{'fraction_non_alphanumeric': 0.03237410071942446, 'fraction_numerical': 0.026618705035971222, 'mean_word_length': 5.23542600896861, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The importance of incorporating threat for efficient targeting and evaluation of conservation investments

We commend Underwood et al. (2009) for evaluating investment returns on billions of public dollars spent on land conservation by comparing actual with optimal conservation spending. However, they use a “maximize gain” prioritization strategy (Murdoch et al. 2007) that omits the likelihood of land-use conversion in the optimization algorithm, thereby implicitly assuming that all areas have the same probability of future development. Because development threat and cost are positively correlated, the “maximize gain” strategy is biased toward inexpensive land where biodiversity is more likely to remain without conservation expenditure. As Wilson et al. (2006) note, the “maximize gain” algorithm underperforms when allocating conservation expenditures where threat is highly variable. Using parcel-level data in Sonoma County, California, we demonstrated that a dynamic reserve site selection model incorporating threat, costs, and benefits significantly outperformed the “maximum gain” approach (costs and benefits only) (Newburn et al. 2006). In California, the same land-cover change data sets used by Underwood et al. (2009) reveal that the threat of habitat conversion varies substantially from almost none to 0.53% conversion of developable land per year among counties (FMMP 2002; C-CAP 2003; see Figure S1). The California data used by Underwood et al. (2009) also show the expected positive correlation between the threat of urban conversion and land cost (see Figure S2). Since these high vulnerability sites are typically more expensive than low vulnerability sites, they will be excluded by the “maximize gain” approach. Because the rate of habitat loss was not included, counties prioritized by Underwood et al. (2009) were less threatened than average, with a mean rank of 33.5 out of 50 counties for their annual rate of habitat conversion. These counties lost an estimated 134 ha per year of habitat to urban development, compared to an average of 1,234 ha per year in the ten fastest-growing counties. Colusa County, which was selected by both funding scenarios in Underwood et al. (2009), was ranked 47th for its annual rate of habitat loss to urban development; in contrast, San Bernardino County, which has high species richness, moderate land costs, and lost the greatest total amount of habitat each year was not selected. The “maximize gain” strategy can be appealing to investors in private land conservation because it results in large landscape acquisitions. For example, close to US$13 million of public funding was used to purchase over 37,000 hectares of conservation easements in Tehama County between 1997 and 2007. However, these actions