Data to replicate: 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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Main Authors: Thorson, James, Arimitsu, Mayumi, Barnett, Lewis, Cheng, Wei, Eisner, Lisa, Haynie, Alan, Hermann, Albert, Holsman, Kirstin, Kimmel, David, Lomas, Mike, Richar, Jon, Siddon, Elizabeth
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2021
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Online Access:https://doi.org/10.5061/dryad.b2rbnzsdc
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author Thorson, James
Arimitsu, Mayumi
Barnett, Lewis
Cheng, Wei
Eisner, Lisa
Haynie, Alan
Hermann, Albert
Holsman, Kirstin
Kimmel, David
Lomas, Mike
Richar, Jon
Siddon, Elizabeth
author_facet Thorson, James
Arimitsu, Mayumi
Barnett, Lewis
Cheng, Wei
Eisner, Lisa
Haynie, Alan
Hermann, Albert
Holsman, Kirstin
Kimmel, David
Lomas, Mike
Richar, Jon
Siddon, Elizabeth
author_sort Thorson, James
collection Zenodo
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 ...
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spelling ftzenodo:oai:zenodo.org:4444003 2025-01-16T21:17:57+00:00 Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models Thorson, James Arimitsu, Mayumi Barnett, Lewis Cheng, Wei Eisner, Lisa Haynie, Alan Hermann, Albert Holsman, Kirstin Kimmel, David Lomas, Mike Richar, Jon Siddon, Elizabeth 2021-01-15 https://doi.org/10.5061/dryad.b2rbnzsdc unknown Zenodo https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.b2rbnzsdc oai:zenodo.org:4444003 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode spatial community reassembly joint dynamic species distribution model empirical orthogonal function analysis vector autoregressive spatio-temporal model info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5061/dryad.b2rbnzsdc 2024-12-06T12:43:19Z 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 ... Other/Unknown Material Bering Sea Copepods Zenodo Bering Sea
spellingShingle spatial community reassembly
joint dynamic species distribution model
empirical orthogonal function analysis
vector autoregressive spatio-temporal model
Thorson, James
Arimitsu, Mayumi
Barnett, Lewis
Cheng, Wei
Eisner, Lisa
Haynie, Alan
Hermann, Albert
Holsman, Kirstin
Kimmel, David
Lomas, Mike
Richar, Jon
Siddon, Elizabeth
Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title_full Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title_fullStr Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title_full_unstemmed Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title_short Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
title_sort data to replicate: forecasting community reassembly using climate-linked spatio-temporal ecosystem models
topic spatial community reassembly
joint dynamic species distribution model
empirical orthogonal function analysis
vector autoregressive spatio-temporal model
topic_facet spatial community reassembly
joint dynamic species distribution model
empirical orthogonal function analysis
vector autoregressive spatio-temporal model
url https://doi.org/10.5061/dryad.b2rbnzsdc