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首页> 外文期刊>Pedobiologia >Modelling distribution patterns of anecic, epigeic and endogeic earthworms at catchment-scale in agro-ecosystems
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Modelling distribution patterns of anecic, epigeic and endogeic earthworms at catchment-scale in agro-ecosystems

机译：农业生态系统中流域尺度上的ec，表皮和内ge的分布模式

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摘要

Species distribution models are useful for identifying driving environmental factors that determine earthworm distributions as well as for predicting earthworm distribution patterns and abundances at different scales. However, due to large efforts in data acquisition, studies on larger scales are rare and often focus on single species or earthworms in general. In this study, we use boosted regression tree models (BRTs) for predicting the distribution of the three functional earthworm types, i.e. anecics, endogeics and epigeics, in an agricultural area in Baden-Württemberg (Southwest Germany).First, we predicted presence and absence and later earthworm abundances, considering predictors depicting land management, topography, and soil conditions as well as biotic interaction by using the abundance of the other functional earthworm types. The final presence-absence models performed reasonably well, with explained deviances between 24 and 51% after crossvalidation. Models for abundances of anecics and endogeics were less successful, since the high small-scale variability and patchiness in earthworm abundance influenced the representativeness of the field measurements. This resulted in a significant model uncertainty, which is practically very difficult to overcome with earthworm sampling campaigns at the catchment scale.Results showed that management practices (i.e. disturbances), topography, soil conditions, and biotic interactions with other earthworm groups are the most relevant predictors for spatial distribution (incidence) patterns of all three functional groups. The response curves and contributions of predictors differ for the three functional earthworm types. Epigeics are also controlled by topographic features, endogeics by soil parameters.
机译：物种分布模型可用于确定决定environmental分布的驱动环境因素，以及预测不同规模的distribution分布模式和丰度。但是，由于在数据获取方面付出了巨大的努力，因此更大规模的研究很少，通常通常只针对单个物种或earth。在这项研究中，我们使用增强回归树模型（BRT）来预测巴登-符腾堡州（德国西南部）的农业地区三种功能性worm的分布，即ec虫病，内ge病和流行病。由于没有其他的later，后来又有了later的数量，因此要考虑使用其他功能性types的丰度来描述土地管理，地形和土壤状况以及生物相互作用的预测因子。最终的缺勤模型表现相当不错，交叉验证后的解释偏差在24％至51％之间。 ec药和内生菌的丰度模型不太成功，因为earth丰度的小规模高变异性和斑块影响了现场测量的代表性。这导致了巨大的模型不确定性，实际上很难通过在流域尺度上的sampling采样活动来克服。结果表明，管理实践（即干扰），地形，土壤条件以及与其他earth类的生物相互作用是最相关的所有三个功能组的空间分布（发生率）模式的预测变量。三种功能earth的响应曲线和预测变量的贡献不同。地形学还受地形特征控制，土壤学也受土壤参数控制。

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