Measure Twice, Estimate Once: Pacific Salmon Population Viability Analysis for Highly Variable Populations
Abstract.—Because many stocks of Pacific salmon Oncorhynchus spp. are listed under the U.S. Endangered Species Act (ESA), research has focused on predicting the future population dynamics for these low-abundance stocks. One method used to make predictions is known as population viability analysis. P...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.571.1432 http://www.webpages.uidaho.edu/fish510/PDF/Model Approaches.pdf |
| Summary: | Abstract.—Because many stocks of Pacific salmon Oncorhynchus spp. are listed under the U.S. Endangered Species Act (ESA), research has focused on predicting the future population dynamics for these low-abundance stocks. One method used to make predictions is known as population viability analysis. Pacific salmon populations exhibit much higher apparent variability than other ESA-listed vertebrates, and high variability increases the probability of extinction. If the high variability is primarily due to counting methods, it could be reduced in model predictions by using methods that correct for measurement error, sampling error, or both. Using data from British Columbia pink salmon O. gorbuscha and Snake River spring-or summer-run Chinook salmon O. tshawytscha and several modeling approaches (Ricker, Dennis, and state-space models), we compared repeated counts of the same population (e.g., spawner and fry, dam and redd counts). We applied the methods to the first half of the time series and compared the predictions with the last half of the time series. The results demonstrated that having counts of all life stages of a Pacific salmon population is no guarantee that variability will be markedly reduced. Measurement error is not the primary cause of high variability in empirical estimates of abundance or in predicted future abundance for the stocks analyzed. The very wide bounds on predicted abundance limit the utility of the model predictions for making management decisions. Furthermore, obtaining more accurate or complete measurements of population |
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