The aim in this study was to develop a model for the probability of finding active golden eagle nests during their breeding season. It was done by using environmental variables derived from expert models which were tested against empirical data. This resulted in a habitat suitability index (HSI), which in this case is assumed to indicate the probability of active nests of golden eagles. The study was conducted together with the County Administrative Board of Västernorrland with the purpose to improve golden eagle’s ecological status.To develop the model, different combinations of several explanatory variables were tested in a model selection process, where the most optimal and parsimonious model was chosen. The tested variables have earlier been shown to affect golden eagles, as slope, aspect, forest age, foraging habitat, suitable flight routes, human population density, roads, railways, power lines, wind power plants, hiking trails and clear cuts. The variables where applied in in ArcMAP at three different scales: nest scale (25 x 25 meter), proximate scale (a circle with the radius of 500 meter) and home range scale (a circle with the radius of 8253 meter). A preliminary test of the variables showed that all golden eagle nests were found in slopes with at least 5֯ degreesas well as in home ranges with human population density not more than 8 people/km2. Due to that a stratified analysis wasperformed. The variables where analysed by multiple logistic regression in R, where the occurrence of golden eagles’ nestswas compared towards random points in the landscape. All variables were also tested one by one by logistic regression. Afterperforming the multiple logistic regression, it was possible to apply its equation into ArcMap to obtain suitability maps withHSI values over Västernorrland’s county.The comparisons of different models show that it is better to combine different spatial scales in the model than only using one spatial scale. The result indicate that three different models might be the best, which all had different combinations of slope and aspect at nest scale and power lines at the proximate scale. Two of these models also include hiking trails and human population density, both at home range scale, in their equation. Since it was some unclarity about the causality between hiking trails and human population density, the conclusion was not to choose any of these as the final model. The final model was more parsimonious and had an additive effect from slope and southern aspect at the nest scale and an antagonistic effect from power lines at the proximate scale.This study clarifies that golden eagles’ habitat preferences for nesting sites during their breeding period is steep slopes (at minimum 5֯ degrees) in more southern aspects with few power lines in the proximate area surrounding the nest. Their homeranges are also situated in areas with less than 8 people/km2. The study also pinpoints a potential conflict between golden eagleand wind power planning, as golden eagles prefer steep slopes and remote areas, which also are valuable areas for wind powerplants. Golden eagles’ preference of remote areas also indicate that they might be affected by human persecution, why certainconservation effort should be focused into this issue. Out from the final model, you can find cluster in the landscape where youcan focus conservation management and restrict exploitation. Due to low number of wind power plants in the landscape, nothingcould be concluded about their effect on golden eagle in this study. An advice from the golden eagle’s perspective is to use theprecautionary principle and further plan wind power plants in areas which already have high disturbance, as for example closeto power lines or roads. The result also indicates that forest age from SLU Forest Map is not suitable for telling where to findgolden eagle nests. GIS-data over forest age would facilitate conservation management for plenty of species connected to theforest.Although good statistical results for the final model, cautions need to be taken in general, since neither population viability analysis have been included, nor changes over time in the landscape. Another issue is the low sample size, where a larger sample size would make it possible to perform profound calibration and validation of the data. To develop a more robust model, the advice is to include these into the model and use a larger sample size.

Conservation managers and planners need the ‘best available science’ to support robust and defensible decisions, ensuring that public resources are appropriately allocated. Spatial planning products and decision-support tools developed for this purpose should enable partner organizations to achieve focus, coordination, and increased effectiveness in their investments and actions. Whereas conservation partnerships have historically created distinct planning tools, there is increasing interest for improved coordination, communication, and unifying biological datasets to improve the cohesiveness of regional management activities. We sought to inform spatial conservation planning efforts in the southeastern United States through the development of species distribution models for focal avian species to prioritize open pine habitat management and a methodological framework to assess alignment between spatial planning products. We offer insight that can help managers maximize data-informed decision making and contribute to more synergistic and effective conservation actions in the future.

