Search

A digital twin is a digital representation of real-world physical product, system, or process. Digital twins potentially offer a much richer capability to model and analyze real-world systems and improve environment sustainability.In this work, an integrated 3D GIS and VR model for scenarios modeling and interactive data visualisation has been developed and implemented through the Digital Twin technology at the Glensaugh research farm. Spatial Multi-criteria Analysis has been applied to decide where to plant new woodlands, recognizing a range of land-use objectives while acknowledging concerns about possible conflicts with other uses of the land. The virtual contents (e.g., forest spatial datasets, monitored climate data, analyzed carbon stocks and natural capital asset index) have been embedded in the virtual landscape model which help raise public awareness of changes in rural areas.The Digital twin prototype for Glensaugh Climate-Positive Farming was used at the STFC workshop 2021, GISRUK 2022, 2022 Royal Highland Show which provides an innovative framework to integrate spatial data modelling, analytical capabilities and immersive visualization.Audience feedback suggested that the virtual environment was very effective in providing a more realistic impression of the different land-use and woodland expansion scenarios and environmental characteristics. This suggests considerable added value from using digital twin technology to better deal with complexity of data analysis, scenarios simulation and enable rapid interpretation of solutions.Findings show this method has a potential impact on future woodland planning and enables rapid interpretation of forest and climate data which increases the effectiveness of their use and contribution to wider sustainable environment.

Peatland is a globally important store of carbon. Peatland restoration efforts are being increasingly undertaken yet effective monitoring of landscape-scale restoration projects has been limited. A particular gap in our understanding is the length of time required before a site reaches the target state. To address this, a classification model based on remote sensing data was developed for a peatland restoration area on blanket bog in northern Scotland, UK, to evaluate whether post-restoration trajectories followed predictable trends over time. The model was trained against a chronosequence of sites within a 20 × 10 km study area that are being restored following drainage and intensive non-native afforestation. Two versions of the model were created to compare the accuracy obtainable from the suite of Sentinel-2 satellite data versus sub-metre resolution aerial imagery from GetMapping (RGB and IR). The Sentinel-2 based model greatly outperformed the aerial imagery-based model. Adding surface slope to the classification did not significantly improve the accuracy of prediction. Prediction of starting and target land covers was very robust, and both the most recent and oldest restoration sites were well predicted spatially. The main uncertainties in the model were within sites of intermediate restoration age, and sites which underwent additional treatments after the initial restoration. Using standard vegetation and wetness indices as indicators, it was possible to track the progression of areas that had been felled and rewetted towards the spectral signal of the control blanket bog locations. A further study examined the use of multiple years of satellite data (2015–2021) and including Sentinel-1 SAR imagery, and confirmed the findings obtained with only a single climatically average year, and furthermore examined the efficacy of different restoration methods. We observed consistent trends of restoration sites beginning to resemble the target hydrologically and ecologically functional blanket bog state after 10–20 years post intervention.

Forest expansion can make an important contribution to the 2015 Paris Agreement, through offsetting Greenhouse gas (GHG) emissions. EU, UK and Scottish forest policy encourages substantial forest expansion. Unfortunately, policy is still inadequately informed by high resolution data, and often assumes a fairly homogenous landscape, uniformly suitable soil types and idealised ‘average’ tree timber yields, while carbon emissions caused by soil disturbance during planting, and changes in climate are rarely adequately considered. Also, the proportional contribution of afforestation targets to national mitigation needs is often overlooked which could lead to over-reliance on tree planting. We address these shortcomings through an integrated modelling approach which estimates net carbon gain for eleven tree species accounting for the interactions between climate, soil and planting practices. We present detailed spatial results for a case study area (Scotland), showing where forest expansion would be likely to result in overall carbon gains, accounting for the differentiated spatial variability of timber yield classes for each one of the species considered including present and future climate. The results showed that upland ecosystems, whose soils are rich in carbon, were vulnerable to net carbon loss, particularly with intensive ground preparation and planting practices. While the prevalence of mineral soils in the lowlands makes them a safer option for planting in theory, these are also areas which might conflict with agricultural activities. Our findings strongly support the notion that both “the right tree in the right place” and “no trees in the wrong place” are important messages for practitioners. In terms of the total UK and Scottish carbon footprints, the magnitude of the offset obtained in 30 years if afforestation goals were fully reached would likely be around 1% of the UK total business as usual GHG footprint and around 10% of the Scottish footprint. Our results can help to improve the targeting of incentives and investments in forest and woodland expansion, but also reinforce the need to pursue emissions reductions in a variety of ways throughout all sectors., This work was funded by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS)

