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dc.contributor.authorNadeau, Christopheren_US
dc.date.accessioned2014-07-28T19:27:54Z
dc.date.available2014-07-28T19:27:54Z
dc.date.issued2014-05-25en_US
dc.identifier.otherbibid: 8641116
dc.identifier.urihttps://hdl.handle.net/1813/37159
dc.description.abstractWildlife management agencies need to adapt management plans to include the potential effects of climate change in order to minimize extinction risk and ensure that future management actions provide long-term benefits for focal species. Climate change vulnerability assessments can help management agencies incorporate climate change into their management plans. Here, I attempt to overcome many of the limitations of existing vulnerability assessments by (1) developing a new method to measure climate change that accounts for changes in multiple weather variables and multiple statistics of climate that are known to be important to the ecology and evolution of species and (2) taking a spatial approach and using expert knowledge to obtain information on rare and poorly studied species. I utilize the spatial aspects of the vulnerability assessment to provide management recommendations for biodiversity and for 113 New York State Species of Greatest Conservation Need in the northeastern United States. In the first chapter, I present a novel index of climate change (climate overlap) that synthesizes changes in multiple weather variables and multiple statistics of climate. Most climate change studies and vulnerability assessments focus only on changes in the means of weather variables. Focusing only on changes in the means could be misleading because the variation, probability of extremes, and correlation between weather variables are known to affect the biology, abundance, diversity, and evolution of organisms. I estimate the overlap between multivariate normal probability distributions representing historical and current or projected future climates. The index is interpreted as the similarity in weather between historical and projected future time periods and is scaled between zero and one, where zero represents completely novel future weather and one represents completely similar future weather. I apply climate overlap to show that current local climates in the continental United States have an average of overlap of 0.442 with historical climates. Much of the change between current and historical climates is due to changes in the variation within and correlation between weather variables. I show that on average the northeastern United States will experience novel local weather (climate overlap < 0.01) by 2054 with 98.9% of the northeastern United States experiencing novel local weather by the end of the century. I also incorporate climate overlap into estimates of climate change velocity to produce the first estimates of climate change velocity to account for changes in multiple weather variables and statistics of climate. Incorporating climate overlap into estimates of climate change velocity decreases estimates of velocity by 59%, when compared to estimates made using only changes in the mean of mean annual temperature. My results demonstrate the importance of accounting for multiple statistics of climate to accurately characterize the magnitude and spatial variation of climate change. In the second chapter, I map spatial indicators of the vulnerability of biodiversity to climate change in the northeastern United States. These spatial indicators combine to describe the amount of climate change predicted for a region and the degree to which landscape features in the region will inhibit species ability to adapt to that change. I then extend this spatial model to rank the relative vulnerability of 113 New York State Species of Greatest Conservation Need to climate change. I combine the relative vulnerability of all the focal species to identify areas on the landscape where decreasing landscape resistance (i.e., decreasing the effect of dispersal barriers) or reducing non-climate threats could help reduce the vulnerability of a large number of focal species and I identify factors that may influence the long-term benefit of species-specific management actions under climate change (i.e., climate-smart management considerations). Last, I use the New England cottontail as a detailed example of how my results can be used to guide species-specific management. ii My results suggest that biodiversity in the northeastern United States is likely to be most vulnerable to climate change in Delaware and least vulnerable in Maine, but that much can be done across all the northeastern United States to reduce vulnerability. Highly vulnerable species (i.e., the top 10%) are vulnerable because their dispersal ability will not allow them to keep pace with climate change velocity and they occur in regions with low local landscape resistance relative to other species. The least vulnerable species (i.e., the lowest 10%) tended to be habitat and dietary generalists and occur in areas where the magnitude of climate change is expected to be low relative to other regions of the northeastern United States. The Hudson Valley of New York State is a hotspot in the northeastern United States for both decreasing landscape resistance and reducing non-climate threats in areas with diverse topoclimates to reduce the vulnerability of the most species included in our analysis. I provide species-specific vulnerability results and management recommendations in Appendix VIII for each of the 113 focal species. iiien_US
dc.language.isoen_USen_US
dc.subjectClimate changeen_US
dc.subjectSpecies of Greatest Conservation Needen_US
dc.subjectVulnerability Assessmenten_US
dc.titleManaging Species Of Conservation Need In The Face Of Climate Change: A Landscape And Trait-Based Approachen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineNatural Resources
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Natural Resources
dc.contributor.chairFuller, Angela K.en_US
dc.contributor.committeeMemberSullivan, Patrick Jen_US
dc.contributor.committeeMemberRosenblatt, Danielen_US


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