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Niche models for British plants and lichens obtained using an ensemble approach

机译:使用集成方法获得的英国植物和地衣的利基模型

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Site-occupancy models that predict habitat suitability for plant species in relation to measurable environmental factors can be useful for conservation planning. Such models can be derived from large-scale presence-absence datasets on the basis of environmental observations or, where only floristic data are available, using plant trait values averaged across a plot. However, the estimated modelled relationship between species presence and environmental variables depends on the type of statistical model adopted and hence can introduce additional uncertainty. Authors used an ensemble-modelling approach to constrain and quantify the uncertainty because of the choice of statistical model, applying generalised linear models (GLM), generalised additive models (GAM), and multivariate adaptive regression splines (MARS). Niche models were derived for over 1000 species of vascular plants, bryophytes and lichens, representing a large proportion of the British flora and many species occurring in continental Europe. Each model predicts habitat suitability for a species in response to climate variables and trait-based scores (evaluated excluding the species being modelled) for soil pH, fertility, wetness and canopy height. An R package containing the fitted models for each species is presented which allows the user to predict the habitat suitability of a given set of conditions for a particular species. Further functions within the package are included so that these habitat suitability scores can be plotted inrelation to individual explanatory variables. A simple case study shows how the R package (MultiMOVE) can be used quickly and efficiently to answer questions of scientific interest, specifically whether climate change will counteract any benefits of sheep-grazing for a particular plant community. The package itself is freely available via http://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9.
机译:可以预测与可测量环境因素相关的植物栖息地适宜性的站点占用模型对于保护规划很有用。这样的模型可以基于环境观测值从大规模的不存在数据集获得,或者在仅可获得植物数据的情况下,使用整个样地的平均植物特征值得出。但是,物种存在与环境变量之间的估计建模关系取决于所采用的统计模型的类型,因此可能引入其他不确定性。由于选择了统计模型,作者使用整体建模方法来约束和量化不确定性,应用了广义线性模型(GLM),广义加性模型(GAM)和多元自适应回归样条(MARS)。生态位模型是从1000多种维管植物,苔藓植物和地衣中获得的,代表了英国植物区系和欧洲大陆许多物种的很大一部分。每个模型都可以根据气候变量和基于土壤的pH值,肥力,湿度和冠层高度的基于性状的得分(评估后的物种除外)来预测某个物种的栖息地适宜性。提出了一个R包,其中包含每个物种的拟合模型,它使用户可以预测特定条件给定条件下栖息地的适宜性。包装内还包含其他功能,以便可以将这些栖息地适应性评分与各个解释变量的关系绘制出来。一个简单的案例研究显示了如何快速有效地使用R包(MultiMOVE)来回答具有科学意义的问题,特别是气候变化是否会抵消特定植物群落放牧绵羊的任何好处。该软件包本身可通过http://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9免费获得。

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