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Stochastic approach to determine spatial patterns of lizard community on a desert island

机译:确定荒岛上蜥蜴群落空间格局的随机方法

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one of the principal sources of error in identifying spatial arrangements is autocorrelation, since nearby points in space tend to have more similar values than would be expected by random change. When a Markovian approach is used, spatial arrangements can be measured as a transition probability between occupied and empty spaces in samples that are spatially dependent. We applied a model that incorporates first-order Markov chains to analyse spatial arrangement of numerical dominance, richness, and abundance on a lizard community at different spatial and temporal scales. We hypothesized that if a spatial dependence on abundance and richness exists in a diurnal desert community, then the Markov chains can predict the spatial arrangement. We found that each pair of values was dependent only on its immediate predecessor segment. In this sense, we found intergeneric differences at temporal and spatial scales of recurrence estimates. Also, in desert scrub, species show higher spatial aggregation and had lower species richness than at the island level; the inverse pattern occurred on rocky hillsides. At the species level, Uta stansburiana is the most abundant species in desert scrub, while Sauromalus slevini is the most abundant species on rocky hillsides. This report attempts to understand, using Markovian spatial models, the effect of nearby samples on local abundance and richness on different scales and over several seasons. (c) 2008 Elsevier Masson SAS. All rights reserved.
机译:自相关是识别空间布置的主要错误来源之一,因为空间中的邻近点往往具有比随机变化所期望的值更相似的值。当使用马尔可夫方法时,空间排列可以作为空间相关样本中占据空间与空白空间之间的转移概率进行测量。我们应用了包含一阶马尔可夫链的模型,以分析蜥蜴群落在不同时空尺度上的优势度,丰富度和丰度的空间排列。我们假设,如果在日间沙漠社区中存在对丰度和丰富度的空间依赖性,那么马尔可夫链可以预测空间排列。我们发现,每对值仅取决于其直接的前一个段。从这个意义上讲,我们在递归估计的时间和空间尺度上发现了属间差异。此外,在荒漠灌木丛中,物种的空间聚集度更高,物种丰富度低于岛屿水平。反模式发生在多岩石的山坡上。在物种水平上,Uta stansburiana是沙漠灌木丛中最丰富的物种,而Sauromalus slevini是多岩石的山坡上最丰富的物种。本报告试图使用马尔可夫空间模型来了解附近样本在不同规模和多个季节对当地丰度和丰富度的影响。 (c)2008年Elsevier Masson SAS。版权所有。

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