Incorporating biophysical ecology into high-resolution restoration targets: Insect pollinator habitat suitability models

Species distribution models can be informative of the biodiversity impacts of changing environments at global, national, and regional scales, but are often constrained in their resolution to extents not relevant to individual, intensive ecological management programs. We constructed a high-resolution topoclimatic model of spring and summer temperatures across a 152km 2 restoration area on the Swan Coastal Plain, Western Australia, and used it to project energetic expenditure and habitat suitability estimates for four major hymenopteran pollinators. For all species, the most heavily modified landscapes were the least suitable, but only for one species, Zapsilothynnus nigripes, was there evidence that the upper thermal tolerance threshold was exceeded broadly. However, at the higher environmental temperatures that we modeled, some species would need to forage up to 10 times their own body mass every hour to meet their energetic requirements. It seems unlikely that the nutritional requirements of most insect pollinators operating at these higher metabolic rates could be met in an impoverished restoration ecosystem, although resource availability remains to be quantified in these habitats. Hence, to increase the likelihood of restoration success by restoring insect pollination networks, nutritional resources may need to be increased during restoration. Accounting for the way that thermoenergetic requirements shape ecological interactions better positions management trajectories aimed at restoring and maintaining key insect pollinators in "novel" ecosystems

Monitoring the Impact of Grazing on Rangeland Conservation Easements Using MODIS Vegetation Indices

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 cost-effective, 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