Mapping the extent and locations of peatland at landscape scale has implications for carbon inventories, conservation and ecosystem services assessments. The main aim of this paper was to model and map the extent of northern peat soils while taking into account its uncertainty, and in particular exploring: 1. the use of radar Sentinel 1 as alternative to optical sensors to reduce problems due to clouds while taking advantage of the seasonality changes and 2. the use of deep learning for peat classification and as application of Digital Soil Mapping. The data sets defining presence or absence of peat in the soil were obtained from different sources and different sampling schemes, densities and distributions. Scotland was used as test case, because of its cloudy weather and fragmented distribution of different types of peat. An extension of the scorpan-kriging approach was used. The trend was estimated with different approaches: Generalized Additive Models, RandomForest and deep learning (convolutional neural networks). Each approach produced the probability of belonging to a class and the predicted class for each pixel. The results were assessed using out-of-sample measures. In this study 108 combinations of data sets and models (including trend approaches, sets of covariates and modelling of the spatial structure) were assessed. Overall, spatially explicit models performed better. The choice of the statistical method can have a significant impact on the predictive performances, while the sets of environmental covariates had a lower impact. Sentinel-1 with morphological features proved to be a good alternative to optical data for peat mapping. It is important to have balanced data sets representing the distribution of the data, because merging heterogeneous sources of data from different populations does not necessarily improve predictions. The use of deep learning and convolutional neural network provided initial promising results. There were large differences in the modelling approaches. These differences and uncertainties need to be taken into account for further modelling such as earth surface modelling or carbon accounting.

Globally, peatlands provide an important sink of carbon in their near natural state but potentially act as a source of gaseous and dissolved carbon emission if not in good condition. There is a pressing need to remotely identify peatland sites requiring improvement and to monitor progress following restoration. A medium resolution model was developed based on a training dataset of peatland habitat condition and environmental covariates, such as morphological features, against information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), covering Scotland (UK). The initial, unrestricted, model provided the probability of a site being in favourable condition. Receiver operator characteristics (ROC) curves for restricted training data, limited to those located on a peat soil map, resulted in an accuracy of 0.915. The kappa statistic was 0.8151, suggesting good model fit. The derived map of predicted peatland condition at the suggested 0.56 threshold was corroborated by data from other sources, including known restoration sites, areas under known non-peatland land cover and previous vegetation survey data mapped onto inferred condition categories. The resulting locations of the areas of peatland modelled to be in favourable ecological condition were largely confined to the North and West of the country, which not only coincides with prior land use intensity but with published predictions of future retraction of the bioclimatic space for peatlands. The model is limited by a lack of spatially appropriate ground observations, and a lack of verification of peat depth at training site locations, hence future efforts to remotely assess peatland condition will require more appropriate ground-based monitoring. If appropriate ground-based observations could be collected, using remote sensing could be considered a cost-efficient means to provide data on changes in peatland habitat condition.

Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore’s Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.

Land degradation leads to ecosystem degradation, reducing ecosystem functioning and depleting ecosystems' resilience. The majority of factors linked to land degradation are closely related with the depletion of below- and above-ground stocks of organic carbon. Organic carbon stock is important for climate change mitigation and for restoring soil functions such as those crucial to support food security. In this study, we mapped carbon stocks to infer land degradation in a small area in the Ethiopian Great Rift Valley. The study aimed to assess carbon stock status and to identify limitations and advantages of using global data in mapping at local scale relative to using local data. Two different datasets were developed; i) a “global dataset” characterised by data from datasets with global coverage data, and ii) a “hybrid dataset” that coupled data from global datasets, soil data derived from a local survey, and land cover data derived from a supervised classification of satellite images. The results showed that i) global datasets introduced inaccuracy that must be taken into account for advocating interventions at a local scale, and ii) global datasets could be used at a small catchment level for decision-making, if a simple rank of values is sufficient, but they might provide an optimistic picture of land degradation because they overestimate stocks.

