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Biased niches – Species response curves and niche attributes from Huisman-Olff-Fresco models change with differing species prevalence and frequency

机译：偏位生态位– Huisman-Olff-Fresco模型的物种响应曲线和生态位属性随物种流行率和频率的不同而变化

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摘要

The study aimed to examine the effects of different numbers of presences and frequencies (proportions) of occurrences of species in a plot data set of forest vegetation on the species response curves and their niche attributes, based on Huisman-Olff-Fresco models (HOF). We modeled responses of 72 to 105 herbaceous forest species along a pH gradient under 14 different random sampling scenarios by varying the number of presences and absences used for model fitting. Mean niche attributes were calculated from 100 repetitive runs for each scenario and species. Re-prediction success of HOF models among the repetitive runs was highest when the total number of plots was high and the frequency of occurrences was low. With low plot numbers and high frequencies, less complicated model types (no response or monotonically increasing/decreasing responses) predominate. Measures of species niche boundaries (limits & borders) and niche width were strongly influenced by changes in sampling characteristics. With an increasing number of presences and an increasing frequency, limits and borders shifted to more extreme values, leading to wider niches. In contrast, species optima showed almost no change between the scenarios. Thus, the detected ecological response of a species often depends on the size of the data set and the relation between presences and absences of a species. In general, high data quantities are required for reliable response curve modeling with HOF models, which prevents the assessment of the responses of many rare species. To avoid undesired bias by differing sampling characteristics when comparing niches between different species or between data sets, the data basis used for model fitting should be adjusted according to the niche attribute in question, for example by keeping the frequency of the species constant.
机译：这项研究旨在基于Huisman-Olff-Fresco模型（HOF），研究森林植被地块数据集中不同物种的存在次数和发生频率（比例）对物种响应曲线及其生态位属性的影响。 。我们通过改变用于模型拟合的存在和不存在的数量，在14种不同的随机采样情况下，沿着pH梯度对72至105种草本林种的响应进行了建模。从每个场景和物种的100次重复运行中计算出平均生态位属性。当地块总数高且发生频率低时，在重复运行中HOF模型的重新预测成功率最高。地块数量少且频率高时，较简单的模型类型（无响应或单调增加/减少响应）占主导地位。物种生态位边界（界限和边界）和生态位宽度的度量受到采样特征变化的强烈影响。随着存在数量的增加和频率的增加，界限和边界转移到了更极端的值，从而导致了更广泛的壁ni。相比之下，物种最优值在两种情况之间几乎没有变化。因此，检测到的物种生态响应通常取决于数据集的大小以及物种存在与否之间的关系。通常，使用HOF模型进行可靠的响应曲线建模需要大量数据，这妨碍了评估许多稀有物种的响应。为了避免在比较不同物种之间或数据集之间的生态位时由于采样特性不同而产生不希望的偏差，应根据所考虑的利基属性来调整用于模型拟合的数据基础，例如，保持物种的频率恒定。

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    Jana Michaelis; Martin R. Diekmann;

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  • 年(卷),期 -1(12),8
  • 年度 -1
  • 页码 e0183152
  • 总页数 16
  • 原文格式 PDF
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