Food webs present complex yet tractable depictions of biodiversity, species interactions, networks of trophic relationships, and ecosystem structure and function. Recent food web models have predicted key topological properties of food webs by relying on two empirical measures of complexity – connectance and species richness. The diet breadth model (DBM) has described the synthesis of foraging behavior and food web theory, wherein the emergence of network properties is attributed to the behavior of individual and, as such, provided a mechanistic explanation of connectance. The small size and relatively high connectance of most food webs compared with other real-world networks has pointed out the types of network topology which can be expected to display various kinds of degree distributions. Adding, removing, or altering species has the potential to rapidly affect many or most species in large complex communities indicative of highly interconnectedness among species within ecosystems. Understanding food web topology may be useful in exploring and predicting functional responses of ecosystems to such structural changes.
Food webs are central to our understanding of the structure, stability, and function of the ecosystem. They are described as networks of feeding links between species (Beckerman et al., 2006).
Food web models highlight the shifts in abundance that result from multiple trophic interactions as well as the energy and biomass transfer through a food web. This includes top-down and bottom-up processes (Matson and Hunter, 1992), trophic cascades (Brett and Goldman, 1996), and more complex interactions across multiple trophic levels (Polis and Strong, 1996). By revealing how consumer–resource interactions lead to trophic cascades, apparent competition, and diversity–stability relationships, they provide a unifying theme for ecology (Terborgh, 2001; Lafferty et al, 2006).
Recent models of food web structure have predicted key topological properties of food webs by relying on two empirical measures of complexity – connectance and species richness (Beckerman et al., 2006).
The Food Web Complexity
One of the key aspects of food web structure is complexity, or connectance. It is the fraction of all realized possible links and represents a standard measure of food web complexity thought to be independent of the trophic species (Martinez, 1992; Warren, 1994; Williams et al., 2002). Beckerman et al. (2006) defines it as the number of links expressed as a proportion of the total possible number of links.
Connectance is linked to the stability of webs and is actually a key parameter in recent models of other aspects of web structure (Beckerman et al., 2006). However, there is yet to have a fundamental biological explanation for this feature in food webs.
Historically, hypotheses on the complexity of food webs were linked to the stability of dynamic processes among species. The instability of highly connected food webs remained likely the dominant explanation for limits on complexity (de Ruiter et al., 2006; McCann, 2000; Pimm, 1982; Warren, 1994; Cohen and Newman, 1988; Williams and Martinez, 2004; Fox and McGrady-Steed, 2002).
Beckerman et al. (2006) proposed an alternative explanation that emerges from considering the species’ foraging in food webs determined by behavioral and morphological characteristics shaped over both ecological and evolutionary time. Links in a food web represent a map of the foraging decisions made by consumers in the specific context of that web. It follows that complexity, when expressed as the number of links in a food web, is a function of the foraging biology of the species in the web (Warren, 1990, 1994, 1996; Schoener, 1989).
They have shown that a simple diet breadth model (DBM) predicts highly constrained values of connectance as an emergent consequence of individual foraging behavior. When combined with features of real food web data, such as taxonomic and trophic aggregation and cumulative sampling of diets, the levels of connectance and scaling of connectance with species richness as seen in real food webs are well predicted. This result is a previously undescribed synthesis of foraging behavior and food web theory, in which network properties emerge from the behavior of individuals and, as such, provides a mechanistic explanation of connectance currently lacking in food web models (Beckerman et al., 2006).
The Network Theory: “Small-world” and “Scale-free”
Most networks from a wide range of physical, biological, and social systems are described as “small-world” and “scale-free” (Dunne et al., 2002). Studies however disagree that food webs possess the characteristic path lengths, clustering coefficients, and degree distributions exhibited by these networks.
Analyses conducted by Dunne et al. (2002) suggest that most food webs do not display a typical small-world topology (Camacho et al., 2002). The apparent deviation is related not only to connectance but to the size of food webs. Food webs generally have much higher complexity and much smaller size than other networks studied (Dunne et al., 2002).
Predicting Species Abundance in Response to Habitat Change
Gotelli and Ellison (2006) provided the first evidence on the significance of trophic structure in controlling abundances of multiple species in an aquatic food web. They demonstrated that models of trophic structure account for better results than do simpler models that focus only on responses of individual species to changes in habitat size or structure, or models that include both food web structure and habitat volume.
