The recent prominence of functional traits in ecological analyses is based on the premise that measurable attributes of an organism's phenotype can take us beyond simple lists of species and closer to valid tests of mechanisms and processes (Cadotte et al., 2011). However, the full potential of trait-based ecology and evolutionary biology is ultimately constrained by incomplete coverage and completeness, particularly in the case of morphological traits (Etard et al., 2020). Filling these gaps in data coverage has proved challenging, with even the best-sampled major taxonomic groups—such as vascular plants—still lacking comprehensive morphological measurements for well over 50% of species worldwide (Hietz et al., 2021; Kattge et al., 2020; Violle et al., 2014). A major step has now been taken towards addressing this challenge with the completion of datasets containing multiple morphological traits for all 11000 bird species (Tobias et al., 2022). The goal of this special issue is to present these data for wider use alongside a series of studies summarising recent advances based on morphological analyses, highlighting their potential application to research and policy.

The most widely used functional traits in macroecological and macroevolutionary analyses are categorical variables, mainly including information on habitat, life-history or diet (Jones et al., 2009; McLean et al., 2021; Wilman et al., 2014). These datasets have been highly influential, yet overall progress has been impeded because many categorical traits are relatively crude and uninformative, reducing their utility as indices of ecological function (Kohli & Jarzyna, 2021). Moreover, they offer an imperfect framework for some statistical models and phylogenetic analyses since many species are assigned the same values and the distance between categories is arbitrary. An obvious solution is to use continuous morphological variables, as these vastly improve the resolution of evolutionary models (Chira et al., 2018) and metrics of community assembly (Blonder et al., 2018; Ricklefs & Travis, 1980). To date, the availability of complete continuous morphological trait datasets has been largely restricted to body mass (Wilman et al., 2014), which is only weakly connected to ecological function (Pigot et al., 2020). A hawk and a duck may share the same body size, for example, but this tells us very little about their functional role in the ecosystem. Analyses based on more detailed compilations of morphological traits have not been possible outside a few well-studied families, leading to a variety of problems including sampling bias and inaccurate evolutionary models (Chang et al., 2020; Mouillot et al., 2021; Tobias et al., 2020).

Birds offer the best opportunity to address the challenge of comprehensive trait coverage for a number of reasons. First, overall species richness (~11,000 species) is far lower than plants, for instance, offering a more achievable target. Second, birds are distributed worldwide across all oceans and terrestrial biomes, where they perform a range of key ecological services (Şekercioǧlu, 2006). Third, because of their visibility and appeal, they are the best-studied clade at this global scale, with extensive datasets now available on distribution, abundance, ecology and life history for almost all species (Bird et al., 2020; Callaghan et al., 2021; Sullivan et al., 2014; Tobias et al., 2020; Tobias & Pigot, 2019; Wilman et al., 2014). Fourth, bird morphology offers a classic system for investigating a range of novel ecological questions because their beaks, legs and wings provide insight into trophic interactions, locomotion and dispersal respectively (Dehling et al., 2016; Pigot, Trisos, et al., 2016; Sheard et al., 2020). Indeed, birds are unique in that specific combinations of traits have been shown to predict key functional characteristics, including dietary niche and foraging behaviour, with far greater accuracy than body mass alone (Kennedy et al., 2020; Pigot et al. 2020).

The power of morphological traits to predict ecology was initially established by a series of papers on bird communities from 1960 onwards (e.g. Miles & Ricklefs, 1984). Although these analyses were based on relatively small samples of species (see Tobias et al., 2022, Figure 1), they provided the conceptual foundation for the field of ‘ecomorphology’ (Bock, 1994; Wainwright & Reilly, 1994) which in turn drove the subsequent (post-2000) development of avian functional ecology based on continuous variables. Over the last two decades, several research groups compiled and analysed bird trait datasets of gradually increasing size, initially targeting manageable samples of a few hundred species (e.g. suboscines: Claramunt, 2010; corvides: Kennedy et al., 2016) or local assemblages (e.g. Manu National Park, Peru: Dehling, Fritz, et al., 2014; Pigot, Bregman, et al., 2016), and more recently spanning thousands of species worldwide (e.g. Cooney et al., 2017; Kennedy et al., 2020; Phillips et al., 2018; Pigot et al. 2020). However, these resources have until now been fragmented, with raw data largely incompatible and unpublished.

