AVOTREX: A global dataset of extinct birds and their traits (v. 1.0)

Human activities have been reshaping the natural world for tens of
thousands of years, leading to the extinction of hundreds of bird species.
Past research has provided evidence of extinction selectivity towards
certain groups of species, but trait information is lacking for the
majority of clades, especially for prehistoric extinctions identified only
through subfossil remains. This incomplete knowledge potentially obscures
the structure of natural communities, undermining our ability to infer
changes in biodiversity across space and time. Biases in currently
available trait data also limit our ability to identify drivers and
processes of extinction. Here we present AVOTREX, an open-access database
of species traits for all birds known to have gone extinct in the last
130,000 years. This database provides detailed morphological information
for 610 extinct species. For each extinct bird species, we provide
information on the taxonomy, geographic location, and period of
extinction. We also present data on island endemicity, flight ability and
body mass, as well as standard measurements of external (matching the
AVONET database of extant birds) and skeletal morphology from museum
specimens where available. To ensure comprehensive morphological data
coverage, we estimate all missing morphological measurements using a data
imputation technique based on machine learning. This method can integrate
measurements from different sources (i.e. skeletal and skin material),
together with taxonomic information, and allows us to provide complete
information on standard morphological traits of all known extinct birds.

数万年来，人类活动持续重塑自然世界，导致数百种鸟类走向灭绝。过往研究已证实物种灭绝存在选择性偏好，但绝大多数支系（clades）的性状信息仍缺失，尤其是仅能通过亚化石（subfossil）遗骸识别的史前灭绝物种相关信息。这种认知的不完备性可能掩盖自然群落的结构特征，削弱我们推断生物多样性在时空维度上变化的能力。当前可用性状数据存在的偏倚，也限制了我们识别灭绝驱动因素与过程的能力。本研究介绍了AVOTREX数据库：这是一个开放获取的物种性状数据库，涵盖已知在过去13万年内灭绝的所有鸟类。该数据库为610个灭绝鸟类物种提供了详尽的形态学信息。针对每一种灭绝鸟类，数据库提供了其分类学信息、地理分布区域以及灭绝时间。此外，数据库还收录了物种的岛屿特有性、飞行能力与体重数据，以及可获取的博物馆标本的外部形态（与现生鸟类AVONET数据库的测量标准一致）和骨骼形态的标准化测量值。为确保形态学数据覆盖的全面性，本研究采用基于机器学习的数据插补技术，对所有缺失的形态学测量值进行估算。该方法可整合不同来源的测量数据（即骨骼与皮肤标本数据），结合分类学信息，从而为所有已知灭绝鸟类提供完整的标准形态学性状信息。