Green space fragmentation is one reason for the failing ecological networks around our planet. It has been argued that the driving force behind the loss of natural habitats by fragmentation is urban development. This essay aims to look at the subject of green space fragmentation in Uppsala municipality and how it affects both humans and roe deer living in the area. This essay will also explore how the efforts to stop wildlife-vehicle collisions impact roe deer’s ability to roam through the area divided thanks to human induced green space fragmentation. This has been done by using a descriptive quantitative analysis combined with a connectivity model, the least-cost approach. Remote sensing was used to collect the data used for the analysis. The results of this essay indicated that green space fragmentation impacts both humans and roe deer in Uppsala municipality since the wildlife-vehicle collisions can be traced to spots where the roe deer are forced to cross the road because of green space fragmentation. The introduced form of wildlife crossing structures, wildlife fences, does not help minimize the effects of the fragmentation, instead it steers the animals into certain points where the collisions occur. To truly minimize the consequences of human induced green space fragmentation, green infrastructure needs to be taken into account whenever planning future developments.

West Nile virus has become a major risk to humans since its first appearance in New York City in 1999. Physicians and state health officials are interested in new and more efficient methods for monitoring disease spread and predicting future outbreaks. This study modeled habitat suitability for mosquitoes that carry West Nile virus. Habitat characteristics were used to derive risk maps for the entire state of Mississippi. Statistical significance tests yielded objective evidence for choosing among many habitat variables. Variables that were significantly correlated with diagnosed human cases for 2002 were combined in weighted linear algebraic models using a geographic information system (GIS). Road density, slope, and summer precipitation minus evaporation (P-E) were the most significant variables. GIS-based model results were compared with results from logistic regression models. The algebraic model was preferred when validated by 2003 human cases. If adopted, GIS-based risk models can help guide mosquito control efforts.

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I compared the performance of habitat models created from data of differing reliability. Because the reliability is dependent on the probability of detecting the species, I experimented to estimate detectability for a salamander species. Based on these estimates, I investigated the sensitivity of habitat models to varying detectability. Models were created using a database of amphibian and reptile observations at Fort A.P. Hill, Virginia, USA. Performance was compared among modeling methods, taxa, life histories, and sample sizes. Model performance was poor for all methods and species, except for the carpenter frog (Rana virgatipes). Discriminant function analysis and ecological niche factor analysis (ENFA) predicted presence better than logistic regression and Bayesian logistic regression models. Database collections of observations have limited value as input for modeling because of the lack of absence data. Without knowledge of detectability, it is unknown whether non-detection represents absence. To estimate detectability, I experimented with red-backed salamanders (Plethodon cinereus) using daytime, cover-object searches and nighttime, visual surveys. Salamanders were maintained in enclosures (n = 124) assigned to four treatments, daytime__low density, daytime__high density, nighttime__low density, and nighttime__high density. Multiple observations of each enclosure were made. Detectability was higher using daytime, cover-object searches (64%) than nighttime, visual surveys (20%). Detection was also higher in high-density (49%) versus low-density enclosures (35%). Because of variation in detectability, I tested model sensitivity to the probability of detection. A simulated distribution was created using functions relating habitat suitability to environmental variables from a landscape. Surveys were replicated by randomly selecting locations (n = 50, 100, 200, or 500) and determining whether the species was observed, based on the probability of detection (p = 40%, 60%, 80%, or 100%). Bayesian logistic regression and ENFA models were created for each sample. When detection was 80 __ 100%, Bayesian predictions were more correlated with the known suitability and identified presence more accurately than ENFA. Probability of detection was variable among sampling methods and effort. Models created from presence/absence data were sensitive to the probability of detection in the input data. This stresses the importance of quantifying detectability and using presence-only modeling methods when detectability is low. If planning for sampling as an input for suitability modeling, it is important to choose sampling methods to ensure that detection is 80% or higher. / Ph. D.

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.

