Cautious optimism for applied conservation genetics and metapopulation viability analysis

Among conservation biologists there is little argument that habitat loss and fragmentation are the greatest threats to future biodiversity. We know from elementary population ecology that, if isolated, even large populations will ultimately be driven to extinction by demographic stochasticity, and that this will occur more rapidly for small populations in small fragments. Theory and observations also show that post-fragmentation species losses can occur slowly, sometimes requiring more than a century for the total ‘extinction debt’ to be realized (Tilman et al., 1994; Vellend et al., 2006). For long-lived animals, such as ambystomatid salamanders, isolated populations doomed to demographic irrelevance and ultimate extinction may persist for decades or longer (Greenwald, 2010). I think of the animals occupying these isolated habitats with little chance of long-term survival as ‘zombie’ populations. As imperfect as our population models are, they represent our best chance for understanding the threats to populations at risk and possibly recovering them from zombie status. Population and metapopulation viability analyses are valuable tools for illustrating, in an understandable way, the risk of extinctions, and for exploring probabilities of local and regional persistence under different scenarios (Morris & Doak, 2002; Marsh, 2008). I see the greatest value of these modeling exercises in their ability to help land managers to evaluate alternative management actions and appreciate their relative costs and likely benefits. As Greenwald (2010) shows, actions to promote connectivity can enhance population viability. Restoring connectivity to fragmented landscapes is often extremely costly (think wildlife overpasses), and avoiding fragmentation completely may be even more controversial (think not building the freeway at all). So, models evaluating the importance of habitat connectivity must be convincing to those people on the hook for making big decisions. The greatest barrier to constructing convincing models is obtaining parameter estimates in which we have confidence, which traditionally means laborand time-intensive mark–recapture studies. Fortunately, the recent work of Greenwald (2010) in addition to others indicates that molecular tools may be surprisingly robust to the challenge of more rapidly and painlessly estimating at least some demographic parameters needed for landscape-scale population modeling. As someone who has invested years in the field marking and recapturing animals, I am optimistic at the promise these methods potentially hold (Trenham et al., 2000; Trenham, Koenig & Shaffer, 2001). The ability of genetic assignment tests to accurately reveal residents and immigrants in samples could change the study of dispersal. My caution in getting too excited about the current abilities of these methods stems from my understanding of several issues that can substantially bias the results. The author addressed the issue of unsampled ‘ghost’ populations. This is a real issue for animals like ambystomatid salamanders where large fractions of the adult population commonly skip breeding and are completely undetectable, thus samples from a single year are unlikely to accurately represent the standing genetic variation present in a population. Despite these issues, one recent study is very encouraging. A detailed study with marked skinks, occupying and dispersing among patches of rocky habitat, showed that assignment tests usually recognized immigrants with high confidence. And further, using skink tissues collected over 3months, the assignment tests yielded estimates of interpatch dispersal rates essentially identical to those estimated over 7 years of mark–recapture efforts (Berry, Tocher & Sarre et al., 2004). At this point, I think there is still need for more cross validation of these methods with mark–recapture studies in other taxa and landscapes, especially if we are to use these data predicting metapopulation viability. If these methods produce robust predictions they have the potential to greatly assist conservation planners. For example, the California tiger salamander Ambystoma californiense is the species about which I know the most, and one whose management would benefit greatly from the types of genetic data and the coupled modeling approach explored