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:

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 occurs Risks 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 temperature Metabolism 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 as predicted –We get s = 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 size decrease with density when growth rate increases?

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