Statistical downscaling of climate data has been widely described in the literature, with the aim of improving the reliability of local climatic parameters from coarse-resolution (often >20 km) global datasets. In this article, we present ClimateDT, a dynamic downscaling web tool for monthly historical and future time series at a global scale. The core of ClimateDT is the 1 km 1981–2010 climatology from CHELSA Climate (version 2.1), where the CRU-TS layers for the period 1901-current are overlayed to generate a historic time series. ClimateDT also provides future scenarios from CMIP5 using UKCP18 projections (rcp2.6 and rcp8.5) and CMIP6 using 5 GCMs, also available on the CHELSA website. The system can downscale the grids using a dynamic approach (scale-free) by computing a local environmental lapse rate for each location as an adjustment for spatial interpolation. Local predictions of temperature and precipitation obtained by ClimateDT were compared with climate time series assembled from 12,000 meteorological stations, and the Mean Absolute Error (MAE) and the explained variance (R²) were used as indicators of performance. The average MAEs for monthly values on the whole temporal scale (1901–2022) were around 1.26 °C for the maximum monthly temperature, 0.80 °C for the average monthly temperature, and 1.32 °C for the minimum monthly temperature. Regarding monthly total precipitation, the average MAE was 19 mm. As for the proportion of variance explained, average R² values were always greater than 0.95 for temperatures and around 0.70 for precipitation due to the different degrees of temporal autocorrelation of precipitation data across time and space, which makes the estimation more complex. The elevation adjustment resulted in very accurate estimates in mountainous regions and areas with complex topography and substantially improved the local climatic parameter estimations in the downscaling process. Since its first release in November 2022, more than 1300 submissions have been processed. It takes less than 2 min to calculate 45 locations and around 8 min for the full dataset (512 records).

Deciding how to establish woodland in forest restoration is not straightforward as different outcomes may be obtained from different establishment approaches, each with cost implications and degree of success limitations attached. Planning restoration requires knowledge of site conditions, including how sites are likely to respond under climate change. For objectives of production and high timber quality it is likely that ground preparation will be used, and planting with forest reproductive material (FRM) of known traits, such as: high survival and growth in establishment, drought tolerance adequate for climate projections, good resistance to pests and pathogens. For objectives associated with biodiversity, carbon sequestration, water supply protection, soil protection, natural regeneration could be a less costly solution with a limited amount of assisted translocation of selected FRM to improve resilience. If objectives are for rewilding forest areas, a degree of natural colonisation perhaps with translocation of some FRM could be a solution. Ignoring site conditions and suitability of available sources of FRM for forest restoration is likely to provide unexpected results with a mix of open ground, scrub and scattered trees resulting from climate, herbivore, and browsing impacts. The recent B4EST EU Horizon 2020 project examined progress in novel rapid approaches for testing the quality of FRM from existing genetic trials. Here we review the work of B4EST to show the opportunities from transformative tree breeding in forest restoration schemes, including: new climate projection ensembles at high temporal and spatial resolution to develop norms of reaction and transfer models with genetic components; multi-environment genotype-phenotype associations and multi-locus genotype-environment associations in identifying drivers of local adaptation; techniques for genomic selection using single nucleotide polymorphism (SNP) arrays to derive functional traits from polygenic associations; work on seed orchard site and climate specific FRM and zones for deployment; and work on some of the forest ecosystem service benefits derived at a landscape scale. We conclude that tree-breeding will provide robust forest restoration for planting, and rewilding (assisted natural regeneration), and if not “ignoring” but instead assisting natural colonisation processes – tree breeding may improve long-term forest resilience under environmental change.

