Beyond species distribution modelling: A landscape genetics approach to investigating range shifts under future climate change

Understanding how biodiversity will respond to future climate change is a major conservation and societal challenge. Climate change is predicted to force many species to shift their ranges in pursuit of suitable conditions. This study aims to use landscape genetics, the study of the effects of environmental heterogeneity on the spatial distribution of genetic variation, as a predictive tool to assess how species will shift their ranges to track climatic changes and inform conservation measures that will facilitate movement. The approach is based on three steps: 1) using Species Distribution Models (SDMs) to predict suitable ranges under future climate change, 2) using the landscape genetics framework to identify landscape variables that impede or facilitate movement, and 3) extrapolating the effect of landscape connectivity on range shifts in response to future climate change. I show how this approach can be implemented using the publicly available genetic dataset of the grey long-eared bat,Plecotus austriacus, in the Iberian Peninsula. Forest cover gradient was the main landscape variable affecting genetic connectivity between colonies. Forest availability is likely to limit future range shifts in response to climate change, primarily over the central plateau, but important range shift pathways have been identified along the eastern and western coasts. I provide outputs that can be directly used by conservation managers and review the viability of the approach. Using landscape genetics as a predictive tool in combination with SDMs enables the identification of potential pathways, whose loss can affect the ability of species to shift their range into future climatically suitable areas, and the appropriate conservation management measures to increase landscape connectivity and facilitate movement

Shifting suitability for malaria vectors across Africa with warming climates

<p>Abstract</p> <p>Background</p> <p>Climates are changing rapidly, producing warm climate conditions globally not previously observed in modern history. Malaria is of great concern as a cause of human mortality and morbidity, particularly across Africa, thanks in large part to the presence there of a particularly competent suite of mosquito vector species.</p> <p>Methods</p> <p>I derive spatially explicit estimates of human populations living in regions newly suitable climatically for populations of two key <it>Anopheles gambiae </it>vector complex species in Africa over the coming 50 years, based on ecological niche model projections over two global climate models, two scenarios of climate change, and detailed spatial summaries of human population distributions.</p> <p>Results</p> <p>For both species, under all scenarios, given the changing spatial distribution of appropriate conditions and the current population distribution, the models predict a reduction of 11.3–30.2% in the percentage of the overall population living in areas climatically suitable for these vector species in coming decades, but reductions and increases are focused in different regions: malaria vector suitability is likely to decrease in West Africa, but increase in eastern and southern Africa.</p> <p>Conclusion</p> <p>Climate change effects on African malaria vectors shift their distributional potential from west to east and south, which has implications for overall numbers of people exposed to these vector species. Although the total is reduced, malaria is likely to pose novel public health problems in areas where it has not previously been common.</p

Measuring urban economic density

At the heart of urban economics are agglomeration economies, which drive the existence and extent of cities and are also central to structural transformation and the urbanization process. This paper evaluates the use of different measures of economic density in assessing urban agglomeration effects, by examining how well they explain household income differences across cities and neighborhoods in six African countries. We examine simple scale and density measures and more nuanced ones which capture in second moments the extent of clustering within cities. The evidence suggests that more nuanced measures attempting to capture within-city differences in the extent of clustering do no better than a simple density measure in explaining income differences across cities, at least for the current degree of accuracy in measuring clustering. However, simple city scale measures such as total population are inferior to density measures and to some degree misleading. We find large household income premiums from being in bigger and particularly denser cities over rural areas in Africa, indicating that migration pull forces remain very strong in the structural transformation process. Moreover, the marginal effects of increases in urban density on household income are very large, with density elasticities of 0.6. In addition to strong city level density effects, we find strong neighborhood effects. For household incomes, both overall city density and density of the own neighborhood matter