On the Use of Demographic Models of Population Viability in Endangered Species Management

TLDR

The study argues that demographic models in PVA for endangered species should be used cautiously, recommends evaluating relative extinction rates, short‑term projections, simple data‑supportable models, and field validation of assumptions, and stresses linking recovery options to model predictions. The authors review analytical, deterministic, stochastic, metapopulation, and spatially explicit PVA models, detailing their structures, data needs, and outputs, and propose starting with simple, data‑supported models and evaluating multiple scenarios. They find that quantitative PVA predictions for endangered species are unreliable because of poor demographic data quality, difficulty estimating rate variance, limited dispersal information, inadequate validation of stochastic models, neglect of environmental trends and density dependence, and divergent outcomes from alternative model structures.

Abstract

We examine why demographic models should be used cautiously in Population Viability Analysis (PVA) with endangered species. We review the structure, data requirements, and outputs of analytical, deterministic single-population, stochastic single-population, metapopulation, and spatially explicit models. We believe predictions from quantitative models for endangered species are unreliable due to poor quality of demographic data used in most applications, difficulties in estimating variance in demographic rates, and lack of information on dispersal (distances, ages, mortality, movement patterns). Unreliable estimates also arise because stochastic models are difficult to validate, environmental trends and periodic fluctuations are rarely considered, the form of density dependence is frequently unknown but greatly affects model outcomes, and alternative model structures can result in very different predicted effects of management regimes. We suggest that PVA (1) evaluate relative rather than absolute rates of extinction, (2) emphasize short-time periods for making projections, (3) start with simple models and choose an approach that data can support, (4) use models cautiously to diagnose causes of decline and examine potential routes to recovery, (5) evaluate cumulative ending functions and alternative reference points rather than extinction rates, (6) examine all feasible scenarios, and (7) mix genetic and demographic currencies sparingly. Links between recovery options and PVA models should be established by conducting field tests of model assumptions and field validation of secondary model predictions.