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In fragmented landscapes, the survival of species and the maintenance of populations with healthy genetic structures will largely depend on movement/dispersal of organisms across matrix areas. In this article, we highlight that effects of fragmentation and climate change occur simultaneously and may enhance or mitigate each other. We systematically analyzed the effect of increasing interannual variation in rainfall on the genetic structure of two neighbouring small mammal subpopulations in a fragmented savanna landscape. The effect of interannual rainfall variation is analyzed for two contrasting scenarios that differ in mean annual rainfall and are both close to a dispersal threshold. Scenario 1 (low mean annual rainfall) lies slightly below this threshold and scenario 2 (high mean annual rainfall) slightly above, i.e. the amount of rainfall in an average rainfall year prevents dispersal in scenario 1, but promotes gene flow in scenario 2. We show that the temporal dynamics of the matrix was crucial for gene flow and the genetic structure of the neighbouring small mammal subpopulations. The most important result is that the increase in rainfall variability could both increase and decrease the genetic difference between the subpopulations in a complex pattern, depending on the scenario and on the amount of variation in rainfall. Finally, we discuss that the relevance of the matrix as temporarily suitable habitat may become a key aspect for biodiversity conservation. We conclude to incorporate temporal changes in matrix suitability in metapopulation theory since local extinctions, gene flow and re-colonization are likely to be affected in fragmented landscapes with such dynamic matrix areas.
Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.