One of the most important ways species interact in an ecosystem? Food Webs. Learn how researchers study paleo and present-day food webs.
From the California Academy of Sciences.
That’s the title of our new paper, hot off the PNAS press. This study was a lot of fun, because it combines my food web work with one of the best known events in the fossil record. The lead author is Jonathan Mitchell, a graduate student at the University of Chicago. Jon became familiar with the food web work via Ken Angielczyk at the Field Museum, also in Chicago, a former post-doctoral researcher in my lab and close collaborator. Jon wondered what Late Cretaceous, dinosaur-bearing communities would look like when subjected to CEG perturbations (just search this blog for info. on CEG!), and presented his results two years ago at the Annual Meeting of the Geological Society of America. The results were so intriguing that we decided then to explore the question in much greater detail, and ask what sorts of community and ecosystem changes unfolded in the years before the Chicxulub impact, and what role they might have played in the subsequent extinctions. And here are the results! I will list the full reference below, and you can obtain a complete copy of the paper from PNAS (sorry, not open access). Also, here are links to some news websites that have covered the paper, as well as the paper’s abstract. Enjoy!
Jonathan S. Mitchell, Peter D. Roopnarine, and Kenneth D. Angielczyk. Late Cretaceous restructuring of terrestrial communities facilitated the End-Cretaceous mass extinction in North America. PNAS, October 29, 2012
The sudden environmental catastrophe in the wake of the end-
Cretaceous asteroid impact had drastic effects that rippled through
animal communities. To explore how these effects may have been
exacerbated by prior ecological changes, we used a food-web
model to simulate the effects of primary productivity disruptions,
such as those predicted to result from an asteroid impact, on ten
Campanian and seven Maastrichtian terrestrial localities in North
America. Our analysis documents that a shift in trophic structure
between Campanian and Maastrichtian communities in North
America led Maastrichtian communities to experience more second-
ary extinction at lower levels of primary production shutdown and
possess a lower collapse threshold than Campanian communities.
Of particular note is the fact that changes in dinosaur richness had
a negative impact on the robustness of Maastrichtian ecosystems
against environmental perturbations. Therefore, earlier ecological
restructuring may have exacerbated the impact and severity of the
end-Cretaceous extinction, at least in North America.
In a recent paper in the Royal Society Proceedings B, Randy Irmis and Jessica Whiteside verify a prediction of the CEG model regarding earliest Triassic terrestrial communities of the Karoo Basin in South Africa. Ken Angielczyk and I were interviewed by Wired Science for an article about the paper. Read it all here!
We predicted that communities in the Lystrosaurus Assemblage Zone would exhibit intrinsic instability in the face of even mild disruptions of primary productivity. More recently (and here), we explained that the intrinsic instability stemmed from the rapid diversification of small to medium-sized synapsid carnivores in the aftermath of the end-Permian mass extinction, coupled with very low species richness of herbivorous tetrapod prey, and the resulting intensity of competitive interactions among the carnivores. The recent Proceedings B paper seems to support our prediction on the basis of relative abundances of species of different trophic ecologies, characterizing those species as “boom and bust”. It’s always great to have model verification!
I think that there are some unresolved questions though:
Whether you can observe a thing or not depends on the theory which you use. (Einstein)
biodiversity, carrying capacity, cascades, competition, extinction, food webs, interaction strength, link distribution, link strength, modeling, networks, paleo-food web, paleontology, Robustness, Scientific models, simulations, Tipping point, trophic guild
My colleague Ken Angielczyk and I have a new paper out in the Royal Society‘s Biology Letters, entitled “The evolutionary palaeoecology of species and the tragedy of the commons“. If you have never read Garrett Hardin’s original paper on the tragedy of the commons, I strongly suggest that you do. It is a principle that I believe has broad application, and would well be worth a re-visit (first visit?!) by today’s leaders and economists. Our paper can be found here or here (first page only). And here is the abstract, as a little teaser!
