運用非監督向量表徵方法揭示都市環境中鳥類多樣性的時空語義

Abstract

都市鳥類生物多樣性是城市生態系統健康的重要指標。過去研究多著重於物種豐富度或特定物種的空間分布，但對於鳥類出現的時空模式及其共現關係較少討論。本研究旨在揭示都市環境中鳥類的時空分佈特性，並探討棲地環境因子對這些特性的影響。 本研究採用數據驅動的非監督式學習模型 Word2vec，建構鳥類時空多維語義空間。在此空間中，每個物種轉化為嵌入向量，向量間的距離即代表物種間的時空相關程度，將其量化即為可計算的相似性分數。藉由結合鳥類生物屬性，本研究進一步提升了該向量空間的可解釋性與討論深度。本研究亦針對都市區域建立了基於「鳥類時空特性」與「行道樹組成」的向量特徵空間。分析結果顯示，棲地環境特性（行道樹組成）與鳥類的時空分佈存在相關。在空間鄰近性的驗證中，本研究比較了「實體空間鄰居」與「棲地語義空間鄰居」的影響力，發現後者對鳥類群社相似度的預測能力顯著優於前者，驗證了特徵向量語義空間在生態分析中的重要性。 本研究的貢獻在於將人工智慧領域的概念與工具引入生態學研究，協助生態學家與決策者了解鳥類物種在時空分佈上的複雜關係，並且可作為後續預測模型的特徵工程，為都市規劃和生態保育提供新的數據驅動視角。

Urban bird biodiversity is a key indicator of urban ecosystems. While previous research has predominantly focused on species richness and the spatial distribution of species, with limited discussion on the spatial-temporal occurrence patterns and interspecific co-occurrence relationships of birds, this study aims to reveal the spatial-temporal characteristics of bird distributions in urban environments and investigate how habitat environmental factors influence these patterns. This study introduces a fully data-driven, unsupervised learning approach, the Word2vec model, to construct a multi-dimensional spatial-temporal semantic space for bird species. Within this space, each species is represented as an embedding vector, with distances between vectors indicating the degree of spatial-temporal relatedness between species, enabling the quantification of these relationships into computable similarity scores. By integrating biological attributes of bird species, we further enhanced the interpretability and analytical depth of the vector space. Furthermore, we established vector feature spaces for urban areas based on both "bird spatial-temporal characteristics" and "street tree composition." The results indicate a correlation between habitat environmental characteristics and the spatial-temporal distribution of birds. In validating spatial proximity, we compared the predictive influence of "physical spatial neighbors" versus "environmental semantic neighbors." We found that the latter significantly outperformed the former in predicting bird community similarity, thereby validating the importance of feature vector semantic spaces in ecological analysis. The primary contribution of this study lies in introducing concepts and tools from artificial intelligence into ecological research. By encoding explicit spatial-temporal characteristics into latent vector spaces, this approach reveals the complex functional relationships governing species and spatial distributions of bird communities. Furthermore, these embeddings serve as a robust foundation for feature engineering, facilitating downstream predictive modeling and providing a data-driven basis for operationalizing biodiversity metrics in urban planning and conservation strategies.