In order to test the mechanisms controlling the abundance of species when habitat is subjected to change (Ellison et al., 2003), a model system of the northern pitcher plant (Sarracenia purpurea) and its associated macroinvertebrate community was established. It was observed that an aquatic food web emerges once the leaves of this carnivorous plant are opened, which usually takes place every 17 days, and are filled with rain water. Starting with the captured arthropod prey, which is predominantly composed of the ants and flies, midge (Metriocnemus knabi) and larvae of sarcophagid fly (Fletcherimyia fletcheri) would shred and partially consume the arthropods, which served as the base of the food web. The shredded arthropods are then processed by bacteria and protozoa, which serve as prey to the filter-feeding rotifers (Habrotrocha rosi) and mites (Sarraceniopus gibsonii). These rotifers, as well as the bacteria and protozoa, would in turn be fed upon by the larvae of the pitcher plant mosquito (Wyeomyia smithii). Ultimately, the smaller larvae of pitcher plant mosquito (W. smithii) and the rotifers are preyed upon by the larger larvae of sarcophagid fly (F. fletcheri). Thus, the pitcher plant food web resembles other aquatic and terrestrial food webs for having the same complex linkages across multiple trophic levels (Gotelli and Ellison, 2006).
Realistic field manipulations of habitat volume (adding or removing water from the leaves) and simplification of the trophic structure of the pitcher plant food web (removal of top trophic levels of the entire aquatic communities) have also been employed. With these manipulations, major alterations in habitat size and community structure that have been studied previously in nonexperimental settings were effected (Terborgh et al., 2001; Gotelli and Ellison, 2006).
Using the model fit statistics, it was found out that the fit of the food web and keystone models did not improve with the incorporation of partial or complete links with habitat volume. The Wyeomyia keystone model turned out to be the single best-fitting model, and the best-fitting group of models was composed of the four food-web models with no volume linkage. These models did not reject the statistical hypothesis of a “close fit” with the variance-covariance structure of the data (Gotelli and Ellison, 2006).
The Wyeomyia keystone model accurately identified the role of mosquito larvae as predators of rotifers and as prey of Fletcherimyia (Bledzki and Ellison, 1998; Gotelli and Ellison, 2006). These path analyses are consistent with other studies suggesting that Wyeomyia is a keystone predator with both top-down and bottom-up effects in this food web (Addicott, 1974; Buckley et al., 2003; Gotelli and Ellison, 2006). The latent variable analysis of the food-web models suggested that strong trophic links were associated with bacteria, which support the view of the Sarracenia food web as a commensal processing chain in which shredders, detritivores, and filter feeders sequentially process and transform arthropod prey (Heard, 1994; Gotelli and Ellison, 2006).
Experimental assessment of the relative importance of autecological responses, keystone species effects, and trophic interactions in accounting for changes in species’ abundance were therefore achieved.
In conclusion, the scaling approach of the DBM, where species possess optimization rules (foraging biology) from which food web complexity emerges, has potential application to understanding emergent properties in all other types of networks.
The small size and relatively high connectance of most food webs compared with other real-world networks has illuminated which types of network topology can be expected to display various kinds of degree distributions. From a more applied, conservation-minded perspective, an understanding of food web topology can be used to explore and predict functional responses of ecosystems to structural changes.
The dynamics of species within ecosystems may be more highly interconnected and that biodiversity loss and species invasions may affect more species than previously thought. Adding, removing, or altering species has the potential to rapidly affect many or most species in large complex communities. Attention to trophic paths between species could help conservation managers by suggesting whether and how species affect each other.
LITERATURE CITED
Addicott JF. 1974. Predation and prey community structure: An experiment study of the effect of mosquito larvae on the protozoan communities of pitcher plants. Ecology 55: 475–492.
Beckerman, Andrew P., Owen L. Petchey, and Philip H. Warren. 2006. Foraging biology predicts food web complexity. Proc Natl Acad Sci 103(37): 13745–13749.
Bledzki LA and Ellison AM. 1998. Population growth and production of Habrotrocha rosa Donner (Rotifera: Bdelloidea) and its contribution to the nutrient supply of its host, the northern pitcher plant, Sarracenia purpurea L (Sarraceniaceae). Hydrobiologia 385: 193–200.
Brett MT and Goldman CR. 1996. A meta-analysis of the freshwater trophic cascade. Proc Natl Acad Sci USA 93: 7723–7726.
Buckley HL, Miller TE, Ellison AM, Gotelli NJ. 2003. Reverse latitudinal trends in species richness of pitcher-plant food webs. Ecol Lett 6: 825–829.
