Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations
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Abstract
Detecting temporal changes in fish abundance is an essential component of fisheries management. Because of the need to understand short‐term and nonlinear changes in fish abundance, traditional linear models may not provide adequate information for management decisions. This study highlights the utility of Bayesian dynamic linear models (DLMs) as a tool for quantifying temporal dynamics in fish abundance. To achieve this goal, we quantified temporal trends of Smallmouth Bass Micropterus dolomieu catch per effort (CPE) from rivers in the mid‐Atlantic states, and we calculated annual probabilities of decline from the posterior distributions of annual rates of change in CPE. We were interested in annual declines because of recent concerns about fish health in portions of the study area. In general, periods of decline were greatest within the Susquehanna River basin, Pennsylvania. The declines in CPE began in the late 1990s—prior to observations of fish health problems—and began to stabilize toward the end of the time series (2011). In contrast, many of the other rivers investigated did not have the same magnitude or duration of decline in CPE. Bayesian DLMs provide information about annual changes in abundance that can inform management and are easily communicated with managers and stakeholders.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations |
| Series title | North American Journal of Fisheries Management |
| DOI | 10.1002/nafm.10051 |
| Volume | 38 |
| Issue | 2 |
| Year Published | 2018 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Coop Res Unit Leetown |
| Description | 9 p. |
| First page | 493 |
| Last page | 501 |
| Country | United States |
| State | Maryland, Pennsylvania, West Virginia |
| Other Geospatial | Allegheny River, Delaware River, Juniata River, Potomac River. Susquehanna River |
| Google Analytic Metrics | Metrics page |