Recent developments in spatial methods and data in biogeographical distribution modelling – advantages and pitfalls

Abstract

Geography has a long tradition in studies of geographical distribution of flora and fauna. Detailed mappings of the distributions of biota over wide regions can produce highly valuable biogeographical data, but are extremely laborious.
These challenges in biogeographical mapping, as well as the need for mitigation tools for the adverse impacts of human disturbance on the landscape and biodiversity, have stimulated the development of new approaches for assessing biogeographical patterns. Particularly, the ability to model distribution patterns of organisms and habitat types has recently increased along with the theoretical and methodological development of biogeography and spatial ecology, and modern spatial techniques and extensive data sets (provided e.g., by earth observation techniques). However, geographical data have characteristics which produce statistical problems and uncertainties in these modelling studies: 1) the data are almost always multivariate and intercorrelated, 2) the data are often spatially autocorrelated, and 3) biogeographical distribution patterns are affected by different factors operating on different spatial and temporal scales. Especially remote sensing and geographic information data provide powerful means for studies of environmental change, but also include pitfalls and may generate biased results. Quantitative analysis and modelling with correct and strict use of spatial statistics should also receive
more attention. The issues discussed in this paper can have relevance in several fields of application of geographical data.