Forecasting community reassembly using climate‐linked spatio‐temporal ecosystem models

Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community...

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Veröffentlicht in:Ecography (Copenhagen) 2021-04, Vol.44 (4), p.612-625
Hauptverfasser: Thorson, James T., Arimitsu, Mayumi L., Barnett, Lewis A. K., Cheng, Wei, Eisner, Lisa B., Haynie, Alan C., Hermann, Albert J., Holsman, Kirstin, Kimmel, David G., Lomas, Michael W., Richar, Jon, Siddon, Elizabeth C.
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container_issue 4
container_start_page 612
container_title Ecography (Copenhagen)
container_volume 44
creator Thorson, James T.
Arimitsu, Mayumi L.
Barnett, Lewis A. K.
Cheng, Wei
Eisner, Lisa B.
Haynie, Alan C.
Hermann, Albert J.
Holsman, Kirstin
Kimmel, David G.
Lomas, Michael W.
Richar, Jon
Siddon, Elizabeth C.
description Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio‐temporal ecosystem model that approximates coupled physical, biological and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate‐linked forecasts), primary producers (spring and fall size‐fractionated chlorophyll‐a), secondary producers (copepods), juveniles (age‐0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort) and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom–up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate ‘ecological teleconnections' (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.
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We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. 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To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio‐temporal ecosystem model that approximates coupled physical, biological and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate‐linked forecasts), primary producers (spring and fall size‐fractionated chlorophyll‐a), secondary producers (copepods), juveniles (age‐0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort) and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom–up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. 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subjects Age
Animal behavior
Aquatic birds
Chlorophyll
Climate models
Copepoda
Ecosystem models
Ecosystems
Empirical analysis
empirical orthogonal function analysis
Environment models
EOF
Fisheries
Fishing
Geographical distribution
Human influences
joint dynamic species distribution model
Juveniles
Microprocessors
Nutrients
Orthogonal functions
Physical characteristics
Physical properties
Pollack
Predators
Prey
spatial community reassembly
Trophic levels
vector autoregressive spatio-temporal model
Weather forecasting
title Forecasting community reassembly using climate‐linked spatio‐temporal ecosystem models
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