Estimating risks in declining populations with poor data.
Census data on endangered species are often sparse, error-ridden, and confined to only a segment of the population. Estimating trends and extinction risks using this type of data presents numerous difficulties. In particular, the estimate of the variation in year-to-year transitions in population size (the "process error" caused by stochasticity in survivorship and fecundities) is confounded by the addition of high sampling error variation. In addition, the year-to-year variability in the segment of the population that is sampled may be quite different from the population variability that one is trying to estimate. The combined effect of severe sampling error and age- or stage-specific counts leads to severe biases in estimates of population-level parameters. I present an estimation method that circumvents the problem of age- or stage-specific counts and is markedly robust to severe sampling error. This method allows the estimation of environmental variation and population trends for extinction-risk analyses using corrupted census counts--a common type of data for endangered species that has hitherto been relatively unusable for these analyses.
Holmes
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Resource Enhancement and Utilization Technologies Division, Northwest Fisheries Science Center, 2725 Montlake Boulevard East, Seattle, WA 98112, USA. eli.holmes@noaa.gov
D000375:Aging; D000818:Animals; D001717:Birds / Q000254:growth & development / Q000502:physiology; D019352:Censuses; D003364:Costa Rica; D004463:Ecology; D005298:Fertility; D006254:Hawaii; D008017:Life Expectancy; D016013:Likelihood Functions; D011156:Population Density; D015203:Reproducibility of Results; D012306:Risk; D012474:Salmon / Q000502:physiology; D012621:Seasons; D015983:Selection Bias; D013223:Statistics as Topic / Q000379:methods*; D013269:Stochastic Processes; D015996:Survival Rate; D014426:Turtles / Q000502:physiology