Ecological regionalisations delineate areas of similar environmental conditions, ecological processes, and biotic communities, and provide a basis for systematic conservation planning and management. Most regionalisations are made based on subjective criteria, and can not be readily revised, leading to outstanding questions with respect to how to optimally develop and define them. Advances in remote sensing technology, and big data analysis approaches, provide new opportunities for regionalisations, especially in terms of productivity patterns through both photosynthesis and structural surrogates. Here we show that global terrestrial productivity dynamics can be captured by Dynamics Habitat Indices (DHIs) and we conduct a regionalisation based on the DHIs using a two-stage multivariate clustering approach. Encouragingly, the derived clusters are more homogeneous in terms of species richness of three key taxa, and of canopy height, than a conventional regionalisation. We conclude with discussing the benefits of these remotely derived clusters for biodiversity assessments and conservation. The clusters based on the DHIs explained more variance, and greater within-region homogeneity, compared to conventional regionalisations for species richness of both amphibians and mammals, and were comparable in the case of birds. Structure as defined by global tree height was also better defined by productivity driven clusters than conventional regionalisations. These results suggest that ecological regionalisations based on remotely sensed metrics have clear advantages over conventional regionalisations for certain applications, and they are also more easily updated.

Adaptive management of large herbivores requires an understanding of how spatial‐temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial‐temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free‐ranging cattle. However, less is known about the spatial‐temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi‐temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite‐derived phenological metrics (pseudo‐daily, using data fusion and interpolation) to model daily mass gains of free‐ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid‐May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10‐year period (n = 138). We found strong relationships between diet quality and the satellite‐derived phenological metrics, especially metrics related to the timing and rate of green‐up and senescence. Satellite‐derived diet quality estimates were strong predictors of monthly mass gains (R² = 0.68) across a wide range of aboveground net herbaceous production. Season‐long predictions of average daily gain and cattle off‐mass had mean absolute errors of 8.9% and 2.9%, respectively. The model performed better temporally (across repeated observations in the same paddock) than spatially (across all paddocks within a given year), highlighting the need for accurate vegetation maps and robust field data collection across both space and time. This study demonstrates that free‐ranging cattle performance in rangelands is strongly affected by diet quality, which is related to the timing of vegetation green‐up and senescence. Senescing vegetation suppressed mass gains, even if adequate forage was available. The satellite‐based pseudo‐daily approach presented here offers new opportunities for adaptive management of large herbivores, such as identifying within‐season triggers to move livestock among paddocks, predicting wildlife herd health, or timing the grazing season to better match earlier spring green‐up caused by climate change and plant species invasion.

Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow dynamics. This study examines grizzly bear (Ursus arctos) spring habitat selection and use across western Alberta, Canada. Made possible by newly available fine-scale snow cover data, this research tests a hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring (den emergence to May 31^st). Using Integrated Step Selection Analysis, a series of models were built to examine whether snow cover information such as fractional snow covered area and date of snow melt improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, we found that the inclusion of fractional snow covered area improved model fit 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes, including fractional snow covered area, date since snow melt, elevation, and distance to road. Results indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions. This study is an example of how fine spatial and temporal scale remote sensing data can be used to improve our understanding of wildlife habitat selection and use in relation to key environmental attributes.

Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.