Accurate estimates of the costs of land protection are useful for understanding where biodiversity conservation goals can be achieved at the lowest cost to society. However, because of the scarcity of high-quality cost maps for tropical countries, conservation planning studies often ignore cost or rely on untested proxies, such as agricultural rent or land-use intensity. Here, we show how analysts can estimate land protection costs using original data of public land acquisitions, global predictor datasets, and simple machine-learning models. For the Colombian Andes, a global biodiversity hotspot, we found that the principal driver of the cost of land protection is urban proximity, not agricultural rent. We derived cost estimates that predict public land protection costs more accurately than available cost proxies and identified new protection priorities for 143 threatened bird species. A more systematic collection of cost records of land protection will help inform public decisions on national and global biodiversity protection priorities.

中文翻译：

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改进热带生物多样性热点地区土地保护成本的估算

准确估计土地保护成本有助于了解在哪些方面可以以最低的社会成本实现生物多样性保护目标。然而，由于热带国家缺乏高质量的成本地图，保护规划研究常常忽略成本或依赖未经测试的替代指标，例如农业租金或土地使用强度。在这里，我们展示了分析师如何使用公共土地征用的原始数据、全球预测数据集和简单的机器学习模型来估算土地保护成本。对于全球生物多样性热点地区哥伦比亚安第斯山脉，我们发现土地保护成本的主要驱动因素是城市邻近性，而不是农业租金。我们得出的成本估算比现有的成本代理更准确地预测了公共土地保护成本，并确定了 143 种受威胁鸟类的新保护优先事项。更系统地收集土地保护成本记录将有助于为有关国家和全球生物多样性保护优先事项的公共决策提供信息。