特质
范畴变量
生态学
模块化设计
生物多样性
多样性(政治)
空格(标点符号)
计算机科学
环境资源管理
数据科学
生物
机器学习
环境科学
社会学
人类学
程序设计语言
操作系统
作者
Carlos P. Carmona,Eleonora Beccari
摘要
Summary Plant traits capture the remarkable diversity of ecological strategies, yet synthesizing this complexity into coherent frameworks remains challenging. Trait spaces have significantly advanced this effort, distilling numerous traits into meaningful ecological dimensions. Recent frameworks integrate above‐ and belowground characteristics, but other dimensions – such as reproduction, phenology, hydraulics, and mycorrhizal associations – remain poorly represented, facing conceptual and methodological hurdles. Resolving these issues requires addressing biases in trait databases, standardizing methodologies, and integrating complex or categorical traits. In this review, we propose the development of a fully unified trait space that is flexible, modular, and scalable, which would greatly enhance global comparisons, improve our predictive capabilities of plant ecological responses to global change, and ultimately strengthen biodiversity monitoring and management.
科研通智能强力驱动
Strongly Powered by AbleSci AI