Can we use food web theory to evaluate how robust communities are to species loss? Dunne et al. (2002) Ecology Letters 5:558

Motivated by network theory that explores how complex networks (eg. Power grid, WWW, neural networks) are influenced by node loss.

Used a set of 16 well characterized food webs differing in richness and connectedness to explore how robust they are to secondary extinctions following species elimination. Are there ‘rivet-like thresholds’ where enough primary extinctions result in community collapse?

S= trophic species (share the same predators/prey) Res = % of taxa identified; O = fraction of species that are omnivores For each food web: Simulate extinction by sequentially removing (trophic) species according to various criteria (e.g., random, most connected spp, least connected spp).

“Stability”: number of potential secondary extinctions, which would occur if a species lost all its prey items.

“Robustness”: How many species do you need to remove to eliminate half of all species in the web (secondary extinctions)

Correlated robustness against food web metrics (S, connectedness, omnivory) Food web robustness strongly influenced by connectance

Robustness here is proportion of primary spp removals necessary to generate 50% extinction from food web. Staniczenko et al. (2010) Structural dynamics and robustness of foodwebs. Ecology Letters13:891-899

Follow up to Dunne et al. (2002)

Many examples where species change diet when competition for prey items changes (examples?)

Asked:

What are the consequences of incorporating predator-prey “rewiring” for food web robustness? Example of foodweb rewiring:

Remove species 4

Competition for Food web is rewired prey of species as sp 6 consumes sp 4 reduced 1 Results

Fraction of sp removals PIR = proportional until no spp remains increase in robustness Staniczenko et al. argue that cannot predict outcome of species loss using a static food web

Compensatory effects have potentially strong impacts on food web robustness.

Robustness then depends on overlap spp that can shift diet when prey species is removed (rather than S or c) . Dunne/Staniczenko work highlights limitation of two approaches to exploring foodweb stability

Lotka-Volterra approach (May, Pimm, Lawton, Morin) - per capita effects difficult to parameterize with emprical data - limited in the trophic species richness that can handle - cannot portray complex topology of food webs

Structural approach (Dunne and later papers) - tractable to empirical data for large numbers of taxa - lack information on population size, dynamics - cannot quantify species links as energy flow or interaction strength Future of food web research (Thompson et al. in press)

Utility of food web research is linking community structure to ecosystem function. Need to:

Incorporate energy flux into food webs to evaluate ecosystem function

Link individual traits to food web structure – for example size or metabolic varition

Incorporate temporal and spatial variation in food web structure

Predict impacts of biodiversity loss or invasion on ecosystem function Summary

Food web studies explore how trophic relationships influence the stability of communities. When linked to explorations of energy flow they can (could) provide a framework for examining top down and bottom up effects in communities

Early modeling efforts made unrealistic assumptions about the distribution of interaction strengths in communities. The distribution of strengths is now better understood but application to food webs is limited.

Food web studies provide opportunities to predict how species losses propagate through communities and influence community stability and ecosystem services. Priority effects and assembly rules

What are the consequences of phenological patterns for interspecific interactions?

Harper (1961) planted two species of grass: Bromus rigidus and B. madritensis either simultaneously or with B. rigidus sown 3 weeks after B. madritensis

Grown together: B. rigidus accounted for 75 % of biomass Sown later: B. rigidus accounted for 10 % of biomass

What might determine a priority effect like this?? Why is it important for understanding species coexistence? Priority effects aren’t always tied to phenology

Schulman (1983) looked at recruitment of marine reef fish from the larval stage on newly created artificial reefs

- Recruitment of fish was inhibited by prior occupation by two species of beaugregory (territorial damselfish) and juvenile snapper

- New territories on the reef open at random. Species that have more settling larvae available when the territory opens up will have a higher probability of filling it.

Other examples of priority effects like this one?? Priority effects are an example of an ‘Assembly Rule’

What is an assembly rule?

Diamond: rules that govern how communities are assembled!

