MODIS time series contribution for the estimation of nutritional properties of alpine grassland

This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Remote Sensing on 17th February 2017, available online: https://doi.org/10.5721/EuJRS20164936Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both cross-validated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported

Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa

Empirical verification of multiple states in drylands is scarce, impeding the design of indicators to anticipate the onset of desertification. Remote sensing‐derived indicators of ecosystem states are gaining new ground due to the possibilities they bring to be applied inexpensively over large areas. Remotely sensed albedo has been often used to monitor drylands due to its close relationship with ecosystem status and climate. Here, we used a space‐for‐time‐substitution approach to evaluate whether albedo (averaged from 2000 to 2016) can identify multiple ecosystem states in African drylands spanning from the Saharan desert to tropical Africa. By using latent class analysis, we found that albedo showed two states (low and high; the cut‐off level was 0.22 at the shortwave band). Potential analysis revealed that albedo exhibited an abrupt and discontinuous increase with increased aridity (1 − [precipitation/potential evapotranspiration]). The two albedo states co‐occurred along aridity values ranging from 0.72 to 0.78, during which vegetation cover exhibited a rapid, continuous decrease from ~90% to ~50%. At aridity values of 0.75, the low albedo state started to exhibit less attraction than the high albedo state. Low albedo areas beyond this aridity value were considered as vulnerable regions where abrupt shifts in albedo may occur if aridity increases, as forecasted by current climate change models. Our findings indicate that remotely sensed albedo can identify two ecosystem states in African drylands. They support the suitability of albedo indices to inform us about discontinuous responses to aridity experienced by drylands, which can be linked to the onset of land degradation.This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19030500), the National Key Research and Development Program of China (Grant 2016YFC0503302), the European Research Council (BIODESERT project, ERC Grant Agreement 647038), the Joint PhD, Training Program of the University of Chinese Academy of Sciences, and the Research Foundation of Henan University of Technology (Grant 31401178)

Woodland Caribou Habitat Selection Patterns in Relation to Predation Risk and Forage Abundance Depend on Reproductive State

The ideal free distribution assumes that animals select habitats that are beneficial to their fitness. When the needs of dependent offspring differ from those of the parent, ideal habitat selection patterns could vary with the presence or absence of offspring. We test whether habitat selection depends on reproductive state due to top‐down or bottom‐up influences on the fitness of woodland caribou (Rangifer tarandus caribou), a threatened, wide‐ranging herbivore. We combined established methods of fitting resource and step selection functions derived from locations of collared animals in Ontario with newer techniques, including identifying calf status from video collar footage and seasonal habitat selection analysis through latent selection difference functions. We found that females with calves avoided predation risk and proximity to roads more strongly than females without calves within their seasonal ranges. At the local scale, females with calves avoided predation more strongly than females without calves. Females with calves increased predation avoidance but not selection for food availability upon calving, whereas females without calves increased selection for food availability across the same season. These behavioral responses suggest that habitat selection by woodland caribou is influenced by reproductive state, such that females with calves at heel use habitat selection to offset the increased vulnerability of their offspring to predation risk

The legacy of past human land use in current patterns of mammal distribution

Multiple environmental factors are known to shape species distributions at the global scale, including climate and topography, but understanding current extents of occurrence and biodiversity patterns requires considering anthropogenic factors as well. Numerous studies have explored the relationship between contemporary human activities and different biodiversity metrics, but the influence of past activities, such as land-use, remains poorly understood despite being one of the oldest human impacts. Here we evaluate the role of past land-use modifications in the current distribution and conservation status of mammals worldwide using spatial data characterizing human land use from c.B.C.6000 to c.A.D.2000. First, we applied a clustering method that revealed three generalized past human land-use trajectories that represent low-, recently- and steadily-used areas widely represented across the globe. Second, we fitted boosted regression trees to predict total and threatened mammalian richness, globally and within trajectory-clusters, testing the role of environmental factors and multiple human land-use metrics reflecting: total used area at different time spans, rates of land-use change, and the occurrence of remarkable land-use shifts. Environmental factors were identified as the main correlates of current mammalian richness, but several proposed metrics of past land-use were also relevant predictors. Overall, these results highlight the likely existence of a land-use legacy in some regions of the world that has influenced the distribution of extant mammals, particularly of those currently classified as threatened. Even if we cannot change that legacy, our results show that we need to account for past human impacts to understand present biodiversity patterns and, arguably, to guide future actions