Cities experience rapid environmental change; thus, vegetation changes in urban areas can provide insights into how vegetation responds to future environmental change. Although previous research has reported a global rise in indirect effects (ωi) of urban vegetation growth, the specific temporal patterns and changes in this growth improvement remain inadequately investigated. To address this, ωi trends over the past two decades were analyzed, and data-driven artificial intelligence models were developed to predict future growth under various climate scenarios in 4,983 cities worldwide. The analysis revealed that ωi exhibited a logistic increase over time, culminating in an approximate 70% enhancement by 2020. However, predictions from the six models indicate a transition to a slower, linear growth pattern moving forward, with additional growth ranging from 16% to 30% between 2020 and 2100. This marks a stark contrast to the rapid growth observed between 2000 and 2020. The analysis identified CO2 as the primary driver of growth enhancement over the past two decades, but its future influence is expected to decline. Instead, land surface temperature, precipitation patterns, and urban population dynamics are projected to become the dominant factors driving future ωᵢ of vegetation growth. These findings offer critical insights into the future acclimation of urban vegetation, supporting improved forecasting and urban planning under global change.

Nitrogen (N)-fixing species, like alder, can influence the leaf litter characteristics of neighbouring non-N-fixers, potentially affecting detritus-based ecosystems. We assessed the effects of black alder presence or absence in plantations on the leaf litter characteristics, and subsequent colonization and decomposition by stream microbes, of three non-N-fixing species: oak, chestnut, and beech. Leaf litter of non-N-fixers was collected from trees in monocultures and mixed plantations with alder (BangorDIVERSE experimental plantations) and chemically and physically characterized. Colonization and decomposition of the leaf litter by stream microbes was assessed in microcosms containing single-species (leaf litter of each non-N-fixing species and origin) and mixed-species (mixture of leaf litter of the three non-N-fixing species either from monocultures or mixed plantations with alder). Effects of alder presence or absence on non-N-fixers are species-dependent. Chestnut leaf litter from mixed plantations had lower polyphenols concentration, higher N concentration, and lower C:N ratio than that from monocultures. Oak leaf litter had higher lignin concentration, while beech leaf litter had lower lignin concentration when originated from mixed plantations than monocultures. Consequently, fungal biomass (in oak), sporulation rates (in oak and chestnut), and decomposition rates (in oak and chestnut) were higher in leaf litter from trees grown in the mixed plantations with alder. In contrast, beech leaf litter showed no effect of alder presence or absence in plantations. The relative differences in leaf litter characteristics, microbial colonization, and decomposition rates among the non-N-fixing species depended on litter origin, leading to different litter mixing effects when litter originated from mixed plantations (synergistic) and monocultures (additive). These findings highlight the potential indirect effects of alder decline or invasion on stream functioning through alterations to leaf litter characteristics for non-N-fixing species. These findings also emphasize the importance of considering litter origin in litter-mixing experiments that assess the effects of forest compositional changes on stream functioning.

Accurate quantitative information on tree species is essential for achieving sustainable forest development and ecological conservation in the Yellow River Basin. Vegetation-based phenology studies provide a critical foundation for monitoring inter-annual variability and long-term trends in vegetation dynamics. However, in mining-forest composite zones, extreme habitat fragmentation and highly artificial vegetation cover exacerbate spectral confusion in tree species identification. Furthermore, the classification potential of multi-temporal data and the extraction and utilization of valuable phenological features from multi-temporal data remain underexplored. In this study, the Huodong mining area was selected as the research site - a representative coal mining region in the middle reaches of the Yellow River. Using medium-resolution, dense time-series Sentinel-2 data spanning 2019-2023 to derive vegetation phenology and canopy structural characteristics. Phenological Trajectories Divergence Time Series (PTDTS) of tree species were comprehensively extracted from four dimensions: temporal, spatial, frequency, and dynamic features, and were used as input variables to compare the classification performance of different machine learning algorithms, ultimately selecting the optimal model for tree species classification. Analysis of multidimensional time-series data, interspecies phenological differences were assessed across different years, and the influence of environmental variables and feature combinations on classification performance was evaluated. The results showed that the optimal feature combination achieved an overall accuracy of 85.71%. Notably, using only spectral features and PTDTS variables alone could reach an accuracy of 85.62%, demonstrating that spectral-temporal phenological trajectory features remain the dominant explanatory information, even when topographic features and Sentinel-1 data are used to supplement our analysis. Additionally, the study revealed five years of continuous data, improving classification accuracy by 8.12% compared to single-year data. These findings underscore the importance of long-term time-series imagery for tree species mapping and the capability of Sentinel-2 data to support accurate regional tree species classification based on phenological trajectory analysis.

