Modeling species response to environmental change: development of integrated, scalable Bayesian models of population persistence

Science Center Objects

Estimating species response to environmental change is a key challenge for ecologists and a core mission of the USGS. Effective forecasting of species response requires models that are detailed enough to capture critical processes and at the same time general enough to allow broad application. This tradeoff is difficult to reconcile with most existing methods. We propose to extend and combine e...

Estimating species response to environmental change is a key challenge for ecologists and a core mission of the USGS. Effective forecasting of species response requires models that are detailed enough to capture critical processes and at the same time general enough to allow broad application. This tradeoff is difficult to reconcile with most existing methods. We propose to extend and combine existing models that operate at different scales and with different levels of data complexity into a modeling framework that will allow robust estimation of population response to environmental change across a species’ range. This integrated modeling is now possible with the increasing development and application of population models in an extremely flexible and powerful statistical framework (hierarchical Bayesian modeling). These models will integrate data across multiple scales, providing critical insight into environmental control of population dynamics and a unique tool for forecasting effects of current and future environmental change. Although we will initially develop models for fish living in stream networks, our integrated modeling approach will be applicable to a wide range of species and could serve as a model for future modeling efforts.

Principal Investigator(s):

Benjamin Letcher (Conte Anadromous Fish Research Laboratory, LSC)

Participant(s):

Robert K Al-Chokhachy (Northern Rocky Mountain Science Center)

Elise Zipkin (Patuxent Wildlife Research Center)

Elizabeth Marschall (Ohio State University)

Jim Peterson (Oregon State University)

Erin Rodgers (Conte Anadromous Fish Research Laboratory, Leetown Science Center)

Andy Royle (Patuxent Wildlife Research Center)

Heather J. Lynch (Stony Brook University)

David Boughton (National Oceanic and Atmospheric Administration)

Steve Munch (National Oceanic and Atmospheric Administration)

Chris Jordan (National Oceanic and Atmospheric Administration)

Mark Scheuerell (National Oceanic and Atmospheric Administration)

Kevin See (National Oceanic and Atmospheric Administration)

James Thorson (National Oceanic and Atmospheric Administration)

Eric Ward (National Oceanic and Atmospheric Administration)

Michael Schaub (Swiss Ornithological Institute)

Ronald D Bassar (Conte Anadromous Fish Research Laboratory, Leetown Science Center)

Krzysztof Sakrejda-Leavitt (Conte Anadromous Fish Research Laboratory, Leetown Science Center)

Paul Schueller (Conte Anadromous Fish Research Laboratory, Leetown Science Center)

Yoichiro Kanno (Conte Anadromous Fish Research Laboratory, Leetown Science Center)

Publication(s):

Kanno, Y., Letcher, B., Hitt, N.P., Boughton, D., Wofford, J.E.B, and Zipkin, E., (2015). Seasonal weather patterns drive population vital rates and persistence in a stream fish. Global Change Biology 21, 1856-1870. doi: 10.1111/gcb.12837



Kanno, Y., Letcher, B.H., Vokoun, J.C., and Zipkin, E.F. (2014). Spatial variability in adult brook trout (Salvelinus fontinalis) survival within two intensively surveyed headwater stream networks, 71(7). doi: 10.1139/cjfas-2013-0358



Letcher, B.H., Schueller, P., Bassar, R.D., Nislow, K.H., Coombs, J.A., Sakrejda, K., Morrissey, M., Sigourney, D.B., Whiteley, A.R.,O'Donnell, M.J., and Dubreuil, T.L., (2014). Robust estimates of environmental effects on population vital rates: an integrated capture-recapture model of seasonal brook trout growth, survival and movement in a stream network. Journal of Animal Ecology. 84. 337-352 doi: 10.1111/1365-2656.12308



Lynch, H.J., Thorson, J.T., and Shelton, A.O. (2014). Dealing with under- and over-dispersed count data in life history, spatial, and community ecology. Ecology. doi: 10.1890/13-1912.1



Thorson, J.T., Jensen, O.P., and Zipkin, E.F. (2014). How variable is recruitment for marine fishes? A hierarchical model for testing life history theory. Canadian Journal of Fisheries and Aquatic Sciences, 71(7): 973-983. doi: 10.1139/cjfas-2013-0645



Thorson, J., Munch, S., and Ono, K. (2014). A Bayesian approach to identifying and compensating for model misspecification in population models. Ecology, 95(2), 329-341. doi: 10.1890/13-0187.1



Thorson, J.T., Scheuerell, M.D., Semmens, B.X., Pattengill-Semmens, C. (2014). Demographic modeling of citizen science data informs habitat preferences and population dynamics of recovering fishes. Ecology. doi: 10.1890/13-2223.1



Zipkin, E., Sillett, T.S., Grant, E.H., Chandler, R., and Royle, A. (2014). Inferences about population dynamics from count data using multistate models: a comparison to capture-recapture approaches. Ecology and Evolution, 4(4), 417-426. doi: 10.1002/ece3.942



Zipkin, E.F., Thorson, J.T., See, K., Lynch, H.J., Grant, E.H.C., Kanno, Y., Chandler, R.B., Letcher, B.H., and Royle, J.A. (2014). Modeling structured population dynamics using data from unmarked individuals. Ecology, 95, 22-29. doi: 10.1890/13-1131.1