The fossil record presents palaeoecological pat-
terns of rise and fall on multiple scales of time
and biological organization. Here, we argue that
the rise and fall of species can result from a tragedy
of the commons, wherein the pursuit of self-inter-
ests by individual agents in a larger interactive
system is detrimental to the overall performance
or condition of the system. Species evolving
within particular communities may conform to
this situation, affecting the ecological robustness
of their communities. Results from a trophic
network model of Permian–Triassic terrestrial
communities suggest that community perform-
ance on geological timescales may in turn
constrain the evolutionary opportunities and
histories of the species within them.
connectance, extinction, food webs, graph, link distribution, metanetwork, Network theory, networks, nonlinear, paleo-food web, power law, probability, real world networks, Robustness, simulations, trophic guild
Roopnarine, P. D. 2010. Networks, extinction and paleocommunity food webs in J. Alroy and G. Hunt, eds., Quantitative Methods in Paleobiology, The Paleontological Society Papers, 16: 143-161. (available here).
The paper is part of a volume, Quantitative Methods in Paleobiology, sponsored by The Paleontological Society. Full details are available here. The volume is also available for sale. Purchase one and support the Society!
A number of earlier posts have discussed food webs of the Permian-Triassic of the Karoo Basin in South Africa. This terrestrial ecosystem was subjected to the devastating end Permian mass extinction. The community which emerged in the aftermath of the extinction, the Lystrosaurus Assemblage Zone (LAZ), has been identified as having very unusual food web dynamics. This first figure compares the CEG dynamics of the end Permian Dicynodon Assemblage Zone (DAZ), LAZ, and the successive Cynognathus Assemblage Zone (CAZ). The implication is that there was a breakdown of perturbation dynamics during and/or right after the extinction episode. LAZ differs from the other communities (and in fact from every other community that we’ve studied so far!) in two ways:
So what causes all this?
The first question we asked ourselves was, is LAZ an unusually bad community or metanetwork, or are the other Karoo communities just exceptionally good? Our approach to addressing this was to generate 1,000 random metanetworks by randomly selecting observed guild richnesses from among our observed communities to fill the richness of a random community. A random community or metanetwork could therefore have guild richnesses that never occur together in any of the observed communities, but every guild richness of a random community is observed in at least one real Karoo community. We then simulated perturbation of 100 SLNs for each random community, and collected data on the first observation above, i.e., the variability of resistance at low levels of perturbation. As we see in the second figure, LAZ really stands out, even among the random communities! Why?
Well, in order to address that, we’ve used a number of regression models to examine the dependence of that variability on proportional guild richness. Proportional guild richness, in contrast to absolute, is the fraction of a community’s total consumer richness encompassed by a particular guild. Several guilds consistently stand out: very large amphibians, very small herbivorous amniotes, very small carnivorous/insectivorous amniotes, small carnivorous/insectivorous amniotes, carnivorous insects, and herbivorous insects. Multiple regression models demonstrate that the herbivorous guilds affect resistance variability negatively, i.e., they dampen the variability, while carnivorous guilds affect it positively! Now here’s the neat part. If we examine the sub-metanetworks of DAZ, LAZ and CAZ comprising these guilds only (see figure), we can immediately see how the communities differed with respect to these crucial guilds. Guilds with a dampening effect are shown in blue, those in red have the opposite effect (producer guilds are brown). And if you think of LAZ as being somehow imbalanced or out of whack, the figures should suggest to you some ways to “restore the balance”. I’ll discuss those in the next post.
The CEG model asserts that food web structure plays a role in extinction. The intricate patterns of relationships among species in a community distribute the effects of changes in one species to others in its community. Therefore, while the ultimate causes of increased extinction in an interval of time may be abiotic, and might affect only some species directly, the effects could be felt more broadly.
