III. Life History Evolution Trade-Offs Components of fitness? - probability of survival - number of offspring - probability that offspring survive

IV. Life History Evolution Trade-Offs 2. Relationships with Energy Budgets METABOLISM GROWTH SURVIVAL METABOLISM REPRODUCTION REPRODUCTION

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction Maximize probability of survival Maximize reproduction GROWTH METABOLISM GROWTH REPRODUCTION METABOLISM REPRODUCTION

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction European Kestrels

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction Cox, R.M., and R. Calsbeek. 2010. Severe costs of reproduction persist in Anolis lizards despite the evolution of a single-egg clutch. Evolution 64: 1321-1330.

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction - Suppose the probability of adult survival is low for other reasons? Can wait Can’t wait

IV. Life History Evolution Trade-Offs 3. Trade-offs Between Survival and Reproduction - Suppose the probability of adult survival is low for other reasons? Can vary within a species in different environments: Guppies

IV. Life History Evolution Trade-Offs 4. Trade-offs Between # offspring and offspring survival METABOLISM REPRODUCTION REPRODUCTION METABOLISM A few large, high prob of survival Lots of small, low prob of survival

IV. Life History Evolution Trade-Offs 4. Trade-offs Between # offspring and offspring survival – Lack Hypothesis Again, diminishing returns

IV. Life History Evolution Trade-Offs 4. Trade-offs Between # offspring and offspring survival – Lack Hypothesis Varies within a species under different environmental conditions: Guppies

III. Life History Evolution Trade-Offs Timing Life History Strategies

Climate and Biomes Evolution and Adaptation Population Ecology

Attributes of Populations Population Ecology Attributes of Populations - Population: A group of potentially interbreeding organisms at the same time and place, that share a common gene pool. - Population size : number of individuals - Population Growth Rate: change in number over time, as a function of (birth +immigration)- (death + emigration)

Attributes of Populations Distributions Population Ecology Attributes of Populations Distributions A. Determining Factors: Environmental Tolerance and the ‘niche’ Realized and fundamental niches Zones of optima, tolerance, intolerance soil moisture temperature performance

A. Determining Factors: Environmental Tolerance and the ‘niche’ B. Changes through time: Climate Change

Changes in elevational range cooler warmer

Craig Moritz,1,2. James L. Patton,1,2 Chris J. Conroy,1 Juan L Craig Moritz,1,2* James L. Patton,1,2 Chris J. Conroy,1 Juan L. Parra,1,2 Gary C. White,3 Steven R. Beissinger1,4. 2008. Impact of a Century of Climate Change on Small-Mammal Communities in Yosemite National Park, USA. Science 322:261-264.

Craig Moritz,1,2. James L. Patton,1,2 Chris J. Conroy,1 Juan L Craig Moritz,1,2* James L. Patton,1,2 Chris J. Conroy,1 Juan L. Parra,1,2 Gary C. White,3 Steven R. Beissinger1,4. 2008. Impact of a Century of Climate Change on Small-Mammal Communities in Yosemite National Park, USA. Science 322:261-264.

C. Modeling the Spatial Structure of Populations 1. Metapopulation Model Subpopulation inhabit separate patches of the same habitat type in a “matrix” of inhospitable habitat.. - immigration causes recolonization of habitats in which population went extinct. So, rates of immigration and local extinction are critical to predicting long-term viability of population.

C. Modeling the Spatial Structure of Populations 2. Source-Sink Model Subpopulations inhabit patches of different habitat quality, so there are ‘source’ populations with surplus populations that disperse to populations in lower quality patches (‘sinks’).

