Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst “perfect” analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

Understanding patterns of biodiversity is crucial for developing appropriate conservation and management plans. The IUCN RedList is looked upon as a source of globally-consistent assessment of species extinction risk, including range maps as part of the extinction risk assessment. Species ranges are a central criterion in determining extinction vulnerability, and consequently apportioning conservation and research efforts. Thus, the accuracy of these maps is crucial to the effective conservation of global biodiversity. Given difficulties in acquiring sufficient, reliable point data and the need for species or diversity maps within many studies, countless papers rely on these centralized expert range maps. However, such efforts are vulnerable to errors if not carefully checked, and the drive to assess as many species as possible rather than to ensure meaningful quality assessment may drive high error rates, with huge implications for species conservation. Recent efforts to account for the over-generalization of species ranges by trimming species ranges with landcover and elevation also makes a number of assumptions on the consistency and accuracy of global data, the lack of politically-driven biases. Here, we analyse the biases present in 50768 animal IUCN and BirdLife maps and provide suggestions on how such analyses could be improved, and flag spatial and taxonomic inconsistencies to enable analysis to acknowledge the limitations of data in further analysis based on these maps. We also discuss effective ways to overcome these biases, the limits of such applications and explore alternative means of mapping diversity patterns.

Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis of these patterns or their consequences exists. As such, our view of ecosystems may be incorrect, undermining countless ecological and evolutionary studies. Using 742 million records of 374,900 species, we explore the global patterns and impacts of accessibility in terrestrial and marine Systems. Pervasive sampling and observation biases exist across animals, with only 6.74% of the globe sampled, and disproportionately poor tropical sampling. High-elevations and deep-seas are comparably unknown. Over 50% of records in most groups account for under 2% of species. Citizen-science exacerbates biases, and normalizing the practice of valuing data publication is essential to bridge this gap and better represent species distributions from more distant and inaccessible areas, and provide the necessary basis for conservation and management.

Proper conceptual framework of community assembly is critical for understanding biodiversity patterns. In this work, we raised the concepts, ‘predominant/ assistant trait’, to characterize distinct trait roles in the processes of community organizing. We further incorporated them into previous theoretical understanding. Upon this theoretical basis, we presented a case study with two rodent datasets in the Hengduan Mountains, China. Due to weak phylogenetic signal and extraordinary habitat heterogeneity, we detected most of the rodent communities appeared random at both phylogenetic and morphological facets, which have prevented us to identify the role of morphological attributes and major process structuring rodent communities. We inferred that the variations in trait role, trait conservatism and habitat heterogeneity are major noise confounding our understanding on community assembly. Based on theoretical modification and empirical demonstration, this work has offered a novel and comprehensive perspective to understand the patterns of phylogenetic and trait-based community structure.