Synthesis of interannual variability in spatial demographic processes supports the strong influence of cold-pool extent on eastern Bering Sea walleye pollock (Gadus chalcogrammus)
Attributing variability in fish demographic processes to environmental conditions is helpful when assessing population status and forecasting changes in ecosystem function. Empirical orthogonal function (EOF) analysis has long been used to explore variability in physical processes, but has been only...
| Published in: | Progress in Oceanography |
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| Main Authors: | , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Elsevier Ltd
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.pocean.2021.102569 http://ecite.utas.edu.au/152640 |
| Summary: | Attributing variability in fish demographic processes to environmental conditions is helpful when assessing population status and forecasting changes in ecosystem function. Empirical orthogonal function (EOF) analysis has long been used to explore variability in physical processes, but has been only recently employed to study variability in biological processes. EOF analysis estimates dominant modes of variability (indices) and produces maps representing the spatial response for the dependent variable to each of these indices. In the eastern Bering Sea (EBS), research has linked demographic processes to the spatial extent of bottom temperatures less than or equal to 2 degrees C (the "cold-pool extent" or "CPE"), but has generally not compared effects among different demographic processes. We applied EOF analysis to four types of data measuring the outcome of demographic processes for EBS walleye pollock (Gadus chalcogrammus) over the period 1982-2019: numerical density (outcome of movement), morphometric condition (outcome of bioenergetics), length-at-age (outcome of growth), and prey-biomass-per-predator-mass (a proxy for stomach contents; outcome of consumption). We first designed exploratory factor analysis (EFA) models that did not include a CPE effect. We then applied confirmatory factor analysis (CFA), which differed from EFA by attributing observed patterns to a spatially varying response of demographic processes to CPE. We inferred that CPE was a proxy for demographic variability when there was a strong correlation between (1) the first or second mode of variability in the EFA and CPE or (2) the spatial map associated with the positive phase of the first or second mode of variability from the EFA model and the spatially varying response of CPE from the CFA model. Results showed that prey-biomass-per-predator-mass had the strongest correlation with CPE, numerical density and morphometric condition were also strongly correlated with CPE, and length-at-age was moderately correlated with CPE. The models also ... |
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