On Lumping Species in Food Webs

Abstract

Species in community food webs are commonly aggregated or lumped on the basis of overlap in predators and prey. This note reports an unexpected result of lumping species in observed food webs and simulated food webs. The main result is that it is much easier to lump species in observed webs than in simulated webs. The reason is that there is much more overlap in predators and prey for species in observed webs than in simulated webs. This points to a fundamental deficiency in web assembly models that assume a random distribution of links.

Simple rules yield complex food webs

“…These properties include the fractions of species at top, intermediate and basal trophic levels, the means and variabilities of generality, vulnerability and food-chain length, and the degrees of cannibalism, omnivory, looping and trophic similarity. Using only two empirical parameters, species number and connectance, our`niche model' extends the existing`cascade model' 3,19 and improves its ®t ten-fold by constraining species to consume a contiguous sequence of prey in a one-dimensional trophic niche 20 .…”

“…We compare the abilities of two earlier models, the random and cascade models 3,19 , and our new niche model to predict a dozen properties for each of seven food webs. The parameters of all models are set to synthesize webs with the empirically observed species number and connectance level.…”

“…In the random model 3,19 , any link among S species occurs with the same probability (P) equal to C of the empirical web. This creates webs as free as possible from biological structuring while maintaining the observed S and C. The cascade model 3,19 assigns each species a random value drawn uniformly from the interval [0,1] and each species has probability P = 2CS/(S -1) of consuming only species with values less than its own.…”

“…This creates webs as free as possible from biological structuring while maintaining the observed S and C. The cascade model 3,19 assigns each species a random value drawn uniformly from the interval [0,1] and each species has probability P = 2CS/(S -1) of consuming only species with values less than its own. This pecking order helps to explain species richness among trophic levels 3 but underestimates inter-speci®c trophic similarity 19 and overestimates food-chain length and number in larger webs 3,18 . The niche model ( Fig.…”

Food web dynamics in an Australian Wet Tropics river

“…As functional categories used in web construction led to lumping of trophic species (Pimm et al 1991), the calculation of food web statistics is not appropriate. Many food web statistics are sensitive to the way in which the webs themselves are constructed (Sugihara et al 1997;Solow and Beet 1998).…”