elliott 1993) • Mean Weight = k abundance s • Theory suggests that s = -4/3 • Results from assuming metabolic rate = k weight b where b = ¾ • Elliott found s to be highly variable but had a 25 year average of -1.33, as predicted

29. Population Level Analysis • Self-thinning (Elliott 1993) • Mean Weight = k abundance s • Theory suggests that s = -4/3 • Elliott found s to be highly variable but had a 25 year average of -1.33 as predicted • We get s = -1.25 for b = ¾, a bit too low • However, our s is sensitive to b in the right way

30. Population Level Analysis • Critical survival time (Elliot 1989) • Elliott found intense density-dependent mortality commencing when trout fry emerge and continuing for from 30 to 70 days

31. Population Level Analysis • Critical survival time (Elliot 1989) • Elliott found intense density-dependent mortality commencing when trout fry emerge and continuing for from 30 to 70 days • In 18 of our 29 simulations we found a critical period, the lengths varied from 30 to 65 days • However, we found no critical period in years of low age zero abundance (the other 11 cases)

32. Population Level Analysis • Population Variation Over Time (House 1995) • Age 0 abundance varying by a factor of 4 • Age 1 least variable age class • Age 2+ most variable

33. Population Level Analysis • Population Variation Over Time (House 1995) • Age 0 abundance varying by a factor of 4 • Age 1 least variable age class • Age 2+ most variable • Age 0 abundance variation similar to House • Age 1 more variable than age 2+ • We have more pools - higher survival for adult fish

34. Population Level Analysis • Population Variation Over Time (House 1995) • Weak correlation between peak winter flow and age 1 abundance the following summer • No correlation between lowest summer flow and abundance

35. Population Level Analysis • Population Variation Over Time (House 1995) • Weak correlation between peak winter flow and age 1 abundance the following summer • No correlation between lowest summer flow and abundance • We found the same though our correlation between winter flow and age 1 abundance was a little stronger

36. Population Level Analysis • Density Dependence in Growth • Elliott (1994) observed abundance and size of age 0 trout and concluded abundance had little effect on growth • Jenkins et al. (1999) observed abundance and growth in natural and controlled streams and concluded abundance had a strong negative effect on size

37. Population Level Analysis • Density Dependence in Growth • Our experiments demonstrate a negative relationship between abundance of age 0 trout and their size in fall (similar to Jenkins), but

38. Population Level Analysis • Density Dependence in Growth • But it is not that simple! • We find a weak positive relationship between growth rate (grams/day) and density

39. Population Level Analysis • Density Dependence in Growth • How can age 0 sizedecrease with density when growth rateincreases?

40. Population Level Analysis • Density Dependence in Growth • Fall mean weight of age 0 trout is related to • Time of emergence • Size-dependent mortality

41. Population Level Analysis • Density Dependence in Growth • Time of emergence • Later emergence means less mortality of age zero trout before census and younger thus smaller trout at the time of the census

42. Population Level Analysis • Density Dependence in Growth • Size-dependent mortality is more important than growth rate in determining average fry weight • When competition for resources (habitat & food) is greater mortality of age 0 trout is higher and the smaller individuals are the most vulnerable • The most prevalent form of mortality is starvation and disease due to poor condition

43. Population Level Analysis • Density Dependence in Growth • Size-dependent mortality is more important than growth rate in determining average fry weight • The per-fish rate of predation mortality is much lower at high fish density than starvation and disease • At low density it is just as important as starvation and disease

44. Population Level Analysis • No Pools Produces Few Large Trout (Bisson & Sedell 1984) • In watersheds with clearcut timber harvests both the pool volume and the abundance of older trout were lower than in comparison control watersheds

45. Population Level Analysis • No Pools Produces Few Large Trout (Bisson & Sedell 1984) • Five year simulation with pools removed resulted in lower abundance of all age classes especially the older ones • Terrestrial predation increased because of the shallower water. • Growth was slower because of the increased energy expenditure in the faster moving water resulting in fewer eggs per spawner.

46. Population Level Analysis • No Pools Produces Few Large Trout (Bisson & Sedell 1984) • Size of age 0 and 1 trout increased when pools were removed • Abundance decreased, so there was less competition for food • Age 1 trout were forced to use faster, shallower habitat where predation risk is higher BUT food intake and growth is higher

47. Potential Applications • Instream flow evaluation: • Assessing cumulative effects of changes in: • flow rate, flow timing, temperature, physical habitat, … • Evaluating habitat restoration actions: • Assessing benefits of changes in nearshore habitat, wood, in-stream objects, etc. • What are the benefits of additional cover for hiding vs. feeding? • Assessing population-level effects of: • Spawning habitat • Stranding

48. Potential Applications • Predicting species interactions: • How does competition among salmonid species/races affect restoration success? • What are interactions between salmonids and non-salmonid species (e.g., striped bass)? • Monitoring & adaptive management framework: • Use model to predict results of management actions • Design monitoring programs to test predictions and the model • Use model to understand why observed responses occurred

49. Population Level Responses Emerging from Processes Acting at the Individual Level • Simple appearing responses at population level may result from complex interactions at the individual level • Density effects on size not explained by food competition • Fewer pools resulted in fewer trout and smaller adults but bigger 0 and 1year olds