Barnes Kevin W, Niemuth Neal D, Iovanna Rich
Habitat and Population Evaluation Team, U.S. Fish and Wildlife Service, Hadley, Massachusetts, USA.
Habitat and Population Evaluation Team, U.S. Fish and Wildlife Service, Bismarck, North Dakota, USA.
Conserv Biol. 2025 Feb;39(1):e14346. doi: 10.1111/cobi.14346. Epub 2024 Aug 21.
Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R = 0.89 [range: 0.83-0.96], root mean squared error [RMSE] = 9.29 ha [range: 2.83-22.77 ha]; proportional loss model: weighted group mean R = 0.74 [range: 0.64-0.87], RMSE = 0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.
草原保护规划通常侧重于高风险景观，但许多草原转化模型并非旨在优化保护规划，因为它们缺乏多维度风险评估，且与生态和保护实施尺度不一致。为辅助草原保护规划，我们在相关尺度上开发了景观尺度模型，以预测未来（2021 - 2031年）未受保护草原转化为农田或用于开发的总面积和比例损失。我们通过将过去的转化情况（2011 - 2021年）与随机森林回归模型中的一组协变量相关联，并将这些模型应用于当代协变量来预测未来损失，从而为美国本土的20个生态区开发了模型。总体而言，草原损失模型表现良好，其解释力在各生态区之间存在空间差异（总面积损失模型：加权组均值R = 0.89 [范围：0.83 - 0.96]，均方根误差[RMSE] = 9.29公顷[范围：2.83 - 22.77公顷]；比例损失模型：加权组均值R = 0.74 [范围：0.64 - 0.87]，RMSE = 0.03 [范围：0.02 - 0.06]）。景观中的作物量以及与城市、乙醇工厂和集约化动物饲养场的距离在两个模型中都具有很高的变量重要性。当景观中存在中等数量的草地、作物或开发区域（约50%）时，草地总损失更大。当草地较少（约<30%）且作物或开发区域较多（约>50%）时，草地比例损失更大。一些变量仅对一部分生态区有较大影响，例如，当景观中约>70%的区域纳入了保护储备计划时，草地损失更大。我们的方法为开发非常适合保护的风险图层提供了一种简单且灵活的方法，该方法可在全球范围内扩展。我们的转化模型可以通过根据风险进行优先级排序来支持保护规划，并且可以通过纳入生物价值和成本进一步优化。