Diamond’s work based on an accumulation of observational data on the distribution of bird species on Bismark islands around New Guinea

Interested to know if only certain sets of spp drawn from a regional species pool can Jared Diamond (1975) coexist at some local level Bismarck Archipelago

Islands N and E of Papua New Guinea Diamond’s approach to examining coexistence

Influenced by MacArthur and his warblers…

Hypothesis: species fit together in a complementary way in communities dictated by the strength of interspecific competition

Incidence functions: the probability that a particular species of interest will occur in a particular community given some attribute of the community

Attribute of the community that predicts occurrence = species richness. Why species richness? Species called High-S require more specialized features of communities that support a variety of other species. ‘Tramp’ species occur islands including those with low spp richness

“Tramp sp” “High-S sp” (cuckoo) (dove)

“Tramp sp” “Supertramp” of occurrence Probability (flower pecker) (cuckoo-dove)

Species richness Diamond’s hypothesis for these patterns:

Species use/consume resources (e.g., food, nesting sites) out of the total resource pool available on the island. Resource pool determined mostly by island size?

Under what conditions would you predict that species can coexist? 4 spp guild with different resource requirements. Solid line represents resource production. Dashed line resource use by each species

Small island - only sp # 3 exists

Larger island spp 2 and 4 but not 2,3,4 can coexist

Larger island, sp # 1 could invade island occupied by spp 2 and 4 Diamond codified the patterns he observed into a set of “Assembly rules”

1. Considering all combinations that could be found for a group of related spp. only certain ones exist in nature

2. Those permissible combinations resist invaders that would transform them to forbidden combinations

3. “Checkerboard rule” - some pairs of species never coexist either by themselves or as part of a larger combination

How would you test if these assembly rules actually operate?? Example of Diamond’s rule that some spp pairs never coexist Various tests of Diamond’s rules using null models

• Connor and Simberloff (1979) and other papers - looked at whether fewer species combinations occurred in nature than expected at random. Could NOT reject the null model

• Gotelli and McCabe (2002) - more complete analysis of particular assembly rules - first test of the checkerboard assembly rule

-Assembled data from 96 studies of species occurrences from scales of 1-1010 m2 used Monte Carlo randomizations to examine whether there are species co-occurrences that are less likely than expected at random

-Found general SUPPORT for assembly rules Assembly of Hawaiian spider communities (Gillespie 2004)

Hawaiian island archipelago – isolated, topographically diverse, and range of island ages (Hawaii <1 Mya – Kauai 5 Mya.

How have communities assembled on these islands?

Tetragnatha radiation of spider species on Hawaii

- One clade of Tetragnatha = ‘spiny leg’ clade (16 spp) – hunting spiders that abandoned web building Assembly of Hawaiian spider communities (Gillespie 2004)

A. Green ecomorph. Leaf dwelling, feed on small insects

B. Maroon ecomorph. Moss- dwelling, weakly flying insects

C. Small brown ecomorph. Twig dwelling, feeds on small insects

D. Large brown. Slow moving, lives on bark, feeds on caterpillars Assembly of Hawaiian spider communities (Gillespie 2004)

Each community: 2-4 ecomorphs, regardless of island/volcano age

Never find 2 spp of the same ecomorph in the same community

Assembly highly non-random Distribution of spider ecomorphs across Hawaiian islands

What processes could generate this pattern? Molecular phylogeny of Tetragnatha

What pattern do you infer? Assembly of Hawaiian spider communities (Gillespie 2004)

Ecomorphs originate within habitats by:

(i) In situ evolution of one ecomorph into another (e.g. green-maroon – Oahu)

(ii) Dispersal without speciation (e.g. quasimodo).

