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首页> 外文期刊>Ecological Modelling >Species detection vs. habitat suitability: Are we biasing habitat suitability models with remotely sensed data? (Review)
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Species detection vs. habitat suitability: Are we biasing habitat suitability models with remotely sensed data? (Review)

机译：物种检测与栖息地适宜性：我们是否利用遥感数据对栖息地适宜性模型进行偏倚？ （评论）

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摘要

Remotely sensed datasets are increasingly being used to model habitat suitability for a variety of taxa. We review habitat suitability models (HSMs) developed for both plants and animals that include remote sensing predictor variables to determine how these variables could affect model projections. For models focused on plant species habitat, we find several instances of unintentional bias in HSMs of vegetation due to the inclusion of remote sensing variables. Notably, studies that include continuous remote sensing variables could be inadvertently mapping actual species distribution instead of potential habitat due to unique spectral or temporal characteristics of the target species. Additionally, HSMs including categorical classifications are rarely explicit about assumptions of habitat suitability related to land cover, which could lead to unintended exclusion of potential habitat due to current land use. Although we support the broader application of remote sensing in general, we caution developers of HSMs to be aware of introduced model bias. These biases are more likely to arise when remote sensing variables are added to models simply because they improve accuracy, rather than considering how they affect the model results and interpretation. When including land cover classifications as predictors, we recommend that modellers provide more explicit descriptions of how habitat is defined (e.g., is deforested land considered suitable for trees?). Further, we suggest that continuous remote sensing variables should only be included in habitat models if authors can demonstrate that their inclusion characterizes potential habitat rather than actual species distribution. Use of the term 'habitat suitability model' rather than 'species distribution model' could reduce confusion about modelling goals and improve communication between the remote sensing and ecological modelling communities.
机译：越来越多地使用遥感数据集来模拟各种分类单元的栖息地适宜性。我们回顾了为动植物开发的栖息地适应性模型（HSM），其中包括遥感预测变量，以确定这些变量如何影响模型预测。对于关注植物物种栖息地的模型，我们发现由于包含遥感变量而导致植被的HSM出现了无意偏见的情况。值得注意的是，由于目标物种独特的光谱或时间特征，包含连续遥感变量的研究可能会无意间绘制出实际物种分布，而不是潜在的栖息地。此外，包括分类在内的HSM很少明确说明与土地覆盖有关的栖息地适宜性，这可能会导致由于当前土地使用而意外排除潜在的栖息地。尽管我们总体上支持广泛的遥感应用，但我们提醒HSM的开发人员注意引入的模型偏差。当将遥感变量添加到模型中时，仅是因为它们提高了准确性，而不是考虑它们如何影响模型结果和解释，所以更有可能出现这些偏差。在将土地覆盖物分类作为预测指标时，我们建议建模人员提供关于生境定义的更明确的描述（例如，被砍伐的土地是否适合树木？）。此外，我们建议，只有在作者能够证明连续性遥感变量包含了潜在的栖息地特征而不是实际物种分布的情况下，才应将其纳入生境模型。使用术语“栖息地适宜性模型”而不是“物种分布模型”可以减少对建模目标的困惑，并改善遥感与生态建模社区之间的交流。

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