Data from: Importance of antecedent environmental conditions in modeling species distributions

Although species distributions can change in an unexpectedly short period of time, most species distribution models (SDMs) use only long-term averaged environmental conditions to explain species distributions. We aimed to demonstrate the importance of incorporating antecedent environmental conditions into SDMs in comparison to long-term averaged environmental conditions. We modeled the presence/absence of 18 fish species captured across 108 sampling events along a 50-km length of the Sagami River in Japan throughout the 1990s (one to four times per site at 45 sites). We constructed and compared the two types of SDMs: (1) a conventional model that uses only long-term averaged (10-year) environmental conditions; and (2) a proposed model that incorporates environmental conditions 2 years prior to a sampling event (antecedent conditions) together with long-term averages linked to life-history stages. These models both included geomorphological, hydrological, and sampling conditions as predictors. A random forest algorithm was applied for modeling and quantifying the relative importance of the predictors. For seven species, antecedent hydrological conditions were more important than the long-term averaged hydrological conditions. Furthermore, the distributions of two species with low prevalence could not be predicted using long-term averaged hydrological conditions but only using antecedent hydrological conditions. In conclusion, incorporating antecedent environmental factors linked with life-history stages at appropriate time scales can better explain changes in species distribution through time.

尽管物种分布的变化可能在远超预期的短时间尺度内发生，但绝大多数物种分布模型（species distribution models, SDMs）仅依赖长期平均环境条件来阐释物种分布格局。本研究旨在对比长期平均环境条件，阐明在物种分布模型中纳入前期环境条件的重要性。我们以20世纪90年代日本相模川50公里河段内的108次采样数据为基础，对该河段45个采样点（每个点位采样1至4次）所捕获的18种鱼类的存在/缺失情况进行建模分析。本研究构建并对比了两类物种分布模型：（1）仅采用10年长期平均环境条件的传统模型；（2）本研究提出的新型模型，该模型纳入采样事件前2年的环境条件（即前期环境条件），并结合与物种生活史阶段相关的长期平均环境条件。两类模型均以地貌、水文及采样条件作为预测变量。本研究采用随机森林（Random Forest）算法开展模型构建，并量化各预测变量的相对重要性。针对7个鱼类物种，前期水文条件的相对重要性均高于长期平均水文条件。此外，对于两个检出率较低的物种，仅采用长期平均水文条件无法准确预测其分布，唯有纳入前期水文条件方可实现有效预测。综上，在适宜的时间尺度下纳入与物种生活史阶段相关的前期环境因子，能够更精准地解释物种分布随时间的动态变化。