A phylogeny-informed characterisation of global tetrapod traits addresses data gaps and biases

Tetrapods (amphibians, reptiles, birds, and mammals) are model systems for global biodiversity science, but continuing data gaps, limited data standardisation, and ongoing flux in taxonomic nomenclature constrain integrative research on this group and potentially cause biased inference. We combined and harmonised taxonomic, spatial, phylogenetic, and attribute data with phylogeny-based multiple imputation to provide a comprehensive data resource (TetrapodTraits 1.0.0) that includes values, predictions, and sources for body size, activity time, micro- and macrohabitat, ecosystem, threat status, biogeography, insularity, environmental preferences, and human influence, for all 33,281 tetrapod species covered in recent fully sampled phylogenies. We assess gaps and biases across taxa and space, finding that shared data missing in attribute values increased with taxon-level completeness and richness across clades. Prediction of missing attribute values using multiple imputation revealed substantial changes in estimated macroecological patterns. These results highlight biases incurred by nonrandom missingness and strategies to best address them. While there is an obvious need for further data collection and updates, our phylogeny-informed database of tetrapod traits can support a more comprehensive representation of tetrapod species and their attributes in ecology, evolution, and conservation research.

The changing landscape of text mining: a review of approaches for ecology and evolution

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution.

The AusTraits plant dictionary

Traits with intuitive names, a clear scope and explicit description are essential for all trait databases. The lack of unified, comprehensive, and machine-readable plant trait definitions limits the utility of trait databases, including reanalysis of data from a single database, or analyses that integrate data across multiple databases. Both can only occur if researchers are confident the trait concepts are consistent within and across sources. Here we describe the AusTraits Plant Dictionary (APD), a new data source of terms that extends the trait definitions included in a recent trait database, AusTraits. The development process of the APD included three steps: review and formalisation of the scope of each trait and the accompanying trait description; addition of trait metadata; and publication in both human and machine-readable forms. Trait definitions include keywords, references, and links to related trait concepts in other databases, enabling integration of AusTraits with other sources. The APD will both improve the usability of AusTraits and foster the integration of trait data across global and regional plant trait databases.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

An extensive database on the traits and occurrences of amphibian species in Turkey

Amphibians are the most endangered taxa among vertebrates, and they face many threats during their complex life cycles. The species' life history traits and occurrence database help understand species responses against ecological factors. Consequently, the species-level-trait database has gained more prominence in recent years as a useful tool for understanding the dimensions of communities, assembly processes of communities, and conserving biodiversity at the ecosystem level against environmental changes. However, in Turkey, there are deficiencies in the knowledge of the ecological traits of amphibians compared to other vertebrate taxa, as most studies have focused on their distribution or taxonomic status. Consequently, there is a need to create such a database for future research on all known extant amphibians in Turkey. We compiled a species-level data set of species traits and occurrences for all amphibians in Turkey using 436 literature sources. We completed 36 trait categories with 5611 occurrence data for 37 amphibian species in Turkey. This study provides an open, useful, and comprehensive database for macroecological and conservation studies on amphibians in Turkey.

ARAapp: filling gaps in the ecological knowledge of spiders using an automated and dynamic approach to analyze systematically collected community data

The ARAMOB data repository compiles meticulously curated spider community datasets from systematical collections, ensuring a high standard of data quality. These datasets are enriched with crucial methodological data that enable the datasets to be aligned in time and space, facilitating data synthesis across studies, respectively, collections. To streamline the analysis of these datasets in a species-specific context, a suite of tailored ecological analysis tools named ARAapp has been developed. By harnessing the capabilities of ARAapp, users can systematically evaluate the spider species data housed within the ARAMOB repository, elucidating intricate relationships with a range of parameters such as vertical stratification, habitat occurrence, ecological niche parameters (moisture and shading) and phenological patterns. Database URL: ARAapp is available at  www.aramob.de/en.

Improper data practices erode the quality of global ecological databases and impede the progress of ecological research

The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared with the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of applicability and traceability likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.

