物种分布模式的异常检测:保护生物多样性的时空方法

IF 0.5 4区 医学
Mingyang He, Hao Chen
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引用次数: 0

摘要

监测和维护生物多样性对生态系统的生态平衡和整体健康至关重要。检测物种分布模式中的异常现象对于确定关注区域和及时实施保护措施至关重要。本文介绍了一种通过整合时间序列分析、机器学习技术和空间统计来检测时空物种活动数据异常的方法。我们的方法可以识别各种物种的分布模式,并将具有相似模式的区域分组,从而将研究区域划分为不同的类别。在这些分类区域内,我们结合使用聚类算法和离群点检测技术,找出物种分布的异常行为。为了确认研究结果的可靠性,我们将研究结果与通过实地观察或其他可靠数据来源获得的验证数据进行交叉对比。这些佐证表明,异常现象往往表明物种数量的突然变化,或者在某些地点和时间的空间分布发生了意想不到的变化。为了更深入地了解物种是如何分布的,我们整理了一个数据集,排除了这些异常观测数据,并利用它开发了一种预测算法。我们的分析表明,与在包含异常数据的数据集上训练的模型相比,在这个经过提炼、不包含异常数据的数据集上训练的预测模型实现了更低的归一化均方误差(NMSE),这表明其预测准确性达到了更高水平。利用这种方法可以促进制定有效的保护计划,有助于实现更可持续的生态系统管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly Detection in Species Distribution Patterns: A Spatio-Temporal Approach for Biodiversity Conservation
Monitoring and maintaining biodiversity are crucial for the ecological balance and overall health of ecosystems. Detecting anomalies in species distribution patterns is essential for identifying areas of concern and implementing timely conservation measures. This paper introduces an approach to detect anomalies in spatiotemporal species activity data by integrating time series analysis, machine learning techniques, and spatial statistics. Our method identifies distribution patterns of various species and groups regions with similar patterns, enabling the segmentation of the study area into distinct categories. Within these categorized regions, we apply a combination of clustering algorithms and outlier detection techniques to pinpoint anomalous behaviors in species distribution. In order to confirm the reliability of our findings, we cross-reference them with verified data acquired through field observations or other credible data sources. These corroborations indicate that anomalies are frequently indicative of sudden variations in species numbers or unexpected alterations in spatial distribution at certain places and times. To gain a more robust understanding of how species are distributed, we curate a data set that excludes these anomalous observations and use it to develop a predictive algorithm. Our analysis shows that a predictive model trained on this refined, anomaly-free dataset achieves a lower normalized mean square error (NMSE), which suggests a higher level of predictive accuracy as compared to a model trained on data containing anomalies. Utilizing this methodology can facilitate the creation of effective conservation plans and contribute to more sustainable ecosystem management.
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来源期刊
Journal of Biobased Materials and Bioenergy
Journal of Biobased Materials and Bioenergy 工程技术-材料科学:生物材料
自引率
0.00%
发文量
60
审稿时长
6 months
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