African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2=0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2=0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2=0.82) and shrub cover (R2=0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2=0.76), shrubs (R2=0.83), and grass (R2=0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

The relationship between resource availability and wildlife movement patterns is pivotal to understanding species behavior and ecology. Movement response to landscape variables occurs at multiple temporal scales, from sub‐diurnal to multiannual. Additionally, individuals may respond to both current and past conditions of resource availability. In this paper, we examine the temporal scale and variation of current and past resource variables that affect movement patterns of African elephants (Loxodonta africana) using sub‐hourly movement data from GPS‐GSM collared elephants in Etosha National Park, Namibia. We created detailed satellite‐based spatiotemporal maps of vegetation biomass, as well as distance from surface water, road and fence. We used step selection functions to measure the relative importance of these landscape variables in determining elephants’ local movement patterns. We also examined how elephants respond to information, in locations they have previously visited, on productivity integrated over different temporal scales: from current to historical conditions. Our results demonstrate that elephants choose patches with higher than average annual productivity and grass biomass, but lower tree biomass. Elephants also prefer to walk close to water, roads, and fences. These preferences vary with time of day and with season, thereby providing insights into diurnal and seasonal behavioral patterns and the ecological importance of the landscape variables examined. We also discovered that elephants respond more strongly to long‐term patterns of productivity than to immediate forage conditions, in familiar locations. Our results illustrate how animals with high cognitive capacity and spatial memory integrate long‐term information on landscape conditions. We illuminate the importance of long‐term high temporal resolution satellite imagery to understanding the relationship between movement patterns and landscape structure.

Animals generally benefit from their gastrointestinal microbiome, but the factors that influence the composition and dynamics of their microbiota remain poorly understood. Studies of nonmodel host species can illuminate how microbiota and their hosts interact in natural environments. We investigated the role of migratory behaviour in shaping the gut microbiota of free-ranging barn swallows (Hirundo rustica) by studying co-occurring migrant and resident subspecies sampled during the autumn migration at a migratory bottleneck. We found that within-host microbial richness (α-diversity) was similar between migrant and resident microbial communities. In contrast, we found that microbial communities (β-diversity) were significantly different between groups regarding both microbes present and their relative abundances. Compositional differences were found for 36 bacterial genera, with 27 exhibiting greater abundance in migrants and nine exhibiting greater abundance in residents. There was heightened abundance of Mycoplasma spp. and Corynebacterium spp. in migrants, a pattern shared by other studies of migratory species. Screens for key regional pathogens revealed that neither residents nor migrants carried avian influenza viruses and Newcastle disease virus, suggesting that the status of these diseases did not underlie observed differences in microbiome composition. Furthermore, the prevalence and abundance of Salmonella spp., as determined from microbiome data and cultural assays, were both low and similar across the groups. Overall, our results indicate that microbial composition differs between migratory and resident barn swallows, even when they are conspecific and sympatrically occurring. Differences in host origins (breeding sites) may result in microbial community divergence, and varied | 4707 TURJEMAN ET Al.

Monitoring the effects of grazing on rangelands is crucial for ensuring sustainable rangeland ecosystem function and maintaining its conservation values. Residual dry matter (RDM), the dry grass biomass left on the ground at the end of the grazing season, is a commonly used proxy for rangeland condition in Mediterranean climates. Moderate levels of RDM are correlated with soil stability, forage production, wildlife habitat, and diversity of native plants. Therefore RDM is widely monitored on rangeland conservation properties. Current ground-based methods for RDM monitoring are expensive, are labor intensive, and provide information in the fall, after the effects of grazing have already occurred. In this paper we present a costeffective, rapid, and robust methodology to monitor and predict RDM using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. We performed a time series analysis of three MODIS-based vegetation indices (VIs) measured over the period 2000-2012: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR). We examined the correlation between the four VIs and fall RDM measured at The Nature Conservancy's Simon Newman Ranch in central California. We found strong and significant correlations between maximum VI values in late spring and RDM in the fall. Among the VIs, LAI values had the most significant correlation with fall RDM. MODIS-based multivariate models predicted up to 63% of fall RDM. Importantly, maximum and sum VIs values were significantly higher in management units with RDM levels in compliance with RDM conservation easement terms compared with units out of compliance. On the basis of these results, we propose a management model that uses time series analysis of MODIS VIs to predict forage quantities, manage stocking rates, and monitor rangeland easement compliance. This model can be used to improve monitoring of rangeland conservation by providing information on range conditions throughout the year.

No abstract