Swedish Species Information Centre

Climate extremes exact a heavy and differential toll on society. Reported economic losses are primarily concentrated in developed economies, whereas reported fatalities occur overwhelmingly in developing economies. Moreover, even at single locations the adverse impacts of extreme climate events are often unequally distributed across the population. Understanding such impacts holds enormous societal and economic value, and is a key step towards climate resilience and adaptation. Recent research advances include improved impact forecasting and enhanced understanding of how the interaction between human and natural systems shapes the impacts of climate extremes. Nonetheless, there are some key challenges that have hindered progress. We focus on three: Limited availability and quality of impact data, difficulties in understanding the processes leading to impacts and lack of reliable impact projections. We argue that newly released datasets and recent methodological and technical advances open a window of opportunity to address several dimensions of these challenges. Notable examples include extracting impact information from textual sources using large language models and developing impact projections using data-driven approaches. Moreover, interdisciplinary collaborations between the social and natural sciences can elucidate processes underlying past climate impacts and enable building storylines of future societal impacts. We call for building momentum in seizing these opportunities for a breakthrough in the study of impacts of climate extremes. Achieving meaningful progress will require interdisciplinary and intersectoral research, and strong collaboration across academic, policy and practitioner communities.

Abstract Habitat loss and nutrient enrichment are major drivers of plant species declines. However, local extinction risks vary widely and are shaped by life‐history trait syndromes. Genomic life‐history traits, which are simple, stable and widely available, offer a promising yet underexplored approach to improving extinction risk assessments and informing conservation efforts. Using a spatial comparison approach, we analysed 256 semi‐natural grassland plant communities in four Swedish agricultural landscapes to examine whether genomic life‐history traits such as ploidy, genome size and chromosome number mediate differences in plant community diversity and composition along independent gradients of grassland fragment size and phosphorus availability. Our findings show that plant diversity and community composition are increasingly dominated by larger genome species both as grassland fragments become smaller and as more phosphorus becomes available. While all genomic life‐history traits proved important, ploidy plays a particularly significant role in explaining differences between observed plant communities. Ploidy mediated deterministic plant community changes along both environmental gradients. Diploid species are especially vulnerable to local extinction, whereas polyploids demonstrate resilience to habitat loss and benefit from increased phosphorus availability, likely leading to the competitive exclusion of diploids. Synthesis . By revealing how genomic traits shape plant community responses to land‐use change and nutrient pollution—two key drivers of extinction—our study introduces a predictive, genomic trait‐based framework for assessing local extinction risks in fragmented landscapes.

Abstract Ensemble forecasts are often calibrated using regression methods known as ensemble model output statistics (EMOS). Most of the EMOS methods described in the literature make the assumption that statistical parameter uncertainty is negligible, although making this assumption has been shown to be detrimental to forecast quality. We use simulations to show that this assumption would be expected to give unreliable forecasts and underprediction of extremes. Previous research has described how parameter uncertainty can be included in EMOS models using bootstrapping. We show that bootstrapping can be replaced with an analytical approach based on right Haar prior (RHP) theory, a branch of objective Bayesian statistics. For two commonly used EMOS models, simple linear regression (SLR) and heteroscedastic linear regression (HLR, also known as non‐homogeneous Gaussian regression), we show that RHP theory leads to perfectly reliable predictions, if the distributional assumptions are met. We apply SLR and HLR methods, with and without parameter uncertainty, to 5 years of past medium‐range forecasts for eight Northern Hemisphere locations and evaluate forecast accuracy and reliability. We find that the RHP version of HLR is the most accurate in terms of both log‐score and continuous ranked probability score. It is also the most reliable, and it reduces the underestimation of extremes seen in the other methods. Our results with regard to the benefits of including parameter uncertainty agree with the previous results based on bootstrapping, although the RHP calculations are faster and more accurate. A free software library is available for applying the various methods, and execution is computationally inexpensive. We conclude that the RHP version of HLR should be used in preference to the other methods tested. Further research would be needed to understand how to use objective Bayesian methods to include parameter uncertainty in other EMOS models.

