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Simulating Metacommunities of Riverine Fishes: Trials and Tribulations (Interim presentation)
Citation:
Beebe, B., J. Ebersole, A. Brookes, AND B. Rashleigh. Simulating Metacommunities of Riverine Fishes: Trials and Tribulations (Interim presentation). Annual Meeting of the American Fisheries Society, virtual, N/A, November 06 - 10, 2021.
Impact/Purpose:
This research aims to advance methods for understanding the potential benefits of improvements in water quality and aquatic habitats. Understanding what people value requires models that can quantify willingness-to-pay for specified endpoints. Translating water quality or habitat improvements into fish endpoints valued by people requires models that can link fish population responses to projected improvements. In this presentation, we outline an approach for linking water quality/habitat improvements to fish populations, and describe some of the challenges in formulating and applying a complex ecological model to watersheds.
Description:
Salmonid populations have undergone prolonged declines even amidst a plethora of conservation efforts. Inevitably, future climate and land use changes will continue to strain our ability to manage fish populations, requiring targeted approaches for conservation activities to focus our efforts and resources more effectively. We are developing a fish community modeling framework, Simulating Metacommunities of Riverine Fishes (SMRF), to showcase our application of assemblage modeling to evaluate changes in fish abundances in response to stream restoration activities, with a focus on recovering threatened salmonid species. Because salmonid recovery is contingent on habitat quality and quantity as well as community dynamics, our model integrates aspects of movement, habitat suitability, and species interactions to increase the element of realism; however, this added complexity also presents some challenges. We explore details of some of the challenges we have encountered with model calibration using imperfect data, highlight our methods for remedying these issues to improve model performance, and discuss anticipated challenges with interpreting and conveying meaningful model results.