A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecas ts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance f or the arboreal marsupial greater glider Petauroides volans with prediction s from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occup ancy were made, assuming no fragmentation effects and based simply on remna nt area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater g liders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA unde r several metapopulation modelling scenarios. The metapopulation models per formed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occup ancy for the four scenarios (P = 0.001-0.006). When the model-derived proba bility of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greate r congruence between actual patch occupancy and the predicted probability o f occupancy. 4. For many patches, probability distribution functions indicated that mode l predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantiall y over-predicted or under-predicted actual abundance. Some important proces ses, such as inter-patch dispersal, that influence the distribution and abu ndance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of spec ies, are required to assess further predictions made using these tools. Thi s will help determine those taxa for which predictions are and are not accu rate and give insights for improving models for applied conservation manage ment.