Presence-absence versus presence-only modelling methods for predicting bird habitat suitability
Presence-absence versus presence-only modelling methods for predicting bird habitat suitability
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DOI：
10.1111/j.0906-7590.2004.03764.x
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发表时间：
2004-08-01
期刊：
影响因子：
5.9
通讯作者：
Hirzel, AH
中科院分区：
文献类型：
--
作者：
Brotons, L;Thuiller, W;Hirzel, AH
生境适宜性模型可以使用需要物种存在或物种存在和不存在信息的方法来生成。了解这些方法的预测性能成为确定它们在不同约束条件下绘制当前物种分布的最佳应用范围的关键问题。本文以加泰罗尼亚的种鸟地图集数据为例，分析了两种方法的相对性能：仅使用存在数据的生态位因子分析（ENFA）和使用存在/不存在数据的广义线性模型（GLM）。模型是在一组具有相似生境要求，但发生率（流行率）和生态位（边际性）不同的森林物种上运行的。我们的结果支持GLM预测比ENFA预测更准确的观点。当物种使用与其适合度成比例的可用栖息地时，这一点尤其正确，这使得缺失数据可靠，有助于加强模型校准。生态位空间的物种边际性也与预测精度相关，即生态需求限制较少的物种比生态需求限制较多的物种建模更准确。这种模式与采用的方法无关。广泛和耐受性物种的模型对缺失数据更为敏感，这表明存在/缺失方法对于预测这类物种的分布可能特别重要。我们的结论是，建模者应该考虑物种的生态特征是决定模型准确性的关键，并且很难准确地预测一般物种的分布，这与所使用的方法无关。生境分布建模方法基于调整数据和数据质量的不同方法，涵盖不同的应用领域，因此很难确定一种应该普遍适用。然而，我们的结果表明，如果缺失数据是可用的，使用这些信息的方法应该在大多数情况下使用。
Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide-ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.