C. Modeling the Spatial Structure of Populations 3. Landscape Model Subpopulations inhabit patches of different habitat quality, so there are ‘source’ populations with surplus populations that disperse to populations in lower quality patches (‘sinks’). However, the quality of the patches is ALSO affected by the surrounding matrix… alternative resources, predators, etc. And, the rate of migration between patches is also affected by the matrix between patches… with some areas acting as favorable ‘corridors’

The rate of population growth is measured as: The derivative of the growth equation: Nt = Noert dN/dt = rNo r = per capita growth (b – d) D. Population Growth 1. exponential growth

Survivorship Curves: Describe age-specific probabilities of survival, as a consequence of age-specific mortality risks. This will influence when selection favors reproduction… creating “life history” characteristics

Net Reproductive Rate = Σ(lxbx) = 10 D. Population Growth 2. Life History Redux: Patterns in survivorship will select for patterns of reproductive output, creating those life-history strategies. x lx bx lxbx xlxbx 1.0 1 0.5 20 10 2 - R = 10 T = 1 r = 2.303 Net Reproductive Rate = Σ(lxbx) = 10 Generation Time – T = Σ(xlxbx)/ Σ(lxbx) = 1.0 rm (estimated) = ln(Ro)/T = 2.303

x lx bx lxbx xlxbx 1.0 1 0.5 20 10 2 - R = 10 T = 1 r = 2.303 x lx bx 2. Life History Redux - increase fecundity, increase growth rate (obvious) x lx bx lxbx xlxbx 1.0 1 0.5 20 10 2 - R = 10 T = 1 r = 2.303 x lx bx lxbx xlxbx 1.0 1 0.5 22 11 2 - R = 11 T = 1 r = 2.398

x lx bx lxbx xlxbx 1.0 1 0.5 22 11 2 - R =11 T = 1 r = 2.398 x lx bx 2. Life History Redux - increase fecundity, increase growth rate (obvious) - decrease generation time (reproduce earlier) – increase growth rate x lx bx lxbx xlxbx 1.0 1 0.5 22 11 2 - R =11 T = 1 r = 2.398 x lx bx lxbx xlxbx 1.0 2 1 0.5 20 10 - R = 12 T = 0.833 r = 2.983

x lx bx lxbx xlxbx 1.0 1 0.5 22 11 2 - R = 11 T = 1 r = 2.398 x lx bx 2. Life History Redux - increase fecundity, increase growth rate (obvious) - decrease generation time (reproduce earlier) – increase growth rate - increasing survivorship – DECREASE GROWTH RATE (lengthen T) x lx bx lxbx xlxbx 1.0 1 0.5 22 11 2 - R = 11 T = 1 r = 2.398 x lx bx lxbx xlxbx 1.0 1 0.5 20 10 2 3 - R = 20 T = 1.5 r = 2.00

2. Life History Redux - increase fecundity, increase growth rate (obvious) - decrease generation time (reproduce earlier) – increase growth rate - increasing survivorship – DECREASE GROWTH RATE (lengthen T) - survivorship adaptive IF: - necessary to reproduce at all - by storing E, reproduce disproportionately in the future x lx bx lxbx xlxbx 1.0  0 1 0.9 2 0.8 200 160 320 3 0.7 100 70 210 4 - R = 230 T = 2.30 r = 2.36 Original r = 2.303

D. Population Growth Robert Malthus 1766-1834 1. Exponential Grwoth 2. Life History Redux 3. Limits on Growth: Density Dependence Robert Malthus 1766-1834

Premise: - as population density increases, resources become limiting and cause an increase in mortality rate, a decrease in birth rate, or both... r > 0 DEATH BIRTH RATE r < 0 DENSITY

Premise: - as population density increases, resources become limiting and cause an increase in mortality rate, a decrease in birth rate, or both... r > 0 RATE r < 0 DEATH BIRTH DENSITY

Premise: - as population density increases, resources become limiting and cause an increase in mortality rate, a decrease in birth rate, or both... r > 0 r < 0 DEATH RATE BIRTH DENSITY

As density increases, successful reproduction declines And juvenile suvivorship declines (mortality increases)

Lots of little plants begin to grow and compete Lots of little plants begin to grow and compete. This kills off most of the plants, and only a few large plants survive.

