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The best local-scale prediction maps for dynamic landscape patterns of aquatic habitats of anopheline larvae in western lowland Kenya.

机译：肯尼亚西部低地按蚊幼虫水生生境动态景观格局的最佳局部尺度预测图。

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摘要

The possibility of anopheline larval control and the need to understand the contribution of larval habitat distribution to the intensity of the malaria transmission cycle have generated inquiry into the relationships of anopheline larval habitats with environmental variables, including those variables that can be remotely-sensed across the landscape. These habitats are spatially predictable but their occurrence is unstable throughout time such that a map of their locations has a short lifespan of high accuracy. In this study, I create a dynamic environmental model of aquatic habitats of anopheline larvae for Asembo, a community in western Kenya, using topography, land-use/land-cover, and rainfall variables that have shown previous success in landscape models of Anopheles spp. habitats. I compare the success of the model's prediction maps when confronted with new data in another year at the same site to the accuracy of nearly-contemporaneous maps of the habitats as well as kriging-interpolated maps that exploit the habitat spatial clustering to increase the predictive power of the map. The dynamic environmental model shows the best predictive power of the three map types tested. The dominant input variable, the topographic position index, is further investigated, showing that the relationship is strongest at the 1710m scale and the predictions are moderately robust to elevation measurement errors. Though the prior knowledge of habitat locations does not accurately predict their future locations for long, I identify significant spatiotemporal autocorrelation in the distribution of the aquatic habitats that could be used in future prediction mapping to fine-tune generalized environmental models to site-specific patterns when some habitats have already been identified.
机译：oph蚊幼虫控制的可能性和了解幼虫栖息地分布对疟疾传播周期强度的贡献的需求引起了对inquiry蚊幼虫栖息地与环境变量之间关系的质疑，其中包括环境变量可以在整个环境中进行遥感。景观。这些栖息地在空间上是可预测的，但它们的出现在整个时间上都是不稳定的，因此其位置图的寿命较短，准确性很高。在这项研究中，我使用地形，土地利用/土地覆盖和降雨变量为肯尼亚西部的一个社区Asembo创建了按蚊幼虫水生生境的动态环境模型，该模型已显示出按蚊属物种景观模型的先前成功。栖息地。我比较了该模型的预测图在同一年在同一地点遇到另一年的新数据时的成功与栖息地的近同期图以及利用栖息地空间聚类来提高预测能力的克里金插值图的准确性。地图。动态环境模型显示了所测试的三种地图类型的最佳预测能力。进一步调查了主要输入变量地形位置指数，表明该关系在1710m尺度上最强，并且预测对高程测量误差具有中等鲁棒性。尽管对生境位置的先验知识不能长期准确地预测其未来位置，但我发现水生生境分布中存在显着的时空自相关性，可用于将来的预测映射中，以便在将广义环境模型微调为特定地点的模式时已经确定了一些栖息地。

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  • 作者

    Smith, Nicole Jean.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Geography.;African studies.
  • 学位 M.S.
  • 年度 2016
  • 页码 90 p.
  • 总页数 90
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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