L15 modern coexistence theory and community assembly

Question 1

What is the focus of today’s lecture?

Answer 1

How Modern Coexistence Theory informs Community Assembly.

Question 2

What does Modern Coexistence Theory (MCT) build on?

Answer 2

The idea that species’ interactions determine coexistence, assuming concepts like stabilizing vs. equalizing mechanisms.

Question 3

What are the three main steps in the community assembly process?

Answer 3

1. Regional Species Pool & Local Patches
2. Environmental Filtering
3. Formation of the Local Community

Question 4

What is a Regional Species Pool & Local Patches?

Answer 4

A large pool of potential species and multiple smaller habitats each harboring a subset of those species.

Question 5

What is Environmental Filtering?

Answer 5

Abiotic conditions acting as a filter—only species whose Fundamental Niche includes those conditions can pass.

Question 6

How is a Local Community defined in community assembly?

Answer 6

The set of species (alpha diversity) present in a patch after abiotic filtering and biotic interactions.

Question 7

Define Alpha Diversity.

Answer 7

Species richness within a local patch.

Question 8

Define Beta Diversity.

Answer 8

Turnover or dissimilarity in species composition between patches.

Question 9

Define Gamma Diversity.

Answer 9

Total species richness across the entire region (the full Regional Species Pool).

Question 10

What is the Fundamental Niche?

Answer 10

The set of abiotic conditions under which a species can persist without biotic interactions.

Question 11

What is the Realized Niche?

Answer 11

The subset of the Fundamental Niche after accounting for biotic interactions (competition, predation, etc.).

Question 12

What role does Local Competition play in community assembly?

Answer 12

After passing the abiotic filter, species compete biotically; weaker competitors are excluded, narrowing the Realized Niche.

Question 13

What is Dispersal Limitation?

Answer 13

Chance-based absence of species in patches due to limited arrival, even if conditions are suitable.

Question 14

What are Priority Effects?

Answer 14

Historical sequence of colonization determining community outcomes when multiple stable states exist (e.g., Lotka–Volterra model scenarios).

Question 15

How do Deterministic and Stochastic factors differ in community assembly?

Answer 15

Deterministic factors yield predictable outcomes from abiotic/biotic filters; stochastic factors involve chance and history (dispersal events, priority).

Question 16

In the hypothetical tree community example, how would environmental filtering shape trait distributions?

Answer 16

Co-occurring species at each site should exhibit similar trait values matching local environmental conditions (phenotypic convergence).

Question 17

What are the five most critical concepts summarized for community assembly?

Answer 17

1. Environmental Filtering drives local establishment
2. Realized Niches are narrower than Fundamental Niches
3. Alpha, Beta, Gamma Diversity quantify local, between-site, and regional richness
4. Dispersal Limitation & Priority Effects introduce stochasticity
5. The balance of Deterministic vs. Stochastic processes is a central debate

Question 18

Why move from species presence–absence to trait-based analysis in community assembly?

Answer 18

Traits are the direct targets of environmental and competitive filters, allowing clearer tests of assembly mechanisms.

Question 19

What is Specific Leaf Area (SLA)?

Answer 19

The ratio of leaf area to dry mass; high SLA means thin, expansive leaves, low SLA means thick, dense leaves.

Question 20

How does high SLA function ecologically?

Answer 20

It supports high light capture and growth rates where water is plentiful by fueling a strong transpiration stream.

Question 21

What ecological advantage does low SLA confer?

Answer 21

Reduced water loss in arid or drought-prone environments.

Question 22

What is the objective of null models and bootstrapping in trait analysis?

Answer 22

To determine if observed within-site trait distributions differ from random expectations.

Question 23

What is the procedure for trait-based null models?

Answer 23

Calculate observed trait statistic per site, randomly reassign species to sites many times, compute the statistic for each draw, and compare to the observed.

Question 24

What does trait clustering (observed variance < null variance) indicate?

Answer 24

Evidence of environmental filtering—co‐occurring species share similar traits.

Question 25

What does trait overdispersion (observed variance > null variance) indicate?

Answer 25

Evidence of niche differentiation—competition drives trait divergence.

Question 26

How was Kraft et al.’s Amazonian forest case study designed?

Answer 26

A 25 ha plot with ~150,000 mapped trees (~1,100 species), divided into 20×20 m quadrats; traits (e.g., SLA, seed mass, wood density) measured per species; species labels shuffled among trees to build null distributions.

Question 27

What did Kraft et al. find about SLA and topography?

Answer 27

SLA clustered by terrain: lower SLA on ridge tops and higher SLA in valleys, showing environmental filtering.

Question 28

How did Kraft et al. detect local competition via seed mass?