<strong class="journal-contentHeaderColor">Abstract.</strong> Teleconnections between the North Atlantic and the Baltic Sea region are shaped by the polar jet stream and are critical drivers of weather and climate in the region, thereby impacting the physical and biogeochemical properties of the Baltic Sea ecosystem. This review synthesizes how key circulation features and modes of climate variability, including the North Atlantic Oscillation, atmospheric blocking and the Atlantic Multidecadal Variability, influence the Baltic Sea region. By examining existing literature data and observational and climate model data, we summarize links to temperature, precipitation, storms and other key indicators from synoptic to multidecadal time scales. We then assess how these climate controls cascade into ecosystem relevant processes, namely oxygen dynamics, primary productivity and ocean acidification. Although physical links are already established, the pathways connecting large-scale atmospheric patterns to biogeochemistry are still poorly constrained, partly because dedicated field studies and targeted model experiments are limited. We outline priority research needs to enhance near-term predictability and reduce uncertainty in future projections for the Baltic Sea.

Rewilding is a transformative conservation strategy that emphasises the restoration of ecological processes and ecosystem resilience. This perspective article addresses limitations in current rewilding monitoring practices, which predominantly rely on quantitative metrics. We argue for the integration of qualitative approaches to better capture the complexity and interdependence of human–nature interactions that shape rewilding outcomes. Drawing on social–ecological systems (SES) thinking, we propose a holistic monitoring framework that combines quantitative and qualitative measures. This approach reflects emerging shifts in conservation mindsets – recognising humans as part of nature, valuing biodiversity intrinsically as well as instrumentally, and embracing uncertainty and complexity over control. We suggest that Holling’s panarchy – a framework for understanding adaptive cycles and cross-scale interactions – can support the design of rewilding interventions and guide monitoring. By focusing on fast- and slow-changing variables, panarchy enhances adaptive management and supports context-sensitive theories of change. This article contributes to the rewilding discourse by offering practical guidance for practitioners and policymakers, promoting a shift towards inclusive, adaptive and transformative monitoring practices.

ABSTRACTPopulation genomics can reveal trends and drivers of biodiversity loss, but it is still unclear how best to use measures of genome variation to understand population vulnerability in insects. Here we study genome variation in three species of Anthophora bees that show contrasting population trends in northern Europe. Two species, Anthophora plagiata and Anthophora retusa , have experienced declines and recoveries of different magnitudes in the last 50 years, whereas a third species, Anthophora quadrimaculata , has relative population stability. We generate highly contiguous genome assemblies and use them to study genome variation in 136 samples of these species collected throughout Sweden. We find exceedingly low genetic variation in A. plagiata , which has experienced a severe recent bottleneck, but high genetic variation in A. retusa , despite a similar recent population trajectory. Fragmented populations of the threatened species A. plagiata appear isolated from each other, but in A. retusa, there is a lack of deep population structure among geographically separated subpopulations. We infer population size in the distant past using MSMC2 and recent past using GONE. These methods are remarkably concordant and indicate ancient fluctuations in population size dating back to the Pleistocene, with moderate expansions in the past century in all three species. These results are comparable to some other studies of endangered insects, which have experienced population declines that predate the modern era. We detect long blocks of identity‐by‐state in A. plagiata , indicative of severe recent inbreeding. Translocations between isolated populations of this species could have a positive effect on their resilience.

Rewilding is a transformative conservation strategy that emphasises the restoration of ecological processes and ecosystem resilience. This perspective article addresses limitations in current rewilding monitoring practices, which predominantly rely on quantitative metrics. We argue for the integration of qualitative approaches to better capture the complexity and interdependence of human–nature interactions that shape rewilding outcomes. Drawing on social–ecological systems (SES) thinking, we propose a holistic monitoring framework that combines quantitative and qualitative measures. This approach reflects emerging shifts in conservation mindsets – recognising humans as part of nature, valuing biodiversity intrinsically as well as instrumentally, and embracing uncertainty and complexity over control. We suggest that Holling’s panarchy – a framework for understanding adaptive cycles and cross-scale interactions – can support the design of rewilding interventions and guide monitoring. By focusing on fast- and slow-changing variables, panarchy enhances adaptive management and supports context-sensitive theories of change. This article contributes to the rewilding discourse by offering practical guidance for practitioners and policymakers, promoting a shift towards inclusive, adaptive and transformative monitoring practices.

