Forecasting Changes in Aquatic Systems and Resilience of Brook Trout
Forecasting Changes in Aquatic Systems and Resilience of Aquatic Populations in the NALCC: Decision-support Tools for Conservation
The objective of this project was to develop tools to assist managers in protecting and restoring streams for brook trout and other aquatic resources in the face of threats such as climate change and development. Deliverables from this project included models of stream temperature, stream flow, and brook trout occurrence for headwaters of the Northeast, including projections of the potential effects of climate change. The investigators worked closely with decision-makers such as state water resource agencies to ensure the tools are useful. Summary of Phase 2 of the project (2014-2016):The goal of this project is to improve natural resources management by providing effective, flexible, portable, and transparent modeling results and decision support tools to managers. The objectives include: 1) Expand existing tools to additional portions of LCC region 2) Integrate models with management and policy The Connecticut DEEP and Massachusetts DEP have agreed to participate in this pilot, which will be designed for adoption by interested managers across the region. Specific tasks will include: a) further adapting stream and fish models; b) customizing maps and graphics for decision support; c) modifying the existing map viewer for prioritization of watersheds; and d) exploring the potential for real-time updates of model results based on state-provided data. Summary of Phase 1 of the project (2011-2014):The objective of this project is to develop a web-based decision support system for evaluating effects of alternative management scenarios on local population persistence of brook trout under different climate change scenarios. The project includes the following tasks: Task 1: Hierarchical modeling framework to account for multiple scales and sources of uncertainty in climate change predictions. UMass will develop the theory and application of a hierarchical Bayesian model to forecast local (catchment scale) population persistence of brook trout. LCC Staff Contact: Scott Schwenk The Regional Stream Temperature/SHEDS workshop was held Feb 22-23 2017 in Hadley, MA. Forty-four people from state, federal and non-profit agencies attended the meeting. The project team explained the stream temperature database, the stream temperature model, and how the stream temperature model informs the occupancy model. Suggestion from participants were incorporated into v2.0 of "ICE". In 2015, the project team launched "SHEDS" - the Spatial Hydro-Ecological Decision System. SHEDS is a web application that seamlessly links hydro-ecological datasets, models, and decision support systems. SHEDS provides tools for gaining insight, improving decision making, and supporting better management of hydro-ecological resources. One of the components of SHEDS is "ICE" - the Integrated Catchment Explorer. ICE is a dynamic visualization tool for exploring catchment characteristics and environmental model predictions across the Northeast. Major products from this project, as well as related projects funded through other sources, have been incorporated into ICE. They include stream temperature models and brook trout occupancy models. Previous updates and information:
The original task list and timeline, from fall 2011, is available here. Documentation from the initial phases of this research was created for the Population Persistence Modelling (Task 3) and Hydrology and Stream Temperature (Task 2) components of the project. In the NewsBrook trout study identifies top climate change pressure factor NALCC-supported research published in Journal of Animal Ecology NALCC Funding: FY 2010: $420,000; FY 2014: $110,000 Other Funding: FY 2010: $200,000; FY 2014: $185,651 Tools"SHEDS" - Spatial Hydro-Ecological Decision System. "ICE" - the Integrated Catchment Explorer Journal ArticlesLetcher, B.H., D.J. Hocking, K. O'Neil, A.R. Whiteley, K.H. Nislow, M.J. O'Donnell. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ 4:e1727; DOI 10.7717/peerj.1727 Yoichiro Kanno, B.H. Letcher, A. L. Rosner, K. P. O’Neal, and Keith H. Nislow. 2015. Environmental Factors Affecting Brook Trout Occurrence in Headwater Stream Segments. Transactions of the American Fisheries Society 144:373–382. Letcher, B.H., P. Schueller, R. Bassar, K.H. Nislow, J.A. Coombs, K. Sakrejda, M. Morrissey, D. Sigourney, A.R. Whitely, M. O'Donnell, T. Dubreuil. In press. 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. doi: 10.1111/1365-2656.12308 Kanno,Y., Letcher, B.H., Hitt, N., Boughton, D., Wofford, J., and Zipkin, E. in Press. Seasonal weather patterns drive population vital rates and persistence in a stream fish. Global change biology Kanno, Y, B. H. Letcher, J.C. Vokoun and E.F. Zipkin, 2014. Spatial variability in survival of adult brook trout within two intensively surveyed headwater stream networks, Canadian Journal of Fisheries and Aquatic Sciences 71: 1010-1019. Zipkin,E., J. Thorson, K. See, H. Lynch, E. Grant, Y. Kanno, R. Chandler, B.H. Letcher, and J. Royle. 2014. Modeling structured population dynamics using data from unmarked individuals. Ecology: 95(1) doi:10.1890/13-1131.1 Kanno, Y., B.H. Letcher, J.A. Coombs, K.H. Nislow, and A.R. Whiteley. 2014. Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogenous stream network. Freshwater Biology 59: 142-154. Kanno, Y., J. C. Vokoun, and B. Letcher. 2013. Paired stream-air temperature measurements reveal fine-scale thermal heterogeneity within headwater brook trout streams networks. River Research and Applications 10.1002/rr. Whiteley, A; Coombs, J; Hudy, M; Robinson, Z; Colton, A; Nislow, K; Letcher, B.H. 2013. Fragmentation and patch size shape genetic structure of brook trout populations. Canadian Journal of Fisheries and Aquatic Sciences. 70(5): 678-688, 10.1139/cjfas-2012-0493. Kanno, Y., J. C. Vokoun, K. E. Holsinger, and B. H. Letcher. 2012. Estimating size-specific brook trout abundance in continuously sampled headwater streams using Bayesian mixed models with zero inflation and overdispersion. Ecology of Freshwater Fish:1–16. Sigourney, D. B., S. B. Munch, and B. H. Letcher. 2012. Combining a Bayesian nonparametric method with a hierarchical framework to estimate individual and temporal variation in growth. Ecological Modelling 247:125–134. Steinschneider, S., A. Polebitski, C. Brown, and B. H. Letcher. 2012. Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change. Water Resources Research 48:W11525. Whiteley, A. R., J. a. Coombs, M. Hudy, Z. Robinson, K. H. Nislow, and B. H. Letcher. 2012. Sampling strategies for estimating brook trout effective population size. Conservation Genetics 13:627-637. Presentations |
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