Assembly hypothesis: Young islands – initial colonization followed by species radiation, then additional colonization increases species richness. Then competition after species accumulate to fine tune community composition?? Community phylogenetics – the rebirth of assembly rules

Last 6-8 years – push to combine phylogenetic analysis of species relationships with community assembly and structure

3 perspectives on how communities assemble:

(1) Niche-assembly rules dictated by local environmental filters and the principle of competitive exclusion (Tilman, Diamond) (2) Neutral assembly (the null model approach) where species are assumed to be ecologically equivalent (Hubbell, Simberloff) (3) History-based assembly. Starting conditions and historical patterns of speciation matter more than local processes (Ricklefs) Phylogenetic approach: identification of processes that underlie community assembly - Emphasis on competitive exclusion/limiting similarity led to convenient assumption that evolutionary processes are not relevant on the time scale of ecological processes.

Cavender-Bares et al. (2009) Ecol. Lett. 12:693-715 The paradox of phenotypic similarity …species of the same genus have usually, though by no means invariably, some similarity in habits and constitution… Darwin (1859)

So…closely related species should also experience strong competitive interactions due to their ecological similarity.

One the one hand environmental filtering will select for species with similar traits in the same environment. On the other hand ecological similarity may prevent closely related species from sharing environments. Community phylogenetics explores the relative importance of competitive exclusion and ecological character displacement in community assembly. What might community phylogenetic structure look like? Scenario #1 Strong phylogenetic signal in community assembly

Clustering is a consequence of trait conservatism – closely related species have similar ecologies

Phenotypic clustering in turn results from environmental filtering What might community phylogenetic structure look like? Scenario #1 Strong phylogenetic signal in community assembly

Clustering is a consequence of trait conservatism – closely related species have similar ecologies

Phenotypic clustering in turn results from environmental filtering What might community phylogenetic structure look like? Communities composed of species from different branches of phylogeny. Why?

Species on different branches converge on similar traits

Environmental filtering controls what traits can occur in a niche/ community What might community phylogenetic structure look like? Communities composed of species from different branches of phylogeny. Why?

Species on different branches converge on similar traits

Environmental filtering controls what traits can occur in a niche/ community Explaining phylogenetic structure (Webb 2002)

If environmental filtering dominates, co-occurring species sharing the same abiotic environment should have more similar traits (phenotypically more similar) than expected (trait clustering) Explaining phylogenetic structure (Webb 2002)

If competitive interactions dominate, co-occurring species sharing the same abiotic environment should be phenotypically less similar than expected (trait overdispersion) Explaining phylogenetic structure (Webb 2002)

Which ecological process (filtering, competition) is important in determining phylogenetic structure also depends on pattern of trait evolution. For phylogenetic overdispersion: competitive interactions must cause overdispersion of conserved traits, or environmental filtering must cause clustering of convergent traits. Cavender-Bares et al. (2004) Phylogenetic overdispersion of oak communities

17 oak species occur in North Central Florida in sites that range in moisture availability.

Environmental filtering – oaks that live in similar environments should show similar phenotypic traits.

But… species that are too similar are unlikely to co-occur because of competitive exclusion

Explored correlations between phylogenetic relatedness of oaks, degree of co-occurrence, and similarity in physiological traits. Floridian oak phylogeny and a mapped on trait (soil moisture preference)

Any evidence for phylogenetic clustering?? Found:

Significant negative correlation between species differences in soil moisture preference and phylogenetic distance… so, distantly related species… converge on the same habitat conditions.

Despite phylogenetic overdispersion there is evidence for environmental filtering in this study:

Bark thickness, radial growth, rhizome resprouting potential, seedling growth rate – all show phenotypic clustering, indicating that co-occurring species across a soil moisture gradient were phenotypically similar. Conclusions:

Assembly rules idea attractive to ecologists but languished until recent development of community phylogenetics

-Very difficult to test for assembly rules based on spp presence- absence patterns (too many explanatory variables that need to be ruled out)

-Phylogenetic approach provides additional insight into mechanisms leading to co-occurrence (dispersal, radiation) and can detect potential effects of competition – a cornerstone of traditional assembly theory, and allows us to incorporate evolutionary processes occurring at larger spatial and temporal scales.