Translocations are an important aspect of the management of natural populations in an increasingly fragmented landscape. Maintaining connectivity and gene flow is beneficial for both contemporary fitness and adaptive potential in the face of environmental change. Genetic rescue (GR) can alleviate inbreeding depression, genetic load, and increase adaptive potential of populations. Here, I have translocated 10 (5 of each sex) brook trout (Salvelinus fontinalis) to four geographically proximate and environmentally similar fragmented stream-dwelling populations of brook trout in Virginia to test for genetic rescue. The translocated brook trout contributed to more families than would be expected under neutral introgression, and hybridization resulted consistently in larger full-sibling family sizes. In the cohort immediately following translocation I observed relatively high (>20%) introgression in 3 of the 4 recipient sites, and in one recipient population 57.7 % of the offspring had at least one migrant parent. During the post-translocation period favorable regional climatic conditions resulted in large cohorts across recipient sites and controls, however the percent increase in juvenile abundance scales to initial genetic diversity and patch size. I observe strong evidence of hybrid vigor through consistently larger body sizes of hybrid offspring. At this point I cannot rule out potential negative effects of translocations such as outbreeding depression with out sampling more cohorts following genetic recombination. However, I provide an empirical and replicated foundation to begin assessing the efficacy of GR-motivated translocations for headwater fish conservation, and make a substantial contribution to the growing body of GR-literature.

To account for the effects of climate change, management plans in the northeast need to incorporate climate adaptation. Conserving climate change refugia is one adaptation strategy. Climate change refugia are areas buffered by climate change that enable the persistence of valued physical, ecological, and cultural resources; preserving these areas could be a potential adaptation strategy. Using a translational ecology approach where researchers and managers from the National Park Service, US Geological Survey, the University of Massachusetts, and elsewhere worked together, we focused on identifying refugia for tree, herbaceous plant, mammal, and bird species in order to prioritize them for conservation action. Results predict shifts in distribution of habitats and species due to climate change, identifying areas to prioritize for invasive species treatment and other management actions. This study highlights priorities for future monitoring and data analysis, providing a model that can be replicated in other regions and motivate future research.

Coastal habitats are highly dynamic and vulnerable to landscape-level disturbances such as storms and restoration projects. Along the east coast of Florida these areas are particularly valuable as they provide significant nesting habitat for two sea turtle species, the threatened loggerhead (Caretta caretta) and the endangered green turtle (Chelonia mydas). This coast was heavily impacted by three major hurricanes in 2004 and in some areas by large restoration projects in 2005. Recent remote sensing methods allow for broad evaluation of the shoreline and thus the ability to assess sea turtle nesting habitat at a landscape scale. I collected nesting data for southern Brevard County, Florida from 1989 – 2005 and for Canaveral National Seashore, Florida from 1995 – 2005. I used LiDAR (Light Detection and Ranging) and IfSAR (Interferometric Synthetic Aperture Radar) remote sensing to map sea turtle nesting habitat in both areas following the 2004 hurricanes and any subsequent restoration. Canaveral National Seashore underwent no restoration while southern Brevard County received extensive restoration. Topographic variables (e.g., total sand volume, width, and slope) derived from the remote sensing data were compared across three time periods (pre-hurricane, posthurricane, and recovery period) and I compared nesting success data from 2004 to 2005. I built regression models for 2004 and 2005 to determine which topographic features influenced loggerhead and green turtle nesting the most. Green turtle nesting success declined from 2004 to 2005 only in highly restored areas while loggerhead nesting sucess declined throughout. Hurricanes caused a reduction in most of the topographic variables and restoration predominantly impacted aspects of the beach profile (e.g. slope and width). Loggerheads responded to profile characteristics (e.g. upper and lower iii beach slopes) though green turtles showed no consistent response to topography. The results indicate that both loggerheads and green turtles are sensitive to beach restoration, although loggerhead nesting is more influenced by beach morphology and green turtle nesting may be influenced more by other dune features such as vegetation cover.

