The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r² = 0.65) and 50-m AGL (r² = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals.

Identification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that reach the ground, potentially creating false planar surfaces within a vegetation stand. We present an application of discriminant analysis (a commonly used statistical tool in decision theory) to classify polygons (derived from LiDAR) as either buildings or a non-building planar surfaces. We conducted our analysis in southern Texas where thornscrub vegetation often prevents a LiDAR beam from fully penetrating the vegetation canopy in and around residential areas. Using discriminant analysis, we grouped potential building polygons into building and non-building classes using the point densities of ground, unclassified, and building points. Our technique was 95% accurate at distinguishing buildings from non-buildings. Therefore, we recommend its use in any locale where distinguishing buildings from surrounding vegetation may be affected by the proximity of dense vegetation to buildings.

Many applied disciplines report treatment means and their standard errors (or confidence intervals) in the presentation of experimental results. Often, overlapping standard error bars (or confidence intervals) are used to draw inferences about statistical differences (or lack thereof) between treatments. This practice can lead to misinterpretations about treatment effects because it lacks type I error rate control associated with formal hypothesis tests. Theoretical considerations and Monte Carlo methods are used to show that the probability that standard error bars overlap is affected by heterogeneity of variances in an unpaired data collection setting; in a paired setting, this probability is affected by heterogeneity of variances, degree and direction of non—independence (covariance) between treatments, and the variance of random pairing effects. As a result, basing inferences on overlapping standard error bars is a decision tool with type I error rates ranging from 16% to 32% in an unpaired setting, and from 0% to 32% in a paired setting. In contrast, type I error rates associated with overlapping 95% confidence intervals are at most 5% and generally much smaller. These considerations apply to one— and two—sided tests of hypotheses. In multivariate applications, non—overlapping hypothesis and error ellipses are reliable indicators of treatment differences.

Interspecific interactions among mesocarnivores can influence community dynamics and resource partitioning. Insights into these interactions can enhance understanding of local ecological processes that have impacts on pathogen transmission, such as the rabies lyssavirus. Host species ecology can provide an important baseline for disease management strategies especially in biologically diverse ecosystems and heterogeneous landscapes. We used a mesocarnivore guild native to the southwestern United States, a regional rabies hotspot, that are prone to rabies outbreaks as our study system. Gray foxes (Urocyon cinereoargenteus), striped skunks (Mephitis mephitis), bobcats (Lynx rufus), and coyotes (Canis latrans) share large portions of their geographic ranges and can compete for resources, occupy similar niches, and influence population dynamics of each other. We deployed 80 cameras across two mountain ranges in Arizona, stratified by vegetation type. We used two-stage modeling to gain insight into species occurrence and co-occurrence patterns. There was strong evidence for the effects of elevation, season, and temperature impacting detection probability of all four species, with understory height and canopy cover also influencing gray foxes and skunks. For all four mesocarnivores, a second stage multi-species co-occurrence model better explained patterns of detection than the single-species occurrence model. These four species are influencing the space use of each other and are likely competing for resources seasonally. We did not observe spatial partitioning between these competitors, likely due to an abundance of cover and food resources in the biologically diverse system we studied. From our results we can draw inferences on community dynamics to inform rabies management in a regional hotspot. Understanding environmental factors in disease hotspots can provide useful information to develop more reliable early-warning systems for viral outbreaks. We recommend that disease management focus on delivering oral vaccine baits onto the landscape when natural food resources are less abundant, specifically during the two drier seasons in Arizona (pre-monsoon spring and autumn) to maximize intake by all mesocarnivores.