Abstract
Ecological regionalisations delineate areas of similar environmental conditions, ecological processes, and biotic communities, and provide a basis for systematic conservation planning and management. Most regionalisations are made based on subjective criteria, and can not be readily revised, leading to outstanding questions with respect to how to optimally develop and define them. Advances in remote sensing technology, and big data analysis approaches, provide new opportunities for regionalisations, especially in terms of productivity patterns through both photosynthesis and structural surrogates. Here we show that global terrestrial productivity dynamics can be captured by Dynamics Habitat Indices (DHIs) and we conduct a regionalisation based on the DHIs using a two-stage multivariate clustering approach. Encouragingly, the derived clusters are more homogeneous in terms of species richness of three key taxa, and of canopy height, than a conventional regionalisation. We conclude with discussing the benefits of these remotely derived clusters for biodiversity assessments and conservation. The clusters based on the DHIs explained more variance, and greater within-region homogeneity, compared to conventional regionalisations for species richness of both amphibians and mammals, and were comparable in the case of birds. Structure as defined by global tree height was also better defined by productivity driven clusters than conventional regionalisations. These results suggest that ecological regionalisations based on remotely sensed metrics have clear advantages over conventional regionalisations for certain applications, and they are also more easily updated.
Highlights
Natural systems are complex and variable over time and space, and understanding the patterns and processes that occur within natural systems, their interactions, and their effects on biodiversity is at the heart of macro-ecology
The Dynamics Habitat Indices (DHIs) reflect a number of environmental parameters, including climate and terrain, as well as information of vegetation production, and in some instances land cover and land use patterns, and that makes them powerful predictors of biodiversity patterns[35]
Because the DHIs are computed based on 8-day variations in fPAR, which is a key productivity indicator, they provide a link with previous experimental, descriptive, and theoretical work that relates productivity to species richness and composition[37,38]
Summary
Natural systems are complex and variable over time and space, and understanding the patterns and processes that occur within natural systems, their interactions, and their effects on biodiversity is at the heart of macro-ecology. With increases in computing power, and the availability of finer-resolution, spatially-explicit datasets of the environment and its biota[17], the potential to develop quantitative rather than qualitative regionalisations has increased substantially and such quantitative regionalisations have the benefit that they are more explicit, repeatable, transferable, and defensible than subjective regionalisations based on human expertise[18] These benefits, in turn, enhance and expand the utility of ecoregions, making them more valuable for certain ecosystem management applications, allowing areas of common environmental characteristics to be grouped, and dissimilar classes compared, as well as supporting quantitative analysis of how unique the delineated regions are, and informing monitoring programs. The DHIs capture: (1) cumulative annual productivity as the integrated landscape productive capacity over a year analogous to the available energy hypothesis which suggests that areas of high vegetation productivity have more resources to partition among competing species, supporting a greater number of species, and higher population densities, than areas with lower productivity[29,30,31] (2) annual minimum productivity as minimum amount of vegetation production over a year, which may compose impositions of inclement climate and seasonally low productivity as constraints on biodiversity and (3) seasonal variation in productivity, which reflects how the vegetation varies within the year, an indicator of climatic variation, and phenology which may be indicative of the capacity of the landscape that may limit permanent resident species[32], but not migratory species[33]
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