1. Ecology 8310 Population (and Community) Ecology

2. Context

3. What accounts for the distributional limit? 1.dispersal (perhaps with Allee effects) 2.biotic and abiotic factors If environmental factors, then: 1.λ(N≈0) > 1 within distribution 2.λ(N≈0) < 1 outside of distribution

4. Context

5. Secondary issue: If environment changes, how will these distributional limits change? As determined by shifts in λ (e.g., from 0)

6. Context λ is a complex "trait" Determined by vital rates: Birth (or recruitment) rate Death (or survival) rate Growth rate These rates may not change concordantly across environmental gradients

7. Context

8. Demographic Compensation

9. Due to what?

10. Demographic Compensation 1.Density – mediated (e.g., sunfish) Small Bluegill Large Bluegill Littoral Invertebrates Zooplankton

11. Demographic Compensation 2.Other drivers of negative correlations: Genetic architecture| Biochemical processes Energetic constraints

12. Daniel D. Heath et al. Science 2003;299:1738-1740 Egg number vs. size

13. Fitness landscapes

14. But this ignores (for the most part) trade-offs

15. Fitness landscapes Trait 1 (Z 1 ) Trait 2 (Z 2 ) Low fitness High fitness X

16. What if there is a trade-off between Z 1 and Z 2 ?

17. Fitness landscapes & trade-offs Trait 1 (Z 1 ) Trait 2 (Z 2 ) Low fitness High fitness X

18. Fitness landscapes & trade-offs Trait 1 (Z 1 ) Trait 2 (Z 2 ) Low fitness High fitness X

19. What if there are different environments (genotypes)? (in which total allocation to Z 1 and Z 2 is greater or lesser?)

20. Fitness landscapes & trade-offs Trait 1 (Z 1 ) Trait 2 (Z 2 ) High resource acquisition Low resources

21. Common empirical pattern: Trait 1 (Z 1 ) Trait 2 (Z 2 ) No trade-off ?

22. Alternative interpretation: Trait 1 (Z 1 ) Trait 2 (Z 2 )

23. Alternative interpretation: Trait 1 (Z 1 ) Trait 2 (Z 2 ) Trade-off hidden by variation in resource acquisition

24. But, back to Villellas et al…

25. Demographic Compensation = Trade-off (between vital rates)

26. Data approach 1)Obtain vital rates for many populations 2)Contribution of vital rate to λ 3)Assess correlations among rates 4)For 26 species with >6 pops.

27. Two analytic challenges 1.Estimation of vital rates 2.Contributions of vita rates (via LTRE)

28. Projection matrix  vital rates Seedbank Seedlings Small juveniles Large juveniles Small adults Large adults Thus seedlings and small juveniles are the same "stage", but different "age"

29. Projection matrix  vital rates s = Pr(survival) g = Pr(growth|survival) (by any amount) r = Pr (revert|survival) (by any amount) k = Pr (growth by >1 step | survival and growth) (h is analogous) l = Pr (growth by >2 steps | survival, grow by >1 step) f = fecundity (includes some survival) z = Pr (germination) Sums to s 4

30. Life Table Response Experiment 1)Convert vital rate to "contribution to growth, C" 2)Originally proposed to compare two treatments in an experiment (Caswell 2001 – classic book) 3)Here, "reference" is the mean across all populations:

31. 1) Could you recreate these calculations (up to the analyses)?

32. A result 24/ 26 studies had significantly more POSITIVE correlations than expected by chance 2) What does this mean?

33. A result 9 (11) / 26 studies had significantly more NEGATIVE correlations than expected by chance 3) What does this mean?

34. A result The 4 experimental studies did NOT show demographic compensation. 4) Why?

35. A result Demographic compensation more likely, if: a) monitored longer; and b) monitored more populations 5) Why?

36. Timescale issues Compare an experimental (short-term) approach with an observational one (in which the populations have existed for many generations) 6) What do you expect?

37. 7) What might this tell us about life history trade- offs? Other results

38. 8) Why is this an interesting questions? 9) Why is var(λ) an "inverse proxy" for potential range size?

39. 10) How do you expect vital rates to co-vary across space?

40. From Kerfoot et al. (1985)

41. Context My view: 1) DC doesn't require negative correlation 2) Little change in distributional limit; but really…

42. This isn't about increase from N~0 (it's about equilibrium)…