Metapopulation viability analysis for amphibians

A landscape-scale approach is critical for the conservation of pond-breeding amphibians. Individual demes at one or a few closely linked breeding sites are highly susceptible to local extinction. Recruitment in pond-breeding amphibians is often sensitive to hydroperiod and predation, and these factors can fluctuate greatly from year-to-year (Semlitsch et al., 1996). As a result, pond-breeding amphibians are subject to a high degree of stochasticity in population size, which can endanger these populations even in the absence of environmental stressors. Over somewhat longer time scales, many amphibian populations will decline due to succession within pond communities or in surrounding terrestrial habitats (Semlitsch, 2002; Skelly et al., 2005). And over even longer periods, ponds may gradually fill and be replaced by new ponds elsewhere in the landscape (Sjögren-Gulve, 1994). Together, all these factors virtually ensure that a single breeding site, or a group of closely linked sites, will not be sufficient for the long-term persistence of amphibians. Approaches to amphibian conservation and management therefore need to focus on population viability at the landscape scale. Indeed, many of the most common strategies for protecting amphibian species are aimed at the metapopulation level. One example is the creation of new breeding ponds, either to mitigate the loss of wetlands to development or to expand the amount of available habitat. Amphibians may readily colonize these new sites from existing ponds, as long as the new sites are not too isolated (Lehtinen & Galatowitsch, 2001; Pechmann et al., 2001). A related strategy is to restore existing ponds by improving habitat quality or by removing introduced predators. In some cases, habitat improvement has resulted in surprisingly rapid recolonization of amphibians from surrounding ponds (Vredenburg, 2004). Lastly, reintroduction of amphibians to breeding sites can be viewed as a metapopulation-based approach, because it acts primarily to increase colonize rates and occupancy across the landscape (e.g. Muths, Johnson & Corn, 2001; Kinne, 2006). The challenge, of course, is to know which of these approaches will be most effective for amphibian conservation. Creating new wetlands can be very costly and is still more of an art than a science. Removing introduced predators is also extremely difficult, as a few escapees can quickly reproduce to saturate a pond. Reintroduction is usually a less expensive way to enhance occupancy, but it carries a risk of spreading pathogens around the landscape (Seigel & Dodd, 2002). Thus, conservation planners need to be able to make predictions about the most productive strategies – they cannot simply try lots of different approaches and see what works. The principal approach to making these predictions is metapopulation viability analysis (MPVA). The basic idea of MPVA is to use data on patterns of occurrence or turnover to parameterize a model for metapopulation dynamics. The model can then be projected forward under a variety of scenarios to determine which conservation strategies will lead to the most desirable outcome. One version of MPVA uses a statistical model, usually logistic regression, to examine the local and landscape predictors of extinction and colonization. Sjögren-Gulve & Ray (1996) used this approach with pool frogs in Sweden and determined that whereas the existing intensity of forestry would not threaten pool frogs, high-intensity forestry could disrupt migration among breeding sites and lead to metapopulation extinction. Another version of MPVA creates a demographic model for each breeding site, and then links these models by dispersal. Hels & Nachman (2002) used this approach to examine the viability of a spadefoot toad metapopulation and found that source–sink dynamics were fundamental to persistence. The third common version of MPVA uses an incidence function model (IFM) instead of logistic regression (Hanski, 1994). The IFM approach has been used on a variety of taxa, including butterflies, birds and mammals, but only rarely with amphibians (Vos, ter Braak & Nieuwenhuizen, 2000; Ter Braak & Etienne, 2003). It is this approach that is taken by Gilioli et al. (2008). The principal advantages of the IFM approach are (1) that it requires only a single snapshot of occupancy data across the landscape and (2) that it parameterizes a