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The EU Biodiversity Strategy 2030 aims to better protect natural ecosystems with high biodiversity and climate change mitigation potential. To achieve this goal, it is crucial to identify forests worth protecting, such as those characterized by long continuity and old age. Here, we propose a robust approach that combines historical maps from the mid‐19th century with remote sensing data to identify areas that have been forested for over 150 years, aiming to reduce the need for detailed and time‐consuming field investigations in potential conservation areas. We tested this approach in a 20,000 km2 region in the Polish Carpathians, an area likely containing many unprotected forests of high conservation value, where historical maps from the mid‐19th century are representative of much of Central Europe. Our results showed that an area of approximately 4200 km2 has been continuously forested since at least the mid‐19th century. Currently, 50% of these forests are outside protected areas, representing an important conservation opportunity. More generally, our approach can support the identification of valuable forests worth protecting before they are lost and provide useful insights for policymakers and stakeholders in the ongoing debate on the challenges of improving forest conservation in the Carpathians, and Europe more widely. [ABSTRACT FROM AUTHOR]

Large‐scale bioenergy plays a key role in climate change mitigation scenarios, but its efficacy is uncertain. This study aims to quantify that uncertainty by contrasting the results of three different types of models under the same mitigation scenario (RCP2.6‐SSP2), consistent with a 2°C temperature target. This analysis focuses on a single bioenergy feedstock, Miscanthus × giganteus, and contrasts projections for its yields and environmental effects from an integrated assessment model (IMAGE), a land surface and dynamic global vegetation model tailored to Miscanthus bioenergy (JULES) and a bioenergy crop model (MiscanFor). Under the present climate, JULES, IMAGE and MiscanFor capture the observed magnitude and variability in Miscanthus yields across Europe; yet in the tropics JULES and IMAGE predict high yields, whereas MiscanFor predicts widespread drought‐related diebacks. 2040–2049 projections show there is a rapid scale up of over 200 Mha bioenergy cropping area in the tropics. Resulting biomass yield ranges from 12 (MiscanFor) to 39 (JULES) Gt dry matter over that decade. Change in soil carbon ranges from +0.7 Pg C (MiscanFor) to −2.8 Pg C (JULES), depending on preceding land cover and soil carbon.2090–99 projections show large‐scale biomass energy with carbon capture and storage (BECCS) is projected in Europe. The models agree that <2°C global warming will increase yields in the higher latitudes, but drought stress in the Mediterranean region could produce low yields (MiscanFor), and significant losses of soil carbon (JULES and IMAGE). These results highlight the uncertainty in rapidly scaling‐up biomass energy supply, especially in dry tropical climates and in regions where future climate change could result in drier conditions. This has important policy implications—because prominently used scenarios to limit warming to 'well below 2°C' (including the one explored here) depend upon its effectiveness. [ABSTRACT FROM AUTHOR]

Soil moisture is central to local climate on land. In situ soil moisture observations are vital for observing vegetation‐relevant root‐zone soil moisture. However, stations included in the International Soil Moisture Network are sparse in regions with strong land‐atmosphere coupling. We apply a machine‐learning‐based procedure for informing future station placement using virtual soil moisture stations in future CMIP6 projections. Stations are placed where the climate is currently most under‐represented. This strategy outperforms random station placement and station placement according to geographical distance. Doubling the current number of stations using this method alleviates the uneven global distribution of stations, increases the skill in the estimation of inter‐annual variability and trends in dry‐season soil moisture, and reduces its differences across climates in future projections. Stations are predominantly placed in tropical climates, especially when optimizing for drying trends. The results can inform future station placement to support climate change mitigation efforts. Plain Language Summary: Water stored in the ground as soil moisture is the main water supply for ecosystems and can influence local weather. Ground observations of soil moisture can measure water in deeper soil layers that roots can access. Many stations are collected in the International Soil Moisture Network database. However, this database mainly contains observation stations from the US and Europe. If we were to put additional stations, we should focus on regions and climates that the current network does not sufficiently cover. Locations in model projections of future climate that currently contain a station ("virtual stations") are used to build a machine‐learning model that predicts soil moisture at locations without stations. We then place new virtual stations where the prediction error of dry‐season soil moisture variability and dry‐season trends is largest. This strategy outperforms randomly placing new stations or placing stations where they are most distant from existing ones. Stations are placed predominantly in the tropics, but prediction improves in all regions. Furthermore, stations are more evenly distributed across the globe, and differences in skill between regions become smaller. This tool can be used to decide where to place future soil moisture stations, which are important for monitoring climate change. Key Points: In situ soil moisture observations are distributed unequally across the globe and sparse in regions with strong land‐atmosphere couplingWe present a machine‐learning based procedure for informing future station placement using virtual soil moisture stationsDoubling the number of stations with this strategy reduces error in soil moisture trends by 0.2–1.4 kg m−2 and reduces global differences [ABSTRACT FROM AUTHOR]

