细粒度预测是准确呈现大陆尺度生物多样性模式的关键

IF 6.3 1区 环境科学与生态学 Q1 ECOLOGY
Jeremy M. Cohen, Walter Jetz
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引用次数: 0

摘要

随着全球变化的加速,准确预测物种分布和生物多样性模式对于限制生物多样性的丧失至关重要。大量研究发现,粗粒度物种分布模型(SDMs)与细粒度模型相比表现较差,原因是它们使环境信息与观测结果不匹配。然而,目前仍不清楚粒度偏差在不同时空的强度是如何变化的,这可能导致对特定区域、季节或物种的预测不准确。例如,粗粒度偏差可能会在斑块状、不连续的地貌中加剧。这种偏差累积起来,可能会对大陆和季节性生物多样性模式产生极具误导性的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fine-Grain Predictions Are Key to Accurately Represent Continental-Scale Biodiversity Patterns

Aim

As global change accelerates, accurate predictions of species distributions and biodiversity patterns are critical to limit biodiversity loss. Numerous studies have found that coarse-grain species distribution models (SDMs) perform poorly relative to fine-grain models because they mismatch environmental information with observations. However, it remains unclear how grain-size biases vary in intensity across space and time, possibly generating inaccurate predictions for specific regions, seasons or species. For example, coarse-grain biases may intensify in patchy, discontinuous landscapes. Such biases may accumulate to produce highly misleading estimates of continental and seasonal biodiversity patterns.

Location

United States and Canada.

Time Period

2004–2021.

Major Taxa Studied

Birds (Aves).

Methods

We fit presence-absence SDMs characterising the summer and winter distributions of 572 bird species native to the US and Canada across five spatial grains from 1 to 50 km, using observations from the eBird citizen science initiative. We combined these predictions to generate seasonal biodiversity estimates across the US and Canada, which we validated using observations from 322 independent sites.

Results

We find that in both seasons, 1 km models more accurately predicted species presence, absence and richness at local sites. Coarse-grain models (even at 3 km) consistently under-predicted range area, potentially missing important habitat. This bias intensified during summer (83%–86% of species) when many birds have smaller ‘operational scales’ via localised home ranges while breeding. Biases were greatest in desert regions with patchier habitat and for range-restricted and habitat-specialist species. Predictions based on coarse-grain models overpredicted avian diversity in the west and underpredicted it in the great plains, prairie pothole region and boreal zones.

Main Conclusions

We demonstrate that coarse-grain models can bias seasonal and continental estimates of biodiversity patterns across space and time and that grain-related biases intensify during summer and in patchier landscapes, especially for range-restricted and habitat specialist species at risk of population declines.

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来源期刊
Global Ecology and Biogeography
Global Ecology and Biogeography 环境科学-生态学
CiteScore
12.10
自引率
3.10%
发文量
170
审稿时长
3 months
期刊介绍: Global Ecology and Biogeography (GEB) welcomes papers that investigate broad-scale (in space, time and/or taxonomy), general patterns in the organization of ecological systems and assemblages, and the processes that underlie them. In particular, GEB welcomes studies that use macroecological methods, comparative analyses, meta-analyses, reviews, spatial analyses and modelling to arrive at general, conceptual conclusions. Studies in GEB need not be global in spatial extent, but the conclusions and implications of the study must be relevant to ecologists and biogeographers globally, rather than being limited to local areas, or specific taxa. Similarly, GEB is not limited to spatial studies; we are equally interested in the general patterns of nature through time, among taxa (e.g., body sizes, dispersal abilities), through the course of evolution, etc. Further, GEB welcomes papers that investigate general impacts of human activities on ecological systems in accordance with the above criteria.
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