...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Accounting for spatial autocorrelation from model selection to statistical inference: Application to a national survey of a diurnal raptor
【24h】

Accounting for spatial autocorrelation from model selection to statistical inference: Application to a national survey of a diurnal raptor

机译：从模型选择到统计推论的空间自相关的解释：在日间猛禽全国调查中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Planning actions for species conservation involves working at both an ecologically meaningful spatial scale and a scale suitable for implementing management or conservation plans. Animal populations and conservation policies often operate across wide areas. Large-extent spatial datasets are thus often used, but their analyses rarely deal with problems inherent to spatial datasets such as residual spatial autocorrelation, which can bias or even reverse results. Here we propose a procedure for analysing a large-scale count dataset integrating residual spatial autocorrelation in a Generalized Linear Model framework by combining and extending previously published methods. The first step concerns the selection of the environmental variables by a modified cross-validation procedure allowing for residual spatial autocorrelation. Then the second step consists in evaluating the spatial effect of the model using a spatial filtering approach based on the variogram parameters. We apply this method to the Black kite (Milvus migrans) to estimate the distribution and population size of this species in France. We found some divergence in estimated population size between spatial and non spatial models, as well as in the distribution map. We also found that the uncertainty of the model was underestimated by the residual spatial autocorrelation. Our analysis confirms previous results, that residual spatial autocorrelation should be always accounted for, especially in conservation where false results may lead to poor management decisions.
机译：物种保护的计划行动包括在具有生态意义的空间规模和适合实施管理或保护计划的规模上开展工作。动物种群和保护政策通常在广泛的地区运作。因此，通常使用大范围的空间数据集，但是它们的分析很少处理空间数据集固有的问题，例如残留空间自相关，这可能会偏差甚至颠倒结果。在这里，我们提出了一种程序，用于通过合并和扩展先前发布的方法来分析在广义线性模型框架中集成了残差空间自相关的大规模计数数据集的过程。第一步涉及通过修改的交叉验证程序选择环境变量，从而允许残留空间自相关。然后，第二步包括使用基于变异函数参数的空间滤波方法评估模型的空间效果。我们将此方法应用于黑鸢（Milvus migrans），以估计该物种在法国的分布和种群数量。我们发现在空间模型和非空间模型之间以及在分布图中，估计的人口规模存在一些差异。我们还发现，模型的不确定性被残差空间自相关低估了。我们的分析证实了先前的结果，应始终考虑残留的空间自相关，尤其是在保存中，错误的结果可能会导致不良的管理决策。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号