Understanding the nature of genetic variation in natural populations is an underlying theme of population genetics. In recent years population genetics has benefited from the incorporation of landscape and environmental data into pre-existing models of isolation by distance (IBD) to elucidate features influencing spatial genetic variation. Many of these landscape genetics studies have focused on populations separated by discrete barriers (e.g., mountain ridges) or species with specific habitat requirements (i.e., habitat specialists). One difficulty in using a landscape genetics approach for taxa with less stringent habitat requirements (i.e., generalists) is the lack of obvious barriers to gene flow and preference for specific habitats. My study attempts to fill this information gap to understand mechanisms underlying population subdivision in generalists, using the squirrel treefrog (Hyla squirella) and a system for classifying ‗terrestrial ecological systems‘ (i.e. habitat types). I evaluate this dataset with microsatellite markers and a recently introduced method based on ensemble learning (Random Forest) to identify whether spatial distance, habitat types, or both have influenced genetic connectivity among 20 H. squirella populations. Next, I hierarchically subset the populations included in the analysis based on (1) genetic assignment tests and (2) Mantel correlograms to determine the relative role of spatial distance in shaping landscape genetic patterns. Assignment tests show evidence of two genetic clusters that separate populations in Florida‘s panhandle (Western cluster) from those in peninsular Florida and southern Georgia (Eastern cluster). Mantel correlograms suggest a patch size of approximately 150 km. Landscape genetic analyses at all three spatial scales yielded improved model fit relative to isolation by distance when including habitat types. A hierarchical effect was identified whereby the importance of spatial distance (km) was the strongest predictor of patterns of genetic differentiation above the scale of the genetic patch. Below the genetic patch, spatial distance was still an explanatory variable but was only iv approximately 30% as relevant as mesic flatwoods or upland oak hammocks. Thus, it appears that habitat types largely influence patterns of population genetic connectivity at local scales but the signal of IBD becomes the dominant driver of regional connectivity. My results highlight some habitats as highly relevant to increased genetic connectivity at all spatial scales (e.g., upland oak hammocks) while others show no association (e.g., silviculture) or scale specific associations (e.g., pastures only at global scales). Given these results it appears that treating habitat as a binary metric (suitable/non-suitable) may be overly simplistic for generalist species in which gene flow probably occurs in a spectrum of habitat suitability. The overall pattern of spatial genetic and landscape genetic structure identified here provides insight into the evolutionary history and patterns of population connectivity for H. squirella and improves our understanding of the role of matrix composition for habitat generalists.

Soils provide many essential functions that support the world. With a decline in soil health, these functions also decrease in efficiency, and can threaten the health of billions of people around the world. Typically, soil health tests do not use biological indicators, however microbes drive and perform vital functions to increase soil health. One way to increase soil health is through the use of cover crops to reduce soil erosion during fallow periods, increasing soil organic matter, as well as collecting nutrients from soil into their biomass. These cover crops are then terminated through various methods such as herbicides, disk tillage, or no tillage. The termination method can have an impact on soil health, by chemically affecting soil microbes with herbicides, disturbing soil, microbial communities, and fungi with tillage, or creating residue barriers on the surface of soil such as with using roller crimping (no tillage). Fertilization can also affect soil health, controlling rates of nutrient turnover and decomposition through the needs of microbes for carbon and nitrogen. This study quantifies the effects of four termination methods and four fertilization treatments on soil biological indicators during one growing season of sweet corn. Plots that were not tilled and terminated using roller crimping showed highest rates of decomposition, as well as increased labile carbon pools to feed microbes slowly throughout the growing season. Microbial activity was also observed to respond to fertilization, as patterns in activity spiked directly after fertilization. This study informs agricultural land management by the usage of biological indicators to further support the use of cover crops to increase soil health along with using no-till termination methods. Root biomass contributions toward soil health was also investigated, and how they may be affected by tillage.

State and federal agencies spend considerable time and resources to enhance and create habitat for wildlife. Understanding how target and non-target species respond to these efforts can help direct the allocation of limited conservation resources. However, monitoring species response to habitat management comes with several logistical challenges that are exacerbated as the area of geographic focus increases. I used autonomous recording units (ARUs) to mitigate these challenges when assessing Eastern Whip-poor-will (Antrostomus vociferus) response to forest management. I deployed 1,265 ARUs across managed and unmanaged public and private forests from western North Carolina to southern Maine. I then applied a machine learned classifier to all recordings to create whip-poor-will daily detection histories for each survey location. I used detection data and generalized linear models to examine regional, landscape, and site factors that influenced whip-poor-will occurrence. Whip-poor-wills were detected at 399 (35%) survey locations. At the regional scale, occupancy decreased with latitude and elevation. At the landscape scale, occupancy was negatively associated with the amount of impervious cover within 500m, and was positively associated with the amount of oak forest and evergreen forest cover within 1,750m. Additionally, whip-poor-will occupancy exhibited a quadratic relationship with the amount of shrub/scrub cover within 1,500m. At the site-level, occupancy was negatively associated with increased basal area and exhibited a quadratic relationship with woody stem density. Whip-poor-will populations can benefit from the implementation of forestry practices that create and sustain early successional forests within forested landscapes, especially those dominated by oak forest types. The use of ARUs helped overcome several challenges associated with intensive broad-scale monitoring efforts for a species with a limited survey window, but also presented new challenges associated with data management, storage, and analyses.

