Few ecological studies have explored landscape suitability using the gradient concept of landscape structure for wildlife species. Identification of conditions influencing the landscape ecology of endangered species allows for development of more robust recovery strategies. Our objectives were to (i) identify the range of landscape metrics (i.e., mean patch area; patch and edge densities; percent land cover; shape, aggregation, and largest patch indices) associated with woody vegetation used by ocelots (Leopardus pardalis), and (ii) quantify the potential distribution of suitable woody cover for ocelots across southern Texas. We used the gradient concept of landscape structure and the theory of slack combined with GPS telemetry data from 10 ocelots. Spatial distribution of high suitable woody cover is comprised of large patches, with low shape-index values (1.07–2.25), patch (27.21–72.50 patches/100 ha), and edge (0–191.50 m/ha) densities. High suitability landscape structure for ocelots occurs in 45.27% of woody cover in southern Texas. Our study demonstrates a new approach for measuring landscape suitability for ocelots in southern Texas. The range of landscape values identified that there are more large woody patches containing the spatial structure used by ocelots than previously suspected, which will aid in evaluating recovery and road planning efforts.

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.

Background: Complexity in landscape structure is often assessed using individual metrics related to ecological processes. However, this rarely incorporates important relationships among metrics and may miss landscape structure effects. Multivariate statistics provide techniques for assessing overall landscape structure effects. We assessed how multivariate statistics could be used to connect landscape structure with an ecological process [bobcat (Lynx rufus) wildlife crossing structure (WCS) use]. We tested how landscape structure at WCS sites compared to the surrounding landscape and how structure affected detections at WCS sites. Our study was conducted in Cameron County, Texas, USA where WCSs are in various stages of construction and monitoring. We used a classified land use/land cover map and aerial LiDAR to calculate configuration and density metrics at WCS and random sites. We created indices for configuration and density using principal components analysis to assess landscape structure effects on camera trap detections at WCSs. Results: Landscape structure at WCSs did not differ from random locations. Wildlife crossing structure use increased with greater woody cover and decreased with increasing vegetation density. Our indices allowed identification of differences in how configuration and density impacted WCS use. Ordination methods helped identify individual contributions of landscape metrics to the overall landscape structure effect. Conclusions: Wildlife crossing structures are permanent fixtures on landscapes, so selecting appropriate locations using broad-scale landscape structure likely increases target species use. Using indices of landscape structure provides planners with a more holistic approach to WCS placement and provides a more comprehensive picture of landscape pattern and process relationships.

Assessment of locations where wildlife species cross highways is a key question in mitigating future wildlife-vehicle mortality. Examination of the spatial structure, complexities, and patterns of vegetation or other land-use types (i.e., cropland, urban areas) near roadways allows scientists to identify any thresholds that influence where animals are likely to die or successfully cross the roadway. We used a historic 1982 to 2017 dataset of ocelot (Leopardus pardalis pardalis) mortality locations and approximate road crossing locations of telemetered ocelots in the Lower Rio Grande Valley in Texas to examine the spatial structure of woody vegetation within a hypothesized road effect zone. We determined if there were differences in the spatial structure of woody cover within a 1050 m buffer of each successful crossing and roadkill location using PERMANOVA and principal component analyses. We used a similarity percentages analysis to determine the relative contribution of each aspect of spatial structure on differences in successful crossing and roadkill locations. We found statistically significant differences in spatial attributes of patches at the locations of successful crossing versus roadkill locations of ocelots at the 150 m spatial extent (pseudo-F_1,41 = 4.85, P(perm) = 0.008, permutations = 9949). Largest patch index contributed most to the differences between successful crossing and roadkill locations (15.94%), followed by mean patch area (15.44%), percent woody cover (15.18%), aggregation indices (14.53%), Euclidean nearest neighbor (13.47%), edge (13.08%) and patch densities (12.36%). Roadkill locations were clustered in locations with lower-quality woody cover within 300 m of the highway. This suggests areas immediately surrounding roads need to contain woody patches that are larger and closer together to reduce the barrier-effects of roads. Such information is important for informing highway planners about where to encourage crossings or to build wildlife crossing structures to promote movement across the highway.