Steven E. Sesnie

A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert, USA

Sonoran Desert bighorn sheep (Ovis canadensis mexicana) occupy rugged upland areas that experience irregular periods of vegetation growth associated with precipitation events. These episodic and often spatially limited events provide important forage and preformed water resources that may be important drivers of animal movement and habitat use. Habitat-use models that incorporate forage phenology would broaden our understanding of desert bighorn ecology and have considerable potential to inform conservation efforts for the species. Field-based methods are of limited utility to characterize vegetation phenology across large areas. Vegetation indices (VI) derived from satellite imagery are a viable alternative, but may be confounded by areas of high relief and shadow effects that can degrade VI values. The varying spatial and temporal resolutions of readily available satellite sensors, such as the Landsat thematic mapper (TM) and moderate-resolution imaging spectrometer (MODIS), present additional challenges. In this study, we sought to minimize degrading effects of terrain on TM- and MODIS-based estimates of vegetation phenology. We compared effects of high topographic relief on time series MODIS- and TM-based VI such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) using VI departures from average (DA) in shaded and unshaded areas. Sun elevation angle negatively impacted TM-derived NDVI and EVI values in areas of steep terrain. In contrast, MODIS-derived NDVI values were insensitive to sun elevation and terrain effects, whereas MODIS-derived EVI was degraded in areas of steep terrain. Time series MODIS NDVI and EVI DA values differed significantly during months of low sun elevation angle. Average MODIS EVI departure values were ≥20% lower than NDVI under these conditions, confounding time series estimates of plant phenology. Our best results were obtained from MODIS 16-day composited NDVI. These remote-sensing-based VI estimates of seasonal plant phenology and productivity can be used to inform models of habitat use and movements of desert bighorn over large areas.

Integrating single-species management and landscape conservation using regional habitat occurrence models: the northern goshawk in the Southwest, USA

Conservation planners and land managers are often confronted with scale-associated challenges when assessing the relationship between land management objectives and species conservation. Conservation of individual species typically involves site-level analyses of habitat, whereas land management focuses on larger spatial extents. New models are needed to more explicitly integrate species-specific conservation with landscape or regional scales. We address this challenge with an example using the northern goshawk (Accipiter gentilis), a forest raptor with circumpolar distribution that is the focus of intense debate regarding forest management on public lands in the southwestern USA. To address goshawk-specific habitat conservation across a management area of 22,800-km2 in northern Arizona, we focused on the territory scale rather than individual nest sites. We compiled a 17-year database of 895 nest sites to estimate territory locations. We then estimated the likelihood of territory occurrence for the entire management area using multiple logistic regression within an expert-driven, spatially balanced, and information-theoretic framework. Our occurrence model incorporated forest structure variables that were derived from USFS Forest Inventory and Analysis plots and high-resolution satellite imagery. Results indicated that high canopy-bulk density, intermediate canopy-base heights, and low variation in tree density were strong predictors of territory occurrence. We used model-averaged parameter estimates for these variables to map and explore patterns of territory distribution across multiple land jurisdictions and ecological subregions. Our iterative modeling approach complements previous demographic studies in the region. It also provides a robust framework for integrating species conservation and landscape management in ongoing and future regional planning efforts.

An iterative and targeted sampling design informed by habitat suitability models for detecting focal plant species over extensive areas.

Prioritizing areas for management of non-native invasive plants is critical, as invasive plants can negatively impact plant community structure. Extensive and multi-jurisdictional inventories are essential to prioritize actions aimed at mitigating the impact of invasions and changes in disturbance regimes. However, previous work devoted little effort to devising sampling methods sufficient to assess the scope of multi-jurisdictional invasion over extensive areas. Here we describe a large-scale sampling design that used species occurrence data, habitat suitability models, and iterative and targeted sampling efforts to sample five species and satisfy two key management objectives: 1) detecting non-native invasive plants across previously unsampled gradients, and 2) characterizing the distribution of non-native invasive plants at landscape to regional scales. Habitat suitability models of five species were based on occurrence records and predictor variables derived from topography, precipitation, and remotely sensed data. We stratified and established field sampling locations according to predicted habitat suitability and phenological, substrate, and logistical constraints. Across previously unvisited areas, we detected at least one of our focal species on 77% of plots. In turn, we used detections from 2011 to improve habitat suitability models and sampling efforts in 2012, as well as additional spatial constraints to increase detections. These modifications resulted in a 96% detection rate at plots. The range of habitat suitability values that identified highly and less suitable habitats and their environmental conditions corresponded to field detections with mixed levels of agreement. Our study demonstrated that an iterative and targeted sampling framework can address sampling bias, reduce time costs, and increase detections. Other studies can extend the sampling framework to develop methods in other ecosystems to provide detection data. The sampling methods implemented here provide a meaningful tool when understanding the potential distribution and habitat of species over multi-jurisdictional and extensive areas is needed for achieving management objectives.