@article {800,
	title = {Modeling wildlife and other trade-offs with biofuel crop production},
	journal = {GCB Bioenergy},
	volume = {4},
	year = {2012},
	month = {2012},
	pages = {330-341},
	abstract = {Biofuels from agricultural sources are an important part of California{\textquoteright}s strategy to reduce greenhouse gas emissions and dependence on foreign oil. Land conversion for agricultural and urban uses has already imperiled many animal species in the state. This study investigated the potential impacts on wildlife of shifts in agricultural activity to increase biomass production for transportation fuels. We applied knowledge of the suitability of California{\textquoteright}s agricultural landscapes for wildlife species to evaluate wildlife effects associated with plausible scenarios of expanded production of three potential biofuel crops (sugar beets, bermudagrass, and canola). We also generated alternative, spatially explicit scenarios that minimized loss of habitat for the same level of biofuel production. We explored trade-offs to compare the marginal changes per unit of energy for transportation costs, wildlife, land and water-use, and total energy produced, and found that all five factors were influenced by crop choice. Sugar beet scenarios require the least land area: 3.5 times less land per liter of gasoline equivalent than bermudagrass and five times less than canola. Canola scenarios had the largest impacts on wildlife but the greatest reduction in water use. Bermudagrass scenarios resulted in a slight overall improvement for wildlife over the current situation. Relatively minor redistribution of lands converted to biofuel crops could produce the same energy yield with much less impact on wildlife and very small increases in transportation costs. This framework provides a means to systematically evaluate potential wildlife impacts of alternative production scenarios and could be a useful complement to other frameworks that assess impacts on ecosystem services and greenhouse gas emissions.},
	keywords = {agroecosystems, biofuels, biomass feedstock, California Wildlife Habitat Relationships system, geographic information systems, habitat suitability, Marxan, renewable energy, trade-offs, water demand},
	isbn = {1757-1707},
	url = {http://dx.doi.org/10.1111/j.1757-1707.2011.01130.x},
	author = {Stoms, David M. and Davis, Frank W. and Jenner, Mark W. and Nogeire, Theresa M. and Kaffka, Stephen R.}
}
@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 {782,
	title = {Geographic analysis of California condor sighting data},
	journal = {Conservation Biology},
	volume = {7},
	year = {1993},
	month = {1993},
	pages = {148-159},
	abstract = {Observation and habitat data were compiled and analyzed in conjunction with recovery planning for the endangered California Condor (Gymnogyps californianus). A geographic information system (GIS) was used to provide a quantitative inventory of recent historical Condor habitats, to measure the association of Condor activity patterns and mapped habitat variables, and to examine spatio-temporal changes in the range of the species during its decline. Only five percent of the study area within the historic range is now used for urban or cultivated agricultural purposes. Observations of Condor feeding perching, and nesting were nonrandomly associated with mapped land cover, in agreement with life history information for the species. The precipitous decline in numbers of Condors in this century produced only a small reduction in the limits of the observed species {\textquoteright}range, as individual birds continued to forage over most of the range. Some critical risk factors such as shooting and lead poisoning are difficult to map and bave not been included in the database Besides the applications demonstrated in this case study, GIS can be a valuable tool for recovery planning, in the design of stratified sampling schemes, or for extrapolation of habitat models over unsurveyed regions. We conclude with recommendations from this case study regarding when to consider using GIS and the importance of pilot studies and sensitivity analysis.},
	keywords = {habitat suitability, sensitivity analysis},
	url = {<Go to ISI>://A1993KR98600021},
	author = {Stoms, D. M. and Davis, F. W. and Cogan, C. B. and Painho, M. O. and Duncan, B. W. and Scepan, J. and Scott, J. M.}
}
@article {763,
	title = {Effects of habitat map generalization in biodiversity assessment},
	journal = {Photogrammetric Engineering and Remote Sensing},
	volume = {58},
	year = {1992},
	month = {1992},
	pages = {1587-1591},
	abstract = {Species richness is being mapped as part of an inventory of biological diversity in California (i.e., gap analysis). Species distributions are modeled with a GIS on the basis of maps of each species{\textquoteright} preferred habitats. Species richness is then tallied in equal-area sampling units. A GIS sensitivity analysis examined the effects of the level of generalization of the habitat map on the predicted distribution of species richness in the southern Sierra Nevada. As the habitat map was generalized, the number of habitat types mapped within grid cells tended to decrease with a corresponding decline in numbers of species predicted. Further, the ranking of grid cells in order of predicted numbers of species changed dramatically between levels of generalization. Areas predicted to be of greatest conservation value on the basis of species richness may therefore be sensitive to GIS data resolution.},
	keywords = {habitat suitability, scale, sensitivity analysis, species richness},
	url = {<Go to ISI>://A1992JV67200007},
	author = {Stoms, D. M.}
}
@article {781,
	title = {Sensitivity of wildlife habitat models to uncertainties in GIS data},
	journal = {Photogrammetric Engineering and Remote Sensing},
	volume = {58},
	year = {1992},
	month = {1992},
	pages = {843-850},
	abstract = {Decision makers need to know the reliability of output products from GIS analysis. For many GIS applications, it is not possible to compare these products to an independent measure of "truth." Sensitivity analysis offers an alternative means of estimating reliability. In this paper, we present a GIS-based statistical procedure for estimating the sensitivity of wildlife habitat models to uncertainties in input data and model assumptions. The approach is demonstrated in an analysis of habitat associations derived from a GIS database for the endangered California condor. Alternative data sets were generated to compare results over a reasonable range of assumptions about several sources of uncertainty. Sensitivity analysis indicated that condor habitat associations are relatively robust, and the results have increased our confidence in our initial findings. Uncertainties and methods described in the paper have general relevance for many GIS applications.},
	keywords = {habitat suitability, sensitivity analysis},
	url = {<Go to ISI>://A1992HX38700006},
	author = {Stoms, D. M. and Davis, F. W. and Cogan, C. B.}
}