K Premise: Result: There is a density at which r = 0 and DN/dt = 0. THIS IS AN EQUILIBRIUM.... r = 0 DEATH RATE BIRTH DENSITY K

Premise 2. Result 3. The Logistic Growth Equation: Exponential: dN/dt = rN N t

Premise 2. Result 3. The Logistic Growth Equation: Exponential: Logistic: dN/dt = rN dN/dt = rN [(K-N)/K] K N N t t

Premise 2. Result 3. The Logistic Growth Equation (Pearl-Verhulst Equation, 1844-45): Logistic: dN/dt = rN [(K-N)/K] When a population is very small (N~0), the logistic term ((K-N)/K) approaches K/K (=1) and growth rate approaches the exponential maximum (dN/dt = rN). K N t

Premise 2. Result 3. The Logistic Growth Equation: Logistic: dN/dt = rN [(K-N)/K] As N approaches K, K-N approaches 0; so that the term ((K-N)/K) approaches 0 and dN/dt approaches 0 (no growth). K N t

Premise 2. Result 3. The Logistic Growth Equation: Logistic: dN/dt = rN [(K-N)/K] Should N increase beyond K, K-N becomes negative, as does dN/dt (the population will decline in size). K N t

Premise 2. Result 3. The Logistic Growth Equation: Logistic: dN/dt = rN [(K-N)/K] [(N-m)/K] Minimum viable population size add (N-m)/N K N m t

D. Population Growth 1. Exponential Growth 2. Life History Redux 3. Density Dependence 4. Spatial Dynamics and Metapopulations

D. Population Growth 1. Exponential Growth 2. Life History Redux 3. Density Dependence 4. Spatial Dynamics and Metapopulations Dealing with small populations with increase chance of stochastic extinction (e). The survival of the metapopulation is dependent on a rate of migration that can ‘rescue’ extinct at a compensatory rate.

Extinction probability is strongly influenced by population size. 4. Spatial Dynamics and Metapopulations Extinction probability is strongly influenced by population size.

What influences extinction probability and colonization rate? E. Spatial Dynamics and Metapopulations What influences extinction probability and colonization rate? Degree of patch isolation affects colonization probability Extinction probability is strongly influenced by population size (and thus patch size).

Greater Patch area means more resources, larger populations, and lower e Low e/c ?? ?? High e/c Closer patches mean higher c

Why is this important?

Why is this important?

Why is this important?

Why is this important?

These are the large intact pieces of natural biome left…

Community Ecology I. Introduction A. Definitions of Community - broad: a group of populations at the same place and time “old-hickory community”

Community Ecology I. Introduction A. Definitions of Community - broad: a group of populations at the same place and time “old-hickory community” - narrow: a “guild” is a group of species that use the same resources in the same way.

Community Ecology I. Introduction A. Definitions of Community - broad: a group of populations at the same place and time “old-hickory community” narrow: a “guild” is a group of species that use the same resources in the same way. complex: communities connected by migration or energy flow

complex: communities connected by migration or energy flow Dragonflies eat pollinators and reduce plant reproduction rates. Fish reverse these effects, increasing plant reproduction.

I. Introduction A. Definitions B. Key Descriptors – what is measured and compared? 1. Species Richness Habitat 1 Habitat 2 species A species B Richness 50 1 2 50 99

I. Introduction A. Definitions B. Key Descriptors 1. Species Richness 2. Species Diversity - relative abundance - Diversity Indices Simpson’s = 1/Σ(pi)2 Habitat 1 Habitat 2 species A species B Richness Simp. Div. 50 1 2 1.02 50 99

I. Introduction A. Definitions B. Key Descriptors 1. Species Richness 2. Species Diversity - relative abundance - Diversity Indices Simpson’s = 1/Σ(pi)2 3. Membership - species list Habitat 1 Habitat 2 species A species B Richness Simp. Div. 50 1 2 1.02 50 99