To provide an integrated resource with broad utility, managers of different bird trait datasets have joined forces to merge their work into AVONET, a compendium of morphological, ecological and geographical data for all bird species published as the flagship article of this special issue (Tobias et al., 2022). AVONET was inspired by the success of the TRY plant trait database, a potent catalyst of high-impact research in ecology and ecosystem science over the last decade (Kattge et al., 2020). To maximise the likelihood of a similar positive impact, and to align with Open Science principles (Gallagher et al., 2020), AVONET is released as individual measurements of specimens as well as species averages, without restrictions on data access.

To some degree, the publication of AVONET marks an endpoint a personal journey. My fascination with bird traits began in the 1980s as a schoolboy walking the tidelines and powerlines of Northumberland in search of corpses for dismembering. I owe a belated debt of thanks to my mother for abiding with bedroom shelves full of skulls and cabinets loaded with malodorous wings and tarsi. But the story of AVONET extends far wider than that, and deeper in time. The completion of this first iteration—AVONET 1.0—is a truly international effort, with vital expertise and data contributed by 115 authors based at 106 institutions in 30 countries. The most important shifts in momentum occurred when the project was joined by colleagues managing their own extensive trait datasets, including Santiago Claramunt (Uruguay), Matthias Schleuning and Susanne Fritz (Germany), Carsten Rahbek (Denmark), Gavin Thomas (United Kingdom) and Gustavo Bravo (Colombia).

A common denominator among these major datasets is their reliance on museum specimens. Across AVONET as a whole, most specimens were measured at the Natural History Museum, London and the American Museum of Natural History, New York, with smaller samples from a further 76 collections (see Tobias et al., 2022, Fig. 4). Indeed, the project would not have been possible without the contributions of countless museum curators, field assistants and specimen collectors since the mid-1800s, some luminaries among them, including Charles Darwin, Alfred Russell Wallace, Ernest Shackleton and John James Audubon, all of whom prepared specimens subsequently measured for trait data. Ultimately, given the key importance of well-preserved specimen material for trait-based ecology, AVONET is a monument to the museum community and the crucial service it provides to scientific research and human society in general (Suarez & Tsutsui, 2004).

Many sources of information were distilled to provide the first detailed summary of morphological, ecological and geographical data contained in AVONET. Using this resource, anyone can now extract traits, ecology and spatial context for any avian taxon or assemblage—indeed, even for the entire radiation of extant birds. The data can be used to fit models, test hypotheses, or to calculate biodiversity metrics, including various dimensions of functional diversity. Comprehensive data improve the validity of these methods and increase the scale at which they can be applied. For example, tests of evolutionary models can be executed not only on well-sampled clades (e.g. Drury et al., 2018; Tobias et al., 2014) but also across far wider tracts of the avian phylogenetic tree (Crouch & Tobias, 2022). Similarly, methods using traits to quantify niche differences among species are no longer limited to smaller samples (e.g. Pigot & Tobias, 2013) and can now be applied across all birds (Drury et al., 2021; Freeman et al., 2022; Pigot et al., 2018).

A unique feature of AVONET is that trait data are presented in alignment with three alternative taxonomic treatments: BirdLife International, Clements and BirdTree (Tobias et al., 2022). In theory, this will be a major time-saver for users, facilitating integration with published geographical range maps and IUCN Red List data, eBird citizen-science data (Sullivan et al., 2014) and the global bird phylogeny (Jetz et al., 2012). Interoperability across these datasets allows an array of research questions to be addressed in novel ways. The following sections summarise recent progress in applying AVONET data across different research fields along with a horizon-scan of emerging opportunities.

