A central task in community ecology is to determine biophysical controls of species richness patterns. Most multivariate models relating environmental characteristics to species richness were developed with sparse point sampling from meteorological stations. However, satellite remote sensing provides an alternative, and in some ways potentially superior, means of estimating appropriate environmental factors and thereby improving predictions of species richness. The frequently used surrogate measures for species richness patterns are net primary productivity (NPP) and actual evapotranspiration (AET). Local spatial variability of NPP and AET, which indicates spatial heterogeneity, is hypothesized as another influence on species richness. The Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) has been shown to be related to NPP and AET in many vegetation types. We examined the relationship between interannual NDVI parameters and species richness of vascular plants, large mammals and birds. Statistical analyses revealed that at small spatial scales (10 x 10 km) higher average NDVI results in lower species richness of mammals and plants, whereas standard deviation of maximum NDVI and coefficient of variation correlated positively with species richness. Conversely, at large spatial scales (55 x 55 km) bird species richness increases as average NDVI increases, whereas higher standard deviation and coefficient of variation result in lower species richness. Thus, NDVI parameters appear to represent environmental factors influencing species richness. In other words, by utilizing remote sensing, our understanding of the spatial variability of species richness has been improved.

There is need to identify ecosystems that support richer assemblages of biological species in order to preserve habitats and protect the greatest number of species. Remotely sensed data hold tremendous potential for mapping species habitats and indicators of biological diversity, such as species richness. Landscape level habitat analysis using remotely sensed data and Geographical Information Systems (GIS) has the potential to aid in explaining species richness patterns at fine-scale resolutions. We used Landsat Thematic Mapper (TM) image and GIS as well as field data to classify habitat types in the Maasai Mara ecosystem, Kenya. The accuracy of the resulting habitat map was assessed and indices of habitat diversity computed. We then determined the relationship between large mammal species richness and habitat diversity indices, and investigated whether this relationship is sensitive to changes in spatial scale (extent and grain size). Statistical analyses show that species richness is positively correlated with habitat diversity indices and changes of scale in calculations of habitat diversity indices influenced the strength of the correlation. The results demonstrate that mammalian diversity can be predicted from habitat diversity derived from satellite remotely sensed data.

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Sustainable resource management requires understanding the factors that increase or decrease species richness. Regional species richness patterns may be predicted by analysing patterns of variation in the environment. A number of studies have shown that bird species richness at a regional scale is in£uenced by climatic variables.We examined environmental correlates of bird species richness at a quarter degree square scale (55 Â55 km). Mean annual potential evapotranspiration accounts for 46% of the observed variation in species richness, while mean annual temperature and range annual potential evapotranspiration are signi¢cantly correlated with species richness and together account for a further 5% of the observed variation. The results are consistent with the hypothesis that environmentally available energy limits regional species richness.Re¨sumeL a gestion soutenable des ressources exige la compre¨hension des facteurs qui augmentent ou re¨duisent la richesse des espe© ces. On peut pre¨dire les sche¨mas re¨gionaux de la richesse en espe© ces en analysant les sche¨mas de variation dans l'environnement. De nombreuses e¨tudes ont montre¨que la richesse en espe© ces d'oiseaux est, a© l'e¨chelle d'une re¨gion, in£uence¨e par les variables climatiques. Nous avons e¨tudie¨les corre¨lations environnementales de la richesse en oiseaux a© l'e¨chelle d'un carre¨de 55 Â55 km. Le potentiel annuel moyen d'e¨vapotranspiration compte pour 46% de la variation observe¨e dans la richesse en espe© ces, tandis que la tempe¨rature annuelle moyenne et la variation annuelle du potentiel d'e¨vapotranspiration sont signi¢cativement lie¨es a© la richesse en espe© ces et ensemble, comptent pour 5% supple¨mentaires de la variation observe¨e. Les re¨sultats concordent avec l'hypothe© se selon laquelle l'e¨nergie environnementale disponible limite la richesse re¨gionale en espe© ces.

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