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Aim Our objective was to identify the distribution of the endangered golden-cheeked warbler (Setophaga chrysoparia) in fragmented oak–juniper woodlands by applying a geoadditive semiparametric occupancy model to better assist decision-makers in identifying suitable habitat across the species breeding range on which conservation or mitigation activities can be focused and thus prioritize management and conservation planning. Location Texas, USA. Methods We used repeated double-observer detection/non-detection surveys of randomly selected (n = 287) patches of potential habitat to evaluate warbler patch-scale presence across the species breeding range. We used a geoadditive semiparametric occupancy model with remotely sensed habitat metrics (patch size and landscape composition) to predict patch-scale occupancy of golden-cheeked warblers in the fragmented oak–juniper woodlands of central Texas, USA. Results Our spatially explicit model indicated that golden-cheeked warbler patch occupancy declined from south to north within the breeding range concomitant with reductions in the availability of large habitat patches. We found that 59% of woodland patches, primarily in the northern and central portions of the warbler’s range, were predicted to have occupancy probabilities ≤0.10 with only 3% of patches predicted to have occupancy probabilities >0.90. Our model exhibited high prediction accuracy (area under curve = 0.91) when validated using independently collected warbler occurrence data. Main conclusions We have identified a distinct spatial occurrence gradient for golden-cheeked warblers as well as a relationship between two measurable landscape characteristics. Because habitat-occupancy relationships were key drivers of our model, our results can be used to identify potential areas where conservation actions supporting habitat mitigation can occur and identify areas where conservation of future potential habitat is possible. Additionally, our results can be used to focus resources on maintenance and creation of patches that are more likely to harbour viable local warbler populations.

Urban development in the Florida Keys, USA, mandates an understanding of how habitat requirements for Florida Key deer (Odocoileus virginianus clavium) interact with vegetation changes caused by development. Our study objectives were to (1) determine Key deer habitat use at different spatial scales, (2) evaluate vegetation changes and identify vegetation types most threatened by development, and (3) provide guidelines to direct land acquisition programs in the future. We identified 6 vegetation types: pineland, hammock, developed, freshwater marsh, buttonwood, and mangrove. Key deer (n = 180; 84 F, 96 M) preferred upland vegetation types (>1 m above mean sea level; pineland, hammock, developed) and avoided tidal or lower‐elevation areas (<1 m above mean sea level; freshwater marsh, buttonwood, mangrove). Analyses of Geographic Information System (GIS) coverages suggested that historical development impacted near‐shore habitats while recent trends pose a greater risk to upland areas (pineland, hammock). Because uplands are preferred by Key deer, conservation measures that include land acquisition and habitat protection of these areas may be needed.

Avian surveys using point sampling for abundance estimation have either focused on distance sampling or more commonly mark-recapture to correct for detection bias. Combining mark-recapture and distance sampling (MRDS) has become an effective tool for line transects, but it has been largely ignored in point sampling literature. We describe MRDS and show that the previously published methods for point sampling are special cases. Using simulated data and golden-cheeked warbler (Dendroica chrysoparia) survey data from Texas, we demonstrate large differences in abundance estimates resulting from different independence assumptions. Data and code are provided in supplementary materials.

Population abundance estimates using predictive models are important for describing habitat use and responses to population‐level impacts, evaluating conservation status of a species, and for establishing monitoring programs. The golden‐cheeked warbler (Setophaga chrysoparia) is a neotropical migratory bird that was listed as federally endangered in 1990 because of threats related to loss and fragmentation of its woodland habitat. Since listing, abundance estimates for the species have mainly relied on localized population studies on public lands and qualitative‐based methods. Our goal was to estimate breeding population size of male warblers using a predictive model based on metrics for patches of woodland habitat throughout the species' breeding range. We first conducted occupancy surveys to determine range‐wide distribution. We then conducted standard point‐count surveys on a subset of the initial sampling locations to estimate density of males. Mean observed patch‐specific density was 0.23 males/ha (95% CI = 0.197–0.252, n = 301). We modeled the relationship between patch‐specific density of males and woodland patch characteristics (size and landscape composition) and predicted patch occupancy. The probability of patch occupancy, derived from a model that used patch size and landscape composition as predictor variables while addressing effects of spatial relatedness, best predicted patch‐specific density. We predicted patch‐specific densities as a function of occupancy probability and estimated abundance of male warblers across 63,616 woodland patches accounting for 1.678 million ha of potential warbler habitat. Using a Monte Carlo simulation, our approach yielded a range‐wide male warbler population estimate of 263,339 (95% CI: 223,927–302,620). Our results provide the first abundance estimate using habitat and count data from a sampling design focused on range‐wide inference. Managers can use the resulting model as a tool to support conservation planning and guide recovery efforts. © 2012 The Wildlife Society.

