Actual and Potential Use of Population Viability Analyses in Recovery of Plant Species Listed under the U.S. Endangered Species Act

Abstract

Use of population viability analyses (PVAs) in endangered species recovery planning has been met with both support and criticism. Previous reviews promote use of PVA for setting scientifically based, measurable, and objective recovery criteria and recommend improvements to increase the framework's utility. However, others have questioned the value of PVA models for setting recovery criteria and assert that PVAs are more appropriate for understanding relative trade-offs between alternative management actions. We reviewed 258 final recovery plans for 642 plants listed under the U.S. Endangered Species Act to determine the number of plans that used or recommended PVA in recovery planning. We also reviewed 223 publications that describe plant PVAs to assess how these models were designed and whether those designs reflected previous recommendations for improvement of PVAs. Twenty-four percent of listed species had recovery plans that used or recommended PVA. In publications, the typical model was a matrix population model parameterized with ≤5 years of demographic data that did not consider stochasticity, genetics, density dependence, seed banks, vegetative reproduction, dormancy, threats, or management strategies. Population growth rates for different populations of the same species or for the same population at different points in time were often statistically different or varied by >10%. Therefore, PVAs parameterized with underlying vital rates that vary to this degree may not accurately predict recovery objectives across a species' entire distribution or over longer time scales. We assert that PVA, although an important tool as part of an adaptive-management program, can help to determine quantitative recovery criteria only if more long-term data sets that capture spatiotemporal variability in vital rates become available. Lacking this, there is a strong need for viable and comprehensive methods for determining quantitative, science-based recovery criteria for endangered species with minimal data availability. Uso Actual y Potencial del Análisis de Viabilidad Poblacional para la Recuperación de Especies de Plantas Enlistadas en el Acta de Especies En Peligro de E.U.A.

The compadrePlant Matrix Database: an open online repository for plant demography

“…), the importance of plant shrinkage for population dynamics (Salguero‐Gómez & Casper ), the first comparative exploration of short‐term dynamics (Stott et al . ) and an assessment of utility for recovery of endangered species (Zeigler, Che‐Castaldo & Neel ).…”

Population viability analyses in New Zealand: a review

“…The temporal extent of the data needs to be considered relative to the lifespan of the taxa of interest and the projection horizon (Zeigler et al 2013;Rueda-Cediel et al 2015), as well as the importance of infrequent catastrophes as determinants of population change (Ralls & Taylor 1997). The data period:projection horizon ratio was 0.01-0.23-1.2, meaning that in most cases the temporal extent of the data was much shorter than the period over which projections were being made.…”

“…GitHub) to help increase transparency and repeatability (see Wood et al 2017). (5) PVA should be based on as long and high-quality data as possible given the context the model is being developed and used in (Coulson et al 2001;Zeigler et al 2013;Rueda-Cediel et al 2015). We do not, however, advocate waiting indefinitely for the 'perfect' dataset, as not making any decision does carry a cost.…”

Cautions on using percentile-based benchmarks of status for data-limited populations of Pacific salmon under persistent trends in productivity and uncertain outcomes from harvest management

“…The extirpation risks and probabilities of correctly identifying conservation concerns estimated in the simulation model should be considered for each benchmark and sensitivity analysis relative to other benchmarks and analyses presented here, instead of in absolute terms as recommended by authors of other simulation models of population extinction (Mace et al, 2008;Zeigler et al, 2013). Absolute extinction risks are difficult to identify when models are parameterized from short and sparse data, especially when periods of low abundances are not available (Zeigler et al, 2013).…”

“…Absolute extinction risks are difficult to identify when models are parameterized from short and sparse data, especially when periods of low abundances are not available (Zeigler et al, 2013). However, relative performance of benchmarks can provide valuable information on the conditions or management decisions that can lead to increased extinction risks, such as in our study when benchmarks based on percentiles of abundances are applied under low or declining productivity and high outcome uncertainties.…”