Camacho J, R Guimerà, and LAN Amaral. 2002. Phys. Rev. Lett. 88: 228102–228105.
Cohen JE and Newman CM. 1988. Ecology 69:1655–1664.
de Ruiter P, Moore JC, Wolters V. 2006. Dynamic Food Webs: Multispecies Assemblages, Ecosystem Development and Environmental Change (Elsevier, Amsterdam).
Dunne EA, RJ Williams, and ND Martinez. 2002. Food-web structure and network theory: the role of connectance and size. Proc Natl Acad Sci 99(20): 12917-12922.
Ellison AM, Gotelli NJ, Brewer JS, Cochran-Stafira DL, Kneitel J, et al. 2003. The evolutionary ecology of carnivorous plants. Adv Ecol Res 33: 1–74.
Fox JW and McGrady-Steed J. 2002. J Anim Ecol 71:749–756.
Gotelli, Nicholas J. and Aaron M. Ellison. 2006. Food-Web Models Predict Species Abundances in Response to Habitat Change. PLoS Biol 4(10): 1869-1873.
Heard SB. 1994. Pitcher plant midges and mosquitoes: A processing chain commensalism. Ecology 75: 1647–1660.
Lafferty, Kevin D., Andrew P. Dobson, and Armand M. Kuris. 2006. Parasites dominate food web links. Proc Natl Acad Sci 103(30): 11211–11216.
Martinez ND. 1992. Am. Nat. 139:1208-1218.
Matson PA and Hunter MD. 1992. The relative contributions of top-down and bottom-up forces in population and community ecology. Ecology 73: 723.
McCann KS. 2000. Nature 405:228–233.
Pimm SL. 1982. Food Webs. Chapman & Hall, London.
Polis GA and Strong DR. 1996. Food web complexity and community dynamics. Am Nat 147: 813–846.
Schoener TW. 1989. Ecology 70:1559–1589.
Terborgh J, Lopez L, Nunez P, Rao M, Shahabuddin G, et al. (2001) Ecological meltdown in predator-free forest fragments. Science 294: 1923–1926.
Warren PH. 1990. Am Nat 136:689–700.
Warren PH. 1994. Trends Ecol Evol 9:136–141.
Williams RJ and Martinez ND. 2004. Eur Phys J B 38:297–303.
Williams RJ, EL Berlow, JA Dunne, AL Barabási, and ND Martinez. 2002. Two degrees of separation in complex food webs. Proc Natl Acad Sci 99(20): 12913-12916.
Synthesis paper for my Bio160 lec class (1st Semester, SY 2007-2008)
Food webs are central to our understanding of the structure, stability, and function of the ecosystem. They are described as networks of feeding links between species (Beckerman et al., 2006).
Food web models highlight the shifts in abundance that result from multiple trophic interactions as well as the energy and biomass transfer through a food web. This includes top-down and bottom-up processes (Matson and Hunter, 1992), trophic cascades (Brett and Goldman, 1996), and more complex interactions across multiple trophic levels (Polis and Strong, 1996). By revealing how consumer–resource interactions lead to trophic cascades, apparent competition, and diversity–stability relationships, they provide a unifying theme for ecology (Terborgh, 2001; Lafferty et al, 2006).
Recent models of food web structure have predicted key topological properties of food webs by relying on two empirical measures of complexity – connectance and species richness (Beckerman et al., 2006).
The Food Web Complexity
One of the key aspects of food web structure is complexity, or connectance. It is the fraction of all realized possible links and represents a standard measure of food web complexity thought to be independent of the trophic species (Martinez, 1992; Warren, 1994; Williams et al., 2002). Beckerman et al. (2006) defines it as the number of links expressed as a proportion of the total possible number of links.
Connectance is linked to the stability of webs and is actually a key parameter in recent models of other aspects of web structure (Beckerman et al., 2006). However, there is yet to have a fundamental biological explanation for this feature in food webs.
Historically, hypotheses on the complexity of food webs were linked to the stability of dynamic processes among species. The instability of highly connected food webs remained likely the dominant explanation for limits on complexity (de Ruiter et al., 2006; McCann, 2000; Pimm, 1982; Warren, 1994; Cohen and Newman, 1988; Williams and Martinez, 2004; Fox and McGrady-Steed, 2002).