Answer 28

Within quadrats, observed seed mass variance exceeded null expectations, indicating overdispersion from competition.

Question 29

What ontogenetic shift did Kraft et al. observe?

Answer 29

Saplings’ trait distributions matched random expectation, while adults showed stronger trait structuring as competition thinned similar individuals.

Question 30

What is phylogenetic clustering and what does it imply?

Answer 30

Co‐occurring species are more closely related than expected, implying environmental filtering on conserved traits.

Question 31

What is phylogenetic overdispersion and what does it imply?

Answer 31

Co‐occurring species are more distantly related than expected, implying competition driving divergence in conserved traits.

Question 32

Why integrate abiotic, biotic, and evolutionary perspectives in community assembly?

Answer 32

Combining filters (abiotic/biotic) with phylogenetic context yields a richer, multiscale understanding of how communities form.

Question 33

Why incorporate phylogeny into community assembly analyses?

Answer 33

Phylogeny reveals evolutionary conservation of traits, helping infer whether filtering or competition drives community patterns.

Question 34

What was the design of the Florida oak community case study?

Answer 34

Seventeen oak species in Florida, grouped into red, white, and live oak clades; measured each species’ mean soil moisture preference and mapped them on a phylogeny.

Question 35

What pattern of soil moisture preference emerged among Florida oaks?

Answer 35

Closely related species often occupied contrasting moisture niches, showing phylogenetic overdispersion.

Question 36

What does phylogenetic overdispersion of a trait indicate?

Answer 36

Divergent adaptation within clades and niche partitioning driving diversification rather than conserved trait filtering.

Question 37

How was field co-occurrence analyzed in the oak study?

Answer 37

By calculating pairwise co-occurrence probabilities and comparing against phylogenetic distances among species.

Question 38

What did the oak co-occurrence analysis reveal within each clade?

Answer 38

Specialists on dry, intermediate, and wet niches evolved in each clade, and co-occurrence was higher among phylogenetically distant specialists.

Question 39

What is a key critique about defining the Regional Species Pool in trait studies?

Answer 39

Arbitrary spatial boundaries can bias results, making 'regional pool' delineations appear random.

Question 40

How does spatial scale sensitivity affect trait-based community assembly findings?

Answer 40

The size of sampling units (e.g., quadrat area) can change whether filtering or competition signals are detected.

Question 41

Why is separating environmental filtering from competition challenging?

Answer 41

Observed trait clustering may conflate abiotic filtering with competitive exclusion if both produce similar patterns.

Question 42

What limitation does experimental realism impose on trait-filter studies?

Answer 42

Only about 15% of studies properly isolate filtering and competition, making causal inference weak.

Question 43

How does Modern Coexistence Theory (MCT) refine classical trait-pattern predictions?

Answer 43

By partitioning coexistence into niche differences (stabilizing) and fitness differences (equalizing), tailoring expectations per trait.

Question 44

Under MCT, what pattern is expected for traits linked to fitness differences?

Answer 44

Phenotypic convergence (clustering) reduces fitness disparities and promotes coexistence.

Question 45

Under MCT, what pattern is expected for traits linked to niche differences?

Answer 45

Phenotypic overdispersion (divergence) maximizes niche separation and promotes coexistence.

Question 46

How do classical vs. MCT perspectives differ on interpreting competition signals?

Answer 46

Classical: competition always yields overdispersion; MCT: competition may yield clustering for fitness traits and overdispersion for niche traits.

Question 47

Describe the algal competition experiment using MCT.

Answer 47

Monoculture growth assays for fitness, mutual invasion trials for invasibility, across 8 freshwater green algae species.

Question 48

Which MCT metrics predicted coexistence in the algal experiment?

Answer 48

Niche differences (invasion-rate divergence) predicted coexistence; fitness differences did not.

Question 49

What was the phylogenetic signal outcome in the algal MCT study?

Answer 49

No phylogenetic signal for coexistence, niche, or fitness differences—key traits were evolutionarily labile.

Question 50

Why is classical trait-clustering vs. overdispersion theory considered overly simplistic?

Answer 50

It ignores trait-specific roles in fitness vs. niche axes and evolutionary lability, leading to mixed or misleading signals.

Question 51

What outstanding challenges remain for community assembly research?

Answer 51

Defining regional pools and scales, linking field trait patterns to coexistence mechanisms, and integrating variable phylogenetic signals.

Question 52

How does trait phylogenetic signal affect assembly predictions?

Answer 52

Conserved traits yield clear clustering/overdispersion interpretations, while labile traits weaken phylogenetic inference.

Question 53

Why must co-occurrence analyses account for clade-level niche partitioning?

Answer 53

Because closely related species may diverge ecologically within clades, masking true filtering or competition drivers.