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

Autor: Thorson JT; Habitat and Ecological Processes Research Program, Alaska Fisheries Science Center, NMFS, NOAA, Seattle, WA, USA., Cheng W; Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA, USA.; Pacific Marine Environmental Laboratory, NOAA, Seattle, WA, USA., Hermann AJ; Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA, USA.; Pacific Marine Environmental Laboratory, NOAA, Seattle, WA, USA., Ianelli JN; Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, NMFS, NOAA, Seattle, WA, USA., Litzow MA; College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Kodiak, AK, USA., O'Leary CA; School of Aquatic and Fisheries Sciences, University of Washington, Seattle, WA, USA., Thompson GG; Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, NMFS, NOAA, Seattle, WA, USA.
Jazyk: angličtina
Zdroj: Global change biology [Glob Chang Biol] 2020 Aug; Vol. 26 (8), pp. 4638-4649. Date of Electronic Publication: 2020 May 28.
DOI: 10.1111/gcb.15149
Abstrakt: 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.
(© 2020 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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