Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data Alexandra D. Syphard a, * , Keith C. Clarke b , Janet Franklin c , Helen M. Regan d , Mark Mcginnis e a Conservation Biology Institute,10423 Sierra Vista Avenue, La Mesa, CA 91941, USA b Department of Geography, University of California, Santa Barbara, CA 93106-4060, USA c School of Geography and Urban Planning, Arizona State University, Tempe, AZ 85287, USA d Department of Biology, University of California, Riverside, CA 92521, USA e Dudek, 605 Third Street, Encinitas, CA 92024, USA article info Article history: Received 4 June 2010 Received in revised form 10 January 2011 Accepted 11 March 2011 Available online 7 April 2011 Keywords: Urban growth model Land use/land cover Conservation Southern California Spatial pattern Landscape metrics abstract The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species’ persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation manage- ment, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the defi- nition and scale of their data to fit their research objectives. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction A primary driver of environmental change and biodiversity loss is the conversion of natural habitat to urban settlements (Vitousek et al., 1997; Sala et al., 2000). Some regions, such as Mediterranean- type ecosystems, may experience disproportionate impacts of land use change on biodiversity due to high species endemism and rapid growth in population density and urban area (Underwood et al., 2009). In spite of the significant attention paid to climate change, land use change may produce far greater short- and long-term impacts on biodiversity (Slaymaker, 2001). The spatial pattern of development at landscape scales, i.e., dispersed, low-density housing vs. clustered, high density housing, may have important, but varying conservation impacts. For example, dispersed development may consume more land and lead to more widespread ecological degradation (Xie et al., 2005), but clustered developments may be dominated by greater proportions of non- native vegetation (Lenth et al., 2006). Fire risk in wildfire-prone regions has also been related to the spatial pattern of development, with the highest risk occurring where there is intermediate housing density (Syphard et al., 2008). The spatial pattern of urban devel- opment can also affect hydrology, nutrient cycling and microclimate (Artur-Hartranft et al., 2003), and thus the provision of ecosystem services that benefit society (Solecki and Oliveri, 2004). To better understand development patterns and to predict where future growth is likely to occur, and what impact it might have, many conservation scientists and land use planners use urban growth models. While urban modeling has a long history (e.g., Tobler, 1970), increased computing power has greatly expanded the range of problems that can be addressed (Guhathakurta, 1999; Ward et al., 2000; Paegelow and Olmedo, 2008). The complexity * Corresponding author. Tel./fax: þ1 619 328 1001. E-mail addresses: asyphard@consbio.orgv (A.D. Syphard), kclarke@geog.ucsb. edu (K.C. Clarke), Janet.Franklin@asu.edu (J. Franklin), helen.regan@ucr.edu (H.M. Regan), mcginnis76@gmail.com (M. Mcginnis). Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman 0301-4797/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2011.03.014 Journal of Environmental Management 92 (2011) 1882e1893