Beckerman et al. (2006) proposed an alternative explanation that emerges from considering the species’ foraging in food webs determined by behavioral and morphological characteristics shaped over both ecological and evolutionary time. Links in a food web represent a map of the foraging decisions made by consumers in the specific context of that web. It follows that complexity, when expressed as the number of links in a food web, is a function of the foraging biology of the species in the web (Warren, 1990, 1994, 1996; Schoener, 1989).
They have shown that a simple diet breadth model (DBM) predicts highly constrained values of connectance as an emergent consequence of individual foraging behavior. When combined with features of real food web data, such as taxonomic and trophic aggregation and cumulative sampling of diets, the levels of connectance and scaling of connectance with species richness as seen in real food webs are well predicted. This result is a previously undescribed synthesis of foraging behavior and food web theory, in which network properties emerge from the behavior of individuals and, as such, provides a mechanistic explanation of connectance currently lacking in food web models (Beckerman et al., 2006).
The Network Theory: “Small-world” and “Scale-free”
Most networks from a wide range of physical, biological, and social systems are described as “small-world” and “scale-free” (Dunne et al., 2002). Studies however disagree that food webs possess the characteristic path lengths, clustering coefficients, and degree distributions exhibited by these networks.
Analyses conducted by Dunne et al. (2002) suggest that most food webs do not display a typical small-world topology (Camacho et al., 2002). The apparent deviation is related not only to connectance but to the size of food webs. Food webs generally have much higher complexity and much smaller size than other networks studied (Dunne et al., 2002).
Predicting Species Abundance in Response to Habitat Change
Gotelli and Ellison (2006) provided the first evidence on the significance of trophic structure in controlling abundances of multiple species in an aquatic food web. They demonstrated that models of trophic structure account for better results than do simpler models that focus only on responses of individual species to changes in habitat size or structure, or models that include both food web structure and habitat volume.
In order to test the mechanisms controlling the abundance of species when habitat is subjected to change (Ellison et al., 2003), a model system of the northern pitcher plant (Sarracenia purpurea) and its associated macroinvertebrate community was established. It was observed that an aquatic food web emerges once the leaves of this carnivorous plant are opened, which usually takes place every 17 days, and are filled with rain water. Starting with the captured arthropod prey, which is predominantly composed of the ants and flies, midge (Metriocnemus knabi) and larvae of sarcophagid fly (Fletcherimyia fletcheri) would shred and partially consume the arthropods, which served as the base of the food web. The shredded arthropods are then processed by bacteria and protozoa, which serve as prey to the filter-feeding rotifers (Habrotrocha rosi) and mites (Sarraceniopus gibsonii). These rotifers, as well as the bacteria and protozoa, would in turn be fed upon by the larvae of the pitcher plant mosquito (Wyeomyia smithii). Ultimately, the smaller larvae of pitcher plant mosquito (W. smithii) and the rotifers are preyed upon by the larger larvae of sarcophagid fly (F. fletcheri). Thus, the pitcher plant food web resembles other aquatic and terrestrial food webs for having the same complex linkages across multiple trophic levels (Gotelli and Ellison, 2006).
Realistic field manipulations of habitat volume (adding or removing water from the leaves) and simplification of the trophic structure of the pitcher plant food web (removal of top trophic levels of the entire aquatic communities) have also been employed. With these manipulations, major alterations in habitat size and community structure that have been studied previously in nonexperimental settings were effected (Terborgh et al., 2001; Gotelli and Ellison, 2006).
Using the model fit statistics, it was found out that the fit of the food web and keystone models did not improve with the incorporation of partial or complete links with habitat volume. The Wyeomyia keystone model turned out to be the single best-fitting model, and the best-fitting group of models was composed of the four food-web models with no volume linkage. These models did not reject the statistical hypothesis of a “close fit” with the variance-covariance structure of the data (Gotelli and Ellison, 2006).
The Wyeomyia keystone model accurately identified the role of mosquito larvae as predators of rotifers and as prey of Fletcherimyia (Bledzki and Ellison, 1998; Gotelli and Ellison, 2006). These path analyses are consistent with other studies suggesting that Wyeomyia is a keystone predator with both top-down and bottom-up effects in this food web (Addicott, 1974; Buckley et al., 2003; Gotelli and Ellison, 2006). The latent variable analysis of the food-web models suggested that strong trophic links were associated with bacteria, which support the view of the Sarracenia food web as a commensal processing chain in which shredders, detritivores, and filter feeders sequentially process and transform arthropod prey (Heard, 1994; Gotelli and Ellison, 2006).
