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Characterization of spatial and temporal variability in fishes in response to climate change

Predicting population responses to climate change requires an understanding of how population dynamics vary over space and time. For instance, a measured indicator may vary among repeated samples from a single site, from site to site within a lake, from lake to lake, and over time. Although variability has historically been viewed as an impediment to understanding population responses to ecological changes, the structure of variation can also be an important part of the response. In this project, we will build upon recently completed analyses of fish population data in the Great Lakes basin to help predict how spatial and temporal variation in fish populations may respond to climate change and other important drivers.

We will evaluate whether a shifting variance structure can be indicative of population-level responses to climate change. We expect that the structure of variation (i.e., variance components themselves), not just the total variance, will be responsive to severe large-scale perturbation, and that this change in variance structure will have implications for how we conduct ecological monitoring. This research will help elucidate the extent to which quantifiable responses in spatial and temporal variability occur in fish population data. The data available for such analyses of fish and other populations are usually non-negative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. Thus, we will explore additional distributional assumptions (e.g., negative binomial, Poisson) within a mixed-model framework to model count data and quantitatively estimate spatial and temporal variation.

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