Chinese Seed Trait Database: a curated resource for diaspore traits in the Chinese flora

Abstract

Introduction

Regeneration marks the beginning of a plant's life cycle, with profound effects on population recruitment, species adaptation, and community resilience to environmental changes (Grubb, 1977). Diaspores, including seeds, fruits, and their appendages (hereafter ‘seeds’), are crucial agents of plant regeneration, possessing a wide range of functional traits related to dispersal and colonisation (Saatkamp et al., 2019). Trait-based approaches have been widely used to investigate responses of plants to both biotic and abiotic conditions from individual to ecosystem scales (Díaz et al., 2016; He et al., 2019). To date, traits of vegetative organs are overrepresented in existing global and regional databases, largely due to the essential roles of leaves in ecological functions and ecosystem services as well as the relative ease of accessing and measuring these traits (Supporting Information Table S1). However, our understanding of plant traits remains incomplete, particularly for those traits that are difficult to measure, such as regenerative traits (known as the ‘Raunkiæran shortfall’; Hortal et al., 2015). Several databases have made efforts to address this gap (Table S1). For instance, the TRY database has compiled over 50 distinct seed traits with decent species representation (Kattge et al., 2020), and the GIFT database has also aggregated substantial seed trait data (Weigelt et al., 2020). Some regional databases, such as Rasgos-CL (Alfaro et al., 2023) and LEDA (Kleyer et al., 2008), with over 20% seed trait records, have contributed to this field. Nevertheless, seed trait data remain relatively sparse both in terms of trait diversity and record coverage (Table S1). Advancing key research questions in plant science necessitates comprehensive seed trait data, along with associated geographic information (Saatkamp et al., 2019). These demands have prompted the recent initiation of several databases focusing on seed germination (Fernández-Pascual et al., 2023), seed dormancy (Rosbakh et al., 2020), and Brazilian rock vegetation seed traits (Ordóñez-Parra et al., 2023). Despite these advances, seed-related data lag far behind those centred on vegetative traits in existing databases (Larson & Funk, 2016; Kattge et al., 2020).

Current research efforts remain disproportionately focused on specific plant communities or habitats within certain regions, leading to heavily regional biases in the current global coverage of seed traits (Silveira et al., 2023). China, which has a wide range of biomes, is renowned for its remarkable floral diversity of over 35 000 vascular plant taxa (Lu & He, 2017). For example, the Qinghai-Tibet Plateau, known as the ‘Roof of the World’, harbours at least 12 000 plant species, of which almost 20% are endemic to the region (Wen et al., 2014). These abundant resources have enabled a wealth of research by Chinese scholars, establishing China as a growing contributor to the international plant science community. According to the Chinese Science Citation Database, > 1000 authorised journals in science and technology are published in Chinese (http://sdb.csdl.ac.cn; Jin & Wang, 1999). However, due to the language barrier and access limitation, this wealth of information remains a rich yet underutilised resource for plant science studies in the international research community (Amano et al., 2016). To date, a number of regional databases have emerged, for example, AusTraits summarises data on plant traits in Australia (Falster et al., 2021), and FunAndes compiles trait data on major plant taxa in the tropical Andes (Báez et al., 2022). Integrating regional databases with global ones can enhance the completeness of trait data and mitigate the geographical biases in global data coverage (Maitner et al., 2023). Unfortunately, limited progress has been made in systematically compiling regional data on seeds. Here, we present a curated repository that compiles an extensive array of seed traits for plant species in China – the Chinese Seed Trait Database (hereafter ‘CSTD’; https://macroeologygroup.shinyapps.io/CSTD). Our goal is to address gaps in current plant trait data and to enhance the accessibility of seed trait data to the international research community.