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首页> 外文期刊>Journal of Biogeography >Modelling the responses of Andean and Amazonian plant species to climate change: the effects of georeferencing errors and the importance of data filtering
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Modelling the responses of Andean and Amazonian plant species to climate change: the effects of georeferencing errors and the importance of data filtering

机译：模拟安第斯和亚马孙植物物种对气候变化的响应：地理配准误差的影响和数据过滤的重要性

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Species distribution models are a potentially powerful tool for predicting the effects of global change on species distributions and the resulting extinction risks. Distribution models rely on relationships between species occurrences and climate and may thus be highly sensitive to georeferencing errors in collection records. Most errors will not be caught using standard data filters. Here we assess the impacts of georeferencing errors and the importance of improved data filtering for estimates of the elevational distributions, habitat areas and predicted relative extinction risks due to climate change of nearly 1000 Neotropical plant species. The Amazon basin and tropical Andes, South America. We model the elevational distributions, or 'envelopes', of 932 Amazonian and Andean plant species from 35 families after performing standard data filtering, and again using only data that have passed through an additional layer of data filtering. We test for agreement in the elevations recorded with the collection and the elevation inferred from a digital elevation model (DEM) at the collection coordinates. From each dataset we estimate species range areas and extinction risks due to the changes in habitat area caused by a 4.5 pC increase in temperature. Amazonian and Andean plant species have a median elevational range of 717 m. Using only standard data filters inflates range limits by a median of 433 m (55%). This is equivalent to overestimating the temperature tolerances of species by over 3 pC - only slightly less than the entire regional temperature change predicted over the next 50-100 years. Georeferencing errors tend to cause overestimates in the amount of climatically suitable habitat available to species and underestimates in species extinction risks due to global warming. Georeferencing error artefacts are sometimes so great that accurately predicting whether species habitat areas will decrease or increase under global warming is impossible. The drawback of additional data filtering is large decreases in the number of species modelled, with Andean species being disproportionately eliminated. Even with rigorous data filters, distribution models will mischaracterize the climatic conditions under which species occur due to errors in the collection data. These errors affect predictions of the effects of climate change on species ranges and biodiversity, and are particularly problematic in mountainous areas. Additional data filtering reduces georeferencing errors but eliminates many species due to a lack of sufficient 'clean' data, thereby limiting our ability to predict the effects of climate change in many ecologically important and sensitive regions such as the Andes Biodiversity Hotspot.
机译：物种分布模型是预测全球变化对物种分布及其造成的灭绝风险的影响的潜在强大工具。分布模型依赖于物种发生与气候之间的关系，因此可能对收集记录中的地理配准错误高度敏感。使用标准数据过滤器不会捕获大多数错误。在这里，我们评估了地理配准误差的影响以及改进的数据过滤对于评估海拔分布，栖息地面积和因近1000种新热带植物物种的气候变化而导致的相对灭绝风险的重要性的重要性。南美洲亚马逊盆地和热带安第斯山脉。在执行标准数据过滤后，我们仅对通过附加数据过滤层的数据进行建模，然后对来自35个科的932种亚马逊和安第斯植物物种的海拔分布或“信封”进行建模。我们测试与集合一起记录的高程以及从数字高程模型（DEM）在集合坐标处推断出的高程中的一致性。根据每个数据集，我们估计物种范围区域和由于温度升高4.5 pC引起的栖息地面积变化而导致的灭绝风险。亚马孙和安第斯植物物种的中位海拔范围为717 m。仅使用标准数据过滤器可将范围限制扩大433 m（55％）。这相当于将物种的温度容忍度高估了3 pC以上-仅略低于未来50-100年所预测的整个区域温度变化。地理配准误差往往导致高估了可供物种使用的气候适宜生境的数量，而低估了由于全球变暖造成的物种灭绝风险。地理配准误差伪影有时如此之大，以至于无法准确预测物种栖息地面积在全球变暖下会减少还是增加。附加数据过滤的缺点是建模物种的数量大大减少，而安第斯物种却被不成比例地消除。即使使用严格的数据过滤器，分布模型也会错误地归因于由于收集数据中的错误而导致发生物种的气候条件。这些错误影响对气候变化对物种范围和生物多样性的影响的预测，在山区尤其成问题。附加的数据过滤可减少地理配准错误，但由于缺乏足够的“干净”数据而消除了许多物种，从而限制了我们预测许多生态重要和敏感​​地区（如安第斯山脉生物多样性热点）的气候变化影响的能力。

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