| 1. |
- Albouy, Camille, et al.
(författare)
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The marine fish food web is globally connected
- 2019
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Ingår i: Nature Ecology & Evolution. - : NATURE PUBLISHING GROUP. - 2397-334X. ; 3:8, s. 1153-
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Tidskriftsartikel (refereegranskat)abstract
- The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish-that is, all possible potential feeding links between co-occurring species-is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.
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| 2. |
- Baiser, Benjamin, et al.
(författare)
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Ecogeographical rules and the macroecology of food webs
- 2019
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Ingår i: Global Ecology and Biogeography. - : Wiley-Blackwell Publishing Inc.. - 1466-822X .- 1466-8238. ; 28:9, s. 1204-1218
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Tidskriftsartikel (refereegranskat)abstract
- AimHow do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well‐studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann's rule, the island rule and Rapoport's rule] are associated with the architecture of food webs.LocationGlobal.Time periodCurrent.Major taxa studiedAll taxa.MethodsWe discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda.ResultsWe derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport's rule), while for others (e.g., Bergmann's and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport's rule, we found support for the prediction that species turnover in food webs is inversely related to latitude.Main conclusionsThe macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.
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| 3. |
- Cirtwill, Alyssa, et al.
(författare)
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A quantitative framework for investigating the reliability of empirical network construction
- 2019
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Ingår i: Methods in Ecology and Evolution. - : WILEY. - 2041-210X. ; 10:6, s. 902-911
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Tidskriftsartikel (refereegranskat)abstract
- Descriptions of ecological networks typically assume that the same interspecific interactions occur each time a community is observed. This contrasts with the known stochasticity of ecological communities: community composition, species abundances and link structure all vary in space and time. Moreover, finite sampling generates variation in the set of interactions actually observed. For interactions that have not been observed, most datasets will not contain enough information for the ecologist to be confident that unobserved interactions truly did not occur. Here, we develop the conceptual and analytical tools needed to capture uncertainty in the estimation of pairwise interactions. To define the problem, we identify the different contributions to the uncertainty of an interaction. We then outline a framework to quantify the uncertainty around each interaction by combining data on observed co-occurrences with prior knowledge. We illustrate this framework using perhaps the most extensively sampled network to date. We found significant uncertainty in estimates for the probability of most pairwise interactions. This uncertainty can, however, be constrained with informative priors. This uncertainty scaled up to summary measures of network structure such as connectance and nestedness. Even with informative priors, we are likely to miss many interactions that may occur rarely or under different local conditions. Overall, we demonstrate the importance of acknowledging the uncertainty inherent in network studies, and the utility of treating interactions as probabilities in pinpointing areas where more study is needed. Most importantly, we stress that networks are best thought of as systems constructed from random variables, the stochastic nature of which must be acknowledged for an accurate representation. Doing so will fundamentally change network analyses and yield greater realism.
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