I. Introduction A. Definitions B. Key Descriptors 1. Species Richness 2. Species Diversity - relative abundance - Diversity Indices Simpson’s = 1/Σ(pi)2 3. Membership 4. Trophic Relationships

Community Ecology I. Introduction A. Definitions B. Development of the Community Concept C. Key Descriptors D. Conceptual Models

D. Conceptual Models 1. Elton - numerical and biomass pyramids Top predators are rare…

E. Conceptual Models 1. Elton - numerical and biomass pyramids Numerical and biomass pyramids can be "inverted": - one tree can be preyed upon by thousands of insect herbivores

E. Conceptual Models 1. Elton - numerical and biomass pyramids Numerical and biomass pyramids can be "inverted": - one tree can be preyed upon by thousands of insect herbivores - a lower trophic level can support more biomass at a higher level IF the rate of biomass production in lower level is high

E. Conceptual Models 1. Elton - numerical and biomass pyramids Numerical and biomass pyramids can be "inverted": - one tree can be preyed upon by thousands of insect herbivores - a lower trophic level can support more biomass at a higher level IF the rate of biomass production in lower level is high - but a productivity pyramid (new biomass/area/time) can't be permanently inverted

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective - energetic conversion rates determine biomass transfer: - endotherm food chains are short; only 10% efficient Only 10% of the biomass consumed by herbivores is converted into herbivore biomass that is available to predators.

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective - energetic conversion rates determine biomass transfer: - endotherm food chains are short; only 10% efficient - ectotherm food chains can be longer, because energy is transfered more efficiently up a food chain (insects - 50% efficient).

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective - energy available in lower level will determine the productivity of higher levels... this is called "bottom-up" regulation. not enough energy to support another trophic level

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective 3. Hairston, Slobodkin, and Smith (HSS) - 1960 - Observation: "The world is green" - there is a surplus of vegetation

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective 3. Hairston, Slobodkin, and Smith (HSS) - 1960 - Observation: "The world is green" - there is a surplus of vegetation - Implication: Herbivores are NOT limited by food... they must be limited by something else...predation?

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective 3. Hairston, Slobodkin, and Smith (HSS) - 1960 - Observation: "The world is green" - there is a surplus of vegetation - Implication: Herbivores are NOT limited by food... they must be limited by something else ....predation? - If herbivore populations are kept low by predators, they must be the variable limiting predator populations - as food. SO: Top Pred's: Limited by Competition Herbivores: Limited by Predation Plants: Limited by Competition

E. Conceptual Models 1. Elton - '20's - numerical and biomass pyramids 2. Lindeman - 40's - energetic perspective 3. Hairston, Slobodkin, and Smith (HSS) - 1960 - Observation: "The world is green" - there is a surplus of vegetation - Implication: Herbivores are NOT limited by food... they must be limited by predation. - If herbivore populations are kept low by predators, they must be the variable limiting predator populations - as food. SO: Top Pred's: Limited by Competition Herbivores: Limited by Predation Plants: Limited by Competition Community structured by "top-down effects" and ‘trophic cascades’

4. Leibold et al. (1997) As primary productivity increases, herbivore biomass increases, consistent with bottom-up theory. When fish were added, herbivores (zooplankton) declined and phytoplankton were released from herbivory and increased; indicating top-down effects once the third level (predators) were added.

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects . Vandermeer 1969 Dynamics in 4-species protist communities of Blepharisma, P caudatum, P.aurelia, and P. bursaria were consistent with predictions from 2-species L-V interactions.

