Empirical realised niche models for British higher and lower plants – development and preliminary testing
Empirical realised niche models for British higher and lower plants – development and preliminary testing
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英国高等植物和低等植物的实证利基模型——开发和初步测试
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
R. Marrs
中科院分区：
文献类型：
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作者：
S. Smart;W. Scott;J. Whitaker;M. Hill;D. Roy;C. Critchley;L. Marini;C. Evans;B. Emmett;E. Rowe;A. Crowe;M. G. Duc;R. Marrs
问：可以有用的实现生态位模型，为英国的植物物种使用气候，冠层高度和平均Ellenberg指数作为解释变量？
地点：英国。
研究方法：广义线性模型的构建使用发生数据，涵盖所有主要的自然和半自然植被类型（n = 40 683样方样本）。配对的物种和土壤记录仅可用于4%的训练数据（n = 1033），因此建模分两个阶段进行。首先，多元回归被用来表示平均Ellenberg值的水分，pH值和肥力，在直接的土壤测量。接下来，物种存在/不存在的建模使用平均指标得分，覆盖加权冠层高度，三个气候变量和这些因素之间的相互作用，但校正每个目标物种的存在，以避免循环。
结果：共模拟高等植物803种，蝙蝠327种。高等植物生态位模型的13%进行了测试，对一个独立的调查数据集不用于建立模型。模型进行更好的预测时，只基于来自每个地块的物种组成，而不是测量的土壤变量的指数。这反映了植被指数的高度变化，而测量的土壤变量无法解释这种变化。
结论：模型应用于估计预期的栖息地适宜性，而不是预测物种的存在。最小的不确定性也与它们作为自然保护区的风险评估和监测工具的使用有关，因为它们可以使用根据现有物种组成计算的平均环境指标来解决，无论是否有气候数据。
Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables?
Location: Great Britain.
Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi-natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover-weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity.
Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables.
Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.