Rapid biodiversity change at a global scale requires enhanced monitoring tools to predict how shifting environmental conditions might alter species’ extinction risk. Emerging remote sensing tools are essential to these efforts and provide the sole mechanism to detect environmental changes and their potential consequences for biodiversity rapidly. Here, I assess the extent to which remote sensing measurements predict species richness globally and within regions, facilitating the establishment of a single framework for monitoring diversity worldwide. I assembled global remote sensing metrics and data on diversity gradients to construct and cross-validate models predicting species richness of birds and mammals within and among the world’s biogeographic zones. Enhanced vegetation Index (EVI), land surface temperature (LST), the first principal component of habitat heterogeneity, and an interaction between energy and habitat heterogeneity are important remotely-sensed environmental measurements for predicting trends of species richness of birds and mammals at all scales, although the intensity of the relationship differs between groups and grain sizes. However, a global model does not explain differences in species richness of birds between distinct zoogeographical realms, indicating a possible threshold in biodiversity change prediction before onset of novel environmental conditions. Measuring potential nonlinear changes in species richness is a useful application of the essential biodiversity variables (EBV) framework for operational monitoring of global and regional biodiversity. The continued production of reliable and consistent remote sensing will facilitate further exploration of current and upcoming drivers of biodiversity change and will help improve macroecological models.

Unprecedented rates of modern species extinction present a serious challenge in the field of conservation biology. While protected areas (PAs) are regarded as key tools to reduce rates of biodiversity loss, it is unclear to what degree PAs can maintain their ecological integrity while experiencing external pressures from outside of their boundaries. Satellite remote sensing essential biodiversity variables (SRS-EBVs) are indicators of biodiversity that can be produced with large spatial coverages and can be used to measure PAs’ capacity to preserve important ecological elements for biodiversity. In this study, I used SRS-EBVs representative of ecosystem structure and function, including productivity, disturbance regimes, ecosystem extent, and ecosystem composition. I tested if PAs preserved these determinants of species survival through time, whether any changes in these variables in PAs were independent of changes in their surrounding areas (buffer zones), and if the management type of PAs influenced either of these patterns. I found that PAs maintained elements of ecosystem structure, including habitat heterogeneity and extent, inside of their boundaries, regardless of changes that occurred in their surroundings. In contrast, PAs were less effective at sustaining elements of ecosystem function and mitigating other forms of human disturbance. Productivity within PAs was the same as that of their surroundings, underscoring the inability of PAs to track shifts in climate regimes that put some species at greater risk of extinction. Fire disturbance trends were maintained across PA boundaries; however, the causes of these fires are unknown, highlighting the importance of supplemental fire census data to tease apart the trends of natural fire regimes compared to harmful burns. Finally, other human pressures thought to be the indirect effects of linear transportation features (ex. edge effects from roads) were observed to have spilled over from buffer zones into PAs. Planning for future development of the global PA network can benefit greatly from the application of SRS-EBVs. Pairing these data products with foundational ecological conservation principles can build a stronger, more efficient PA network for the preservation of Earth’s species.