When Do Traits Tell More Than Species about a Metacommunity? A Synthesis across Ecosystems and Scales

AbstractLinking species traits with the variation in species assemblages across habitats has often proved useful for developing a more mechanistic understanding of species distributions in metacommunities. However, summarizing the rich tapestry of a species in all of its nuance with a few key ecological traits can also lead to an abstraction that provides less predictability than when using taxonomy alone. As a further complication, taxonomic and functional diversities can be inequitably compared, either by integrating taxonomic-level information into the calculation of how functional aspects of communities vary or by detecting spurious trait-environment relationships. To remedy this, we here synthesize analyses of 80 datasets on different taxa, ecosystems, and spatial scales that include information on abundance or presence/absence of species across sites with variable environmental conditions and the species' traits. By developing analyses that treat functional and taxonomic diversity equitably, we ask when functional diversity helps to explain metacommunity structure. We found that patterns of functional diversity explained metacommunity structure and response to environmental variation in only 25% of the datasets using a multitrait approach but up to 59% using a single-trait approach. Nevertheless, an average of only 19% (interquartile range = 0%-29%) of the traits showed a significant signal across environmental gradients. Species-level traits, as typically collected and analyzed through functional diversity patterns, often do not bring predictive advantages over what the taxonomic information already holds. While our assessment of a limited advantage of using traits to explain variation in species assemblages was largely true across ecosystems, traits played a more useful role in explaining variation when many traits were used and when trait constructs were more related to species' status, life history, and mobility. We propose future research directions to make trait-based approaches and data more helpful for inference in metacommunity ecology.

A roadmap to define and select aquatic biological traits at different scales of analysis

Trait-based approaches are a powerful tool, as they not only improve understanding of ecological complexity and functioning but also allow comparison across different ecosystems and biogeographical regions. They may be used to unveil ecosystem processes and assess community structures, but their great potential becomes limited when dealing with scattered trait data and historically unstandardised trait nomenclature. The lack of standardisation allows authors to use the terminology of their preference, which inevitably leads to ambiguous misunderstandings and limits comparison between different studies. There have been some attempts to organise the trait vocabulary, but even these are mostly created from the perspective of a single ecosystem, which limits their applicability. In this work, we conducted a systematic literature review that identified and compiled 1127 traits across 37 datasets of fishes, invertebrates and zooplankton from freshwater, marine and transitional ecosystems. This dataset was then used to build on the Marine Species Traits Wiki and to propose a new, unified approach to a trait vocabulary based directly on readily available trait data. We propose a single standardised designation for all the different traits identified and provide a list of all the different synonyms commonly used for these traits. A roadmap to help the trait selection process is also provided, offering a guide through four main steps and important questions for choosing an adequate set of traits at the beginning of any study, which constitutes one of the main challenges in functional ecology research. Overall, this proposal will provide a solid baseline for tackling gaps in trait nomenclature and ensuring a clearer future for functional ecology studies.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Host functional traits as the nexus for multilevel infection patterns

Understanding pathogen transmission and infection patterns at multiple biological scales is a central issue in disease ecology and evolution. Here, we suggest that functional traits of host species readily affect infection patterns of species, communities, and landscapes, and thus serve as a linkage for multilevel studies of infectious disease.

The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos

Biological trait analysis (BTA) is a valuable tool for evaluating changes in community diversity and its link to ecosystem processes as well as environmental and anthropogenic perturbations. Trait-based analytical techniques like BTA rely on standardised datasets of species traits. However, there are currently only a limited number of datasets available for marine macrobenthos that contain trait data across multiple taxonomic groups. Here, we present an open-access dataset of 16 traits for 235 macrozoobenthic species recorded throughout multiple sampling campaigns of the Dutch Wadden Sea; a dynamic soft bottom system where humans have long played a substantial role in shaping the coastal environment. The trait categories included in this dataset cover a variety of life history strategies that are tightly linked to ecosystem functioning and the resilience of communities to (anthropogenic) perturbations and can advance our understanding of environmental changes and human impacts on the functioning of soft bottom systems.

