Understanding and addressing shortfalls in European wild bee data

Understanding and addressing shortfalls in European wild bee data

One. Introduction

Biodiversity is declining globally. However, data availability and suitability often severely restrict a full evaluation of biodiversity trends, requiring information on taxonomic identity, distribution, ecology, population dynamics, interactions between species as well as their evolutionary relatedness. However, spatial and temporal sampling biases are ubiquitous features of species occurrence data, partly due to lack of digitalization of massive amounts of data that are collected and stored in public or private collections, and partly due to lack of

ABSTRACT

Understanding and reversing biodiversity decline in the Anthropocene requires robust data on species taxonomic identity, distribution, ecology, and population trends. Data deficits hinder biodiversity assessments and conservation, and despite major advances over the past few decades, our understanding of bee diversity, decline and distribution in Europe is still hampered by such data shortfalls. Using a unique digital dataset of wild bee occurrence and ecology, we identify seven critical shortfalls which are an absence of knowledge on geographic distributions, (functional) trait variation, population dynamics, evolutionary relationships, biotic interactions, species identity, and tolerance to abiotic conditions. We describe "BeeFall," an interactive online Shiny app tool, which visualizes these shortfalls and highlights missing data. We also define a new impediment, the Keartonian Impediment, which addresses an absence of high-quality in situ photos and illustrations with diagnostic characteristics and directly affects the outlined shortfalls. Shortfalls are highly correlated at both the provincial and national scales, identifying key areas in Europe where knowledge gaps can be filled. This work provides an important first step towards the long-term goal to mobilize and aggregate European wild bee data into a multi-scale, easy access, shareable, and updatable database which can inform research, practice, and policy actions for the conservation of wild bees.

sampling. Historically, the primary source of data has been opportunistic collections. Even within digitized data, there can be significant taxonomic, geographic, temporal, and methodological biases. For example, evidence shows that there has been a historical focus of taxonomists on larger, more charismatic species such as birds and mammals, and research on insects has been lagging behind. Within insect groups however, Western Europe and North America have seen greater collection and digitization of data notably through opportunistic sampling such as citizen science and voluntary recording schemes that generate millions of occurrence records every year. Historical data collected by naturalists is also often biased towards rarer species and species with greater morphological differences between genders and developmental stages. While citizen science approaches have a high intrinsic value to raise awareness about the diversity of living organisms around us, such opportunistic samplings are associated with important observation and detection biases because of the variable sampling intensity per survey event, uneven temporal distribution, the different levels of discoverability among target species, the observational skills of the recorders, and the reliability of downstream validation opportunities by experts.

Navigating databases consisting of non-random occurrence data is perhaps one of the most pressing challenges of contemporary biodiversity research. To provide a framework on how to systematically address and fill the persistent knowledge gaps, Hortal et al. proposed a classification of "biodiversity shortfalls" into seven categories that best represent the multifaceted nature of the problem, with a focus on species identity, their distribution, population dynamics, evolution, behavioral/ecological traits, environmental tolerances, and interactions. These shortfalls are a crucial hindrance to biodiversity research as they cause difficulties for predicting declines and assessing threat status. This, in turn, prevents a comprehensive understanding of species niches and their evolutionary histories, and so remains an obstacle towards a global characterization of known and yet undiscovered insect diversity. Because these data gaps are not mutually exclusive in both their origins and solutions, it is of pivotal importance to acknowledge and quantify these shortfalls both individually and in combination, through novel and refined approaches of data collection, sharing and mapping.

Despite major advances over the past few decades in our understanding of bee diversity, their key role in the pollination of wild flowering plants and many crop species, and the drivers of their decline, important biases and knowledge gaps still plague the field of wild bee research and hamper targeted conservation efforts. Wild bee observation data are key for understanding ecological requirements, modeling projected changes under global change, understanding their role as pollinators, resolving their evolutionary relationships, informing conservation planning, practice, and policy, and raising public awareness to their importance. Last, the availability of tools tailored to the analyses and for outreach is key to stimulating the collection of data and to prioritize where data acquisition efforts are most needed. To date, available wild bee biological observations are primarily sourced from opportunistic collections or from detailed, local, scientific surveys, resulting in knowledge gaps, which follow geographical patterns. To ensure the maintenance of wild bee diversity in a context of global changes, long-term monitoring is essential to fill the existing gaps.

Here, we use a unique occurrence dataset for the two thousand plus species of wild bees in Europe, to one provide a first list and quantification of seven shortfalls relevant to European wild bees, and two develop the "BeeFall" tool, an interactive online map, available as a Shiny app, designed to visualize shortfalls independently and report missing data. We also describe an additional new shortfall related to the availability of high-quality in situ photographs and diagnostic traits illustration, a key asset to promote biological recording through citizen sciences. We discuss how the "BeeFall" tool can be used to prioritize field surveys, research efforts and conservation actions.