...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Marine ecology progress series >The relative importance of spatial and temporal variation in predicting community structure at different scales as estimated from Markov chain models
【24h】

The relative importance of spatial and temporal variation in predicting community structure at different scales as estimated from Markov chain models

机译：马尔可夫链模型估计时空变化在不同尺度下预测群落结构的相对重要性

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

Models that incorporate complex interactions are useful tools for estimating the relative importance of ecological processes in changing ecological communities. Multispecies Markov chain models (MMCMs) use transition probabilities to explore how complex interactions influence community structure. However, the accuracy of MMCMs over different spatial and temporal scales is not well known and likely influenced by the scales over which species interactions differ. Here we explore how MMCM predictions vary with scale in the Gulf of Maine intertidal by quantifying transition probabilities at increasing temporal (annual, biannual, seasonal) and spatial (local, meso-, regional) scales. Model predictions were accurate at local scales, even with data from coarse sampling frequencies (annual), suggesting short-term variations in ecological processes do not strongly affect stable-stage community composition over these time scales. Models were less accurate when incorporating spatial variation beyond local scales. Geographic differences in species persistence, mortality, and colonization probabilities reduced MMCM predictive capacity. Our work demonstrates that MMCMs can predict local community structure, even with data from coarse sampling intervals, and may also be useful for identifying regional differences in ecological processes that might be responsible for shifts in community structure.
机译：包含复杂相互作用的模型是评估生态过程在变化的生态群落中的相对重要性的有用工具。多物种马尔可夫链模型（MMCM）使用过渡概率来探索复杂的相互作用如何影响社区结构。但是，MMCM在不同时空尺度上的准确性尚不为人所知，并且很可能受到物种相互作用不同的尺度的影响。在这里，我们通过量化时间（年度，半年度，季节性，季节性）和空间（局部，中尺度，区域）尺度上的过渡概率，来探索缅因湾潮间带的MMCM预测如何随尺度变化。即使使用粗采样频率（年度）的数据，模型预测在本地范围内也是准确的，这表明生态过程中的短期变化不会在这些时间范围内强烈影响稳定阶段的群落组成。当合并超出局部尺度的空间变化时，模型的准确性较差。物种持久性，死亡率和定植概率的地理差异降低了MMCM的预测能力。我们的工作表明，即使使用粗采样间隔的数据，MMCM仍可以预测当地的社区结构，也可能有助于识别可能导致社区结构发生变化的生态过程的区域差异。

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