Topographic secondary extinction.–The narrow definition of secondary extinction, where a species becomes extinct because it has lost all its resources, is termed topological secondary extinction (Roopnarine, 2009). Topological refers to the dependence of extinction solely upon the topology (pattern) of the network. Note that topological secondary extinction affects the network only in a bottom-up fashion, that is, in the direction of energy flow from producers to consumers of increasing trophic level. Measuring or estimating topological secondary extinction in a food web, in response to a particular perturbation, can be approached in three ways, again depending on whether one assumes accuracy of a higher-level representation of the food web (e.g. a metanetwork) or precision of a species-level network. Here I will outline a probabilistic approach using metanetworks.
Let a perturbation of magnitude be equal to the number of species removed randomly from the network. The probability that a species will become secondarily extinct is the probability that all its links are to species that are a subset of the set. This is determined from a hypergeometric distribution, where we can first ask: Given an in-degree of , what is the probability that of them will be lost?
where there are S-1 other species in the network and . The probability of becoming extinct occurs when ,
The formula can be re-stated interestingly as
where trophic breadth is the number of species consumed by species , out of a pool of potential prey (species richness). For example, in a network of 10 species, and , and the probability of extinction increases as , and decreases as (see figure). Species with more resources are thus more resistant to topological secondary extinction. This is the same as saying that the more trophically generalized a species, the greater its resistance to extinction.
This explains in part the suggestion that food webs of greater connectance (C) are more resistant to topological secondary extinction (Dunne et al., 2002). Overall food web or network resistance to this type of extinction has been termed structural robustness (Dunne and Williams, 2009). Food web connectance does not increase, however, because of uniform increases in the in-degrees of all species in the network, but increases instead because of the presence of highly linked species. The skewed, long-tailed in-link distributions discussed earlier indicate the non-uniformity of in-degrees within real food webs. The above formula for extinction shows that , that is, is of greater in-degree than . This will also be the case if the species on which preys are more resistant to extinction, even if . The presence of generalist consumers therefore enhances robustness both because of their own greater resistance, and the resistance which they confer upon their consumers.
The final step in food web construction is the generation of species-level networks (SLNs). A SLN is considered here a single potential pattern of community interactions in any given place at an instant of time, and may be constructed in two distinctly different ways. First, using empirical observations, one could construct the SLN of a community. This is typically the fashion in which SLNs are reconstructed for modern communities; workers observe and record the community’s trophic interactions. SLNs of this type are precise and without error, though usually taxonomically incomplete, and we cannot have similar confidence in their accuracies because of the sources of uncertainty described above. Capturing their variability requires repeated observations, which is possible under some circumstances. For example, there has been documentation of seasonal variation in food webs. Repeat observations are impossible for paleo-food webs. The best that can be done is to measure spatial or temporal variation in taxonomic composition. The latter of course could describe variability on only the longest of ecological timescales. Dunne et al. (2008) (see previous post) compiled SLNs of two Cambrian food webs derived from the Burgess Shale and Chenjiang lagerstatten, comprising 142 and 85 taxa respectively. The taxa in both these networks were subsequently aggregated into trophic species, 48 and 33 respectively, on the basis that species within the trophic species have identical consumers and resources. As argued above, it is impossible to validate this claim for fossil taxa. Trophic species-level links were ranked according to uncertainty in these networks, but there was no explicit attention paid to uncertainty at the level of species within the trophic species.
The CEG model takes an alternative approach to SLN reconstruction, generating multiple plausible SLNs from the metanetwork and hypothetical or underlying principles of food web networks as gleaned from modern food webs. This type of SLN generation requires a trophic in-link distribution for each guild. Recall that a trophic in-link distribution describes the number of prey per species within a guild. This number ranges from 1 (a heterotrophic species must prey upon at least 1 other species) up to the total species-richness of all guilds that are specified as prey of the guild in the metanetwork. SLN-generation requires initially that species within a guild be treated neutrally, that is, they have no distinguishing trophic properties. Stochastic draws from specific guild trophic link distributions then determine the number of prey to be assigned to each species. The prey species themselves are drawn randomly from the pool of prey guilds of the predatory species. The result is a directed graph or network in which each species in the community has been assigned prey, and many therefore also have predators (see figure). SLNs capture the uncertainty associated with the reconstruction of fossil food webs, and in fact any food web, in a manner in which static or unvarying trophic link determinations cannot. Repeated stochastic generation of SLNs accounts for the sources of uncertainty discussed earlier, namely uncertainty of the particular interactions of a species, and the temporal and spatial variability of a community type. Also, even though any two SLNs derived from any moderately complex metanetwork are unlikely to share the same exact topology (isomorphic), they are drawn from the same ensemble, as discussed earlier for Erdӧs-Renyi random graphs. Whether the argument can then be extended to claim that they will also have the same behavior on average, as with random graphs, is an interesting question, because the ensemble is the range of variation possible for a paleocommunity’s food web based on paleontological uncertainty. The next few posts will therefore deal with a description of the ensemble, and the ecological dynamics of the SLNs in an ensemble.
