Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long‐term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time‐series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.

Aim: Spatial and temporal biases in species‐occurrence data can compromise broad‐scale biogeographical research and conservation planning. Although spatial biases have been frequently scrutinized, temporal biases and the overall quality of species‐occurrence data have received far less attention. This study aims to answer three questions: (1) How reliable are species‐occurrence data for flowering plants in Africa? (2) Where and when did botanical sampling occur in the past 300 years? (3) How complete are plant inventories for Africa?Location: Africa.Methods: By filtering a publicly available dataset containing 3.5 million records of flowering plants, we obtained 934,676 herbarium specimens with complete information regarding species name, date and location of collection. Based on these specimens, we estimated inventory completeness for sampling units (SUs) of 25 km × 25 km. We then tested whether the spatial distribution of well‐sampled SUs was correlated with temporal parameters of botanical sampling. Finally, we determined whether inventory completeness in individual countries was related to old or recently collected specimens.Results: Thirty‐one per cent of SUs contained at least one specimen, whereas only 2.4% of SUs contained a sufficient number of specimens to reliably estimate inventory completeness. We found that the location of poorly sampled areas remained almost unchanged for half a century. Moreover, there was pronounced temporal bias towards old specimens in South Africa, the country that holds half of the available data for the continent. There, high inventory completeness stems from specimens collected several decades ago.Main conclusions: Despite the increasing availability of species occurrence data for Africa, broad‐scale biogeographical research is still compromised by the uncertain quality and spatial and temporal biases of such data. To avoid erroneous inferences, the quality and biases in species‐occurrence data should be critically evaluated and quantified prior to use. To this end, we propose a quantification method based on inventory completeness using easily accessible species‐occurrence data.

Artisanal fisheries occur all over the tropics and provide an important source of protein and income for many coastal communities. However, varied types and magnitudes of anthropogenic impacts threaten the environmental, economic, and socio-cultural sustainability of this poorly studied fishing practice. This article reviews the scattered literature on tropical artisanal coastal fisheries, identifying key challenges to ensure future sustainability. Despite huge data shortfalls, there is considerable evidence that artisanal fisheries have a significant influence on the distribution and abundance of target and by-catch species, in addition to wider impacts on biodiversity, biomass, assemblage structure, community dynamics, and ecosystem functioning. Despite these immediate and considerable threats, regulation and management of artisanal fisheries are problematic. Local communities in the coastal tropics are frequently very poor, and families frequently rely on fishing for food security and income. Ensuring social and environmental sustainability therefore entails models of governance that are able to adaptively manage these complex socio-ecological systems. Such models are being developed, but it is unclear whether there are sufficient resources and technical capacity to widely implement them before the widespread collapse of fisheries with potentially serious consequences for the communities that rely on them.

The remarkable biodiversity of the Brazilian Amazon is poorly documented and threatened by deforestation. When undocumented areas become deforested, in addition to losing the fauna and flora, we lose the opportunity to know which unique species had occupied a habitat. Here we quantify such knowledge loss by calculating how much of the Brazilian Amazon has been deforested and will likely be deforested until 2050 without having its tree flora sufficiently documented. To this end, we analysed 399 147 digital specimens of nearly 6000 tree species in relation to official deforestation statistics and future deforestation scenarios. We find that by 2017, 30% of all the localities where tree specimens had been collected were mostly deforested. Some 300 000 km ² (12%; 485 25 × 25 km grid cells) of the Brazilian Amazon had been deforested by 2017, without having a single tree specimen recorded. An additional 250 000–900 000 km ² of severely under‐collected rainforest will likely become deforested by 2050. If future tree sampling is to cover this area, sampling effort has to increase two‐ to six‐fold. Nearly 255 000 km ² or 7% of rainforest in the Brazilian Amazon is easily accessible but does yet but remain under‐collected. Our study highlights how progressing deforestation increases the risk of losing undocumented species of a hyper‐diverse tree flora.