@article {880,
	title = {Climate change, wine, and conservation},
	journal = {Proceedings of the National Academy of Sciences},
	year = {2013},
	author = {Hannah, L. and P. R. Roehrdanz}
}
@article {869,
	title = {Modeling plant species distributions under future climates: how fine scale do climate projections need to be?},
	journal = {Global Change Biology},
	volume = {19},
	year = {2012},
	month = {11/2012},
	pages = {10},
	type = {primary research article},
	chapter = {473},
	abstract = {<p>Recent studies suggest that species distribution models (SDMs) based on fine-scale climate data may provide markedly different estimates of climate-change impacts than coarse-scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse-scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000-fold range of spatial scales (0.008\&ndash;16 km2). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate-data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine- and coarse-scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species\&$\#$39; range size. On average, SDMs projected onto 4 km climate data predicted 42\% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.</p>
},
	keywords = {biodiversity; California; climate change; downscaling; habitat; impacts; spatial resolution; terrain; topography},
	doi = {DOI: 10.1111/gcb.12051},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/gcb.12051/abstract},
	author = {Franklin, J. and Davis, F. W. and Ikegami, M. and Flint, L. E. and Flint, A. L. and Hannah, L.}
}
@article {881,
	title = {Saving a Million Species: Extinction risk from climate change},
	year = {2012},
	author = {Hannah, L.}
}
@article {882,
	title = {The impact of climate change on California timberlands.},
	journal = {Climatic Change},
	volume = {109},
	year = {2011},
	chapter = {429-443.},
	author = {Hannah, L. and C. Costello}
}
@article {695,
	title = {The Impact of Climate Change on California Timberlands},
	year = {2009},
	month = {2009},
	pages = {52},
	institution = {Conservation International and University of California Santa Barbara},
	abstract = {<p>California timber production has been declining in an era of warming, increased wildfires, land use change, and growing emphasis on recreation. Climate change has the potential to further affect the California timber production and prices. The direction and magnitude of change will depend on individual site characteristics and projected climate change. Examples of potential climate change effects include changes in individual tree growth rates, forest dieback, and shifts in species ranges and ecosystem composition. When coupled with changes in global timber prices, which themselves are the result of productivity changes, this leads to important consequences to California\&$\#$39;s private timberlands. The ecological responses to climate change are dynamic and these complexities should be considered when predicting future timber production in California. Past attempts have modeled climate change impacts on the timber industry in California but did not consider dynamic land-use change or biologically relevant spatial resolution. This study uses models that project tree species productivity and movement across the landscape under climate change, coupled with economic models of landowner adaptation and returns from multiple harvest strategies. Our results show that under likely price scenarios, climate change will result in an overall decline in the value of harvested timber in the state, with decreases of 4.9 percent to 8.5 percent by the end of the century, depending on climate change scenario, price scenario and management option, with dollar losses totaling up to $8.1 billion. There is great spatial variation within these statewide averages. Many areas of the state show substantial declines in timber value, while a smaller number of areas show modest increases in value, under price scenarios that reflect the impact of climate change. If prices are not affected by climate change, more areas experience gains in value. Management options influence the degree of loss, indicating that programs fostering adaptation to climate change may pay important economic benefits. Declining timber value corresponds disproportionately to areas already experiencing conversion of timberlands to housing or agriculture. Policy measures to stem conversion of timberlands due to climate change may warrant consideration.</p>
},