Worthen and Moore (1991) Indirect, non-additive competitive effects. D. falleni and D. tripunctata each exert negative competitive effects on D. putrida in pairwise contests, but D. putrida does better with BOTH competitors present than with either alone       NON-ADDITIVE ADDITIVE

D. putrida D. tripunctata D. falleni Worthen and Moore (1991) Indirect, non-additive competitive effects. D. falleni and D. tripunctata each exert negative competitive effects on D. putrida in pairwise contests, but D. putrida does better with BOTH competitors present than with either alone   D. putrida   D. tripunctata   D. falleni

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects 1. Indirect Effects - mediated through changes in abundance 2. Higher Order Interactions - mediated through changes in the competitive interaction (coefficient), itself; not abundance consider 2 species, and the effect of N2 on N1 as aN2. N1 N2

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects 1. Indirect Effects - mediated through changes in abundance 2. Higher Order Interactions - mediated through changes in the competitive interaction (coefficient), itself; not abundance Now, suppose we add species 3 HERE, as shown... N1 N3 N2

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects 1. Indirect Effects - mediated through changes in abundance 2. Higher Order Interactions - mediated through changes in the competitive interaction (coefficient), itself; not abundance So NOW, N2 may shift AWAY from N1, reducing its competitive effect. N1 N3 N2

2. Higher Order Interactions - Wilbur 1972 Ambystoma tremblay Ambystoma laterale Ambystoma maculatum

Mean mass of 32 A. laterale 2. Higher Order Interactions - Wilbur 1972 32 A. laterale alone = 0.940 g 0.686 g 0.608 g 0.589 g Abundances are constant, so the non-additive effect must be by changing the nature of the interaction Mean mass of 32 A. laterale w/ 32 A. tremblay w/ 32 A. maculatum w/both

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects 1. Indirect Effects - mediated through changes in abundance 2. Higher Order Interactions - mediated through changes in the competitive interaction (coefficient), itself; not abundance 3. Mechanisms: Change size of organisms and affect their competitive pressure Change activity level and affect their resource use Change behavior... and resource use

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects C. Results

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects C. Results 1. Niche Partitioning at the Community Level: Species Packing There should be a non-random ordering of species along some resource axis or associated morphological axis. This can be tested through nearest neighbor analyses. What would you see if they were ordered randomly? Then compare.

Worthen and Jones (2006, 2007)

Williams (1994) V-test, v = 0.007, p < 0.05 Worthen (2009) Williams (1994) V-test, v = 0.007, p < 0.05 Mean Perch Height (cm) Amberwing Pondhawk Blue Dasher Goldenwing Slaty Saddlebags

Dayan et al., 1989. Species packing in weasels in Israel. 1. Niche Partitioning at the Community Level: Species Packing Dayan et al., 1989. Species packing in weasels in Israel.

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level A. Additive Competitive Effects B. Non-Additive Competitive Effects C. Results 1. Niche Partitioning in Communities: Species Packing 2. Optimal Size

2. Optimal Size For most groups of animals there is a 'right skew' to the frequency distribution of species ordered by size (log scale) % of Species SIZE

metabolic conversion to offspring 2. Optimal Size For most groups of animals there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown) - Large animals: lots of energy absorbed, but metabolic conversion to offspring is slow metabolic conversion to offspring SIZE

Why? Trade offs in reproductive work (Brown) 2. Optimal Size For most groups of animals, there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown) - Large animals: lots of energy absorbed, but metabolic conversion to offspring is slow - Small animals: good efficiency, but limited by energy they can collect SIZE

Why? Trade offs in reproductive work (Brown) 2. Optimal Size For most groups of animals, there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown) - result: there is a MOST EFFICIENT SIZE for a type of animal SIZE

Why? Trade offs in reproductive work (Brown, 1993) 2. Optimal Size For most groups of animals, there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown, 1993) - result: there is a MOST EFFICIENT SIZE for a type of animal

Why? Trade offs in reproductive work (Brown) 2. Optimal Size For most groups of animals, there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown) - result: there is a MOST EFFICIENT SIZE for a type of animal NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size

Why? Trade offs in reproductive work (Brown) 2. Optimal Size For most groups of animals, there is a 'right skew' to the frequency distribution of species ordered by size (log scale) Why? Trade offs in reproductive work (Brown) - result: there is a MOST EFFICIENT SIZE for a type of animal NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...small size is constrained... but large is not.....