A key assertion of the CEG model is that a paleocommunity’s trophic network can never be specified by a single topology (Roopnarine, 2006; Roopnarine, 2009). There is uncertainty associated with the biotic interactions of a fossil species because no one was there to observe them. Preserved evidence of interactions such as bite marks, gut contents or leaf damage record a subset of the possible range of interactions. Moreover, the topology specified for a single community is expected to vary spatially and temporally. The strength and direction of interspecific interactions of extant species are known to vary according to physical conditions, the presence or absence of other species in the community, relative population sizes, and the incumbency of species when addition to the community is asynchronous. These uncertainties must be incorporated into any realistically complex model of a community food web. In the CEG model, species are therefore grouped into trophic guilds based on the most accurate trophic interpretations available, into trophic guilds. Trophic guilds are defined as the trophic habits and habitats of member species, for example, the “very small carnivorous amniotes” of a Late Permian terrestrial community.
The resulting guild structure represents a species aggregation scheme. The most common aggregation scheme is to assemble species into groups called “trophic species”. Trophic species group species that are assumed to have the same prey and predators. The motivation for this grouping is unclear in cases where link data are available at the species level. One advantage, however, may be to avoid biases introduced by an undersampling of poorly resolved links. Patterns of connection among trophic species may also illuminate patterns of energy and nutrient flow among major species ecotypes in the community. There is no guarantee, though, and in fact no expectation for the preservation of network topology in the conversion of species-level data to a trophic species network. It is always preferable to use species-level data to represent true community complexity. Furthermore, aggregation into trophic species is an inference the strength of which cannot be justified for fossil taxa, and the scheme should be avoided in paleo-food webs. Given that species-level data are rarely available for fossil species, however, and are basically never complete, aggregation is necessary. Dunne et al. (2008) therefore converted species-level data to trophic species in their study of Cambrian food webs. The CEG model aggregates species into trophic guilds, groups of species that cannot be distinguished trophically on the basis of available data. An example would be “epifaunal, seagrass-dwelling suspension feeding bivalves”. Those species, in a particular community, potentially share the same predators and prey. Trophic guilds are similar but not equivalent to trophic species, yet it is clear that if a trophic species is an accurate representation of the species which it comprises, then the composition of a similar trophic guild will approach the composition of the trophic species as the species data become more precise. A network of trophic guilds is termed a metanetwork, and is an hierarchically higher level representation of a species-level network. Two trophic guilds linked in a metanetwork contain species that are potentially trophic interactors. A metanetwork therefore summarizes the most accurate and precise data available for a paleocommunity’s food web.
The contrast between the two aggregation schemes is reduced to one of accuracy and precision. The trophic species scheme assumes a high level of precision, thereby justifying an assumption of trophic neutrality among species within the trophic species. This level of precision is unlikely to be available for fossil taxa, and in any case can never be tested. The metanetwork and trophic guild scheme assumes that the understanding of a species trophic habit is accurate, even though its precise interspecific interactions may be unknown or known incompletely. These uncertainties, stemming from incomplete data and temporal-spatial variance of the data, will be addressed in a future post by exploring the range of species-level food webs implied by the metanetwork.