Cost‐effectiveness of strategies to establish a European bison metapopulation in the Carpathians

Abstract

Summary 1.Where populations are confined to fragmented, human-dominated landscapes, preventing declines and extirpations will often rely on metapopulation management. Spatially-explicit population viability analyses provide tools to evaluate how well the local management efforts can be combined to conserve metapopulations across large areas. Yet, metapopulation models have rarely been combined with tools to assess the cost-effectiveness of different conservation strategies. 2. European bison Bison bonasus only occur in small, fragmented populations, making their long-term survival dependent on establishing a metapopulation across eastern Europe. We parameterized a European bison metapopulation model based on time-series of bison demography and a habitat suitability map to assess the viability of bison populations in the Carpathians and the relative cost-effectiveness of (i) reintroductions, (ii) wildlife overpasses and (iii) anti-poaching measures in establishing a viable bison metapopulation. 3. Our results suggest that the Carpathians could support a viable metapopulation of European bison provided that active efforts are taken to safeguard bison and connect isolated herds. With such steps, our model forecasts that bison numbers could increase substantially over the next 100 years as local populations increase and bison recolonize parts of the Carpathians. 4. Reintroductions appear to be the most cost-effective approach for establishing a viable bison metapopulation among our scenarios, especially when coupled with wildlife overpasses to improve connectivity among herds. The most promising region for a bison metapopulation in the Carpathians was south-eastern Poland, Ukraine and northern Romania. We identified several candidate regions for reintroductions and wildlife overpasses, especially in the border region of Romania and Ukraine. Site-specific assessments of both habitat suitability, and the costs and benefits of a large bison population, should target those regions. 5. Synthesis and applications. Our results highlight how careful conservation planning can identify solutions to preserve large mammals in human-dominated landscapes. Choosing the most effective option from a range of management strategies is a central challenge for wildlife managers. We have shown that incorporating cost-effectiveness analyses into metapopulation models can elucidate the relative value (gain per unit cost) of different conservation management options, allowing decision makers to choose cost-effective options to preserve large mammals. Our model projections also provide hope for establishing a viable free-ranging European bison population in the Carpathians, one of the last relatively wild areas in Europe.

The effect of protected areas on forest disturbance in the Carpathian Mountains 1985–2010

“…All rights reserved. 4 endangered species, and viable populations of wolf, lynx, bear, and European bison (Turnock 2002;Kuemmerle et al 2011b), as well as important ecosystem services (Kuemmerle et al 2011a).…”

Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data

“…Habitat suitability as a proxy for resistance to movement.-A plausible way to empirically estimate relationships between connectivity and environmental conditions is to assume that habitat quality has a direct (inverse) relationship with resistance to movement (e.g., Pullinger and Johnson 2010, Kuemmerle et al 2011, MateoSánchez et al 2014b). We used this approach in our first parameterization scenario (habitat scenario), in which we created a resistance surface where resistance to movement was obtained through an inverse function of habitat suitability.…”

A two-stage translocation strategy for improving juvenile survival of Hawaiian monk seals

“…Lubow 1996, Swart & Lawes 1996, Pedrono et al 2004, Bretagnolle & Inchausti 2005, Kuemmerle et al 2011. Actual estimates of extinction risk from such models may be highly suspect due to limited data for estimating input parameters and their error (e.g.…”