H. Scott Butterfield

Species distribution models of an endangered rodent offer conflicting measures of habitat quality at multiple scales

Summary 1. The high cost of directly measuring habitat quality has led ecologists to test alternate methods for estimating and predicting this critically important ecological variable. In particular, it is frequently assumed but rarely tested that models of habitat suitability (‘species distribution models’, SDMs) may provide useful indices of habitat quality, either from an individual animal or manager’s perspective. Critically, SDMs are increasingly used to estimate species’ ranges, with an implicit assumption that areas of high suitability will result in higher probability of persistence. This assumption underlies efforts to use SDMs to design protected areas, assess the status of cryptic species or manage responses to climate change. Recent tests of this relationship have provided mixed results, suggesting SDMs may predict abundance but not other measures of high-quality habitat (e.g. survival, persistence). 2. In this study, we created a suite of SDMs for the endangered giant kangaroo rat Dipodomys ingens at three distinct scales using the machine-learning method Maxent. We compared these models with three measures of habitat quality: survival, abundance and body condition. 3. Species distribution models were not correlated with survival, while models at all scales were positively correlated with abundance. Finer-scale models were more closely correlated with abundance than the largest scale. Body condition was not correlated with habitat suitability at any scale. The inability of models to predict survival may be due to a lack of information in environmental covariates; unmeasured community processes or stochastic events; or the inadequacy of using models that predict species presence to also predict demography. 4. Synthesis and applications. Species distribution models (SDMs), especially fine scale ones, may be useful for longer-term management goals, such as identifying high-quality habitat for protection. However, short-term management decisions should be based only on models that use covariates appropriate for the necessary temporal and spatial scales. Assumptions about the relationship between habitat suitability and habitat quality must be made explicit. Even then, care should be taken in inferring multiple types of habitat quality from SDMs.

Experimental Use of Remote Sensing by Private Range Managers and Its Influence on Management Decisions

Abstract Although remote sensing has many potential applications for range management, its use by range managers thus far has been limited. To investigate the factors that encourage use of remote sensing and to examine its influence on decision making by individuals who manage privately owned rangeland, we evaluated the decision-making processes of 3 ranch owners and 1 professional ranch manager who were introduced to remote sensing while collaborating with us in a rangeland stewardship program in California. Two of the participants had extensive ranching experience (11 to > 20 years) and managed large cattle ranches (1 000 to > 2 000 ha), and 2 had less experience and managed smaller sheep ranches (< 200 ha). During the 5-year program, the participants implemented a series of new management practices, including prescribed burning, rotational grazing, and seeding of native grasses, with the aim of reducing noxious weeds and increasing productivity. We used remote sensing to quantify the effect of these practices and provided ranch-wide remote sensing analyses to each manager on a password-protected Web site. Using case study methodologies, we found that managers of larger, commercially active ranches found the experimental use of remote sensing to be a highly positive experience that convinced them that this technology could help address difficult management situations and increase ranch profitability. This suggests that the broad use of remote sensing by managers of privately held, commercial rangelands may be limited in part by the simple lack of opportunity to test these technologies. Programs that assist ranchers in obtaining appropriate remote sensing products thus may be a cost-effective way to enhance conservation on private rangelands. Our findings suggest that voluntary self-analysis by ranchers of the landscape dynamics of their own properties is likely to lead to more engaged conservation efforts than will top-down prescriptions.

Contemporary Drought and Future Effects of Climate Change on the Endangered Blunt-Nosed Leopard Lizard, Gambelia sila

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.

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

ABSTRACT 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 Conservancys 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.