Forest ecosystems play a vital role in global carbon (C) sequestration, which is intricately linked to root nitrogen (N) uptake. However, the strategies that forest vegetation employ to take up different forms of soil N, and the implications for forest management, remain insufficiently understood. To address this issue, we employed duallabelled (13C-15N) tracers for three forms of available N (NH4+, NO3- , and glycine) in field experiments conducted in both natural and afforested stands in northeast China. As the growing season progressed, significant but nonuniform changes were observed in the N content, natural abundance of 15N in plant roots, and soil N properties, including soil NH4+, NO3- , amino acids, microbial biomass N, and the natural abundance of 15N. Whether in natural or artificial forests, the uptake rates and patterns of NH4+, NO3 - and glycine by plant roots also varied, resulting in N niche differentiation among the coexisting plant species in terms of N form and timing. Principal component analysis and percentage similarity in N uptake patterns further revealed distinct N niche differentiation. Our results suggest that the uptake of different forms of soil N is likely driven by opportunistic rather than strictly preferential responses. This could provide an important basis for the N niche differentiation among coexisting plant species in the forest communities. Recognizing the temporal differentiation of N uptake niches during afforestation is essential to foster inter-specific coexistence, particularly in N-limited habitats. These findings provide critical insights for optimizing species composition in afforestation practices, thereby alleviating inter-specific competition, facilitating species coexistence, and maintaining productive forest ecosystems.

Understanding the spatial variation,temporal changes,and their underlying driving forces of carbon sequestration in various forests is of great importance for understanding the carbon cycle and carbon management options.How carbon density and sequestration in various Cunninghamia lanceolata forests,extensively cultivated for timber production in subtropical China,vary with biodiversity,forest structure,environment,and cultural factors remain poorly explored,presenting a critical knowledge gap for realizing carbon sequestration supply potential through management.Based on a large-scale database of 449 permanent forest inventory plots,we quantified the spatial-temporal heterogeneity of aboveground carbon densities and carbon accumulation rates in Cunninghamia lanceolate forests in Hunan Province,China,and attributed the contributions of stand structure,environmental,and management factors to the heterogeneity using quantile age-sequence analysis,partial least squares path modeling（PLS-PM）,and hot-spot analysis.The results showed lower values of carbon density and sequestration on average,in comparison with other forests in the same climate zone（i.e.,subtropics）,with pronounced spatial and temporal variability.Specifically,quantile regression analysis using carbon accumulation rates along an age sequence showed large differences in carbon sequestration rates among underperformed and outperformed forests(0.50 and 1.80 Mg·ha -1 ·yr -1 ).PLS-PM demonstrated that maximum DBH and stand density were the main crucial drivers of aboveground carbon density from young to mature forests.Furthermore,species diversity and geotopographic factors were the significant factors causing the large discrepancy in aboveground carbon density change between low-and high-carbon-bearing forests.Hotspot analysis revealed the importance of culture attributes in shaping the geospatial patterns of carbon sequestration.Our work highlighted that retaining largesized DBH trees and increasing shade-tolerant tree species were important to enhance carbon sequestration in C.lanceolate forests.