Non‐native plant pests and pathogens are increasing exponentially, causing extirpation of foundation species. The impact of large‐scale declines in a single host on associated biodiversity is widely documented. However, the impact of multiple host loss on biodiversity and whether these impacts are multiplicative has not been assessed. Ecological theory suggests that systems with greater functional redundancy (alternative hosts) will be more resilient to the loss of sympatric hosts. We test this theory and show its importance in relation to pest/pathogen impact assessments. We assessed the potential impact on biodiversity of the loss of two widely occurring sympatric European tree species, Fraxinus excelsior and Quercus petraea/robur, both of which are currently threatened by a range of pests and pathogens. At the UK scale, the total number of associated species at risk of extirpation from plant diseases affecting these two sympatric hosts is greater than the sum of the associated species at risk from declines in either host alone. F. excelsior hosts 45 obligate species (species only found on that host) and Q. petraea/robur hosts 326. However, a decline in both these trees would impact 512 associated species, across multiple taxon groups, a 38% increase. Assessments at a local scale, 24 mixed F. excelsior–Q. petraea/robur woodlands revealed that these impacts may be even greater due to a lack of functional redundancy. Only 21% of sites were able to provide functional redundancy for F. excelsior and Q. petraea/robur associated species which can use other tree species. In most woodlands, the tree species required to provide functional redundancy were not present, although the site conditions were often suitable for them to grow. Synthesis. Understanding of functional redundancy should be applied to assessments of pests/pathogens impact on biodiversity. In risk assessments, higher impact scores should be given to pests/pathogens affecting hosts occurring with other host plant species already impacted by pests/pathogens. Current pest/pathogen risk assessment approaches that ignore the cumulative, cascading effects shown in this study may allow an insidious, mostly overlooked, driver of biodiversity loss to continue.

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate‐related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross‐validated (50%:50% ‐ training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS‐weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak‐stand locations as a training dataset; the national forest inventory (NFI) “published regional reports” of oak woodland area; and an “NFI map” of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from “NFI survey” sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.

The existing native forests of Scotland are fragmented and highly modified and none are ‘natural’. There is considerable interest in expanding the area of this oceanic boreal forest and restoring forest habitat networks to benefit biodiversity. However, unlike regions with substantial remaining natural forest, it is difficult to provide reference values for forest composition and structure using methods related to historical variability. An alternative approach is to combine models that predict woodland type from knowledge of site conditions, and disturbance regime from knowledge of the disturbance agents (particularly abiotic agents). The applicability of this approach was examined as part of a public participatory planning exercise in a highly managed landscape in Eastern Scotland. Models of site suitability (Ecological Site Classification) and wind disturbance (ForestGALES) were combined to determine potential woodland composition and structure, and derive options for native woodland expansion. The land use of the upper Strathdon catchment is currently dominated by agriculture and planted forests of non-native species, and only small fragments of semi-natural woodland remain (< 0.5% of the land area). Model results indicated that a very substantial proportion of the land area could support woodland (> 90%) but of a restricted range of native woodland types, with Scots pine communities predominant. Structural types likely to be present included wind-induced krummholz (treeline) forest, forest with frequent stand replacement by wind, and also a large area where gap phase (or some other disturbance) would predominate. The merits of the approach are discussed, together with the difficulties of validation, and the implications for the management of existing forests.

Shifts in weather patterns causing more severe and frequent climatic extreme events, particularly periods of hot drought and late-spring frost, cause stress and damage to many tree species. Here we use 12 members of the UKCP Regional (12km) ensemble to assess the changing risk of tree stress posed by hot droughts and late-spring frosts. As an exemplar tree species, we use Norway spruce (Picea abies) to demonstrate a method that links the European distribution of the species to extremes in the projected climate data ensemble at the continental scale. Monthly climate projections were used to calculate the standardised precipitation and evapotranspiration index (SPEI) and bias corrected daily temperature projections were used to calculate a frost buffer of the number of days between last date of spring frost and the date of leafing of Norway spruce. The UKCP Regional climate model ensemble shows substantial changes in the distribution of SPEI likelihood and severity between the pseudo-global warming threshold temperatures of 1⁰C and 2⁰C indicating concern in relation to drought severity and frequency in Alpine, Atlantic, and Mediterranean biogeographic regions. In addition, late spring frosts particularly in the Atlantic and Continental biogeographic regions will continue to affect sensitive young trees such as Norway spruce. The approach removes the time series estimation of climate extremes providing a single method of estimating effects of the climate emergency across shared socio-economic pathways. Finally, the approach could be useful to direct the deployment of suitable selected forest reproductive material from tree-breeding programmes in Europe.