最近生态分析中功能性状的突出是基于这样一个前提，即生物体表型的可测量属性可以使我们超越简单的物种列表，更接近于机制和过程的有效测试（Cadotte 等人，2011 年）。然而，基于特征的生态学和进化生物学的全部潜力最终受到不完全覆盖和完整性的限制，特别是在形态特征的情况下（Etard 等人，2020 年）。事实证明，填补数据覆盖范围内的这些空白具有挑战性，即使是采样率最高的主要分类群（例如维管植物），仍然缺乏对全球超过 50% 物种的全面形态测量（Hietz 等人，2021 年；Kattge 等人。 , 2020; Violle 等人，2014 年）。现在已经朝着应对这一挑战迈出了重要一步，完成了包含所有 11000 种鸟类的多种形态特征的数据集（Tobias 等人，2022 年）。本期特刊的目标是展示这些数据以供更广泛地使用，同时进行一系列研究，总结基于形态分析的最新进展，突出它们在研究和政策中的潜在应用。

在宏观生态学和宏观进化分析中最广泛使用的功能特征是分类变量，主要包括栖息地、生活史或饮食信息（Jones 等人，2009；McLean 等人，2021；Wilman 等人，2014）。这些数据集具有很大的影响力，但整体进展受到阻碍，因为许多分类特征相对粗糙且信息不足，降低了它们作为生态功能指标的效用（Kohli & Jarzyna，2021）。此外，它们为一些统计模型和系统发育分析提供了一个不完善的框架，因为许多物种被分配了相同的值并且类别之间的距离是任意的。一个明显的解决方案是使用连续形态变量，因为这些变量极大地提高了进化模型的分辨率（Chira 等人，2018 年）和群落组装指标（Blonder 等人，2018 年；Ricklefs 和 Travis，1980 年）。迄今为止，完整的连续形态特征数据集的可用性很大程度上仅限于体重（Wilman 等人，2014 年），而体重与生态功能的联系很弱（Pigot 等人，2020 年））。例如，鹰和鸭的体型可能相同，但这并不能告诉我们它们在生态系统中的功能作用。基于更详细的形态特征汇编的分析在少数经过充分研究的科系之外是不可能的，这导致了包括抽样偏差和不准确的进化模型在内的各种问题（Chang et al., 2020 ; Mouillot et al., 2021 ; Tobias等人，2020 年）。

出于多种原因，鸟类提供了应对全面性状覆盖挑战的最佳机会。首先，总体物种丰富度（约 11,000 种）远低于植物，例如，提供了一个更可实现的目标。其次，鸟类分布在世界各地的所有海洋和陆地生物群落中，它们在那里提供一系列关键的生态服务（Şekercioǧlu，2006 年）。第三，由于它们的知名度和吸引力，它们是全球范围内研究得最好的进化枝，现在拥有关于几乎所有物种的分布、丰度、生态和生活史的广泛数据集（Bird 等人，2020 年；Callaghan 等人） ., 2021 ; Sullivan 等人, 2014 ; Tobias 等人, 2020 ; Tobias & Pigot,2019 年；威尔曼等人，2014 年）。第四，鸟类形态学为研究一系列新的生态问题提供了一个经典系统，因为它们的喙、腿和翅膀分别提供了对营养相互作用、运动和扩散的洞察（Dehling et al., 2016 ; Pigot, Trisos, et al., 2016 ; Sheard 等人，2020 年）。事实上，鸟类的独特之处在于，已证明特定性状组合可以预测关键功能特征，包括饮食生态位和觅食行为，其准确度远高于单独的体重（Kennedy 等人，2020 年；Pigot 等人，2020 年）。

形态特征预测生态学的能力最初是由 1960 年以来关于鸟类群落的一系列论文确立的（例如 Miles & Ricklefs，1984 年）。尽管这些分析是基于相对较小的物种样本（参见 Tobias 等人，2022 年，图 1），但它们为“生态形态学”领域提供了概念基础（Bock，1994 年；Wainwright 和 Reilly，1994 年）推动了后续（2000 年后）基于连续变量的鸟类功能生态学的发展。在过去的二十年里，几个研究小组编译和分析了逐渐增加的鸟类特征数据集，最初针对的是几百种物种的可管理样本（例如 suboscines：Claramunt，2010 年；corvides：Kennedy 等人，2016 年）或当地组合（例如，秘鲁马努国家公园：Dehling，Fritz 等人，2014 年；Pigot，Bregman 等人，2016 年），以及最近跨越全球数千种物种（例如 Cooney 等人，2017；Kennedy 等人，2020；Phillips 等人，2018；Pigot 等人，2020）。然而，到目前为止，这些资源一直是零散的，原始数据在很大程度上不兼容且未发布。