A global assessment of the Raunkiaeran shortfall in plants: geographic biases in our knowledge of plant traits

This article is part of the Special Collection ‘Global plant diversity and distribution’. See https://www.newphytologist.org/global-plant-diversity for more details.

A dataset on African bats' functional traits

Trait-based approaches are becoming extremely common in ecological modeling and the availability of traits databases is increasing. However, data availability is often biased towards particular regions and taxa, with many taxa (e.g., bats) often under-represented. Here, we present the AfroBaT dataset, a compilation of trait data on 320 African bat species containing 76,914 values for 86 traits focusing on morphology, reproduction, life-history, trophic ecology, and species distributions. All data were gathered from published literature following the ecological trait-data standard procedure. Missing data for both numerical and categorical traits were imputed with a machine learning approach including species phylogeny. Trophic ecology traits showed the highest coverage in the literature (72% of the species averaged over all traits), while reproductive traits the lowest. Our data imputation improved the coverage of AfroBaT especially for reproductive traits, going from 27% to 58% of the species covered. AfroBaT has a range of potential applications in macroecology and community ecology, and the availability of open-access data on African bats will enable collaboration and data-sharing among researchers.

MOSAIC - A Unified Trait Database to Complement Structured Population Models

Despite exponential growth in ecological data availability, broader interoperability amongst datasets is needed to unlock the potential of open access. Our understanding of the interface of demography and functional traits is well-positioned to benefit from such interoperability. Here, we introduce MOSAIC, an open-access trait database that unlocks the demographic potential stored in the COMADRE, COMPADRE, and PADRINO open-access databases. MOSAIC data were digitised and curated through a combination of existing datasets and new trait records sourced from primary literature. In its first release, MOSAIC (v. 1.0.0) includes 14 trait fields for 300 animal and plant species: biomass, height, growth determination, regeneration, sexual dimorphism, mating system, hermaphrodism, sequential hermaphrodism, dispersal capacity, type of dispersal, mode of dispersal, dispersal classes, volancy, and aquatic habitat dependency. MOSAIC includes species-level phylogenies for 1,359 species and population-specific climate data. We identify how database integration can improve our understanding of traits well-quantified in existing repositories and those that are poorly quantified (e.g., growth determination, modularity). MOSAIC highlights emerging challenges associated with standardising databases and demographic measures.

AraDiv: a dataset of functional traits and leaf hyperspectral reflectance of Arabidopsis thaliana

Data from functional trait databases have been increasingly used to address questions related to plant diversity and trait-environment relationships. However, such databases provide intraspecific data that combine individual records obtained from distinct populations at different sites and, hence, environmental conditions. This prevents distinguishing sources of variation (e.g., genetic-based variation vs. phenotypic plasticity), a necessary condition to test for adaptive processes and other determinants of plant phenotypic diversity. Consequently, individual traits measured under common growing conditions and encompassing within-species variation across the occupied geographic range have the potential to leverage trait databases with valuable data for functional and evolutionary ecology. Here, we recorded 16 functional traits and leaf hyperspectral reflectance (NIRS) data for 721 widely distributed Arabidopsis thaliana natural accessions grown in a common garden experiment. These data records, together with meteorological variables obtained during the experiment, were assembled to create the AraDiv dataset. AraDiv is a comprehensive dataset of A. thaliana's intraspecific variability that can be explored to address questions at the interface of genetics and ecology.

A species-level trait dataset of bats in Europe and beyond

Knowledge of species' functional traits is essential for understanding biodiversity patterns, predicting the impacts of global environmental changes, and assessing the efficiency of conservation measures. Bats are major components of mammalian diversity and occupy a variety of ecological niches and geographic distributions. However, an extensive compilation of their functional traits and ecological attributes is still missing. Here we present EuroBaTrait 1.0, the most comprehensive and up-to-date trait dataset covering 47 European bat species. The dataset includes data on 118 traits including genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution. We compiled the bat trait data obtained from three main sources: (i) a systematic literature and dataset search, (ii) unpublished data from European bat experts, and (iii) observations from large-scale monitoring programs. EuroBaTrait is designed to provide an important data source for comparative and trait-based analyses at the species or community level. The dataset also exposes knowledge gaps in species, geographic and trait coverage, highlighting priorities for future data collection.

