用深度学习从现有调查数据推算任意尺度的物种丰富度。
Multi-scale species richness estimation with deep learning
- 融合采样理论与深度学习，统一建模多尺度物种积累
- 在欧洲35万份植被调查上使预测误差降低61%
- 适合生态评估、保护规划与全球变化研究者使用
生物多样性评估严重依赖于物种丰富度的测量尺度。物种丰富度随采样面积的增长受自然和人为过程影响，这些过程的作用在不同空间尺度上各异。这种累积动态由物种-面积关系（SAR）描述，但难以准确评估，因为大多数生态调查覆盖的区域远小于这些过程作用的尺度。本文结合采样理论与深度学习，提出一种名为MuScaRi的模型，可从现有生态调查中估算任意地理尺度下的物种丰富度。我们将在欧洲约35万份植被调查数据上应用该模型。经独立区域植物名录验证，相较于传统估算方法，MuScaRi将维管植物丰富度估计的均方根误差降低61%，预测偏差显著减小，并生成多尺度丰富度地图及空间显式物种积累速率估计，后者是生物多样性保护的关键指标。通过在一个统一框架内涵盖所有生态相关空间尺度，MuScaRi为全球变化背景下的可靠生物多样性评估与预测提供了关键工具。
原文摘要 · Abstract (English)
Biodiversity assessments depend critically on the spatial scale at which species richness is measured. How species richness accumulates with sampling area is influenced by natural and anthropogenic processes whose effects vary across spatial scales. These accumulation dynamics, described by the species-area relationship (SAR), are challenging to assess because most biodiversity surveys cover sampling areas far smaller than the scales at which these processes operate. Here, we combine sampling theory with deep learning to estimate species richness at arbitrary spatial scales across geographic space from existing ecological surveys. We apply our model, named MuScaRi, to ~350k vegetation surveys across Europe. Validated against independent regional plant inventories, MuScaRi reduces root mean squared error of vascular plant richness estimates by 61% relative to conventional estimators, yields substantially less biased predictions, and produces multi-scale richness maps alongside spatially explicit estimates of the species accumulation rate, a key indicator for biodiversity conservation. By encompassing the full spectrum of ecologically relevant spatial scales within a single unified framework, MuScaRi provides an essential tool for robust biodiversity assessments and forecasts under global change.
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