Inferring trends in pollinator distributions across the Neotropics from publicly available data remains challenging despite mobilization efforts

Deep learning for identifying bee species from images of wings and pinned specimens

One of the most challenging aspects of bee ecology and conservation is species-level identification, which is costly, time consuming, and requires taxonomic expertise. Recent advances in the application of deep learning and computer vision have shown promise for identifying large bumble bee (Bombus) species. However, most bees, such as sweat bees in the genus Lasioglossum, are much smaller and can be difficult, even for trained taxonomists, to identify. For this reason, the great majority of bees are poorly represented in the crowdsourced image datasets often used to train computer vision models. But even larger bees, such as bumble bees from the B. vagans complex, can be difficult to separate morphologically. Using images of specimens from our research collections, we assessed how deep learning classification models perform on these more challenging taxa, qualitatively comparing models trained on images of whole pinned specimens or on images of bee forewings. The pinned specimen and wing image datasets represent 20 and 18 species from 6 and 4 genera, respectively, and were used to train the EfficientNetV2L convolutional neural network. Mean test precision was 94.9% and 98.1% for pinned and wing images respectively. Results show that computer vision holds great promise for classifying smaller, more difficult to identify bees that are poorly represented in crowdsourced datasets. Images from research and museum collections will be valuable for expanding classification models to include additional species, which will be essential for large scale conservation monitoring efforts.

An operational workflow for producing periodic estimates of species occupancy at national scales

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

The Present and Future of Insect Biodiversity Conservation in the Neotropics: Policy Gaps and Recommendations

Emerging evidence suggests that insect populations may be declining at local and global scales, threatening the sustainability of the ecosystem services that insects provide. Insect declines are of particular concern in the Neotropics, which holds several of the world's hotspots of insect endemism and diversity. Conservation policies are one way to prevent and mitigate insect declines, yet these policies are usually biased toward vertebrate species. Here, we outline some key policy instruments for biodiversity conservation in the Neotropics and discuss their potential contribution and shortcomings for insect biodiversity conservation. These include species-specific action policies, protected areas and Indigenous and Community Conserved Areas (ICCAs), sectoral policies, biodiversity offsetting, market-based mechanisms, and the international policy instruments that underpin these efforts. We highlight that although these policies can potentially benefit insect biodiversity indirectly, there are avenues in which we could better incorporate the specific needs of insects into policy to mitigate the declines mentioned above. We propose several areas of improvement. Firstly, evaluating the extinction risk of more Neotropical insects to better target at-risk species with species-specific policies and conserve their habitats within area-based interventions. Secondly, alternative pest control methods and enhanced monitoring of insects in a range of land-based production sectors. Thirdly, incorporating measurable and achievable insect conservation targets into international policies and conventions. Finally, we emphasise the important roles of community engagement and enhanced public awareness in achieving these improvements to insect conservation policies.

We need to talk about nonprobability samples

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.