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...small size is constrained... but large is not.....RESULT: RIGHT SKEW

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...small size is constrained... but large is not.....RESULT: RIGHT SKEW Think about the Fretwell-Lucas model of habitat selection... the optimum is used first, and when this "size niche" is full, less optimal niches are colonized.

NOW: Consider multiple species filling up the environment... - each species will be selected to attain the optimum size - but since size is an important correlate to resource use, at some point a species will do better "off the optimum", rather than competing with lots of species on the optimum....this is not as great a size class, so species will move to new size class to avoid competition more rapidly...small size is constrained... but large is not.....RESULT: RIGHT SKEW Think about the Fretwell-Lucas model of habitat selection... the optimum is used first, and when this "size niche" is full, less optimal niches are colonized. Size correlates with so many patterns of resource use that it is a good generic proxy for niche use.

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level III. Multispecies Interactions across Trophic Levels

Community Ecology I. Introduction II. Multispecies Interactions with a Trophic Level III. Multispecies Interactions across Trophic Levels A. Keystone Predators

1. Paine (1966) - the rocky intertidal A. Keystone Predators 1. Paine (1966) - the rocky intertidal Arrows show energy flow; point to consumer.

A. Keystone Predators 1. Paine (1966) - the rocky intertidal - Pisaster prefers mussels

A. Keystone Predators 1. Paine (1966) - the rocky intertidal - Pisaster prefers mussels - When predators are excluded, mussels outcompete other species and the diversity of the system crashes to a single species - a mussel bed

A. Keystone Predators 1. Paine (1966) - the rocky intertidal - Pisaster prefers mussels - When predators are excluded, mussels outcompete other species and the diversity of the system crashed to a single species - a mussel bed - When predators are present, the abundance of mussels is reduced, space is opened up, and other species can colonize and persist.

A. Keystone Predators 1. Paine (1966) - the rocky intertidal - Pisaster prefers mussels - When predators are excluded, mussels outcompete other species and the diversity of the system crashed to a single species - a mussel bed - When predator is present, the abundance of mussels is reduced, space is opened up, and other species can colonize and persist. So, although Pisaster does eat the other species (negative effect) it exerts a bigger indirect positive effect by removing the dominant competitor

Littorina littorea feeding on algae A. Keystone Predators 2. Lubchenco (1978) Littorina littorea feeding on algae

A. Keystone Predators 2. Lubchenco (1978) - Snails prefer Enteromorpha to Chondrus - E is dominant in tide pools, - C is dominant on exposed rock

A. Keystone Predators 2. Lubchenco (1978) - Snails prefer Enteromorpha to Chondrus - E is dominant in tide pools, - C is dominant on exposed rock In pools, snails are feeding on the dominant and you get a keystone effect from low to intermediate snail densities; then they are so abundant they eat everything.

A. Keystone Predators 2. Lubchenco (1978) - Snails prefer Enteromorpha to Chondrus - E is dominant in tide pools, - C is dominant on exposed rock In pools, snails are feeding on the dominant and you get a keystone effect from low to intermediate snail densities; then they are so abundant they eat everything. On rock, snails feed on competitive subordinate and Enteromorpha is whacked by competition AND predation, and diversity declines with increase snail abundance.

A. Keystone Predators 2. Lubchenco (1978) - Snails prefer Enteromorpha to Chondrus - E is dominant in tide pools, - C is dominant on exposed rock In pools, snails are feeding on the dominant and you get a keystone effect from low to intermediate snail densities; then they are so abundant they eat everything. On rock, snails feed on competitive subordinate and Enteromorpha is whacked by competition AND predation, and diversity declines with increase snail abundance. Effects depend on competitive dynamics, feeding preferences, and densities

A. Keystone Predators 3. Morin - 1983 Dr. Peter Morin number of predatory salamanders Community Ecology

A. Keystone Predators 4. Worthen - 1989

What effect will an introduced species have on a community? What effect will the loss of a species have on a community? “The first rule of the tinkerer is to save all the pieces” – Aldo Leopold