pone.0246222.s009.pdf (113.86 kB)

Species interaction inference model intercomparison.

journal contribution

posted on 2021-10-20, 17:25 authored by Jie Li, Matteo Convertino

A: causal interaction inference CCM model developed by [63]. B: linear association between species computed as Pearson correlation coefficient. TE-based causal interaction inference using Kernel and Gaussian estimators (C and D, respectively) in JIDT developed by [62].

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Temperature increase drives critical slowing down of fish ecosystems

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work provides data order phase transition interaction patterns derived horse mackerel </dynamic dominant eigenvalue decreasing dominant eigenvalues created pressing challenges becoming exceedingly abundant atemporal seasonal sensitivity assess ecosystem susceptibility enhanced community variability fish ecosystem experiences global food security global ecosystem health despite higher time 20 ° c coastal fish community threaten species existence species interaction organization commercially valuable species fish stock despite monitoring fish ecosystems season critical transitions high temperatures states div >< p distinct temperature ranges cooperative decaying species salient fish biodiversity based species interactions community abundance due fish community fish ecosystems cooperative species global warming summer due spectrum transitions salient links local 20 higher eco critical slowing carbon stock biodiversity dynamics venomous species species dynamics keystone species invasive species via cooperative marine ecosystems topologically different term trends term reduction taxonomic richness point resilience observations show maizuru bay lower cooperation low temperatures locally stable large decay japanese anchovy important role globally unstable free compared ecological productivity driven tools critically important climatic memory 21st century 2014 underpins

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