Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank‐reduction techniques (a.k.a. empiri...

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Veröffentlicht in:Global change biology 2020-08, Vol.26 (8), p.4638-4649
Hauptverfasser: Thorson, James T., Cheng, Wei, Hermann, Albert J., Ianelli, James N., Litzow, Michael A., O'Leary, Cecilia A., Thompson, Grant G.
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container_end_page 4649
container_issue 8
container_start_page 4638
container_title Global change biology
container_volume 26
creator Thorson, James T.
Cheng, Wei
Hermann, Albert J.
Ianelli, James N.
Litzow, Michael A.
O'Leary, Cecilia A.
Thompson, Grant G.
description Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank‐reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub‐fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population‐scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum‐likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early‐life survival (“recruitment”) of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end‐of‐century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades. We develop a new statistical technique that simultaneously fits to observed and projected environmental conditions as well as vital rates for fish and wildlife populations. The method identifies typical patterns of environmental variability, as well as environmental conditions that are associated with higher‐than‐aver
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Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades. We develop a new statistical technique that simultaneously fits to observed and projected environmental conditions as well as vital rates for fish and wildlife populations. The method identifies typical patterns of environmental variability, as well as environmental conditions that are associated with higher‐than‐average vital rates. We then demonstrate the method using projected climate change in the northern Pacific Ocean. 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However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank‐reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub‐fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population‐scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum‐likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early‐life survival (“recruitment”) of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end‐of‐century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades. We develop a new statistical technique that simultaneously fits to observed and projected environmental conditions as well as vital rates for fish and wildlife populations. The method identifies typical patterns of environmental variability, as well as environmental conditions that are associated with higher‐than‐average vital rates. We then demonstrate the method using projected climate change in the northern Pacific Ocean. 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Fisheries Abstracts (ASFA) 3: Aquatic Pollution &amp; Environmental Quality</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Global change biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thorson, James T.</au><au>Cheng, Wei</au><au>Hermann, Albert J.</au><au>Ianelli, James N.</au><au>Litzow, Michael A.</au><au>O'Leary, Cecilia A.</au><au>Thompson, Grant G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections</atitle><jtitle>Global change biology</jtitle><addtitle>Glob Chang Biol</addtitle><date>2020-08</date><risdate>2020</risdate><volume>26</volume><issue>8</issue><spage>4638</spage><epage>4649</epage><pages>4638-4649</pages><issn>1354-1013</issn><eissn>1365-2486</eissn><abstract>Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. 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EOF regression is fitted using maximum‐likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early‐life survival (“recruitment”) of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end‐of‐century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades. We develop a new statistical technique that simultaneously fits to observed and projected environmental conditions as well as vital rates for fish and wildlife populations. The method identifies typical patterns of environmental variability, as well as environmental conditions that are associated with higher‐than‐average vital rates. We then demonstrate the method using projected climate change in the northern Pacific Ocean. 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subjects Annual variations
Biological activity
Biology
Communication
delta‐correction method
Dynamic models
Dynamics
Ecologists
Ecosystem management
Empirical analysis
empirical orthogonal function (EOF)
end‐of‐century projection
Environmental conditions
Freshwater fishes
Gadus chalcogrammus
Gadus macrocephalus
Indicators
Marine ecosystems
Marine fishes
Oceanographers
Orthogonal functions
Physical oceanography
Pollack
Population
Population (statistical)
Population biology
Population dynamics
Recruitment
Recruitment (fisheries)
Regional Ocean Modeling System (ROMS)
Regression analysis
Ricker model
Sea surface
Sea surface temperature
Statistical analysis
stock–recruit analysis
Surface temperature
Survival
Time series
Weather forecasting
title Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections
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