Persistence of historical population structure in an endangered species despite near-complete biome conversion in California's San Joaquin Desert

Genomic responses to habitat conversion can be rapid, providing wildlife managers with time‐limited opportunities to enact recovery efforts that use population connectivity information that reflects predisturbance landscapes. Despite near‐complete biome conversion, such opportunities may still exist for the endemic fauna and flora of Californias San Joaquin Desert, but comprehensive genetic data sets are lacking for nearly all species in the region. To fill this knowledge gap, we studied the rangewide population structure of the endangered blunt‐nosed leopard lizard Gambelia sila, a San Joaquin Desert endemic, using restriction site‐associated DNA (RAD), microsatellite and mtDNA data to test whether admixture patterns and estimates of effective migration surfaces (EEMS) can identify land areas with high population connectivity prior to the conversion of native xeric habitats. Clustering and phylogenetic analyses indicate a recent shared history between numerous isolated populations and EEMS reveals latent signals of corridors and barriers to gene flow over areas now replaced by agriculture and urbanization. Conflicting histories between the mtDNA and nuclear genomes are consistent with hybridization with the sister species G. wislizenii, raising important questions about where legal protection should end at the southern range limit of G. sila. Comparative analysis of different data sets also adds to a growing list of advantages in using RAD loci for genetic studies of rare species. We demonstrate how the results of this work can serve as an evolutionary guidance tool for managing endemic, arid‐adapted taxa in one of the worlds most compromised landscapes.

A multi-scale distribution model for non-equilibrium populations suggests resource limitation in an endangered rodent.

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.

Testing a Remote Sensing-Based Interactive System for Monitoring Grazed Conservation Lands

On the Ground Many public agencies and land trusts that manage grazing lands are interested in using remote sensing technologies to make their monitoring programs more efficient but lack the expertise to do so. In California annual grasslands, using remote sensing is especially challenging because the dominant vegetation is not detectable by standard technologies at a key time of year for monitoring. The Nature Conservancy of California (TNC) has developed RDMapper, an easy-to-use web-based tool that uses satellite-based productivity estimates, rainfall records, and compliance history to identify management units at risk of being below the required level of residual dry matter (RDM). TNC successfully used RDMapper in 2015 and 2016 to predict compliance across approximately 47,000 hectares of conservation easement grasslands, while reducing monitoring costs by 42%. We also applied RDMapper on six non-TNC properties (approximately 5,700 hectares) owned by two public agencies. We correctly predicted RDM compliance on 74% of the management units and found the method to be successful overall, with several challenges mainly relating to meeting RDMappers data requirements. Our study illuminated potential benefits, hurdles, and best practices for landowners interested in using RDMapper to increase monitoring efficiency, and made recommendations to improve it. Adding RDMapper to conventional monitoring toolkits could be game-changing for public lands management agencies that currently struggle to manage vast grasslands.

Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management

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.

A trail camera imagery dataset of contrasting shrub and open microsites within the Carrizo Plain National Monument, San Luis Obispo County, California

Background Carrizo Plain National Monument is one of the largest remaining patches of San Joaquin Desert left within the Central Valley of California. It is home to many threatened and endangered species including the San Joaquin kit fox, blunt-nosed leopard lizard, and giant kangaroo rat. The dominant plant lifeform is shrubs. The species Ephedra californica comprises a major proportion of the community within this region and likely also provides key ecosystem services. We used motion sensor trail cameras to examine interactions between animals and these shrubs. This technology is a less invasive alternative to other animal surveying methods such as line transects, radio tracking, and spotlight surveys. Cameras were placed within the shrub understory and in the open (i.e. non-canopied) microhabitats at ground level to estimate animal activity. Findings Trail cameras were successful in detecting the presence of animal species at shrub and open microhabitats. A total of 20 cameras were deployed from April 1st, 2015 to July 5th, 2015 at paired shrub/open microsites at three locations along Elkhorn Road in Carrizo Plain National Monument (35.1914° N, 119.7929° W). Each independent site was approximately 1 km2. Over 440,000 pictures (both of animals and triggers from vegetation moving in the wind) were taken during this time. The trigger rate was very high on the medium sensitivity camera setting in this desert ecosystem, and the rates did not differ between shrub and open microsites. The raw data (.jpeg images) are publicly available for download from GigaDB. Conclusions Motion sensor trail cameras are an effective, non-invasive alternative survey method for collecting data on presence/absence of desert animals. We detected mammals, reptiles, birds, and also insects in 0.4% of the images. We also successfully detected the Federally-listed blunt-nosed leopard lizard. A more extensive array of cameras within the Carrizo Plain National Monument could thus be an effective tool to estimate the presence of this species along with the presence of other animals.