为了提供具有广泛用途的综合资源，不同鸟类特征数据集的管理人员联手将他们的工作合并到 AVONET 中，这是所有鸟类的形态、生态和地理数据概要，作为本期特刊的旗舰文章发表（Tobias 等等人，2022 年）。AVONET 的灵感来自于 TRY 植物性状数据库的成功，该数据库是过去十年生态学和生态系统科学高影响力研究的有力催化剂（Kattge 等人，2020 年）。为了最大限度地提高产生类似积极影响的可能性，并与开放科学原则保持一致（Gallagher 等人，2020 年），AVONET 作为标本的个体测量值和物种平均值发布，对数据访问没有限制。

在某种程度上，AVONET 的发布标志着个人旅程的终点​​。我对鸟类特征的迷恋始于 1980 年代，当时我还是一名小学生，在诺森伯兰郡的潮汐线和电力线中寻找要肢解的尸体。我欠我母亲迟来的一份感谢，因为她在卧室的架子上摆满了头骨，橱柜里装满了臭气熏天的翅膀和跗节。但 AVONET 的故事远不止于此，而且在时间上更深入。第一次迭代（AVONET 1.0）的完成是一项真正的国际努力，其重要的专业知识和数据由来自 30 个国家/地区的 106 个机构的 115 位作者提供。当管理他们自己广泛的特征数据集的同事加入该项目时，势头发生了最重要的转变，包括圣地亚哥克拉拉蒙（乌拉圭），

这些主要数据集的一个共同点是它们对博物馆标本的依赖。在整个 AVONET 中，大多数标本是在伦敦自然历史博物馆和纽约美国自然历史博物馆进行测量的，还有来自另外 76 个馆藏的较小样本（参见 Tobias 等人，2022，图 4）。事实上，如果没有自 1800 年代中期以来无数博物馆馆长、现场助理和标本收藏家的贡献，其中一些杰出人物，包括查尔斯·达尔文、阿尔弗雷德·罗素·华莱士、欧内斯特·沙克尔顿和约翰·詹姆斯·奥杜邦，这个项目就不可能实现。他们准备了随后测量性状数据的标本。最终，鉴于保存完好的标本材料对于基于特征的生态学的关键重要性，AVONET 是博物馆社区的一座纪念碑，也是它为科学研究和整个人类社会提供的关键服务（Suarez & Tsutsui，2004 年）。

提炼了许多信息来源，以提供 AVONET 中包含的形态、生态和地理数据的第一个详细摘要。使用这种资源，现在任何人都可以提取任何鸟类分类群或组合的特征、生态和空间背景——事实上，甚至是现存鸟类的整个辐射。这些数据可用于拟合模型、检验假设或计算生物多样性指标，包括功能多样性的各个维度。综合数据提高了这些方法的有效性并增加了它们的应用范围。例如，进化模型的测试不仅可以在采样良好的进化枝上执行（例如 Drury 等人，2018 年；Tobias 等人，2014 年）) 但也跨越更广泛的鸟类系统发育树 (Crouch & Tobias, 2022 )。同样，使用性状来量化物种间生态位差异的方法不再局限于较小的样本（例如 Pigot & Tobias，2013 年），现在可以应用于所有鸟类（Drury 等人，2021 年；Freeman 等人，2022 年；Pigot等人，2018 年）。

AVONET 的一个独特之处在于，性状数据与三种替代分类处理方法一致：BirdLife International、Clements 和 BirdTree（Tobias 等人，2022 年）。从理论上讲，这将为用户节省大量时间，促进与已发布的地理范围图和 IUCN 红色名录数据、eBird 公民科学数据 (Sullivan et al., 2014 ) 和全球鸟类系统发育 (Jetz et al. , 2012 年）。这些数据集之间的互操作性允许以新颖的方式解决一系列研究问题。以下部分总结了在不同研究领域应用 AVONET 数据的最新进展以及对新兴机会的横向扫描。