Hierarchical multi‐population viability analysis

Abstract

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long‐term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi‐population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub‐models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi‐pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi‐population viability analysis bridges the gap between site‐level field observations and population‐level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

Application of multiple‐population viability analysis to evaluate species recovery alternatives

“…We identified exceptions to these Conservation Biology Volume 34, No. 2, 2020 Leasure et al (2018). general rules, either due to variable environmental conditions or stochasticity ignored in simple habitat extent thresholds.…”

“…Higher peak flows had a weak positive effect on population growth rates (Leasure et al 2018). Riparian vegetation was assessed by the satellite-based normalized difference vegetation index (NDVI) (Pettorelli et al 2011) calculated from Landsat images with the Google Earth engine (Gorelick et al 2017); NDVI does not detectably effect LCT carrying capacities (Leasure et al 2018).…”

“…For other streams, we used a moving weighted-average density for years without data. Non-native trout densities have a significant negative effect on LCT carrying capacities (i.e., an increase in the strength of density dependence) (Leasure et al 2018).…”

“…This has essentially the same form as the classic Ricker model (Ricker 1954;Wenger et al 2017;Leasure et al 2018), but because it is implemented in a stochastic framework constrained by covariates and linked to process and observation models one should not necessarily expect behavior like that of a traditional deterministic Ricker model.…”

Extinction risk to lake minnow (Eupallasella percnurus) due to habitat loss: Eastern Poland case study

“…They allow for the identification of the most important spatial parameters that influence extinction. For example, in population viability analysis (PVA), remote sensing can be used to assess different environmental indicators that affect the existence of a given population in a certain area (Leasure et al 2019; Loannidou and O’Hanley 2019; Roberts et al 2016). GIS techniques also allow for the identification of which habitat patches have the greatest effect on metapopulation viability and to run extinction simulations under various model conditions (McCusker et al 2017) of executed analysis based on historical data to identified parameters that may affect fish extinction in the present (Klippel et al 2016; Tedesco et al 2005).…”