With a new focus on flow regulation by the Tennessee Valley Authority (TVA) in reservoir tailwaters, it is now possible to recover many mussel species that once occurred in these reaches. Before flows can be modified to create habitat for freshwater mussels, suitable microhabitat conditions must be defined. In this study, I used multiple approaches to define suitable microhabitats for species in the free-flowing upper Clinch River, Virginia and Tennessee, where reproducing mussel populations persist. During summer low flows in 2003 and 2004, I measured flow and substrate conditions in over 1000 microhabitat patches (0.25 m² quadrat samples) across five river reaches. Flow characteristics and embeddedness were significantly different between microhabitats occupied and unoccupied by the most abundant species (MRPP, p < 0.05). Comparison of simple and multiple logistic regression models with Akaike's Information Criteria (AIC) demonstrated that increasing Fleisswasserstammtisch (FST) hemisphere number (a measure of shear stress), decreasing degree of embeddedness, and increasing mean column velocity best explained species occurrences in a microhabitat patch. Subtle differentiation in habitat use among species was observed; however, most species appeared to be microhabitat generalists. Species were grouped into three habitat guilds using corresponding canonical analysis and cluster analysis: fast-flow specialists (FFS), fast-flow generalists (FFG), and slow-flow tolerant (SFT). I used the same data set to develop and test transferability of Habitat Suitability Criteria (HSC) for three habitat guilds and seven species of adult freshwater mussels. Nonparametric tolerance limits were used to define the range of suitable and optimal habitat during summer low flows. Optimal habitat was defined as those ranges of FST hemisphere number, mean column velocity, and embeddedness occupied by the central 50% of independent observations for a species or guild, whereas suitable habitat was defined by those ranges occupied by the central 90% of observations. The transferability of criteria to other reaches of the Clinch River was assessed using one-sided Chi-square tests. Criteria developed for the fast-flow specialist (FFS) and fast-flow generalist (FFG) guilds, as well as most criteria for species in those guilds, transferred to destination reaches. In contrast, criteria developed for the slow-flow tolerant (SFT) guild and individual constituent species consistently failed to transfer. Criteria for FFS and FFG guilds and their constituent species should be incorporated into flow simulation models such as PHABSIM to gauge the effect of minimum flows on mussel habitat quality and quantity. These criteria could also be used to determine suitable sites for mussel translocations. However, my criteria require further testing in other rivers before they can be transferred beyond the Clinch River. Behavior and physiological responses to laboratory manipulations of flow velocity and substrate particle size were used to elucidate microhabitat preferences of Actinonaias pectorosa, Potamilus alatus, and Ptychobranchus subtentum. These species appeared less stressed in the fastest flow treatment, demonstrating significantly higher oxygen consumption and oxygen-to-nitrogen (O:N) ratios than in slower flow treatments. Only P. alatus demonstrated a preference for substrate particle size, and consistently selected finer particle sizes. Actinonaias pectorosa and P. subtentum demonstrated preference for fast-flow microhabitats by readily burrowing in those conditions, while abandoning slow-flow conditions. The lack of preference for substrate particle size demonstrated by A. pectorosa and P. subtentum supports conclusions of previous studies that substrate particle size is of little or secondary importance for explaining mussel microhabitat use. These results, along with previous studies in the Clinch River, demonstrate that the stable habitats of riffles and runs; characterized by fast flows during summer low flows, low percent bedrock, and low embeddedness, are the most suitable habitats for mussel assemblages. To create and maintain suitable habitat conditions in tailwaters, releases should maintain flow over riffles at a minimum depth of no less than 30 cm in riffles that provide higher shear stress conditions (FST number > 7) and velocities (> 0.70 m/s). Periodic releases that are sufficient to transport silt and sand, but not high enough to transport larger substrate should be adequate to maintain substrates with a low degree of embeddedness. Doing so would create suitable habitat for all mussels, from the most to least specialized. Additionally, HSC developed for FFS and FFG guilds can be used to determine suitable destination sites for translocations of species belonging to these guilds. / Master of Science

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