Combining citizen science data and literature to build a traits dataset of Taiwan's birds.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-03 DOI:10.1038/s41597-024-03928-3

Shu-Wei Fu, Meng-Chieh Feng, Po-Wei Chi, Tzung-Su Ding

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引用次数: 0

Abstract

Trait-based methodologies are gaining traction in the field of ecology, providing deeper insights into ecosystem structure and functions. To this end, trait databases tailored to specific taxonomic groups have become foundational. In Taiwan, the collaborative efforts of avian researchers and dedicated citizen scientists have led to the compilation of a vast array of data. This includes web-sourced images from social media, spatial distribution records from eBird, and morphological metrics from banded birds and specimens. Enriched by peer-reviewed literature, we have meticulously assembled a comprehensive trait dataset encompassing 454 bird species across 73 families. This dataset covers a wide range of traits, including foraging ecology, morphological characteristics, territorial behaviors, breeding attributes, and the roles of bird species in ecosystem regulation. As an invaluable resource, this dataset lays the foundation for in-depth exploration of functional diversity, trait-based community ecology, ecosystem function, and critical insights needed to shape conservation strategies.

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结合公民科学数据和文献，建立台湾鸟类的特征数据集。

以性状为基础的方法在生态学领域日益受到重视，为生态系统的结构和功能提供了更深入的见解。为此，为特定分类群定制的性状数据库已成为基础。在台湾，鸟类研究人员和热心的公民科学家共同努力，汇编了大量数据。其中包括来自社交媒体的网络图片、来自 eBird 的空间分布记录以及来自带环鸟类和标本的形态指标。通过同行评议文献的补充，我们精心组建了一个全面的性状数据集，涵盖 73 个科 454 种鸟类。该数据集涵盖了广泛的特征，包括觅食生态学、形态特征、领地行为、繁殖属性以及鸟类在生态系统调节中的作用。作为宝贵的资源，该数据集为深入探讨功能多样性、基于性状的群落生态学、生态系统功能以及制定保护策略所需的重要见解奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.