Wildlife biologists use knowledge about wildlife‐habitat relationships to create habitat models to predict species occurrence across a landscape. Researchers attribute limitations in predictive ability of a habitat model to data deficiencies, missing parameters, error introduced by specifications of the statistical model, and natural variation. Few wildlife biologists, however, have incorporated intra‐ and interspecific interactions (e.g., conspecific attraction, competition, predator‐prey relationships) to increase predictive accuracy of habitat models. Based on our literature review and preliminary data analysis, conspecific attraction can be a primary factor influencing habitat selection in wildlife. Conspecific attraction can lead to clustered distributions of wildlife within available habitat, reducing the predictive ability of habitat models based on vegetative and geographic parameters alone. We suggest wildlife biologists consider incorporating a parameter in habitat models for the clustered distribution of individuals within available habitat and investigate the mechanisms leading to clustered distributions of species, especially conspecific attraction.

Summary 1.Research on habitat selection has focused on the role of vegetative and geologic characteristics or antagonistic behavioural interactions. 2. Conspecifics can confer information about habitat quality and provide positive densitydependent effects, suggesting habitat selection in response to the presence of conspecifics can be an adaptive strategy. 3. We conducted a manipulative field experiment investigating use of conspecific location cues for habitat selection and consequent reproductive outcomes for the endangered golden-cheeked warbler (Setophaga chrysoparia). We investigated the response in woodlands across a range of habitat canopy cover conditions typically considered suitable to unsuitable and using vocal cues presented during two time periods: pre-settlement and post-breeding. 4. Warblers showed a strong response to both pre-settlement and post-breeding conspecific cues. Territory density was greater than four times higher in treatment sample units than controls. The magnitude of response was higher for cues presented during the pre-settlement period. Positive response to conspecific cues was consistent even in previously unoccupied areas with low canopy cover typically considered unsuitable, resulting in aggregations of warblers in areas generally not considered potential habitat. 5. Pairing and reproductive success of males was not correlated with canopy cover, as commonly thought. Pairing success and fledging success increased with increasing territory density suggesting that conspecific density may be more important for habitat selection decisions than the canopy cover conditions typically thought to be most important. These results suggest the range of habitat within which birds can perform successfully may be greater than is typically observed. 6. Our results suggest the territory selection process may not be substantially influenced by competition in some systems. Settlement in response to conspecific cues produced aggregations within larger areas of similar vegetative characteristics. Understanding what cues drive habitat selection decisions and whether these cues are correlated with habitat quality is critical for conserving fitness-enhancing habitats, avoiding creation of ecological traps, generating accurate predictions of species distributions and understanding how occupancy relates to habitat suitability.

. Using LiDAR-derived vegetation metrics for high-resolution, species distribution models for conservation planning. Ecosphere 4(3):42. http://dx.doi.org/10.1890/ES12-000352.1Abstract. Advances in remotely sensed data for characterizing habitat have enabled development of spatially explicit predictive species distribution models (SDM) that can be essential tools for management. SDMs commonly use coarse-grain metrics, such as forest patch size or patch connectivity, over broad spatial extents. However, species distributions are likely driven in part by local, fine-grained habitat conditions. Conservation and management are often planned and applied locally, where coarse predictions may be uninformative or not sufficiently precise. We investigated the integration of high-resolution LiDAR (Light Detection and Ranging) with avian point sampling data to develop a detection-corrected occupancy model to quantify habitat-occurrence relationships for two species with different habitats: the endangered golden-cheeked warbler (Setophaga chrysoparia) and black-capped vireo (Vireo atricapilla) on a military installation in central Texas. We compared occupancy models that used only the more conventional, coarse remotely sensed metrics to models that also incorporated high-resolution LiDAR-derived metrics for vegetation height and canopy cover, to assess their use for predicting distributions. Models including LiDAR-derived vegetation height and canopy cover metrics were competitive for both species, and models without LiDAR-derived vegetation height had substantially lower model weights and explanatory strength. Area under curve estimates for the highest ranked models were high for warblers (0.864) and moderate for vireos (0.746). Using the best supported models for each species, we predicted the occurrence distribution for both species. The resulting predictions provide a decision support tool that enables assessment of the status, impacts, and mitigation of impacts to endangered species habitat on the installation due to land management and military training activities that is more standardized and accurate than current assessment approaches based on visual gestalt of habitat and expert opinion. Additionally, although previous species distribution models have been created for our focal species, most fail to match the grain and extent of most management actions or include local, fine-grained metrics that influence distributions. In contrast, we demonstrate that use of LiDAR with species occurrence data can provide precision and resolution at a scale that is relevant ecologically and to the operational scale of most conservation and management actions.