Predicting bird species richness using remote sensing in boreal agricultural-forest mosaics

One of the main goals in nature conservation and land use planning is to identify areas important for biodiversity. One possible cost-effective surrogate for deriving appropriate estimates of spatial patterns of species richness is provided by predictive modeling based on remote sensing and topographic data. Using bird species richness data from a spatial grid system (105 squares of$0.25 km^2$within an area of$26.25 km^2$), we tested the usefulness of Landsat TM satellite-based remote sensing and topographic data in bird species richness modeling in a boreal agricultural-forest mosaic in southwestern Finland. We built generalized linear models for the bird species richness and validated the accuracy of the models with an independent test area of 50 grid squares (12.5 km2). We evaluated separately the modeling performance of habitat structure, habitat composition, topographical-moisture variables and all variables in the model-building and model-test areas. Areas of high observed and predicted bird species richness in the boreal agricultural-forest mosaic were mainly concentrated along river valleys in the grid squares with a high habitat diversity and steep topography. This landscape type also has the highest cover of habitats important for nature conservation: seminatural grasslands, deciduous forests, and watercourses. The covers of deciduous forests and seminatural grassland were included as explanatory variables of the bird species richness model, although they both cover only about 5% of the land in the study area. When the four models were evaluated by fitting them to the model test area, the explanatory power of the topography-moisture model decreased clearly, whereas the habitat-composition, habitat-structure, and all-variables models were more rigorous. Finally, we extrapolated the models to the whole study area of$600 km^2$and produced bird species richness probability maps using GIS techniques. We conclude that, instead of scattered study plots in which birds are counted, predictive modeling requires large study areas where the variation within the whole landscape can be taken into account. A spatial grid system with several environmental variables derived from remote sensing data produces the most reliable data sets, which can be used when predicting species richness in other landscapes.