Ecology 8310 Population (and Community) Ecology

Context

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

Context

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

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

Context

Demographic Compensation

Due to what?

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

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

Daniel D. Heath et al. Science 2003;299: Egg number vs. size

Fitness landscapes

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

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

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

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

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

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

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

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

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

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

But, back to Villellas et al…

Demographic Compensation = Trade-off (between vital rates)

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.

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

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"

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

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:

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

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

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

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

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

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?

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

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

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

From Kerfoot et al. (1985)

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

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