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.
1.Univ Wisconsin, Dept Forest & Wildlife Ecol, SILVIS Lab, 1630 Linden Dr, Madison, WI 53706 USA 2.NextGIS, Moscow, Russia 3.Univ British Columbia, Dept Forest Resources Management, Integrated Remote Sensing Studio, Vancouver, BC V6T 1Z4, Canada 4.Radboud Univ Nijmegen, Inst Water & Wetland Res, Dept Anim Ecol & Physiol, NL-6500 GL Nijmegen, Netherlands 5.Int Union Conservat Nat, Gland, Switzerland 6.Univ Philippines Los Banos, World Agroforestry Ctr ICRAF, Laguna 4031, Philippines 7.Univ Tasmania, Inst Marine & Antarctic Studies, Hobart, Tas 7001, Australia 8.Univ Wisconsin, Dept Stat, Madison, WI 53706 USA 9.Auburn Univ, Dept Biol, Montgomery, AL 36124 USA 10.Swiss Fed Res Inst WSL, CH-8903 Birmensdorf, Switzerland 11.Univ Wisconsin, Dept Integrat Biol, Madison, WI 53706 USA 12.Univ Calif Merced, Dept Life & Environm Sci, Merced, CA 95343 USA 13.Walialok Univ, Dept Biol, 222 Thaiburi, Thasala, Nakhon Si Thamm, Thailand 14.NatureServe, 4600 N Fairfax Dr, Arlington, VA 22203 USA
Recommended Citation:
Radeloff, V. C.,Dubinin, M.,Coops, N. C.,et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity[J]. REMOTE SENSING OF ENVIRONMENT,2019-01-01,222:204-214