Water scarcity is a prevalent issue all over the world. Growing water abstractions combined with uncertain effects of climate increase competition for scarce water resources worldwide, especially in arid and semiarid regions. It is crucial to assess and manage available water resources to ensure its sustainability. There is a need for integrated water management at a watershed scale. Watershed models are a useful tool to support sustainable water management and investigate effects of hydrologic responses at various scales under climate change conditions and to simulate effects of the management decisions. This study aims to assess the sustainability of water resources in the Tuul River Basin in Mongolia using SWAT (Soil Water Assessment Tool) model to understand ecohydrological processes in the basin. The model is used to analyze the trends in water usage on a watershed and subwatershed basis. The water supply and demand dynamics at each sub watershed levels are analyzed to develop a sustainability index based on specific criteria of water sustainability. Sustainability index was used for better water management by targeting areas of the watershed. Using the analysis, strategies for water demand management for the Tuul River basin area were developed. I expect the results of the study with transform water resource situation in the region through better information on the dynamics of the system and will help in alleviating water issues in similar regions of the country and of the world. The model can be a useful tool to support decision makers and to simulate and analyze the effects of water management practices.

Late-successional forests provide a unique opportunity to explore adaptive management approaches that mitigate atmospheric carbon dioxide levels through carbon storage while also enhancing ecological resilience to novel climate and disturbances. Typical benchmarks for adaptive forest management include species diversity and structural complexity, which are widely considered to increase ecosystem stability and productivity. However, the role of functional trait diversity (e.g., variation in leaf and stem traits) in driving forest productivity and ecosystem resilience remains underexplored. We leveraged existing continuous forest inventory (CFI) data and collected local functional trait observations from CFI plots within late-successional forests in western Massachusetts to explore links between aboveground carbon storage and different types of forest diversity. We then fit a linear model within a Bayesian hierarchical framework applying functional diversity, species diversity, and structural complexity as predictors of live aboveground biomass (AGB) within CFI plots. Our framework integrates local functional trait information with database species mean trait values using a multivariate structure to account for inherent trait syndromes and estimate functional diversity in each plot. Across 626 plot-timepoints, we found that integrating individual functional trait information from co-located plots yielded the best predictions of live AGB. Contrary to expectations, functional diversity had a negative relationship with live AGB. Whereas plots with low functional diversity and higher AGB were dominated by mid-to-late successional hardwood species, plots with high functional diversity had more shade-intolerant species and lower AGB mediated by recent small-scale disturbances. Our results reveal an ontogenetic shift in the effects of functional diversity on AGB productivity over the course of succession in northeastern temperate forests. Corroborating with classical models of biomass development in late-successional northern hardwood forests, our findings support the need for adaptive forest carbon management to facilitate a mosaic of different forest successional stages across the landscape to maximize live aboveground carbon benefits in northeastern mixed hardwood forests.

Globalization is recognized as a world-encompassing phenomenon, even as its benefits are debated. Sustainability, the capacity to maintain high standards of living through generations, is at stake. This paper examines the problems of sustainability with globalization from several perspectives. High statistical correlation between indices of globalization and environmental degradation (r2 = 0.977, p < .001) is found using multi-dimensional scaling software. The socially destabilizing, culture flattening effects of globalization are examined, and the terms ‘nationalism’ and ‘terrorism’ are defined. On the basis of its medial position among the indices of both globalization and environmental degradation, Chile is explored in a case study of the interaction. Conclusions regarding Chile’s vulnerabilities are reached, and the country’s environmental, social, and economic ‘weak spots’ are identified. The ethical positions of globalization and sustainability are considered, and the conclusion that there is very little that can be done to alter the nature of the interaction is drawn. It is suggested that globalization minimizes the prospects of success in efforts toward sustainability by maximizing vulnerabilities among sustainability’s components.

The purpose of this research is to identify factors that contribute to wood fuel consumption as a space heating source and estimate a county-wide proportion for wood fuel consuming households. In addition, environmental problems associated with deforestation such as erosion and loss of wildlife habitat are delineated; moreover, air pollution resulting from wood fuel emissions are discussed. An exhaustive literature review provided the basis for the study. Data on Warren County wood fuel consumption patterns were derived from a mail survey. Proportion estimates were tested by using a classical two-tail test of hypothesis. Subsequently, factors were identified and used in a multiple regression analysis. The study found that low income households equipped with electric space heating systems located in rural areas are the most wood intensive. Unlike homes equipped with other alternate heating systems, electric space heat equipped households tend to consume wood fuel proportionally to income. The study also found that 26.3 percent of single family residences in the county use wood for space heating.