Plant community assembly at small scales: Spatial vs. environmental factors in a European grassland
Plant community assembly at small scales: Spatial vs. environmental factors in a European grassland
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DOI：
10.1016/j.actao.2015.01.004
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发表时间：
2015-02-01
影响因子：
1.8
通讯作者：
Caruso, Tancredi
中科院分区：
文献类型：
--
作者：
Horn, Sebastian;Hempel, Stefan;Caruso, Tancredi
扩散限制和环境条件是植物物种分布和建立的关键驱动力。由于这些因素在不同的空间尺度上起作用，我们问：已知的环境因素，以确定社区集会在广泛的规模在细尺度（几米）？这些因素在多大程度上解释了精细尺度上的群落变异？生物和非生物的相互作用以何种方式驱动物种组成的变化？我们调查了沿着一个非常陡峭的土壤特性，如pH值和养分梯度的干草原内的植物群落。我们使用了一个空间显式抽样设计，基于三个重复的macroplots的15 × 15，12 × 12和12 × 12米的范围。采集土壤样本以量化若干土壤特性（碳、氮、植物有效磷、pH值、含水量和脱氢酶活性，作为总体微生物活性的代表）。我们进行方差划分，以评估这些变量对植物组成的影响，并通过特征向量映射的空间自相关进行统计控制。我们还应用零模型分析，以测试非随机模式的物种共现使用随机化计划，占模式下预期的物种interactions.At细空间尺度，环境因素解释18%的变化时，控制植物物种分布的空间自相关，而纯粹的空间过程占14%的变化。模型分析表明，物种在空间上以非随机的方式分离，这些空间格局可能是由于环境过滤和生物相互作用的组合。我们的草原研究表明，在大尺度研究中发现的直接相关的环境因素也存在于小尺度上，但辅以空间过程和更直接的相互作用，如竞争。(C)2015年Elsevier Masson SAS。All rights reserved.
Dispersal limitation and environmental conditions are crucial drivers of plant species distribution and establishment. As these factors operate at different spatial scales, we asked: Do the environmental factors known to determine community assembly at broad scales operate at fine scales (few meters)? How much do these factors account for community variation at fine scales? In which way do biotic and abiotic interactions drive changes in species composition?We surveyed the plant community within a dry grassland along a very steep gradient of soil characteristics like pH and nutrients. We used a spatially explicit sampling design, based on three replicated macroplots of 15 x 15, 12 x 12 and 12 x 12 m in extent. Soil samples were taken to quantify several soil properties (carbon, nitrogen, plant available phosphorus, pH, water content and dehydrogenase activity as a proxy for overall microbial activity). We performed variance partitioning to assess the effect of these variables on plant composition and statistically controlled for spatial autocorrelation via eigenvector mapping. We also applied null model analysis to test for non-random patterns in species co-occurrence using randomization schemes that account for patterns expected under species interactions.At a fine spatial scale, environmental factors explained 18% of variation when controlling for spatial autocorrelation in the distribution of plant species, whereas purely spatial processes accounted for 14% variation. Null model analysis showed that species spatially segregated in a non-random way and these spatial patterns could be due to a combination of environmental filtering and biotic interactions. Our grassland study suggests that environmental factors found to be directly relevant in broad scale studies are present also at small scales, but are supplemented by spatial processes and more direct interactions like competition. (C) 2015 Elsevier Masson SAS. All rights reserved.