Projected decline in European bumblebee populations in the twenty-first century.

Habitat degradation and climate change are globally acting as pivotal drivers of wildlife collapse, with mounting evidence that this erosion of biodiversity will accelerate in the following decades1-3. Here, we quantify the past, present and future ecological suitability of Europe for bumblebees, a threatened group of pollinators ranked among the highest contributors to crop production value in the northern hemisphere4-8. We demonstrate coherent declines of bumblebee populations since 1900 over most of Europe and identify future large-scale range contractions and species extirpations under all future climate and land use change scenarios. Around 38-76% of studied European bumblebee species currently classified as 'Least Concern' are projected to undergo losses of at least 30% of ecologically suitable territory by 2061-2080 compared to 2000-2014. All scenarios highlight that parts of Scandinavia will become potential refugia for European bumblebees; it is however uncertain whether these areas will remain clear of additional anthropogenic stressors not accounted for in present models. Our results underline the critical role of global change mitigation policies as effective levers to protect bumblebees from manmade transformation of the biosphere.

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence-Belgium as a study case.

BACKGROUND: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. METHODS: To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio-temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. RESULTS: Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. CONCLUSION: Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.

Quantifying climate feedbacks in polar regions.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

Using climate models to estimate the quality of global observational data sets.

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.