The agriculture sector can contribute to climate change mitigation by reducing its own greenhouse gas (GHG) emissions, sequestering carbon in vegetation and soils, and providing biomass to substitute for fossil fuels and other GHG‐intensive products. The sector also needs to address water, soil, and biodiversity impacts caused by historic and current practices. Emerging EU policies create incentives for cultivation of perennial plants that provide biomass along with environmental benefits. One such option, common in northern Europe, is to include grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. Here, we apply a spatially explicit model on >81,000 sub‐watersheds in EU27 + UK (Europe) to explore the effects of widespread deployment of such systems. Based on current accumulated SOC losses in individual sub‐watersheds, the model identifies and quantifies suitable areas for increased grass cultivation and corresponding biomass‐ and protein supply, SOC sequestration, and reductions in nitrogen emissions to water as well as wind and water erosion. The model also provides information about possible flood mitigation. The results indicate a substantial climate mitigation potential, with combined annual GHG savings from soil‐carbon sequestration and displacement of natural gas with biogas from grass‐based biorefineries, equivalent to 13%–48% of current GHG emissions from agriculture in Europe. The environmental co‐benefits are also notable, in some cases exceeding the estimated mitigation needs. Yield increases for annual crops in modified rotations mitigate the displacement effect of increasing grass cultivation. If the grass is used as feedstock in lieu of annual crops, the displacement effect can even be negative, that is, a reduced need for annual crop production elsewhere. Incentivizing widespread deployment will require supportive policy measures as well as new uses of grass biomass, for example, as feedstock for green biorefineries producing protein concentrate, biofuels, and other bio‐based products. [ABSTRACT FROM AUTHOR]

Aim: Forest herbs might be unable to track shifts in habitat suitability due to rapid climate change and habitat fragmentation. In this study, we quantified the role of dispersal limitation and the potential mitigating effect of large‐scale reforestation on the redistribution of the herbaceous forest plant species Primula elatior under climate change. Location: Europe. Methods: High resolution (100 m) landscape‐scale and macro‐climatic variables were combined to predict range‐wide habitat suitability using Maxent. Dispersal limitation was modelled, based on isolation‐by‐resistance (IBR) principles through integration of circuit theory and genomic data, to assess patch accessibility and metapopulation stability under climate change. Large‐scale reforestation was evaluated as a potential mitigating strategy by incorporating a land use change scenario into the distribution and dispersal models. Results: Landscape‐scale variables contributed significantly to the distribution of P. elatior (78.33%) and to the accuracy of our model (AUC = 0.81). Isolation‐by‐resistance (R2cond =.92) was driven by land use (45.5%), distance from rivers (36.4%) and elevation (18.2%). It was estimated that 46.4 ± 13.9% (mean ± SD of climate change scenarios) of the total distribution area would be lost due to climate change by 2050 and an additional 15.6 ± 1.7% (mean ± SD) of the distribution would not be accessible through migration. The median latitude of the patch distribution shifted 183.2 ± 34.8 km (mean ± SD) northwards and 58.1 ± 9.3 km (mean ± SD) to more maritime regions. The patch accessibility was low in these regions and the metapopulation stability decreased considerably in the south of the distribution. Reforestation mitigated 54.1 ± 18.2% (mean ± SD) of the accessible distribution area loss and 49.5 ± 4.2% (mean ± SD) of the decrease in metapopulation stability. Main conclusion: To alleviate the loss of the accessible distribution area of P. elatior under climate change, it will be required to integrate climate mitigation strategies (RCP 2.6), range‐wide afforestation, restoration of ecological connectivity and focused assisted migration to newly available habitat. [ABSTRACT FROM AUTHOR]