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Can We Use Food Web Theory to Evaluate How Robust Communities Are to Species Loss? Dunne Et Al

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Can We Use Food Web Theory to Evaluate How Robust Communities Are to Species Loss? Dunne Et Al 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” Probability 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
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