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Reports of major losses in insect biodiversity have stimulated an increasing interest in temporal population changes. Existing datasets are often limited to a small number of study sites, few points in time, a narrow range of land-use intensities and only some taxonomic groups, or they lack standardised sampling. While new monitoring programs have been initiated, they still cover rather short time periods. Daskalova et al. 2021 (Insect Conservation and Diversity, 14, 1-18) argue that temporal trends of insect populations derived from short time series are biased towards extreme trends, while their own analysis of an assembly of shorter- and longer-term time series does not support an overall insect decline. With respect to the results of Seibold et al. 2019 (Nature, 574, 671–674) based on a 10-year multi-site time series, they claim that the analysis suffers from not accounting for temporal pseudoreplication. Here, we explain why the criticism of missing statistical rigour in the analysis of Seibold et al. (2019) is not warranted. Models that include ‘year’ as random effect, as suggested by Daskalova et al. (2021), fail to detect non-linear trends and assume that consecutive years are independent samples which is questionable for insect time-series data. We agree with Daskalova et al. (2021) that the assembly and analysis of larger datasets is urgently needed, but it will take time until such datasets are available. Thus, short-term datasets are highly valuable, should be extended and analysed continually to provide a more detailed understanding of insect population changes under the influence of global change, and to trigger immediate conservation actions.

Reports of major losses in biodiversity have stimulated an increasing interest in temporal population changes, particularly in insects, which had received little attention in the past. Existing long-term datasets are often limited to a small number of study sites, few points in time, a narrow range of land-use intensities and only some taxonomic groups, or they lack standardized sampling. While new multi-site monitoring programs have been initiated, most of them still cover rather short time periods. Daskalova et al. 20201 argue that temporal trends of insect populations derived from short time series are biased towards extreme trends, while their own analysis of an assembly of shorter- and longer-term time series does not support an overall insect decline. With respect to the results of Seibold et al.2 based on a 10-year multi-site time series, they claim that the analysis suffers from not accounting for temporal pseudoreplication. In this note, we explain why the criticism of missing statistical rigour in the analysis of Seibold et al.2 is not warranted. Models that include ‘year’ as random effect, as suggested by Daskalova et al. 2020, fail to detect non-linear trends and assume that consecutive years are independent samples which is questionable for insect time-series data. We agree with Daskalova et al. 2020 that the assembly and analysis of larger datasets is urgently needed, but it will take time until such datasets are available. Thus, short-term datasets like ours are highly valuable, should be extended and analysed continually to provide a more detailed understanding of how insect populations are changing under the influence of global change, and to trigger immediate conservation actions.

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.

Verrucomicrobia affiliated with ���Candidatus Udaeobacter��� belong to the most abundant soil bacteria worldwide. Although the synthesis of antibiotics presumably evolved in soil, and environmental pollution with antimicrobials increases, the impact of these complex molecules on ���Ca. Udaeobacter��� remains to be elucidated. In this study, we demonstrate that ���Ca. Udaeobacter��� representatives residing in grassland as well as forest soil ecosystems show multidrug resistance and even take advantage of antibiotics release. Soils treated with up to six different antibiotics exhibited a higher ���Ca. Udaeobacter��� abundance than corresponding controls after 3, 8, and 20 days of incubation. In this context, we provide evidence that ���Ca. Udaeobacter��� representatives may utilize nutrients which are released due to antibiotic-driven lysis of other soil microbes and thereby reduce energetically expensive synthesis of required biomolecules. Moreover, genomic analysis revealed the presence of genes conferring resistance to multiple classes of antibiotics and indicated that ���Ca. Udaeobacter��� representatives most likely oxidize the trace gas H2 to generate energy. This energy might be required for long-term persistence in terrestrial habitats, as already suggested for other dominant soil bacteria. Our study illustrates, for the first time, that globally abundant ���Ca. Udaeobacter��� benefits from release of antibiotics, which confers advantages over other soil bacteria and represents a so-far overlooked fundamental lifestyle feature of this poorly characterized verrucomicrobial genus. Furthermore, our study suggests that ���Ca. Udaeobacter��� representatives can utilize H2 as an alternative electron donor.

Trait-based research spans from evolutionary studies of individual-level properties to global patterns of biodiversity and ecosystem functioning. An increasing number of trait data is available for many different organism groups, being published as open access data on a variety of file hosting services. Thus, standardization between datasets is generally lacking due to heterogeneous data formats and types. The compilation of these published data into centralised databases remains a difficult and time-consuming task. We reviewed existing trait databases and online services, as well as initiatives for trait data standardization. Together with data providers and users we identified a need for a minimal trait-data terminology that is flexible enough to include traits from all types of organisms but simple enough to be adopted by different research communities. In order to facilitate reproducibility of analyses, the reuse of data and the combination of datasets from multiple sources, we propose a standardized vocabulary for trait data that is compatible with existing ontologies. We tested the vocabulary using trait datasets from several research groups working on different taxa and questions in a large project (the Biodiversity Exploratories, www.biodiversity-exploratories.de). By relying on unambiguous identifiers, the proposed minimal vocabulary for trait data captures the different degrees of resolution and measurement detail for multiple use cases of trait-based research. It further encourages the use of global Uniform Resource Identifiers (URI) for taxa and trait definitions, methods and units, thereby following the standards for a semantic web of scientific data. In addition, we developed an R-based tool to convert any trait dataset into the proposed standard format. The R-package facilitates the upload of own data to hosting services but also simplifies the access to published trait data. It also offers direct access to trait datasets that have been published in the public domain or under creative commons licenses. All these products are available through the Github platform (https://github.com/EcologicalTraitData) with the aim of a continuous collaboration and improvement with the research community. KEYWORDS: traits, standardization, ontology, semantic web, tools, distributed data, R package, Biodiversity Exploratories

Conservation priorities that are based on species distribution, endemism, and vulnerability may underrepresent biologically unique species as well as their functional roles and evolutionary histories. To ensure that priorities are biologically comprehensive, multiple dimensions of diversity must be considered. Further, understanding how the different dimensions relate to one another spatially is important for conservation prioritization, but the relationship remains poorly understood. Here, we use spatial conservation planning to (i) identify and compare priority regions for global mammal conservation across three key dimensions of biodiversity—taxonomic, phylogenetic, and traits—and (ii) determine the overlap of these regions with the locations of threatened species and existing protected areas. We show that priority areas for mammal conservation exhibit low overlap across the three dimensions, highlighting the need for an integrative approach for biodiversity conservation. Additionally, currently protected areas poorly represent the three dimensions of mammalian biodiversity. We identify areas of high conservation priority among and across the dimensions that should receive special attention for expanding the global protected area network. These high-priority areas, combined with areas of high priority for other taxonomic groups and with social, economic, and political considerations, provide a biological foundation for future conservation planning efforts.