Understanding how biodiversity is impacted by natural and man-made factors is essential for developing ecological theory and planning conservation efforts, especially in the face of rapid global changes. Biodiversity is thought to be more strongly predicted by biotic drivers (e.g., competition) at local scales and by abiotic drivers (e.g., climate) at broader spatial scales, yet this theory has not been sufficiently tested from local to continental scales. In addition, natural and man-made disturbances, as well as past land use alter expected relationships between these drivers and biodiversity. The overarching hypothesis for this research is that disturbance history and past land use will have a strong influence on niche overlap, and therefore biodiversity at local scales, whereas climate and geodiversity—the variation in Earth’s abiotic features and processes—should mediate this relationship at intermediate and broader scales. To address this hypothesis, we are leveraging the hierarchical spatial design of the National Ecological Observatory Network (NEON), using NEON data on intraspecific trait variation (ITV) and diversity, and collecting additional measurements from NEON organismal samples. Although NEON measures many abiotic drivers of biodiversity (e.g., climate, soil attributes), it lacks spatially explicit data on past land use, disturbances, and geodiversity. Without these key data, it is challenging to interpret biodiversity patterns and capture important cross-scale interactions among drivers. We are addressing this gap by generating open and accessible novel geospatial techniques and spatial data on disturbance regimes, land use histories, climate, and geodiversity from local to continental scales.
Results/Conclusions
We developed R packages and geospatial workflows across scales with satellite remotely-sensed imagery to (1) detect and attribute disturbances, and to (2) quantify geodiversity. For example, the geodiv R package provides the first open-source software for calculating gradient surfaces and metrics to measure geodiversity on any continuous imagery. Preliminary results from disturbance analysis with Landsat show that leveraging spatial information can improve detect and attribute specific forest disturbance types and that the techniques are transferable across multiple ecoregions. Preliminary results from geodiversity analyses with NDVI and SRTM elevation show that scale-dependent gradient surface metrics can effectively identify regions with low vs. high spatial heterogeneity in a variety of remotely-sensed variables. Taken together, these techniques provide effective ways to quantify drivers of biodiversity and ITV across scales. Ultimately, these resources will be vital to interpreting and forecasting spatial and temporal variation in biodiversity across NEON and beyond, especially with ongoing climate and land use change.