Soluble protein plays a significant role in the release of carbon dioxide (CO2) in soils. However, our understanding of the respiration and C use efficiency (CUE) characteristics of soluble protein-derived C by soil microorganisms is limited. To address this issue, we sampled surface soils (0-10 cm) from seven tree monocultures and examined the temporal dynamics of turnover of C-14-labelled soluble protein-derived C by soil microorganisms. Two double first-order exponential kinetic decay models were applied to analyze the mineralization data (i.e., with and without abiotic protein-surface interactions). The model incorporating the immobilization of protein on the non-living solid phase exhibited the best fit to the experimental data (R-2 > 99.6%). Our results suggest that 66.1-73.9% of the soluble protein-derived C was immobilized by the non-living solid phase in soils. After uptake by the soil microbial community, 8.0-13.8% of the C was rapidly respired as CO2, while 15.0-20.8% was used in anabolic processes, resulting in a CUE of 55.1-70.2%. However, there was little effect of forest type on protein turnover rate in the soil. The C:N ratio of soil microbial biomass (C/N-mic) was positively related to the CUE of protein and exhibited less variation within a forest type. Compared with soil microbial biomass C and N, C/N-mic could serve as a better indicator of the CUE of protein by soil microorganisms. This study sheds light on the respiration and CUE characteristics of soluble protein-derived C by soil microorganisms at afforested sites and enhances our understanding of the trade-off between the metabolism of protein-derived C by soil microorganisms and its immobilization by the non-living solid phase in soils.

Little is known about how drought-related mortality influences light absorption of surviving trees and consequent changes in tree species interactions. Here, we used the detailed tree-level light model (Maestra) in combination with measurements of tree dimensions, crown architectures, and stand structures to examine experimental mixing effects of Fagus sylvatica, Alnus glutinosa, and Betula pendula on light dynamics following a drought in Bangor, Wales. The experimental stands, planted in 2004, were composed of clusters with one to three species in different combinations. Droughts occurred in 2011 and 2014 during the growing seasons, and trees were measured in 2014 and 2015. Species mixing resulted, on average, in higher tree growth, absorption of photosynthetically active radiation (APAR), and light-use efficiency (LUE) compared with the mean of the monocultures. An exception was the monoculture of B. pendula, which was the most productive species and had higher growth, APAR, or LUE than some mixtures. Drought-related mortality reduced the stand basal area across all plots by an average of 8.3% and tree density by 11%. This moderate change in the structure did not result in significant increases in individual tree APAR, LUE, or growth. From a management perspective, mortality might need to reduce stand density more strongly than it did in this study before light absorption or LUE is altered.

Organic nitrogen (N) is the most important N component of soil organic matter. However, knowledge on how tree species with different successional stages affect organic N transformations in soils remains limited. To address this issue, we sampled mineral soils (0-10 cm) under monocultures composed of tree species from different successional stages, including early (black alder and silver birch), early to mid (sycamore and European ash), and late (sweet chestnut, pedunculate oak and European beech), and measured the potential protease activity, the microbial uptake and respiration of 14C-labeled organic N (L-alanine and L-trialanine), and the mineralization of L-alanine N. The activities of alanine aminopeptidase and leucine aminopeptidase (153.8-341.9 and 91.6-147.9 nmol/g/h, respectively), the half-life of the uptake of alanine and trialanine (26.7-39.6 and 60.8-78.6 min, respectively), the half-life of the mineralization of alanine and trialanine (1.98-2.45 and 2.98-4.13 h, respectively) by soil microbes were altered by tree species from different successional stages, systematically changing the transformation chain of soil organic N. The turnover rates of soil organic N under trees of early to late successional stage appeared to decrease and the half-life appeared to increase significantly. The C:N ratio of soil microbial biomass was positively related to the half-life of 14C-labeled alanine and trialanine mineralization, and was negatively related to the carbon (C) use efficiency of alanine, suggesting that microbial demand for C could partially drive the assimilation of soil organic N. These results suggest that the successional stage of tree species play an important role in regulating the turnover rates of soil organic N. An improved understanding of how tree species from different successional stages influence microbial function and soil organic N cycling is beneficial to future afforestation and forest management, alleviating the impacts of global change on the ecosystem.

Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests. Field-based measurements of forest above-ground biomass (AGB) can be costly and difficult to conduct. Multi-source remote sensing data offers the potential to improve the accuracy of modelled AGB predictions. Here, four machine learning methods: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Trees (CART), and Minimum Distance (MD) were used to construct forest AGB models of Taiyue Mountain forest, Shanxi Province, China using single and multi-sourced remote sensing data and the Google Earth Engine platform. Results showed that the machine learning method that most accurately predicted AGB were GBDT and spectral index for coniferous (R-2 = 0.99; RMSE = 65.52 Mg/ha), broadleaved (R-2 = 0.97; RMSE = 29.14 Mg/ha), and mixed-species (R-2 = 0.97; RMSE = 81.12 Mg/ha) forest types. Models constructed using bivariate variable combinations that included the spectral index improved the AGB estimation accuracy of mixed-species (R-2 = 0.99; RMSE = 59.52 Mg/ha) forest types and reduced slightly the accuracy of coniferous (R-2 = 0.99; RMSE = 101.46 Mg/ha) and broadleaved (R-2 = 0.97; RMSE = 37.59 Mg/ha) forest AGB estimation. Overall, parameterizing machine learning algorithms with multi-source remote sensing variables can improve the prediction accuracy of mixed-species forests.

Background Australian immigration policy resulted in large numbers of children being held in locked detention. We examined the physical and mental health of children and families who experienced immigration detention. Methods Retrospective audit of medical records of children exposed to immigration detention attending the Royal Children’s Hospital Immigrant Health Service, Melbourne, Australia, from January 2012 –December 2021. We extracted data on demographics, detention duration and location, symptoms, physical and mental health diagnoses and care provided. Results 277 children had directly (n = 239) or indirectly via parents (n = 38) experienced locked detention, including 79 children in families detained on Nauru or Manus Island. Of 239 detained children, 31 were infants born in locked detention. Median duration of locked detention was 12 months (IQR 5–19 months). Children were detained on Nauru/Manus Island (n = 47/239) for a median of 51 (IQR 29–60) months compared to 7 (IQR 4–16) months for those held in Australia/Australian territories (n = 192/239). Overall, 60% (167/277) of children had a nutritional deficiency, and 75% (207/277) had a concern relating to development, including 10% (27/277) with autism spectrum disorder and 9% (26/277) with intellectual disability. 62% (171/277) children had mental health concerns, including anxiety, depression and behavioural disturbances and 54% (150/277) had parents with mental illness. Children and parents detained on Nauru had a significantly higher prevalence of all mental health concerns compared with those held in Australian detention centres. Conclusion This study provides clinical evidence of adverse impacts of held detention on children’s physical and mental health and wellbeing. Policymakers must recognise the consequences of detention, and avoid detaining children and families.

There is great potential for the use of terrestrial laser scanning (TLS) to quantify aspects of habitat structure in the study of animal ecology and behaviour. Viewsheds-the area visible from a given position-influence an animal's perception of risk and ability to respond to potential danger. The management and conservation of large herbivores and their habitats can benefit greatly from understanding how vegetation structure shapes viewsheds and influences animal activity patterns and foraging behaviour. This study aimed to identify how woodland understory structure influenced horizontal viewsheds at deer eye height. Mobile TLS was used in August 2020 to quantify horizontal visibility-in the form of Viewshed Coefficients (VC)-and understory leaf area index (LAI) of 71 circular sample plots (15-m radius) across 10 woodland sites in North Wales (UK) where fallow deer (Dama dama) are present. The plots were also surveyed in summer for woody plant size structure, stem density and bramble (Rubus fruticosus agg.). Eight plots were re-scanned twice in winter to compare seasonal VC values and assess scan consistency. Sample plots with higher densities of small stems had significantly reduced VC 1 m from the ground. Other stem size classes, mean percentage bramble cover and understory LAI did not significantly affect VC. There was no difference in VC between summer and winter scans, or between repeated winter scans. The density of small stems influenced viewsheds at deer eye height and may alter behavioural responses to perceived risk. This study demonstrates how TLS technology can be applied to address questions in large herbivore ecology and conservation.

Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series.

Aim Soil microorganisms are essential for the functioning of terrestrial ecosystems. Although soil microbial communities and functions are linked to tree species composition and diversity, there has been no comprehensive study of the generality or context dependence of these relationships. Here, we examine tree diversity-soil microbial biomass and respiration relationships across environmental gradients using a global network of tree diversity experiments. Location Boreal, temperate, subtropical and tropical forests. Time period 2013. Major taxa studied Soil microorganisms. Methods Soil samples collected from 11 tree diversity experiments were used to measure microbial respiration, biomass and respiratory quotient using the substrate-induced respiration method. All samples were measured using the same analytical device, method and procedure to reduce measurement bias. We used linear mixed-effects models and principal components analysis (PCA) to examine the effects of tree diversity (taxonomic and phylogenetic), environmental conditions and interactions on soil microbial properties. Results Abiotic drivers, mainly soil water content, but also soil carbon and soil pH, significantly increased soil microbial biomass and respiration. High soil water content reduced the importance of other abiotic drivers. Tree diversity had no effect on the soil microbial properties, but interactions with phylogenetic diversity indicated that the effects of diversity were context dependent and stronger in drier soils. Similar results were found for soil carbon and soil pH. Main conclusions Our results indicate the importance of abiotic variables, especially soil water content, for maintaining high levels of soil microbial functions and modulating the effects of other environmental drivers. Planting tree species with diverse water-use strategies and structurally complex canopies and high leaf area might be crucial for maintaining high soil microbial biomass and respiration. Given that greater phylogenetic distance alleviated unfavourable soil water conditions, reforestation efforts that account for traits improving soil water content or select more phylogenetically distant species might assist in increasing soil microbial functions.

Natural approaches to flood risk management are gaining interest as sustainable flood mitigation options. Targeted tree planting has the potential to reduce local flood risk, however attention is generally focused on the hydrological impacts of catchment afforestation linked to generic tree features, whilst the species-specific impacts of trees on soil hydrology remain poorly understood. This study compared effects of different tree species on soil hydraulic properties. Monocultures of Alnus glutinosa (common alder), Fraxinus excelsior (European ash), Fagus sylvatica (European beech), Betula pendula (silver birch), Castanea sativa (sweet chestnut), Quercus robur (English oak) and Acer pseudoplatanus (sycamore maple) were used to determine effects of tree species identity on soil hydraulic properties (near-saturated K and soil water retention) in a sandy loam soil, North Wales, United Kingdom. The interaction of F. excelsior root properties and soil class on hydraulic conductivity was also examined in four different soils (Rendzic Leptosol, Haplic Luvisol, Dystric Fluvic Cambisol and Dystric Gleysol) across England and Wales. Fine root biomass (FRB) and morphological characteristics were determined at three depths (0-0.1, 0.1-0.2 and 0.2-0.3 m) and complemented by in situ surface measurement of soil hydraulic conductivity. Root morphological traits were closely associated with species identity and pore-size distribution, and FRB was strongly correlated with soil hydraulic conductivity (R-2 = 0.64 for 0-0.1 m depth FRB; R-2 = 0.69 for 0.1-0.2 m depth FRB). Fine root biomass of F. excelsior was sixfold greater than C. sativa (p < 0.001), and the frequency of 0.01 mm radius soil pores under F. excelsior was twice that of Q. robur. Near-saturated hydraulic conductivity under F. excelsior was 7.91 +/- 1.23 cm day(-1), double the mean rate of the other species. Soil classification did not significantly influence FRB (p = 0.056) or near-saturated hydraulic conductivity (p = 0.076) in the 0.0-0.1 m depth soil, but soil water retention varied with depth. Species-specific traits of trees should be considered in landscape design to maximise the local hydrological benefits of trees.

