US-IALE 2018

AUTHORS: Quentin D. Read*, Michigan State University; Sydne Record, Bryn Mawr College; Kyla Dahlin, Michigan State University; Phoebe L. Zarnetske, Michigan State University; Sparkle L. Malone, Florida International University; Keith Gaddis, National Aeronautics and Space Administration; John M. Grady, Bryn Mawr College; Jennifer Costanza, North Carolina State University; Martina L. Hobi, Swiss Federal Research Institute; Andrew Latimer, University of California-Davis; Stephanie Pau, Florida State University; Adam M. Wilson, University at Buffalo; Andrew Finley, Michigan State University; Scott Ollinger, University of New Hampshire

ABSTRACT: Goals: Biodiversity, the variety and distribution of species and their functions, is intimately related to geodiversity, the variety and distribution of nonliving features and processes. Novel remote sensing data products generated by NASA and other agencies show promise for increasing our understanding of this relationship. How we conceive of geodiversity and how we measure it are both critical for understanding and predicting biodiversity. We need to establish ways to conceptualize and measure landscape diversity to better conserve the living and nonliving diversity of the physical world.Approach: Here, we investigate several ways of measuring geodiversity of continuous variables, using topography as an example. We compare how well simple "spatially naive" metrics like standard deviation that ignore spatial arrangement of pixels predict variation in biodiversity compared to with more complex "spatially conscious" metrics that incorporate spatial arrangement of pixel values. Next, we compare different kernel sizes (spatial grains) ranging from 5 km to 100 km. We present relationships between these geodiversity metrics and the alpha-diversity (average species richness) and beta-diversity (variation in species across sites) of trees and birds in the continental USA.Results/Discussion: We found that spatially naive metrics of geodiversity predict biodiversity roughly as well as the spatially conscious metrics. In addition, increasing the radius over which the metrics are calculated provides different information. Interestingly, geodiversity explains little variation in alpha-diversity, but is strongly positively related to beta-diversity and gamma-diversity. This effect is especially pronounced in trees relative to birds, which may be due to their relatively poor mobility.The data processing pipeline we have developed to calculate many different geodiversity metrics at different kernel sizes is available on GitHub; we welcome collaboration and feedback.