Experimental assessment of the relative importance of autecological responses, keystone species effects, and trophic interactions in accounting for changes in species’ abundance were therefore achieved.
In conclusion, the scaling approach of the DBM, where species possess optimization rules (foraging biology) from which food web complexity emerges, has potential application to understanding emergent properties in all other types of networks.
The small size and relatively high connectance of most food webs compared with other real-world networks has illuminated which types of network topology can be expected to display various kinds of degree distributions. From a more applied, conservation-minded perspective, an understanding of food web topology can be used to explore and predict functional responses of ecosystems to structural changes.
The dynamics of species within ecosystems may be more highly interconnected and that biodiversity loss and species invasions may affect more species than previously thought. Adding, removing, or altering species has the potential to rapidly affect many or most species in large complex communities. Attention to trophic paths between species could help conservation managers by suggesting whether and how species affect each other.
LITERATURE CITED
Addicott JF. 1974. Predation and prey community structure: An experiment study of the effect of mosquito larvae on the protozoan communities of pitcher plants. Ecology 55: 475–492.
Beckerman, Andrew P., Owen L. Petchey, and Philip H. Warren. 2006. Foraging biology predicts food web complexity. Proc Natl Acad Sci 103(37): 13745–13749.
Bledzki LA and Ellison AM. 1998. Population growth and production of Habrotrocha rosa Donner (Rotifera: Bdelloidea) and its contribution to the nutrient supply of its host, the northern pitcher plant, Sarracenia purpurea L (Sarraceniaceae). Hydrobiologia 385: 193–200.
Brett MT and Goldman CR. 1996. A meta-analysis of the freshwater trophic cascade. Proc Natl Acad Sci USA 93: 7723–7726.
Buckley HL, Miller TE, Ellison AM, Gotelli NJ. 2003. Reverse latitudinal trends in species richness of pitcher-plant food webs. Ecol Lett 6: 825–829.
Camacho J, R Guimerà, and LAN Amaral. 2002. Phys. Rev. Lett. 88: 228102–228105.
Cohen JE and Newman CM. 1988. Ecology 69:1655–1664.
de Ruiter P, Moore JC, Wolters V. 2006. Dynamic Food Webs: Multispecies Assemblages, Ecosystem Development and Environmental Change (Elsevier, Amsterdam).
Dunne EA, RJ Williams, and ND Martinez. 2002. Food-web structure and network theory: the role of connectance and size. Proc Natl Acad Sci 99(20): 12917-12922.
Ellison AM, Gotelli NJ, Brewer JS, Cochran-Stafira DL, Kneitel J, et al. 2003. The evolutionary ecology of carnivorous plants. Adv Ecol Res 33: 1–74.
Fox JW and McGrady-Steed J. 2002. J Anim Ecol 71:749–756.
Gotelli, Nicholas J. and Aaron M. Ellison. 2006. Food-Web Models Predict Species Abundances in Response to Habitat Change. PLoS Biol 4(10): 1869-1873.
Heard SB. 1994. Pitcher plant midges and mosquitoes: A processing chain commensalism. Ecology 75: 1647–1660.
Lafferty, Kevin D., Andrew P. Dobson, and Armand M. Kuris. 2006. Parasites dominate food web links. Proc Natl Acad Sci 103(30): 11211–11216.
Martinez ND. 1992. Am. Nat. 139:1208-1218.
Matson PA and Hunter MD. 1992. The relative contributions of top-down and bottom-up forces in population and community ecology. Ecology 73: 723.
McCann KS. 2000. Nature 405:228–233.
Pimm SL. 1982. Food Webs. Chapman & Hall, London.
Polis GA and Strong DR. 1996. Food web complexity and community dynamics. Am Nat 147: 813–846.
Schoener TW. 1989. Ecology 70:1559–1589.
Terborgh J, Lopez L, Nunez P, Rao M, Shahabuddin G, et al. (2001) Ecological meltdown in predator-free forest fragments. Science 294: 1923–1926.
Warren PH. 1990. Am Nat 136:689–700.
Warren PH. 1994. Trends Ecol Evol 9:136–141.
Williams RJ and Martinez ND. 2004. Eur Phys J B 38:297–303.
Williams RJ, EL Berlow, JA Dunne, AL Barabási, and ND Martinez. 2002. Two degrees of separation in complex food webs. Proc Natl Acad Sci 99(20): 12913-12916.
Synthesis paper for my Bio160 lec class (1st Semester, SY 2007-2008)
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