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The importance of incorporating threat for efficient targeting and evaluation of conservation investments

The importance of incorporating threat for efficient targeting and evaluation of conservation... We commend Underwood (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 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 (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 2006 ). In California, the same land‐cover change data sets used by Underwood (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 (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 (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 (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 are expected to have little effect on future rates of habitat loss because of low human population growth projections and existing land‐use policies ( Byrd 2009 ). Due to the low land values in this area, the “cost‐efficient” funding scenario proposed by Underwood (2009) would increase investment levels in Tehama County to $360 million. Of course, despite high threat levels, some sites come at too high a price. Therefore, spatial conservation targeting models must attempt to minimize loss by making trade‐offs between land costs, biological benefits, and the probability of habitat loss, which generally result in prioritizing biodiversity‐rich areas in moderate‐to‐high threat sites and for moderate costs ( Newburn 2005 ). Editor : Dr. Kerrie Wilson http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Conservation Letters Wiley

The importance of incorporating threat for efficient targeting and evaluation of conservation investments

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Publisher
Wiley
Copyright
"Copyright © 2009 Wiley Subscription Services, Inc., A Wiley Company"
eISSN
1755-263X
DOI
10.1111/j.1755-263X.2009.00073.x
Publisher site
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Abstract

We commend Underwood (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 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 (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 2006 ). In California, the same land‐cover change data sets used by Underwood (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 (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 (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 (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 are expected to have little effect on future rates of habitat loss because of low human population growth projections and existing land‐use policies ( Byrd 2009 ). Due to the low land values in this area, the “cost‐efficient” funding scenario proposed by Underwood (2009) would increase investment levels in Tehama County to $360 million. Of course, despite high threat levels, some sites come at too high a price. Therefore, spatial conservation targeting models must attempt to minimize loss by making trade‐offs between land costs, biological benefits, and the probability of habitat loss, which generally result in prioritizing biodiversity‐rich areas in moderate‐to‐high threat sites and for moderate costs ( Newburn 2005 ). Editor : Dr. Kerrie Wilson

Journal

Conservation Letters – Wiley

Published: Oct 1, 2009

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