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首页> 外文期刊>Canadian Journal of Zoology >Landscape effects on anuran pond occupancy in an agricultural countryside: barrier-based buffers predict distributions better than circular buffers
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Landscape effects on anuran pond occupancy in an agricultural countryside: barrier-based buffers predict distributions better than circular buffers

机译:景观对农业农村无水池塘占用的影响:基于屏障的缓冲比圆形缓冲能更好地预测分布

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摘要

Species movement and occupancy of habitat patches are dependent on landscape permeability. Some land-use types (e.g., roads) may be barriers to animal movement. Analyses of the effect of landscape structure on patch occupancy usually use circular buffers around focal patches. The main assumption of this methodological approach is that species are affected by a particular landscape element equally in every direction from a given patch. This assumption is likely not to hold if animal movement is restricted by barriers because barriers reduce movement patterns and reshape the ideal circular buffer into a noncircular buffer. We developed a method to determine the effect of landscape variables oil the distribution of two amphibian species that explicitly takes dispersal barriers into account. We extracted landscape variables within (i) circular buffers (CB) and (ii) barrier-based buffers (BBB). BBB were produced by reducing the boundaries of CB according to major impassable barriers. The BBB approach almost doubled the explained deviance of multiple regression models in comparison with the CB approach. Moreover. CB and BBB models included different predictor variables. We Suggest that the BBB approach is more Useful than the traditional CB analyses of species-habitat relationships because ecological barriers are explicitly taken into account..
机译:物种移动和栖息地斑块的占用取决于景观的渗透性。某些土地利用类型(例如道路)可能会阻碍动物活动。对景观结构对斑块占用的影响的分析通常使用焦点斑块周围的圆形缓冲区。这种方法论方法的主要假设是,物种在给定斑块的各个方向上均受到特定景观元素的均等影响。如果动物的运动受到障碍物的限制,则该假设可能不会成立,因为障碍物会减少运动方式并将理想的圆形缓冲区重新成形为非圆形缓冲区。我们开发了一种方法来确定景观变量对两个两栖动物物种分布的影响,其中明确考虑了分散障碍。我们在(i)循环缓冲区(CB)和(ii)基于障碍的缓冲区(BBB)中提取了景观变量。 BBB是根据主要不可逾越的障碍通过减少CB的边界而生产的。与CB方法相比,BBB方法几乎使多重回归模型的解释偏差增加了一倍。此外。 CB和BBB模型包括不同的预测变量。我们建议,BBB方法比传统的CB分析物种-栖息地关系更有用,因为已明确考虑了生态障碍。

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