Can morphological traits explain species-specific differences in meta-analyses? A case study of forest beetles

Meta-analyses have become a valuable tool with which to synthesize effects across studies, but in ecology and evolution, they are often characterized by high heterogeneity, where effect sizes vary between studies. Much of this heterogeneity can be attributed to species-specific differences in responses to predictor variables. Here, we aimed to incorporate a novel trait-based approach to explain species-specific differences in a meta-analysis by testing the ability of morphological traits to explain why the effectiveness of flight-intercept trap design varies according to beetle species, a critical issue in forest pest management. An existing morphological trait database for forest beetles was supplemented, providing trait data for 97 species, while data from a previous meta-analysis on capture rates of bark or woodboring beetles according to different trap designs were updated. We combined these sources by including nine morphological traits as moderators in meta-analysis models, for five different components of trap design. Traits were selected based on theoretical hypotheses relating to beetle movement, maneuverability, and sensory perception. We compared the performance of morphological traits as moderators versus guild, taxonomic family, and null meta-analysis models. Morphological traits for the effect of trap type (panel vs. multiple-funnel) on beetle capture rates improved model fit (AIC_c ), reduced within-study variance (σ² ), and explained more variation (McFadden's pseudo-R² ) compared with null, guild, and taxonomic family models. For example, morphological trait models explained 10% more of the variance (pseudo-R² ) when compared with a null model. However, using traits was less informative to explain how detailed elements of trap design such as surface treatment and color influence capture rates. The reduction of within-study variance when accounting for morphological traits demonstrates their potential value for explaining species-specific differences. Morphological traits associated with flight efficiency, maneuverability, and eye size were particularly informative for explaining the effectiveness of trap type. This could lead to improved predictability of optimal trap design according to species. Therefore, morphological traits could be a valuable tool for understanding species-specific differences in community ecology, but other causes of heterogeneity across studies, such as forest type and structure, require further investigation.

A trait-based framework for dung beetle functional ecology

Traits are key for understanding the environmental responses and ecological roles of organisms. Trait approaches to functional ecology are well established for plants, whereas consistent frameworks for animal groups are less developed. Here we suggest a framework for the study of the functional ecology of animals from a trait-based response-effect approach, using dung beetles as model system. Dung beetles are a key group of decomposers that are important for many ecosystem processes. The lack of a trait-based framework tailored to this group has limited the use of traits in dung beetle functional ecology. We review which dung beetle traits respond to the environment and affect ecosystem processes, covering the wide range of spatial, temporal and biological scales at which they are involved. Dung beetles show trait-based responses to variation in temperature, water, soil properties, trophic resources, light, vegetation structure, competition, predation and parasitism. Dung beetles' influence on ecosystem processes includes trait-mediated effects on nutrient cycling, bioturbation, plant growth, seed dispersal, other dung-based organisms and parasite transmission, as well as some cases of pollination and predation. We identify 66 dung beetle traits that are either response or effect traits, or both, pertaining to six main categories: morphology, feeding, reproduction, physiology, activity and movement. Several traits pertain to more than one category, in particular dung relocation behaviour during nesting or feeding. We also identify 136 trait-response and 77 trait-effect relationships in dung beetles. No response to environmental stressors nor effect over ecological processes were related with traits of a single category. This highlights the interrelationship between the traits shaping body-plans, the multi-functionality of traits, and their role linking responses to the environment and effects on the ecosystem. Despite current developments in dung beetle functional ecology, many knowledge gaps remain, and there are biases towards certain traits, functions, taxonomic groups and regions. Our framework provides the foundations for the thorough development of trait-based dung beetle ecology. It also serves as an example framework for other taxa.

