|Document Type:||Journal Article|
|Title:||Reducing bias and improving precision in species extinction forecasts|
|Author:||K. E. See, E. E. Holmes|
|Keywords:||population viability analysis,sampling design,spatial replication,state-space model|
Forecasting the risk of population decline is crucial in the realm of biological conservation and figures prominently in population viability analyses (PVA). The most common form of available data for a PVA is population counts through time. Previous research has suggested that improving estimates of population trends depends on longer time series (Fieberg and Ellner 2000, Holmes et al. 2007, Ellner and Holmes 2008, Humbert et al. 2009), but that is often impractical or undesirable. Spatial replication of observations is an alternative way to gather more data without extending the time series. In this paper, we examine the tradeoff between the length of the time period over which observations of the population have been taken, and the total number of observations or samples that have been recorded through an analysis of simulated data. We found that when there is a low process to non-process error variance ratio there are benefits to sampling less frequently but extending the period over which counts are gathered, but when that ratio is high more precise estimates of quasi-extinction risks can be obtained if replicated observations are taken at each time step. These results can be used to efficiently design effective monitoring schemes for species of conservation concern.
This journal article is a simulation study investigating the effects of time-series length and replicated observations on the accuracy and precision of estimates of the probability of quasi-extinction.
|Theme:||Ecosystem approach to improve management of marine resources|
Assess ecosystem status and trends.