Restoring habitat in retired farmland could reduce water demand and provide ecosystem services for farmers and local communities.

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.

Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define "available" habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining "available" habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales relevant to theoretical and applied ecologists.

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics.

Extreme weather events can provide unique opportunities for testing models that predict the effect of climate change. Droughts of increasing severity have been predicted under numerous models, thus contemporary droughts may allow us to test these models prior to the onset of the more extreme effects predicted with a changing climate. In the third year of an ongoing severe drought, surveys failed to detect neonate endangered blunt-nosed leopard lizards in a subset of previously surveyed populations where we expected to see them. By conducting surveys at a large number of sites across the range of the species over a short time span, we were able to establish a strong positive correlation between winter precipitation and the presence of neonate leopard lizards over geographic space. Our results are consistent with those of numerous longitudinal studies and are in accordance with predictive climate change models. We suggest that scientists can take immediate advantage of droughts while they are still in progress to test patterns of occurrence in other drought-sensitive species and thus provide for more robust models of climate change effects on biodiversity.

A recent global trend toward retirement of farmland presents opportunities to reclaim habitat for threatened and endangered species. We examine habitat restoration opportunities in one of the world's most converted landscapes, California's San Joaquin Desert (SJD). Despite the presence of 35 threatened and endangered species, agricultural expansion continues to drive habitat loss in the SJD, even as marginal farmland is retired. Over the next decades a combination of factors, including salinization, climate change, and historical groundwater overdraft, are projected to lead to the retirement of more than 2,000 km2 of farmland in the SJD. To promote strategic habitat protection and restoration, we conducted a quantitative assessment of habitat loss and fragmentation, habitat suitability, climatic niche stability, climate change impacts, habitat protection, and reintroduction opportunities for an umbrella species of the SJD, the endangered blunt-nosed leopard lizard (Gambelia sila). We use our suitability models, in conjunction with modern and historical land use maps, to estimate the historical and modern rate of habitat loss to development. The estimated amount of habitat lost since the species became protected under endangered species law in 1967 is greater than the total amount of habitat currently protected through public ownership and conservation easement. We document climatic niche contraction and associated range contraction away from the more mesic margins of the species' historical distribution, driven by the anthropogenic introduction of exotic grasses and forbs. The impact of exotic species on G. sila range dynamics appears to be still unfolding. Finally, we use NASA fallowed area maps to identify 610 km2 of fallowed or retired agricultural land with high potential to again serve as habitat. We discuss conservation strategies in light of the potential for habitat restoration and multiple drivers of ongoing and historical habitat loss.