Silvopastoral agroforestry and the strategic placement of trees and hedgerows offers potential to improve livestock welfare and production efficiency through the provision of shelter in livestock farming systems. The aim of this study was to investigate the relationship between shelter-seeking behaviour of ewes during the lambing period and the microclimate influenced by landscape shelter features. Artificial and natural shelter was provided to Aberfield ewes (n = 15) on an upland sheep farm in Wales, UK, which were then continuously monitored for 14 days using global positioning system tracking devices. Modelling of microclimate influenced by topographical shelter features at the test site was used to generate a 1 m resolution wind field for geospatial statistical analysis of localised wind speed. Ewes demonstrated an increased preference for natural (3.4-fold; p < 0.01) and artificial (3.0-fold; p < 0.05) shelter zones five times the height of the shelter, compared to the exposed area of the trial site. Wind-chill and modelled local-scale wind speeds were found to have the greatest influence on shelter-seeking behaviour, with temperature and field-scale wind speed significantly influencing livestock behaviour. Mean wind-chill temperature during the trial was 3.7 °C (min −5.3 °C; max 13.1 °C), which is within the cold stress temperature threshold (−3 and 8 °C) that requires thermoregulatory strategies such as shelter-seeking behaviour. An improved understanding of the relationship between microclimate and shelter-seeking behaviour in sheep, demonstrated through the agent-based model developed in this project, shall better inform the economic incentives (e.g., reduction in lamb mortality and forage requirements) behind silvopastoral practices that benefit farm productivity, livestock welfare and the environment.

ABSTRACT Some of the most prominent exotic pathogens in temperate forest regions belong to the genus Phytophthora. The pathogen, which is nourished by the enzymatic destruction of living plant cells, can cause mortality in >150 plant species including many temperate forest trees. However, studies have demonstrated the natural disease resistance of trees can be directly induced or facilitated using methods to deploy biochemical compounds that include foliar sprays, trunk injection or bark application, and soil amendments. This systematic review identified and analysed the efficacy of novel treatments to induce a natural resistance against different Phytophthora species in temperate trees. Results showed that treatments reduced Phytophthora infection symptoms compared to controls in all but one of the experiments reviewed. Trunk injections demonstrated the highest cumulative efficacy with a pooled effect size of 1.85 ± 0.56 (Hedge’s g ± 95% confidence interval). Foliar sprays had the second highest efficacy, with a pooled effect size of 1.11 ± 0.28. Finally, soil amendments had the lowest cumulative efficacy, with a pooled effect size of 0.61 ± 0.36. This review supports the use of treatments on trees in nurseries, urban forests, orchards, and arboreta; however, success is dependent upon the application of optimal doses.

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory βc = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies.

In this study we assess the potential for farmland hedgerows to provide climate mitigation via carbon (C) storage, using soil carbon dioxide (CO2) efflux to improve upscaling validity. Two contrasting sites, freelydraining (FD) versus seasonally-wet (SW), situated in mixed-livestock farms (Conwy, Wales, UK), were selected. We measured soil CO2 efflux associated with three field boundaries: hedgerow on SW soil; hedgerow on FD soil; stone wall (abiotic control) on FD soil, quantifying the influence of distance from field boundary and grazing occurrence (grazed pasture versus un-grazed zone adjacent to hedgerows) on annual C budgets based on soil CO2 flux and net primary productivity. For the FD site, the annual C budget showed that pasture was a net source of C emissions (11 +/- 1.5 t CO2 ha(-1) yr(-1)) and the un-grazed zone adjacent to the hedgerow a net sink (-0.9 +/- 2.2 t CO2 ha(-1) yr(-1)). For the SW site, pasture acted as a small net sink of C (-0.1 +/- 1.3 t CO2 ha(-1) yr(-1)) and the hedgerow zone a net source (5.8 +/- 0.8 t CO2 ha(-1) yr(-1)), due entirely to a spike in soil CO2 efflux associated with a relatively unusual summer drought. To investigate the effect of this observed summer drought on more typical (for the UK maritime climate) annual C source-sink dynamics, we modelled soil CO2 efflux for a summer-drought-excluded year for both FD and SW soils. With greater hedgerow cover (modelled prediction compared with a baseline of no hedgerows), annual CO2 flux became more negative (greater net sink) in fields on FD soil (by 1 t CO2 ha(-1) yr(-1) at 8% hedgerow cover), with drought limiting the effect size. In SW soils, greater hedgerow cover also led to a more negative annual CO2 flux (by 0.4 t CO2 ha(-1) yr(-1) at 8% hedgerow cover) when drought was excluded, but a more positive flux (net C source) with drought included (by 0.5 t CO2 ha(-1) yr(-1) at 8% hedgerow cover). This study illustrates the importance of the interaction between soil type and seasonal events such as drought on the ability of hedgerows to act as a net C sink.

