Comparison of parameter estimation methods for detecting climate-induced changes in productivity of Pacific salmon (Oncorhynchus spp.)
By Randall M. Peterman, Brian J. Pyper, J. Grout. 2000
89 citations recorded by the source corpus.
Subfields: Genetics, Global and Planetary Change, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/b2cbad8d1d7df3a647c0919a6729bd58d576ef45
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- B. MacGregor
- Brigitte Dorner
- J. Rosenfeld
- Randall M. Peterman
- Brian J. Pyper
- M. Webster
- E. Fleishman
- J. Seeb
In Global and Planetary Change
- Randall M. Peterman
- Brigitte Dorner
- Brian J. Pyper
- J. King
- B. MacGregor
- J. Rosenfeld
- M. Webster
- D. Schindler
In Nature and Landscape Conservation
- Randall M. Peterman
- Brigitte Dorner
- Brian J. Pyper
- J. King
- B. MacGregor
- J. Rosenfeld
- M. Webster
- D. Schindler
Citations in the corpus
- Climate Change, Ecosystem Impacts, and Management for Pacific Salmon (2008, background): “Some such management strategies have been suggested as ways to account for climatically-driven changes in salmon production, without the need to understand the intricacies of climate impacts on salmon ecosystems (e.g., Walters and Parma 1996; Peterman et al. 2000).”
- A widespread decrease in productivity of sockeye salmon (Oncorhynchus nerka) populations in western North America (2012, methodology): “Previous simulations (Peterman et al. 2000) and empirical analyses (Peterman et al.”
- Use of the Kalman filter to reconstruct historical trends in productivity of Bristol Bay sockeye salmon (Oncorhynchus nerka) (2003, methodology): “That analysis demonstrated that if the Ricker a parameter varies over time, a Kalman filter (state–space) formulation of the Ricker model with a timevarying a parameter performs better at tracking those changes than the standard Ricker model, which assumes that parameters are constant (Peterman et a”
- Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling (2015, unknown): “For example, a modified Ricker model that allows parameters to randomly drift over time (37) will explain variations in the data better than the static alternative, because doing so can indirectly track nonlinear state dependence.”
- REPORT OF THE STUDY GROUP ON FISHERIES AND ECOSYSTEM RESPONSES TO RECENT REGIME SHIFTS (2005, background): “However, the preferred strategy is a regime-specific harvest rate (Spencer 1997; Peterman et al. 2000; MacCall 2002).”