Recommendations for use of annotations and persistent identifiers in taxonomy and biodiversity publishing

The paper summarises many years of discussions and experience of biodiversity publishers, organisations, research projects and individual researchers, and proposes recommendations for implementation of persistent identifiers for article metadata, structural elements (sections, subsections, figures, tables, references, supplementary materials and others) and data specific to biodiversity (taxonomic treatments, treatment citations, taxon names, material citations, gene sequences, specimens, scientific collections) in taxonomy and biodiversity publishing. The paper proposes best practices on how identifiers should be used in the different cases and on how they can be minted, cited, and expressed in the backend article XML to facilitate conversion to and further re-use of the article content as FAIR data. The paper also discusses several specific routes for post-publication re-use of semantically enhanced content through large biodiversity data aggregators such as the Global Biodiversity Information Facility (GBIF), the International Nucleotide Sequence Database Collaboration (INSDC) and others, and proposes specifications of both identifiers and XML tags to be used for that purpose. A summary table provides an account and overview of the recommendations. The guidelines are supported with examples from the existing publishing practices.

Quantifying mammalian diets

We propose a quantitative approach for categorising mammalian diets based on the taxonomy of food items and parts consumed (the form of food). Our analysis covers 82% of the mammalian species alive today. The diet information comes from different data sources—textbooks, datasets and peer-reviewed literature and includes a transformation of narrative quantitative data into qualitative data. We link a database on nutrient composition of diet items of the quantitative diet data and analyse the distribution of macronutrients of diets across taxonomic groups and map them to the dental morphology of the eaters. The results show associations between dental complexity and the concentrations of some nutrients. Our analysis highlights omnivory as a multi-faceted concept—there are many kinds of omnivores within the dietary space we report. The developed dataset and the proposed approach relating the chemical composition of diets offers a basis for future comparative studies of living and fossil mammals. With this study, we make the accompanying large-scale dietary data publicly available online (https://www.mammalbase.net).

Essential Biodiversity Variables: extracting plant phenological data from specimen labels using machine learning

Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML).

A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).

What are mycorrhizal traits?

Traits are inherent properties of organisms, but how are they defined for organismal networks such as mycorrhizal symbioses? Mycorrhizal symbioses are complex and diverse belowground symbioses between plants and fungi that have proved challenging to fit into a unified and coherent trait framework. We propose an inclusive mycorrhizal trait framework that classifies traits as morphological, physiological, and phenological features that have functional implications for the symbiosis. We further classify mycorrhizal traits by location - plant, fungus, or the symbiosis - which highlights new questions in trait-based mycorrhizal ecology designed to charge and challenge the scientific community. This new framework is an opportunity for researchers to interrogate their data to identify novel insights and gaps in our understanding of mycorrhizal symbioses.

Past and future uses of text mining in ecology and evolution

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

The World Spider Trait database: a centralized global open repository for curated data on spider traits

Spiders are a highly diversified group of arthropods and play an important role in terrestrial ecosystems as ubiquitous predators, which makes them a suitable group to test a variety of eco-evolutionary hypotheses. For this purpose, knowledge of a diverse range of species traits is required. Until now, data on spider traits have been scattered across thousands of publications produced for over two centuries and written in diverse languages. To facilitate access to such data, we developed an online database for archiving and accessing spider traits at a global scale. The database has been designed to accommodate a great variety of traits (e.g. ecological, behavioural and morphological) measured at individual, species or higher taxonomic levels. Records are accompanied by extensive metadata (e.g. location and method). The database is curated by an expert team, regularly updated and open to any user. A future goal of the growing database is to include all published and unpublished data on spider traits provided by experts worldwide and to facilitate broad cross-taxon assays in functional ecology and comparative biology. Database URL:https://spidertraits.sci.muni.cz/.

The rise of big data in disease ecology

Big data have become readily available to explore patterns in large-scale disease ecology. However, the rate at which these public databases are exploited remains unknown. We highlight trends in big data usage in disease ecology during the past decade and encourage researchers to integrate big data into their study framework.