Abstract. Teleconnections between the North Atlantic and the Baltic Sea region are shaped by the polar jet stream and are critical drivers of weather and climate in the region, thereby impacting the physical and biogeochemical properties of the Baltic Sea ecosystem. This review synthesizes how key circulation features and modes of climate variability, including the North Atlantic Oscillation, atmospheric blocking and the Atlantic Multidecadal Variability, influence the Baltic Sea region. By examining existing literature data and observational and climate model data, we summarize links to temperature, precipitation, storms and other key indicators from synoptic to multidecadal time scales. We then assess how these climate controls cascade into ecosystem relevant processes, namely oxygen dynamics, primary productivity and ocean acidification. Although physical links are already established, the pathways connecting large-scale atmospheric patterns to biogeochemistry are still poorly constrained, partly because dedicated field studies and targeted model experiments are limited. We outline priority research needs to enhance near-term predictability and reduce uncertainty in future projections for the Baltic Sea.

Habitat features associated with species niches are commonly used as biodiversity proxies. Such proxies usually describe habitat diversity and have seldom been evaluated in a forestry context. As demands on forests to mitigate climate change and biodiversity loss increase, Conservation Value Assessments (CVAs) based on habitat diversity are increasingly utilised to guide conservation and resource extraction decisions. In central Sweden, we investigated the effectiveness of two widely used CVA protocols that assess forest habitat diversity to determine their ability to predict the occurrence of red-listed species for conservation prioritisation. We found that the tested CVAs effectively predicted the occurrence of red-listed wood-inhabiting bryophytes and fungi but were less effective for epiphytic and epixylic lichens or for predicting the occurrence of red-listed species across these three taxo-ecological groups. For red-listed bryophytes and fungi, we were able to identify ecological transition points where the probability of species occurrence had its most rapid increase. In addition, we identified threshold values above which the probability of finding one red-listed species was higher than not finding any red-listed species. These findings define conservation priority zones, which can guide prioritisation and restoration targets. Moreover, our study clearly shows that sites occupied by red-listed species had higher local species richness of non red-listed species in the same organism group. In conclusion, CVAs based on habitat diversity are valuable for identifying and prioritising areas for conservation of certain taxo-ecological groups, broader biodiversity assessments require complementary approaches to encompass the full taxo-ecological diversity in forests. • Habitat diversity was tested as an indicator of red-listed species occurrence. • Habitat diversity predicted red-listed fungi and bryophyte occurrence on deadwood. • Habitat diversity could not predict red-listed lichen occurrence. • Occurrence of red-listed species indicated high local species richness. • Thresholds of habitat diversity can identify areas of conservation priority.

Rewilding is a transformative conservation strategy that emphasises the restoration of ecological processes and ecosystem resilience. This perspective article addresses limitations in current rewilding monitoring practices, which predominantly rely on quantitative metrics. We argue for the integration of qualitative approaches to better capture the complexity and interdependence of human–nature interactions that shape rewilding outcomes. Drawing on social–ecological systems (SES) thinking, we propose a holistic monitoring framework that combines quantitative and qualitative measures. This approach reflects emerging shifts in conservation mindsets – recognising humans as part of nature, valuing biodiversity intrinsically as well as instrumentally, and embracing uncertainty and complexity over control. We suggest that Holling’s panarchy – a framework for understanding adaptive cycles and cross-scale interactions – can support the design of rewilding interventions and guide monitoring. By focusing on fast- and slow-changing variables, panarchy enhances adaptive management and supports context-sensitive theories of change. This article contributes to the rewilding discourse by offering practical guidance for practitioners and policymakers, promoting a shift towards inclusive, adaptive and transformative monitoring practices.