Department of Earth and Environmental Sciences, Celestijnenlaan 200E, Leuven, Belgium.
KU Leuven Plant Institute, KU Leuven, Leuven, Belgium.
Ecol Lett. 2023 Dec;26(12):2043-2055. doi: 10.1111/ele.14312. Epub 2023 Oct 3.
Species distributions are conventionally modelled using coarse-grained macroclimate data measured in open areas, potentially leading to biased predictions since most terrestrial species reside in the shade of trees. For forest plant species across Europe, we compared conventional macroclimate-based species distribution models (SDMs) with models corrected for forest microclimate buffering. We show that microclimate-based SDMs at high spatial resolution outperformed models using macroclimate and microclimate data at coarser resolution. Additionally, macroclimate-based models introduced a systematic bias in modelled species response curves, which could result in erroneous range shift predictions. Critically important for conservation science, these models were unable to identify warm and cold refugia at the range edges of species distributions. Our study emphasizes the crucial role of microclimate data when SDMs are used to gain insights into biodiversity conservation in the face of climate change, particularly given the growing policy and management focus on the conservation of refugia worldwide.
物种分布通常使用开阔区域测量的粗粒度宏气候数据进行建模，这可能导致有偏差的预测，因为大多数陆地物种都生活在树荫下。对于整个欧洲的森林植物物种，我们将基于常规宏气候的物种分布模型 (SDM) 与针对森林小气候缓冲作用进行修正的模型进行了比较。结果表明，高空间分辨率的基于小气候的 SDM 比使用较粗分辨率的宏气候和小气候数据的模型表现更好。此外，基于宏气候的模型在模型化物种响应曲线方面引入了系统偏差，这可能导致错误的范围转移预测。对于保护科学来说至关重要的是，这些模型无法在物种分布范围边缘识别温暖和寒冷的避难所。我们的研究强调了在气候变化背景下使用 SDM 来深入了解生物多样性保护时，小气候数据的关键作用，特别是考虑到政策和管理日益关注全球范围内的避难所保护。