For flux's sake: General considerations for energy-flux calculations in ecological communities

Global change alters ecological communities with consequences for ecosystem processes. Such processes and functions are a central aspect of ecological research and vital to understanding and mitigating the consequences of global change, but also those of other drivers of change in organism communities. In this context, the concept of energy flux through trophic networks integrates food-web theory and biodiversity-ecosystem functioning theory and connects biodiversity to multitrophic ecosystem functioning. As such, the energy-flux approach is a strikingly effective tool to answer central questions in ecology and global-change research. This might seem straight forward, given that the theoretical background and software to efficiently calculate energy flux are readily available. However, the implementation of such calculations is not always straight forward, especially for those who are new to the topic and not familiar with concepts central to this line of research, such as food-web theory or metabolic theory. To facilitate wider use of energy flux in ecological research, we thus provide a guide to adopting energy-flux calculations for people new to the method, struggling with its implementation, or simply looking for background reading, important resources, and standard solutions to the problems everyone faces when starting to quantify energy fluxes for their community data. First, we introduce energy flux and its use in community and ecosystem ecology. Then, we provide a comprehensive explanation of the single steps towards calculating energy flux for community data. Finally, we discuss remaining challenges and exciting research frontiers for future energy-flux research.

Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta-analysis of studies examining population-level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population-level comparisons) we examined, there was a moderate (R 2 = 0.39) relationship between genomic differentiation (F ST ) and phenotypic differentiation (P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross-system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes "strong" differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.

Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database (www.spibirds.org)-a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language). The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.

Reproductive trait differences drive offspring production in urban cavity-nesting bees and wasps

The contrasting and idiosyncratic changes in biodiversity that have been documented across urbanization gradients call for a more mechanistic understanding of urban community assembly. The reproductive success of organisms in cities should underpin their population persistence and the maintenance of biodiversity in urban landscapes. We propose that exploring individual-level reproductive traits and environmental drivers of reproductive success could provide the necessary links between environmental conditions, offspring production, and biodiversity in urban areas. For 3 years, we studied cavity-nesting solitary bees and wasps in four urban green space types across Toronto, Canada. We measured three reproductive traits of each nest: the total number of brood cells, the proportion of parasite-free cells, and the proportion of non-emerged brood cells that were parasite-free. We determined (a) how reproductive traits, trait diversity and offspring production respond to multiple environmental variables and (b) how well reproductive trait variation explains the offspring production of single nests, by reflecting the different ways organisms navigate trade-offs between gathering of resources and exposure to parasites. Our results showed that environmental variables were poor predictors of mean reproductive trait values, trait diversity, and offspring production. However, offspring production was highly positively correlated with reproductive trait evenness and negatively correlated with trait richness and divergence. This suggests that a narrow range of reproductive traits are optimal for reproduction, and the even distribution of individual reproductive traits across those optimal phenotypes is consistent with the idea that selection could favor diverse reproductive strategies to reduce competition. This study is novel in its exploration of individual-level reproductive traits and its consideration of multiple axes of urbanization. Reproductive trait variation did not follow previously reported biodiversity-urbanization patterns; the insensitivity to urbanization gradients raise questions about the role of the spatial mosaic of habitats in cities and the disconnections between different metrics of biodiversity.

Lots of movement, little progress: a review of reptile home range literature

Reptiles are the most species-rich terrestrial vertebrate group with a broad diversity of life history traits. Biotelemetry is an essential methodology for studying reptiles as it compensates for several limitations when studying their natural history. We evaluated trends in terrestrial reptile spatial ecology studies focusing upon quantifying home ranges for the past twenty years. We assessed 290 English-language reptile home range studies published from 2000-2019 via a structured literature review investigating publications' study location, taxonomic group, methodology, reporting, and analytical techniques. Substantial biases remain in both location and taxonomic groups in the literature, with nearly half of all studies (45%) originating from the USA. Snakes were most often studied, and crocodiles were least often studied, while testudines tended to have the greatest within study sample sizes. More than half of all studies lacked critical methodological details, limiting the number of studies for inclusion in future meta-analyses (55% of studies lacked information on individual tracking durations, and 51% lacked sufficient information on the number of times researchers recorded positions). Studies continue to rely on outdated methods to quantify space-use (including Minimum Convex Polygons and Kernel Density Estimators), often failing to report subtleties regarding decisions that have substantial impact on home range area estimates. Moving forward researchers can select a suite of appropriate analytical techniques tailored to their research question (dynamic Brownian Bridge Movement Models for within sample interpolation, and autocorrelated Kernel Density Estimators for beyond sample extrapolation). Only 1.4% of all evaluated studies linked to available and usable telemetry data, further hindering scientific consensus. We ultimately implore herpetologists to adopt transparent reporting practices and make liberal use of open data platforms to maximize progress in the field of reptile spatial ecology.

