Woodpeckers are a group of avian species that cause damage to wooden power utility structures. In the southeastern United States, Tennessee Valley Authority (TVA), has accrued an estimated $5 million USD annually from woodpecker damage. Previous work has focused on effectiveness of reactive mitigation and restoration efforts with little investigation of preventative methods. To address this knowledge gap, this study will i) use species distribution model techniques to predict damage suitability across the TVA service area, ii) use Bayesian hierarchical community model techniques to estimate species richness of the woodpecker community in the service area, and iii) recommend target areas for increased preventative measures in the service area. The suitability map indicated that damage was most likely to occur in the southwestern portions of the TVA service area. Woodpecker species richness was stable across the environmental covariate values estimated with 2-3 species found throughout the service area.

No description available.

No description available.

No description available.

No description available.

No description available.

Spatial information about invasive species abundance is critical for estimating impact and understanding risk to ecosystems and economies. Unfortunately, at landscape and regional scales, most distribution datasets provide limited information about abundance. However, national and regional invasive plant occurrence datasets are increasingly available and spatially extensive. We aim to test whether the frequency of these point occurrences can be used as a proxy for abundance of invasive plants. We compiled both occurrence and abundance data for nine regionally important invasive plants in the northeast US using a combination of herbarium records, surveys of expert knowledge, and various invasive species spatial databases. We integrated all available abundance information based on infested area, percent cover, or qualitative descriptions into abundance rankings ranging from 0 (absent) to 4 (highly abundant). Within equal area grid cells of 800 m, we counted numbers of occurrence points and used an ordinal regression to test whether higher numbers of occurrence points were positively correlated with abundance rankings. We compiled a total 49,341 occurrence points in 18,533 cells, of which 12,183 points (25%) within 4,278 cells (32%) had associated abundance information. In six of nine study species we found slight but significant positive overall relationships between abundance rank and occurrence frequency at high abundance ranks. However, at low abundance rankings the relationship tended to be negative and the magnitude of the overall difference in occurrence frequency was too small to be relevant to management. My results suggest that currently available occurrence datasets are unlikely to serve as effective proxies for abundance, and models derived from invasive plant occurrence datasets should not be interpreted as indicative of plant abundance and associated impact. Increased efforts to collect and report invasive species abundance information, and/or higher densities of occurrence points in heavily infested areas are strongly needed for regional scale assessments of potential abundance and associated impact.

Golden eagles (Aquila chrysaetos canadensis Linnaeus 1758) are declining in some areas throughout their Nearctic range (Sauer et al. 2011). This reduction is linked to changes in their habitat caused by human activities. Golden eagles inhabit an extensive range of environments (Watson 1997, Kochert et al. 2002). In the American Continent, the golden eagle's range encompasses Alaska, Canada, the United States and the Northern and Central portions of Mexico. Northern golden eagle populations migrate during winter to southern grounds, crossing international boundaries of Canada, the US and Mexico and therefore, their conservation is of trilateral concern. Golden eagles are protected by domestic laws in the three North American countries where they occur (FWCA 1997, BGEPA 1940, MBTA 1918, Lacey Act 1900, DOF 2002) and although the IUCN list the species as Least Concern, the A. c. canadensis subspecies has been protected by CITES since 1975 (Birdlife International 2012). While intensively studied in the United States, very little is known in Mexico about golden eagle ecology and their populations. As the national bird of Mexico, its conservation has been a priority for the Mexican government since its inclusion in the Endangered Species List in 1994 (SEDESOL 1994). Several threats jeopardize golden eagle populations throughout their range in North America: habitat alteration and fragmentation, electrocution, collisions with vehicles, collision with windmills and wires, poisoning from lead ingestion, drowning, shooting and trapping, and poaching for illegal wildlife trade. Mexican experts believe that a dramatic decline occurred over recent decades and that the remaining pairs have been restricted to remnant suitable habitat patches (SEMARNAP-INE 1999). Long-term survival of golden eagles largely depends on the effectiveness of current conservation efforts of habitat at a landscape level. Successful conservation and management requires accurate information on ecology of the species upon which decisions can be based. This study investigated habitat characteristics of the areas occupied by golden eagles and developed strategies for habitat management and protection to improve golden eagle viability in Chihuahua State. I surveyed a portion of Chihuahuan Desert Ecoregion in Mexico to locate golden eagle territories during 2009 and 2010. I located 30 golden eagle nesting territories and found similar composition of cover type, vegetation structure and prey indices between the territory cores and their buffer zones. Distance to most anthropogenic disturbance sources was similar between golden eagle sites and random areas (n=60). Grassland was the most common cover type, occurring in 100% of the nesting territories, and comprising 58% of the territories' area, suggesting a disproportionate use of this cover type compared to its overall availability (25% of the state area). I used landscape attributes such as topographic characteristics and human disturbances to model the probability of occurrence of golden eagles across the landscape. I used logistic regression to model the occurrence of golden eagles at two different landscape scales and selected the best model at a home range scale based on AIC values to develop a predictive map of golden eagle distribution in Chihuahua, Mexico. I found that at a home range scale, golden eagles' occurrence was positively related to open areas and terrain ruggedness and negatively to human settlements, while at a larger scale it was positively related to open areas and negatively related to forested areas. The results confirm that golden eagles are dependent on grasslands and rugged terrain. I developed predictive maps of golden eagle occurrence using a logistic regression and a Mahalanobis distance approach using the variables from the model chosen to compare the performance and output with logistic regression modeling. I analyzed the Mexican National Plan for Golden Eagle Recovery (PACE - Ã guila Real) and proposed a conservation strategy oriented to protect golden eagle populations and their habitat in Chihuahua, Mexico. This strategy integrates ecologic knowledge developed in the first two chapters and incorporates social participation of all stakeholders. The strategy recognizes the potential limitations of conservation implementation programs in Mexico and explores the potential opportunities to protect golden eagles populations and their habitat. / Ph. D.

