Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat

Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California’s Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance...

高度移动性物种（如候鸟）会通过迁徙至更优质的栖息地，以应对资源可获得性的季节与年际波动。尽管资源阈值的重要性已得到学界广泛认可，但现有物种分布模型（Species Distribution Model）通常仅依赖长期平均的栖息地条件，这主要是因为大范围、高时间分辨率的环境数据难以获取。近年来遥感技术的进步使得将更频繁的景观动态监测数据纳入模型成为可能，但此类建模与处理工作往往需要付出相应成本，且这类举措的附加价值尚未明确。本研究旨在验证：相较于使用长期平均数据，纳入实时环境数据是否能够提升物种分布模型的预测能力。本研究以加利福尼亚州中央谷地的滨鸟为研究对象，基于高时间分辨率（每16天一次）与17年长期平均的地表水数据，构建并对比了两类物种分布模型。基于丰度数据……