Metadata Made Easy: Develop and Use Domain-Specific Metadata Schemes by following the dmdScheme approach

Metadata plays an essential role in the long-term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain-specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co-development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet.We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain-specific scheme. We describe how to create a domain-specific scheme from this template, including the iterative co-development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation.The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML.Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain-specific schemes. A near-future priority is to provide methods to interface domain-specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.

What does a threatened saproxylic beetle look like? Modelling extinction risk using a new morphological trait database

The extinction of species is a non-random process, and understanding why some species are more likely to go extinct than others is critical for conservation efforts. Functional trait-based approaches offer a promising tool to achieve this goal. In forests, deadwood-dependent (saproxylic) beetles comprise a major part of threatened species, but analyses of their extinction risk have been hindered by the availability of suitable morphological traits. To better understand the mechanisms underlying extinction in insects, we investigated the relationships between morphological features and the extinction risk of saproxylic beetles. Specifically, we hypothesised that species darker in colour, with a larger and rounder body, a lower mobility, lower sensory perception and more robust mandibles are at higher risk. We first developed a protocol for morphological trait measurements and present a database of 37 traits for 1,157 European saproxylic beetle species. Based on 13 selected, independent traits characterising aspects of colour, body shape, locomotion, sensory perception and foraging, we used a proportional-odds multiple linear mixed-effects model to model the German Red List categories of 744 species as an ordinal index of extinction risk. Six out of 13 traits correlated significantly with extinction risk. Larger species as well as species with a broad and round body had a higher extinction risk than small, slim and flattened species. Species with short wings had a higher extinction risk than those with long wings. On the contrary, extinction risk increased with decreasing wing load and with higher mandibular aspect ratio (shorter and more robust mandibles). Our study provides new insights into how morphological traits, beyond the widely used body size, determine the extinction risk of saproxylic beetles. Moreover, our approach shows that the morphological characteristics of beetles can be comprehensively represented by a selection of 13 traits. We recommend them as a starting point for functional analyses in the rapidly growing field of ecological and conservation studies of deadwood.

Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny

Understanding species' roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, and reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high accuracy. We synthesize data from community-wide gut content analyses of tropical coral reef fishes worldwide, resulting in diet information from 13,961 individuals belonging to 615 reef fish. We then use network analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. Finally, we use machine learning to test whether pairwise trophic interactions can be predicted with accuracy. Our models achieved a misclassification error of less than 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, as well as high-resolution probabilities of trophic interactions. By applying our framework to the most diverse vertebrate consumer group, we show that it can be applied to other organismal groups to advance reproducibility in trait-based ecology. Our work thus provides a viable approach to account for the complexity of predator-prey interactions in highly diverse ecosystems.

Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data

Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

Global wildlife trade permeates the Tree of Life

Legal and illegal wildlife trade is a multibillion dollar industry that is driving several species toward extinction. Even though wildlife trade permeates the Tree of Life, most analyses to date focused on the trade of a small selection of charismatic vertebrate species. Given that vertebrate taxa represent only 3% of described species, this is a significant bias that prevents the development of comprehensive conservation strategies. In this short contribution, we discuss the significance of global wildlife trade considering the full diversity of organisms for which data are available in the IUCN database. We emphasize the importance of being fast and effective in filling the knowledge gaps about non-vertebrate life forms, in order to achieve an in-depth understanding of global trading patterns across the full canopy of the Tree of Life, and not just its most appealing twig.

