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Bainbridge Island, WA, United States

Stein B.A.,National Wildlife Federation | Staudt A.,National Wildlife Federation | Cross M.S.,Wildlife Conservation Society | Dubois N.S.,Defenders of Wildlife | And 6 more authors.
Frontiers in Ecology and the Environment | Year: 2013

The emerging field of climate-change adaptation has experienced a dramatic increase in attention as the impacts of climate change on biodiversity and ecosystems have become more evident. Preparing for and addressing these changes are now prominent themes in conservation and natural resource policy and practice. Adaptation increasingly is viewed as a way of managing change, rather than just maintaining existing conditions. There is also increasing recognition of the need not only to adjust management strategies in light of climate shifts, but to reassess and, as needed, modify underlying conservation goals. Major advances in the development of climate-adaptation principles, strategies, and planning processes have occurred over the past few years, although implementation of adaptation plans continues to lag. With ecosystems expected to undergo continuing climate-mediated changes for years to come, adaptation can best be thought of as an ongoing process, rather than as a fixed endpoint. © The Ecological Society of America.

Chinnadurai S.K.,North Carolina State University | Birkenheuer A.J.,North Carolina State University | Blanton H.L.,North Carolina State University | Maggi R.G.,North Carolina State University | And 4 more authors.
Journal of Wildlife Diseases | Year: 2010

Trapper-killed North American river otters (Lontra canadensis) in North Carolina, USA, were screened for multiple vector-borne bacteria known to be pathogenic to mammals. Blood was collected from 30 carcasses in 2006, from 35 in 2007, and from one live otter in 2008. Samples were screened using conventional polymerase chain reaction (PCR) tests for DNA from Bartonella spp., Ehrlichia spp., and spotted fever group Rickettsia spp. All samples were negative for Rickettsia spp. Twelve of 30 samples from 2006 produced amplicons using the assay designed to detect Ehrlichia spp., but sequencing revealed that the amplified DNA fragment was from a novel Wolbachia sp., thought to be an endosymbiote of a Dirofilaria sp. Between 2006 and 2007, DNA from a novel Bartonella sp. was detected in 19 of 65 animals (29%). Blood from one live otter captured in 2008 was found positive for this Bartonella sp. by both PCR and culture. The pathogenicity of this Bartonella species in river otters or other mammals is unknown. © Wildlife Disease Association 2010.

Mcleod E.,The Nature Conservancy | Szuster B.,University of Hawaii at Manoa | Tompkins E.L.,University of Southampton | Marshall N.,James Cook University | And 9 more authors.
Coastal Management | Year: 2015

Climate change threatens tropical coastal communities and ecosystems. Governments, resource managers, and communities recognize the value of assessing the social and ecological impacts of climate change, but there is little consensus on the most effective framework to support vulnerability and adaptation assessments. The framework presented in this research is based on a gap analysis developed from the recommendations of climate and adaptation experts. The article highlights social and ecological factors that affect vulnerability to climate change; adaptive capacity and adaptation options informing policy and conservation management decisions; and a methodology including criteria to assess current and future vulnerability to climate change. The framework is intended for conservation practitioners working in developing countries, small island nations, and traditional communities. It identifies core components that assess climate change impacts on coastal communities and environments at the local scale, and supports the identification of locally relevant adaptation strategies. Although the literature supporting vulnerability adaptation assessments is extensive, little emphasis has been placed on the systematic validation of these tools. To address this, we validate the framework using the Delphi technique, a group facilitation technique used to achieve convergence of expert opinion, and address gaps in previous vulnerability assessments. © 2015, Copyright © Taylor & Francis Group, LLC.

Lawler J.J.,University of Washington | Tear T.H.,Nature Conservancy | Pyke C.,CTG Energetics | Shaw R.M.,Nature Conservancy | And 8 more authors.
Frontiers in Ecology and the Environment | Year: 2010

Climate change is altering ecological systems throughout the world. Managing these systems in a way that ignores climate change will likely fail to meet management objectives. The uncertainty in projected climate-change impacts is one of the greatest challenges facing managers attempting to address global change. In order to select successful management strategies, managers need to understand the uncertainty inherent in projected climate impacts and how these uncertainties affect the outcomes of management activities. Perhaps the most important tool for managing ecological systems in the face of climate change is active adaptive management, in which systems are closely monitored and management strategies are altered to address expected and ongoing changes. Here, we discuss the uncertainty inherent in different types of data on potential climate impacts and explore climate projections and potential management responses at three sites in North America. The Central Valley of California, the headwaters of the Klamath River in Oregon, and the barrier islands and sounds of North Carolina each face a different set of challenges with respect to climate change. Using these three sites, we provide specific examples of how managers are already beginning to address the threat of climate change in the face of varying levels of uncertainty. © The Ecological Society of America.

Thorne J.H.,University of California at Davis | Seo C.,Seoul National University | Basabose A.,International Gorilla Conservation Programme | Gray M.,International Gorilla Conservation Programme | And 3 more authors.
Ecosphere | Year: 2013

Endangered species conservation planning needs to consider the effects of future climate change. Species distribution models are commonly used to predict future shifts in habitat suitability. We evaluated the effects of climate change on the highly endangered mountain gorilla (Gorilla beringei beringei) using a variety of modeling approaches, and assessing model outputs from the perspective of three spatial habitat management strategies: status quo, expansion and relocation. We show that alternative assumptions about the ecological niche of mountain gorillas can have a very large effect on model predictions. 'Standard' correlative models using 18 climatic predictor variables suggested that by 2090 there would be no suitable habitat left for the mountain gorilla in its existing parks, whereas a 'limiting-factor' model, that uses a proxy of primary productivity, suggested that climate suitability would not change much. Species distribution models based on fewer predictor variables, on alternative assumptions about niche conservatism (including or excluding the other subspecies Gorilla beringii graueri), and a model based on gorilla behavior, had intermediate predictions. These alternative models show strong variation, and, in contrast to the standard approach with 18 variables, suggest that mountain gorilla habitat in the parks may remain suitable, that protected areas could be expanded into lower (warmer) areas, and that there might be climactically suitable habitat in other places where new populations could possibly be established. Differences among model predictions point to avenues for model improvement and further research. Similarities among model predictions point to possible areas for climate change adaptation management. For species with narrow distributions, such as the mountain gorilla, modeling the impact of climate change should be based on careful evaluation of their biology, particularly of the factors that currently appear to limit their distribution, and should avoid the naïve application of standard correlative methods with many predictor variables. © 2013 Thorne et al.

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