Food web visualisation: Heat map, interactive graph and animated flow network

Pawluczuk, None Łukasz; Iskrzyński, Mateusz

doi:10.1111/2041-210x.13839

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…We insist on the fact that network representation goes beyond visualisation (e.g. Pawluczuk & Iskrzyński, 2022 ) because it also deals with network layout problem. In addition, our package allows dealing with different scales of the metanetwork.…”

Section: Discussionmentioning

confidence: 99%

“…Although network visualisation tools are now widely available (e.g. Bastian et al, 2009 ; Csardi & Nepusz, 2006 ; Pawluczuk & Iskrzyński, 2022 ; Perrone et al, 2020 ), current network layout methods highlighting hierarchical structure of trophic networks remain scarce (but see Hudson et al, 2013 ). They mainly rely on force‐directed algorithms, as Fruchterman and Reingold ( 1991 ) that is based on vertex repulsion or Kamada and Kawai ( 1989 ) and Gansner et al ( 2004 ) that consists in spring embedding.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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metanetwork: A R package dedicated to handling and representing trophic metanetworks

Ohlmann,

Garnier,

Vuillon

2023

Ecology and Evolution

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Trophic networks describe interactions between species at a given location and time. Due to environmental changes, anthropogenic perturbations or sampling effects, trophic networks may vary in space and time. The collection of network time series or networks in different sites thus constitutes a metanetwork. We present here the R package metanetwork, which will ease the representation, the exploration and analysis of trophic metanetwork data sets that are increasingly available. Our main methodological advance consists in suitable layout algorithm for trophic networks, which is based on trophic levels and dimension reduction in a graph diffusion kernel. In particular, it highlights relevant features of trophic networks (trophic levels, energetic channels). In addition, we developed tools to handle, compare visually and quantitatively and aggregate those networks. Static and dynamic visualisation functions have been developed to represent large networks. We apply our package workflow to several trophic network data sets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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metanetwork: A R package dedicated to handling and representing trophic metanetworks

Ohlmann,

Garnier,

Vuillon

2023

Ecology and Evolution

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“…The histogram here represents the pool used for the simulations in Figure 3, and the patch networks are representatives from a single time step of the no dispersal simulation. Network visualisation uses foodwebviz (Pawluczuk & Iskrzynski, 2022).…”

Section: Methodsmentioning

confidence: 99%

Increased dispersal explains increasing local diversity with global biodiversity declines

Fagan,

Pitchford,

Stepney

et al. 2023

Global Change Biology

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The narrative of biodiversity decline in response to human impacts is overly simplistic because different aspects of biodiversity show different trajectories at different spatial scales. It is also debated whether human‐caused biodiversity changes lead to subsequent, accelerating change (cascades) in ecological communities, or alternatively build increasingly robust community networks with decreasing extinction rates and reduced invasibility. Mechanistic approaches are needed that simultaneously reconcile different aspects of biodiversity change, and explore the robustness of communities to further change. We develop a trophically structured, mainland‐archipelago metacommunity model of community assembly. Varying the parameters across model simulations shows that local alpha diversity (the number of species per island) and regional gamma diversity (the total number of species in the archipelago) depend on both the rate of extirpation per island and on the rate of dispersal between islands within the archipelago. In particular, local diversity increases with increased dispersal and heterogeneity between islands, but regional diversity declines because the islands become biotically similar and local one‐island and few‐island species are excluded (homogenisation, or reduced beta diversity). This mirrors changes observed empirically: real islands have gained species (increased local and island‐scale community diversity) with increased human‐assisted transfers of species, but global diversity has declined with the loss of endemic species. However, biological invasions may be self‐limiting. High‐dispersal, high local‐diversity model communities become resistant to subsequent invasions, generating robust species‐community networks unless dispersal is extremely high. A mixed‐up world is likely to lose many species, but the resulting ecological communities may nonetheless be relatively robust.

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“…For example, food web visualisation should allow us to gain an understanding of the structure of foodwebs and provide insight into the detail of the complexity; however, current approaches tend to simplify the structure and therefore cannot provide the insight needed. To address some of these challenges, Pawluczuk and Iskrzyński (2023) propose methods for visualising increasingly complex foodweb (and other network) structures by combining heatmaps, interactive and animated graphs. Alternatively, Van Moorter et al (2023) have developed the package ConScape (in Julia) which allows users to efficiently analyse and visualise landscape and habitat connectivity more simply.…”

Section: Broad Themesmentioning

confidence: 99%