Patterns in research and data sharing for the study of form and function in caviomorph rodents

The combination of morphometrics, phylogenetic comparative methods, and open data sets has renewed interest in relating morphology to adaptation and ecological opportunities. Focusing on the Caviomorpha, a well-studied mammalian group, we evaluated patterns in research and data sharing in studies relating form and function. Caviomorpha encompasses a radiation of rodents that is diverse both taxonomically and ecologically. We reviewed 41 publications investigating ecomorphology in this group. We recorded the type of data used in each study and whether these data were made available, and we re-digitized all provided data. We tracked two major lines of information: collections material examined and trait data for morphological and ecological traits. Collectively, the studies considered 63% of extant caviomorph species; all extant families and genera were represented. We found that species-level trait data rarely were provided. Specimen-level data were even less common. Morphological and ecological data were too heterogeneous and sparse to aggregate into a single data set, so we created relational tables with the data. Additionally, we concatenated all specimen lists into a single data set and standardized all relevant data for phylogenetic hypotheses and gene sequence accessions to facilitate future morphometric and phylogenetic comparative research. This work highlights the importance and ongoing use of scientific collections, and it allows for the integration of specimen information with species trait data. Recientemente ha resurgido el interés por estudiar la relación entre morfología, ecología, y adaptación. Esto se debe al desarrollo de nuevas herramientas morfométricas y filogenéticas, y al acceso a grandes bases de datos para estudios comparados. Revisamos 41 publicaciones sobre ecomorfología de roedores caviomorfos, un grupo diverso y bien estudiado, para evaluar los patrones de investigación y la transparencia para la liberación de datos. Registramos los tipos de datos que se utilizaron para cada estudio y si los datos están disponibles. Cuando estos datos se compartieron, los redigitalizamos. Nos enfocamos en los ejemplares consultados, y en datos que describen rasgos ecológicos y morfológicos para las especies estudiadas. Los estudios que revisamos abarcan el 63% de las especies de caviomorfos que actualmente existen. Encontramos que raramente fueron compartidos los datos que se tomaron para especies, y menos aún para ejemplares. Los datos morfológicos y ecológicos eran demasiado heterogéneos e exiguos para consolidar en un solo banco de datos; debido a esta circunstancia, creamos tablas relacionales con los datos. Además, enlazamos todas las listas individuales de especímenes para crear un solo banco de datos y estandarizamos todos los datos pertinentes a hipótesis filogenéticas, así como los números de acceso de secuencias genéticas, para así facilitar eventuales estudios comparados de morfometría y filogenia. Este trabajo resalta la importancia de las colecciones científicas y documenta su uso, además permitiendo la futura integración de datos derivados de ejemplares con datos sobre rasgos ecomorfológicos a nivel de especie.

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

Trait-Based Assessments of Climate-Change Impacts on Interacting Species

Plant-animal interactions are fundamentally important in ecosystems, but have often been ignored by studies of climate-change impacts on biodiversity. Here, we present a trait-based framework for predicting the responses of interacting plants and animals to climate change. We distinguish three pathways along which climate change can impact interacting species in ecological communities: (i) spatial and temporal mismatches in the occurrence and abundance of species, (ii) the formation of novel interactions and secondary extinctions, and (iii) alterations of the dispersal ability of plants. These pathways are mediated by three kinds of functional traits: response traits, matching traits, and dispersal traits. We propose that incorporating these traits into predictive models will improve assessments of the responses of interacting species to climate change.

Towards a New Generation of Trait-Flexible Vegetation Models

Plant trait variability, emerging from eco-evolutionary dynamics that range from alleles to macroecological scales, is one of the most elusive, but possibly most consequential, aspects of biodiversity. Plasticity, epigenetics, and genetic diversity are major determinants of how plants will respond to climate change, yet these processes are rarely represented in current vegetation models. Here, we provide an overview of the challenges associated with understanding the causes and consequences of plant trait variability, and review current developments to include plasticity and evolutionary mechanisms in vegetation models. We also present a roadmap of research priorities to develop a next generation of vegetation models with flexible traits. Including trait variability in vegetation models is necessary to better represent biosphere responses to global change.