Airborne laser altimetry and multispectral imagery for modeling Golden‐cheeked Warbler (Setophaga chrysoparia) density

Abstract

Abstract. Robust models of wildlife population size, spatial distribution, and habitat relationships are needed to more effectively monitor endangered species and prioritize habitat conservation efforts. Remotely sensed data such as airborne laser altimetry (LiDAR) and digital color infrared (CIR) aerial photography combined with well-designed field studies can help fill these information voids. We used point count-based distance sampling survey data and LiDAR-fused CIR aerial photography to model density of the Golden-cheeked Warbler (Setophaga chrysoparia), an endangered songbird, on the 10 000-ha Balcones Canyonlands National Wildlife Refuge (BCNWR). We developed a novel set of candidate models to explain Golden-cheeked Warbler detection probability and density using habitat covariates characterizing vegetation structure, composition, and complexity as well as habitat fragmentation, topography, and human infrastructure. We had the most model support for covariates calculated using focal means representing a 3.2 ha territory size (100 m radius) vs. 1.8 and 7.0 ha territory sizes. Detection probability decreased with canopy cover and increased with topographic roughness. Golden-cheeked Warbler density increased with canopy cover, was highest at a 7:3 ratio of Ashe juniper (Juniperus ashei) to broadleaf tree canopy cover, and decreased with global solar radiation. Predicted warbler densities using 3 min point counts were similar to six estimates from independently collected warbler territory mapping on BCNWR with a mean difference of 6% and a Root Mean Squared Error of 1.88 males/40 ha. The total population size for BCNWR was estimated at 884 Golden-cheeked Warbler males (95% CI 662, 1206) and predicted densities across the refuge ranged from 0.0 to 0.50 male warblers per ha. On the basis of observed habitat relationships, we defined high quality habitat as having at least 60% canopy cover with Ashe juniper comprising 50-90% of the canopy. We estimated 48% of the area at BCNWR managed for Golden-cheeked Warblers was in high quality habitat conditions and identified patches within the lower habitat quality areas (14% of warbler management areas) that had the greatest potential to become high quality habitat with management. Our approach combined robust wildlife surveys with highly scalable remotely sensed data to examine habitat relationships, estimate population size, and identify existing areas of high quality habitat. This method can be applied to other species of conservation interest and can be used with multiple years of remotely sensed data to assess changes in habitat at local to regional scales.

Density and nest survival of golden‐cheeked warblers: Spatial scale matters

“…The amount of total woodland cover and canopy height at the forest scale and the amount of mixed woodland at the landscape scale were significantly positively related to warbler density. Similar density relationships were observed on Fort Hood, Texas (Peak and Thompson ) and Balcones Canyonlands National Wildlife Refuge, Texas (Sesnie et al ). Collectively, these studies show mature, closed‐canopy mixed woodlands support the highest densities of breeding males.…”

“…Measuring these attributes in the field is time‐consuming and therefore limited to small scales; however, continuing advancements in remote sensing will likely allow for measurement of increasingly fine‐scale vegetation attributes, such as tree structure, in the future (Lee et al , Sesnie et al ). Our results are consistent with previous studies that showed warblers prefer areas with a high juniper component but also some oaks (not pure juniper stands; Kroll , Marshall et al , Sesnie et al ) and areas with taller canopy height (DeBoer and Diamond , Farrell et al ). Although juniper is clearly a necessary component of warbler habitat, our results demonstrate that oaks are also essential to provide optimal habitat; however, oaks should only make up a minor component of the overall tree composition.…”

“…We predicted that detection may be inversely related to tree volume and slope steepness because bird song likely carries farther in areas of lower tree volume and across open space. Sesnie et al () reported support for variables measuring similar qualities (canopy cover and topographic roughness). We then included the most supported detection model while evaluating vegetation and landscape covariates in the density models.…”

“…Warbler habitat is generically described as juniper‐oak woodland, but there is substantial variation within woodland vegetation structure and composition that may affect perceived or realized habitat quality as measured by density or nest (or territory) success (Peak and Thompson , ; Stewart et al ; Reidy et al ; Sesnie et al ). Previous studies focused separately on relationships between vegetation structure or woodland characteristics on warbler occupancy (Magness et al , Farrell et al ), density (Peak and Thompson , Reidy et al , Sesnie et al ), and productivity (Peak and Thompson ), and only one investigated multiple spatial scales with fine‐scale and coarse‐scale vegetation, and terrain data (DeBoer and Diamond ). Collectively these studies highlight the conservation value of contiguous mature woodland to occupancy, density, and nest success, but they do not provide the level of inference that can be gained by simultaneously considering both density and nest survival at multiple spatial scales.…”

Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance

“…Associated with the resolution of these data, the studied areas were usually large and the input data rarely had a higher resolution than 30 m. Hence, small-scale landscape features, such as hedges, shrubs, and small tree stands were often not mapped, which resulted in a loss of information on habitat-species dependencies [14]. With few exceptions (harnessing , and Worldview-2 data [33]), studies linking fine-grained habitat to spatial patterns of bird species diversity or abundance have been relying on the use of airborne observation data, such as aerial images [34,35] or LiDAR data [36,37]. A low spatial resolution of the spaceborne remote sensing data is therefore a key bottleneck, and ecologically relevant small-scale habitat features need to be considered more.In this study, we focus on an area in the Russian Far East that hosts populations of several globally threatened bird species [38].…”