Forest restoration and afforestation on degraded lands are receiving tremendous research efforts globally as a climate change mitigation option. There is a growing interest in mixed species plantation to ensure sustainable ecosystem services and biodiversity. However, successful mixture of achieving these potential benefits is rare. We studied the polyculture of two pioneer fast growing species (i.e. B. pendula, and A. glutinosa- of which A. glutinosa is N-fixing) and one shed tolerant species with slow juvenile growth (i.e. F. sylvatica) to examine the effects of species mixture on biomass production and quality of soil organic C stock following the replacement series approach. Standing woody biomass in polyculture demonstrated no over-yielding, presumably due to concurrent impacts of suppression of F sylvatica by two fast growing species and competitive reduction benefits in A. glutinosa. Similarly, standing fine root biomass production and turnover showed no significant mixture effect. Although the quantity of soil organic C stock was unaffected by tree mixture, the vertical distribution of biodegradable C fractions was differed between mono and polyculture stands, most probably due to slow decay rate of mixed litter. We found that species mixture decreased soil C lability in the upper soil layers, and increased recalcitrant C in deep soil (>40 cm) that has enormous potential for long-term sequestration. We concluded that contrasting growth responses can result in no biomass over-yielding in polyculture stands but the mixed litter can affect soil C quality.Key words: Mixture effects, Tree polyculture, biomass, over-yielding, C quality, recalcitrant C

Climate change is predicted to increase temperature and seasonal temperature variance in Great Britain (GB). Sitka spruce (Picea sitchensis (Bong.) Carr) is the most important tree species used in commercial plantations throughout Europe and GB. Frosts that occur outside the winter dormancy period can negatively affect trees, since they happen after dehardening. Damage can be especially severe at bud burst, before emerging needles mature and form protective barriers. Here, we modelled the impact of climate change on frost sensitivity in Sitka spruce with temperature data from five climate projections. The UKCP09 climate model HadRm3 uses emission scenario SRESA1B for the years 2020–2099. The global and downscaled versions of the UKCP18 HadGem3 model use the emissions scenario RCP 8.5. The global model CMCC-CM uses the RCP 4.5 and RCP 8.5 emissions scenarios. The predictions based on these models were compared with results from gridded historical data for the period 1960–2015. Three indicators that assessed the frost sensitivity of Sitka spruce were explored: the total number of frosts between the onset of dehardening and the end of summer, which use three different temperature thresholds (Index 10°C, 1–3°C, 1–5°C); the total number of frosts after bud burst (Index 2); the number of days with minimum temperatures below the resistance level (backlashes) during the hardening–dehardening period (September–August) (Index 3). The indices were validated with historical data for frost damage across GB, and Index 1–3°C, Index 1–5°C and Index 3 were shown to be significantly correlated. The frequency of all frosts and backlashes is expected to decrease with climate change, especially under higher emissions scenarios. Post-bud burst frosts have been historically very rare in GB and remain so with climate change. Downscaled regional climate models detect geographic variability within GB and improve prediction of overall trends in frost damage in comparison to global climate change models for GB.