Data from: Hypothesis-driven and field-validated method to prioritize fragmentation mitigation efforts in road projects

The active field of connectivity conservation has provided numerous methods to identify wildlife corridors with the aim of reducing the ecological effect of fragmentation. Nevertheless, these methods often rely on untested hypotheses of animal movements, usually fail to generate fine-scale predictions of road crossing sites, and do not allow managers to prioritize crossing sites for implementing road fragmentation mitigation measures. We propose a new method that addresses these limitations. We illustrate this method with data from southwestern Gabon (central Africa). We used stratified random transect surveys conducted in two seasons to model the distribution of African forest elephant (Loxodonta cyclotis), forest buffalo (Syncerus caffer nanus), and sitatunga (Tragelaphus spekii) in a mosaic landscape along a 38.5 km unpaved road scheduled for paving. Using a validation data set of recorded crossing locations, we evaluated the performance of three types of models (local suitability, local least-cost movement, and regional least-cost movement) in predicting actual road crossings for each species, and developed a unique and flexible scoring method for prioritizing road sections for the implementation of road fragmentation mitigation measures. With a data set collected in <10 weeks of fieldwork, the method was able to identify seasonal changes in animal movements for buffalo and sitatunga that shift from a local exploitation of the site in the wet season to movements through the study site in the dry season, whereas elephants use the entire study area in both seasons. These three species highlighted the need to use species- and season-specific modeling of movement. From these movement models, the method ranked road sections for their suitability for implementing fragmentation mitigation efforts, allowing managers to adjust priority thresholds based on budgets and management goals. The method relies on data that can be obtained in a period compatible with environmental impact assessment constraints, and is flexible enough to incorporate other potential movement models and scoring criteria. This approach improves upon available methods and can help inform prioritization of road and other linear infrastructure segments that require impact mitigation methods to ensure long-term landscape connectivity.当前活跃的连通性保护（connectivity conservation）领域已提出诸多方法，用于识别野生动物廊道（wildlife corridors），以期降低栖息地破碎化带来的生态影响。然而，此类方法往往依赖未经验证的动物运动假说，通常无法生成道路穿越点位的精细尺度预测，也无法帮助管理者优先开展道路破碎化缓解措施的布设工作。针对上述局限，我们提出一种全新方法。我们以非洲加蓬西南部（中非地区）的实测数据为例，对该方法进行了演示。我们在两个季节开展分层随机样带调查，对一段计划实施路面铺装的38.5千米未铺装道路沿线的镶嵌景观中，非洲森林象（Loxodonta cyclotis）、森林水牛（Syncerus caffer nanus）以及薮羚（Tragelaphus spekii）的分布进行建模。利用已记录的穿越点位验证数据集，我们评估了三类模型——局部适宜性模型、局部最小成本移动模型与区域最小成本移动模型——对各物种实际道路穿越情况的预测性能，并开发了一种独特且灵活的评分方法，用于优先排序需布设道路破碎化缓解措施的路段。仅通过不足10周的野外调查所收集的数据集，该方法便可识别出水牛与薮羚的季节性行为变化：湿季时它们仅在局部利用研究区域，干季则会穿越研究区域；而大象在两个季节均会使用整个研究区域。这三个物种的案例表明，需针对物种与季节分别开展运动建模。基于上述运动模型，该方法可为各路段的破碎化缓解措施适配性进行排序，使管理者可根据预算与管理目标调整优先级阈值。该方法所需的数据采集周期可匹配环境影响评价的时限要求，且具备足够灵活性，可纳入其他潜在的运动模型与评分准则。此方法优化了现有技术手段，可为需开展影响缓解措施以保障长期景观连通性的道路及其他线性基础设施路段的优先级排序提供决策依据。