Rigorous modeling of the spatial species distributions is critical in biogeography, conservation, resource management, and assessment of climate change. The goal of chapter 2 of this dissertation was to evaluate the potential of using historical samples to develop high-resolution species distribution models (SDMs) of stream fishes of the United States. I explored the spatial transferability and temporal transferability of stream–fish distribution models in chapter 3 and chapter 4 respectively. Chapter 2 showed that the discrimination power of SDMs for 76 non-game fish species depended on data quality, species' rarity, statistical modeling technique, and incorporation of spatial autocorrelation. The area under the Receiver-Operating-Characteristic curve (AUC) in the cross validation tended to be higher in the logistic regression and boosted regression trees (BRT) than the presence-only MaxEnt models. AUC in the cross validation was also higher for species with large geographic ranges and small local populations. Species prevalence affected discrimination power in the model training but not in the validation. In chapter 3, spatial transferability of SDMs was low for over 70% of the 21 species examined. Only 24% of logistic regression, 12% of BRT, and 16% of MaxEnt had AUC > 0.6 in the spatial transfers. Friedman's rank sum test showed that there was no significant difference in the performance of the three modeling techniques. Spatial transferability could be improved by using spatial logistic regression under Lasso regularization in the training of SDMs and by matching the range and location of predictor variables between training and transfer regions. In chapter 4, testing of temporal SDM transfer on independent samples resulted in discrimination power of the moderate to good range, with AUC > 0.6 for 80% of species in all three types of models. Most cool water species had good temporal transferability. However, biases and misspecified spread occurred frequently in the temporal model transfers. To reduce under- or over-estimation bias, I suggest rescaling the predicted probability of species presence to ordinal ranks. To mitigate inappropriate spread of predictions in the climate change scenarios, I recommended to use large training datasets with good coverage of environmental gradients, and fine-tune predictor variables with regularization and cross validation. / Ph. D.

Aedes aegypti is the principal vector for dengue transmission across large areas of the world. Understanding where this critically important vector lives is paramount to preventing the continued expansion of dengue globally. Using 1981-2010 bioclimatic (bioclim) variables, as well as additional environmental variables and elevation data, multiple MaxEnt models were constructed for Ae. aegypti within the South American continent to identify ecologically important variables and areas of persistent high suitability. All models had acceptable AUC scores (> 0.70), indicating accurate model fitting. Annual mean precipitation and temperature were found to be important model variables; however, when added, humidity became the more predictive variable. Elevation had minimal, if any, impact on model construction predictability. When utilizing MaxEnt models, non-traditional climatic variables, i.e., humidity, were significantly more predictive for Ae. aegypti than the standard bioclim variables. Optimal variable selection is critical to understanding current and potential future areas of high transmission risk.