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Abstract

Remotely sensed data can help to identify both suitable habitat for individual species, and environmental conditions that foster species richness, which is important when predicting how biodiversity will respond to global change. The question is how to summarize remotely sensed data so that they are most relevant for biodiversity analyses, and the Dynamic Habitat Indices are three metrics designed for this. Our goals here were to a) derive, for the first time, the Dynamic Habitat Indices (DHIs) globally, and b) use these to evaluate three hypotheses (available energy, environmental stress, and environmental stability) that attempt to explain global variation in species richness of amphibians, birds, and mammals. The three DHIs summarize three key measures of vegetative productivity: a) annual cumulative productivity, which we used to evaluate the available energy hypothesis that more energy is associate with higher species richness; b) minimum productivity throughout the year, which we used to evaluate the environmental stress hypothesis that higher minima cause higher species richness, and c) seasonality, expressed as the annual coefficient of variation in productivity, which we used to evaluate the environmental stability hypothesis that less intra-annual variability causes higher species richness. We calculated the DHIs globally at 1-km resolution from MODIS vegetation products (NDVI, EVI, LAI, fPAR, and GPP), based on the median of the good observations of all years from the entire MODIS record for each of the 23 or 46 possible dates (8- vs. 16-day composites) during the year, and calculated species richness for three taxa (amphibians, birds, and mammals) at 110-km resolution from species range maps from the IUCN Red List. We found marked global patterns of the DHIs, and strong support for all three hypotheses. The three DHIs for a given vegetation product were well correlated (Spearman rank correlations ranging from −0.6 (cumulative vs. variation DHIs) to −0.93 (variation vs. minimum DHI)). Similarly, DHI components derived from different MODIS vegetation products were well correlated (0.8–0.9), and correlations of the DHIs with temperature and precipitation were moderate and strong respectively. All three DHIs were well correlated with species richness, showing in ranked order positive correlations for cumulative DHI based on GPP (Spearman rank correlations of 0.75, 0.63, and 0.67 for amphibians, resident birds, and mammals respectively) and minimum DHI (0.73, 0.83, and 0.62), and negative for variation DHI (−0.69, −0.83, and −0.59). Multiple linear models of all three DHIs explained 67%, 65%, and 61% of the variability in species richness of amphibians, resident birds, and mammals, respectively. The DHIs, which are closely related to well-established ecological hypotheses of biodiversity, can predict species richness well, and are promising for application in biodiversity science and conservation.

Item Type:	Article
Authors/Creators:	Radeloff, VC and Dubinin, M and Coops, NC and Allen, AM and Brooks, TM and Clayton, MK and Costa, GC and Graham, CH and Helmers, DP and Ives, AR and Kolesov, D and Pidgeon, AM and Rapacciuolo, G and Razenkova, E and Suttidate, N and Young, BE and Zhu, L and Hobi, ML
Keywords:	biodiversity, conservation, global, MODIS, phenology, productivity, remote sensing
Journal or Publication Title:	Remote Sensing of Environment
Publisher:	Elsevier Science Inc
ISSN:	0034-4257
DOI / ID Number:	https://doi.org/10.1016/j.rse.2018.12.009
Copyright Information:	© 2018 Elsevier Inc. All rights reserved.
Related URLs:	UTAS Profile: Brooks, TM
Item Statistics:	View statistics for this item

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