Anomaly Detection in Species Distribution Patterns: A Spatio-Temporal Approach for Biodiversity Conservation

Abstract

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.