Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity (original) (raw)
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Predicting Mammal Species Richness from Remotely Sensed Data at Different Spatial Scales
The Open Remote Sensing Journal, 2009
Spatial variability in species richness has been postulated to depend upon environmental factors such as climatic variability, Net primary productivity and habitat heterogeneity. The Advanced Very High Resolution Radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) has been shown to be correlated with climatic variability, Net primary productivity and habitat heterogeneity. Moreover, Landsat Thematic Mapper (TM) derived habitat diversity indices have been used to reflect habitat heterogeneity. Interannually average NDVI and its variability (standard deviation and coefficient of variation) as well as Landsat Thematic Mapper derived habitat diversity index were correlated with mammal species richness at landscape scale. Species richness related unimodally to interannual average NDVI and positively to variability of NDVI and habitat diversity index. Conversely, at regional scale mammal species richness were correlated with interannually average NDVI and coefficient of variation of NDVI. Species richness related negatively to the latter and positively to interannually average NDVI. Though these relationships are indirect, they apparently operate through the green vegetation cover. Understanding such relationships can help in estimating changes in species richness in response to global climatic change.
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Proceedings of the National Academy of Sciences, 2013
Identifying priority areas for biodiversity is essential for directing conservation resources. Fundamentally, we must know where individual species live, which ones are vulnerable, where human actions threaten them, and their levels of protection. As conservation knowledge and threats change, we must reevaluate priorities. We mapped priority areas for vertebrates using newly updated data on >21,000 species of mammals, amphibians, and birds. For each taxon, we identified centers of richness for all species, small-ranged species, and threatened species listed with the International Union for the Conservation of Nature. Importantly, all analyses were at a spatial grain of 10 × 10 km, 100 times finer than previous assessments. This fine scale is a significant methodological improvement, because it brings mapping to scales comparable with regional decisions on where to place protected areas. We also mapped recent species discoveries, because they suggest where as-yet-unknown species might be living. To assess the protection of the priority areas, we calculated the percentage of priority areas within protected areas using the latest data from the World Database of Protected Areas, providing a snapshot of how well the planet's protected area system encompasses vertebrate biodiversity. Although the priority areas do have more protection than the global average, the level of protection still is insufficient given the importance of these areas for preventing vertebrate extinctions. We also found substantial differences between our identified vertebrate priorities and the leading map of global conservation priorities, the biodiversity hotspots. Our findings suggest a need to reassess the global allocation of conservation resources to reflect today's improved knowledge of biodiversity and conservation. endemism | species distributions | conservation planning | biogeography
2016
Abstract: Spatial variability in species richness has been postulated to depend upon environmental factors such as cli-matic variability, Net primary productivity and habitat heterogeneity. The Advanced Very High Resolution Radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) has been shown to be correlated with climatic variability, Net primary productivity and habitat heterogeneity. Moreover, Landsat Thematic Mapper (TM) derived habitat diversity indices have been used to reflect habitat heterogeneity. Interannually average NDVI and its variability (standard deviation and coefficient of variation) as well as Landsat Thematic Mapper derived habitat diversity index were correlated with mammal species richness at landscape scale. Species richness related unimodally to interannual average NDVI and posi-tively to variability of NDVI and habitat diversity index. Conversely, at regional scale mammal species richness were cor-related with interannually average NDVI and coeffic...
Satellite remote sensing to monitor species diversity: potential and pitfalls
Remote Sensing in Ecology and Conservation, 2015
Assessing the level of diversity in plant communities from field-based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma-diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments.