Aggregation of incomplete food web data may help to suggest sampling strategies

Patonai, Katalin; Jordán, Ferenc

doi:10.1016/j.ecolmodel.2017.02.024

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Our systemic view provides a quantitatively holistic view on the structure of ecosystems. Earlier studies have shown that REGE can result in taxonomically both homogeneous and heterogeneous groups [14]: Heterogeneous groups can reveal ecological functional similarity not reflected in taxonomic closeness. We are suggesting that comparing biological and mathematical definitions of similarity and the consequent aggregation methods can provide standards and, at the same time, consider biological knowledge for better understanding of ecological functionality.…”

Section: Discussionmentioning

confidence: 96%

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Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

et al. 2018

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Species are embedded in a web of intricate trophic interactions. Understanding the functional role of species in food webs is of fundamental interests. This is related to food web position, so positional similarity may provide information about functional overlap. Defining and quantifying similar trophic functioning can be addressed in different ways. We consider two approaches. One is of mathematical nature involving network analysis where unique species can be defined as those whose topological position is very different to others in the same food web. A species is unique if it has very different connection pattern compared to others. The second approach is of biological nature, based on trait-based aggregations. Unique species are not easy to aggregate with others because their traits are not in common with the ones of most others. Our goal here is to illustrate how mathematics can provide an alternative perspective on species aggregation, and how this is related to its biological counterpart. We illustrate these approaches using a toy food web and a real food web and demonstrate the sensitive relationships between those approaches. The trait-based aggregation focusing on the trait values of size (sv) can be best predicted by the mathematical aggregation algorithms.

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Section: Discussionmentioning

confidence: 96%

“…Research on aggregation had already been focused on its effects on network properties from an empirical perspective [1,8,9] and from a modelling perspective [10][11][12][13][14]. According to the result of these studies, aggregation can have a huge impact on food web properties, thus it would be of great value to standardize methodology.…”

Section: Introductionmentioning

confidence: 99%

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

et al. 2018

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“…When only partial food web data are available (Patonai & Jord'an, 2017), the summary statistics in ABC can be used to infer these food web structures from the ADBM. It would be possible to use gut content data of only some of the species in a food web to parameterize the ADBM and predict the food web structure.…”

Section: Incorporating Other Observed Datamentioning

confidence: 99%

Simultaneously estimating food web connectance and structure with uncertainty

Gupta

Furrer

Petchey

2022

Ecology and Evolution

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Food web models explain and predict the trophic interactions in a food web, and they can infer missing interactions among the organisms. The allometric diet breadth model (ADBM) is a food web model based on the foraging theory. In the ADBM, the foraging parameters are allometrically scaled to body sizes of predators and prey. In Petchey et al. (Proceedings of the National Academy of Sciences, 2008; 105: 4191), the parameterization of the ADBM had two limitations: (a) the model parameters were point estimates and (b) food web connectance was not estimated. The novelty of our current approach is: (a) We consider multiple predictions from the ADBM by parameterizing it with approximate Bayesian computation, to estimate parameter distributions and not point estimates. (b) Connectance emerges from the parameterization, by measuring model fit using the true skill statistic, which takes into account prediction of both the presences and absences of links. We fit the ADBM using approximate Bayesian computation to 12 observed food webs from a wide variety of ecosystems. Estimated connectance was consistently greater than previously found. In some of the food webs, considerable variation in estimated parameter distributions occurred and resulted in considerable variation (i.e., uncertainty) in predicted food web structure. These results lend weight to the possibility that the observed food web data is missing some trophic links that do actually occur. It also seems likely that the ADBM likely predicts some links that do not exist. The latter could be addressed by accounting in the ADBM for additional traits other than body size. Further work could also address the significance of uncertainty in parameter estimates for predicted food web responses to environmental change.

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“…An initial step here is establishing the known nodes and their connections [20] which was done using a literature search (Web Of Science), a book on Italian fish [21], and field monitoring survey data. Our aims were to i) describe each section of the river by mapping the connectance webs; ii) aggregate the raw (heterogenous) networks into more compact models, based on taxonomic aggregation following previously described methods [9,22]; iii) describe the river sections using network analysis; and iv) compare the Po River food web with the Danube River food web, the second largest river in Europe [9]. In food webs, it is also possible to aggregate nodes based on structural similarity, which create"trophic guilds" [23][24][25], as an aggregation from an ecological perspective.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of the Po River food web and its comparison with the Danube River food web

et al. 2023

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Freshwater ecosystems are experiencing unprecedented pressure globally. To address environmental challenges, systematic and comparative studies on ecosystems are needed, though mostly lacking, especially for rivers. Here, we describe the food web of the Po River (as integrated from the white literature and monitoring data), describe the three river sections using network analysis, and compare our results with the previously compiled Danube River food web. The Po River food web was taxonomically aggregated in five consecutive steps (T1-T5) and it was also analyzed using the regular equivalence (REGE) algorithm to identify structurally similar nodes in the most aggregated T5 model. In total, the two river food webs shared 30 nodes. Two network metrics (normalized degree centrality [nDC]) and normalized betweenness centrality [nBC]) were compared using Mann-Whitney tests in the two rivers. On average, the Po River nodes have larger nDC values than in the Danube, meaning that neighboring connections are better mapped. Regarding nBC, there were no significant differences between the two rivers. Finally, based on both centrality indices, Carassius auratus is the most important node in the Po River food web, whereas phytoplankton and detritus are most important in the Danube River. Using network analysis and comparative methods, it is possible to draw attention to important trophic groups and knowledge gaps, which can guide future research. These simple models for the Po River food web can pave the way for more advanced models, supporting quantitative and predictive—as well as more functional—descriptions of ecosystems.

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Aggregation of incomplete food web data may help to suggest sampling strategies

Cited by 9 publications

References 26 publications

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

Simultaneously estimating food web connectance and structure with uncertainty

Spatial variability of the Po River food web and its comparison with the Danube River food web

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