	keywords = {California, carbon credit, climate change, growth rate, timber},
	isbn = {CEC-500-2009-045-F},
	url = {http://www.energy.ca.gov/2009publications/CEC-500-2009-045/CEC-500-2009-045-F.PDF},
	author = {Hannah, L. and Costello, Chris and Guo, Chris and Ries, Lydia and Kolstad, Charles and Snider, Nathan}
}
@article {886,
	title = {Climate change adaptation for conservation in Madagascar},
	journal = {Biology Letters},
	volume = {4},
	year = {2008},
	author = {Hannah, L. and Radhika Dave and Porter P. Lowry and Lowry, I.I. and Sandy Andelman and Michele Andrianarisata}
}
@article {885,
	title = {Protected areas and climate change.},
	journal = {Year in Ecology and Conservation Biology},
	volume = {1134},
	year = {2008},
	chapter = {201-212},
	author = {Hannah, L.}
}
@article {887,
	title = {Protected area needs in a changing climate},
	journal = {Frontiers in Ecology and the Environment},
	volume = {5},
	year = {2007},
	chapter = {131-138.},
	author = {Hannah, L. and G. Midgley}
}
@article {891,
	title = { Designing Landscapes and Seascapes for Change. Climate Change and Biodiversity},
	year = {2005},
	author = {Hannah, L. and L. A. Hansen}
}
@article {890,
	title = {Modeling Impacts of Climate Change on Tropical Forests. Tropical Forest Responses to Climate Change},
	journal = {J. Flenley and M. B. Bush},
	year = {2005},
	author = {Hannah, L. and R. A. Betts}
}
@article {833,
	title = {Planning for climate change: Identifying minimum-dispersal corridors for the Cape proteaceae},
	journal = {Conservation Biology},
	volume = {19},
	year = {2005},
	month = {2005},
	pages = {1063-1074},
	abstract = {Climate change poses a challenge to the conventional approach to biodiversity conservation, which relies on fixed protected areas, because the changing climate is expected to shift the distribution of suitable areas for many species. Some species will persist only if they can colonize new areas, although in some cases their dispersal abilities may be very limited. To address this problem we devised a quantitative method for identifying multiple corridors of connectivity through shifting habitat suitabilities that seeks to minimize dispersal demands first and then the area of land required. We applied the method to Proteaceae mapped on a 1-minute grid for the western part of the Cape Floristic Region of South Africa, to supplement the existing protected areas using Worldmap software. Our goal was to represent each species in at least 35 grid cells (approximately 100 km(2)) at all times between 2000 and 2050 despite climate change. Although it was possible to achieve the goal at reasonable cost, caution will be needed in applying our method to reserves or other conservation investments until there is further information to support or refine the climate-change models and the species{\textquoteright} habitat-suitability and dispersal models.},
	keywords = {area-selection algorithms, bioclimatic modeling, biodiversity, biodiversity conservation, connectivity, Conservation, distance, distribution models, distributions, floristic region, habitat suitability, plant migration, Protected areas, reserve selection algorithms, south-africa, species persistence},
	url = {<Go to ISI>://000231118600013},
	author = {Williams, P. and Hannah, L. and Andelman, S. and Midgley, G. and Araujo, M. and Hughes, G. and Manne, L. and Martinez-Meyer, E. and Pearson, R.}
}
@article {889,
	title = {The View from the Cape: Exinction risk, protected areas and climate change},
	journal = {BioScience},
	volume = {53},
	year = {2005},
	chapter = {231-242.},
	author = {Hannah, L. and G. F. Midgley}
}
@article {894,
	title = {Climate change-integrated conservation strategies},
	journal = {Global Ecology \& Biogeography},
	volume = {11},
	year = {2002},
	chapter = {485-495.},
	author = {Hannah, L. and G. F. Midgley}
}
@article {893,
	title = {Conservation of Biodiversity in a Changing Climate},
	journal = {Conservation Biology},
	volume = {16},
	year = {2002},
	chapter = {11-15.},
	author = {Hannah, L. and G. F. Midgley}
}
@article {895,
	title = {The Role of a Global Protected Areas System in Conserving Biodiversity in the Face of Climate Change},
	journal = {Climate Change and protected areas},
	year = {2001},
	author = {Hannah, L.}
}
@article {896,
	title = {A Preliminary Inventory of Human Disturbance of World Ecosystems},
	journal = {Ambio},
	volume = {23},
	year = {1994},
	chapter = {246},
	author = {Hannah, L. and D. Lohse}
}