Current estimates suggest that the world is on track for ~3°C of heating relative to pre-industrial levels by 2100. This is likely to bring great disruption to earth systems, leading to increased natural hazard risks, crop failures, civil unrest and population migration. There is, however, a high degree of uncertainty about the impacts that such events may have on land use and ecosystems in individual countries. Integrated assessment modelling (IAM) of scenarios like the Shared Socioeconomic Pathways (SSPs) offers one way to address this uncertainty, allowing outcomes such as the relative land cover under food production or forestry to be compared for each scenario. However, global and continental-scale IAMs need to be complemented by landscape scale spatial modelling to inform national and regional policy making. In this paper, we demonstrate impacts and trade-offs of future land cover change in Scotland, a UK region with a high degree of political autonomy, using downscaled SSPs from Europe to the national and finally the regional level. Our methods integrate participatory knowledge co-construction approaches with land-use modelling. Firstly, a stakeholder workshop held in November 2018 led to the development of detailed narratives for 5 UK SSPs. Two contrasting UK SSPs, SSP1 (Sustainability), and SSP5 (Fossil-Fuelled Development) were then adapted to the case of Scotland and simulated to the year 2040 using a land use change model (APoLUS). Land use demands for each scenario were quantified based on historical tendencies, narrative information derived from the workshop, and future Scottish Government targets. Results highlight trade-offs between forest cover, grasslands, natural areas including marginal peatlands important for carbon sequestration, and cropland for food production and the drinks industry. We discuss these preliminary findings, highlight key areas of uncertainty and present pathways for future work.

Damage to peatland globally causes significant contributions to the current net greenhouse gas emissions and pose a further future risk as such damaged peatlands are vulnerable to future climatic stress. Globally, peatland restoration efforts are rapidly increasing in scale as natural climate solutions, yet relatively little effort has been it into effective monitoring of landscape scale restoration projects. We developed a classification model that uses remote observations (Sentinel-2 or national scale aerial imagery from Getmapping) to detect restoration efficacy by training it against a dataset from a chronosequence of spatially collocated peatland restoration sites that had previously been converted to plantation forestry. The Sentinel-2 based model greatly outperformed the aerial imagery-based model (RGB and IR, 25 and 50 cm, respectively). Adding slope to the classification improved kappa by less than 0.02. Prediction of the starting (forestry) and target (restored) state was very robust, and both recent and the oldest restoration sites were spatially well predicted. The main model uncertainties lie with sites of intermediate age, where on-the-ground restoration trajectories based on vegetation composition also differ the most, and with sites where additional layers of management after the initial restoration management have been applied.

Forests and woodlands offer many benefits to people. They can provide timber and food, store carbon to help deal with the effects of climate change, decrease flooding and soil erosion, and provide recreation for people and habitat for a multitude of species we care to conserve. Scottish forests cover roughly 19% of the country. The Scottish government has the ambition to add several thousand hectares a year over the next decades, to support the rural economy, the environment, and communities. It is important that a substantial proportion of the expansion is made up by native trees and shrub species due to better habitat for wildlife.These challenges were explored with a case study of virtual forest landscape from Cairngorms National Park (CNP) which was used to test preferences for scenarios of future woodland expansion. Spatial Multi-criteria Analysis (sMCA) has been applied to decide where to plant new forests and woodlands, recognizing a range of land-use objectives while acknowledging concerns about possible conflicts with other uses of the land. The tools used in the development and implementation of the 3D model were PC and Mobile based, and enable the incorporation of interactive functionality for manipulating features. Model inputs comprise 5m DTM, 25cm Aerial Imagery, 3D Tree Species, GIS layers of Current Forest and Woodland Expansion inside CNP. Afforestation animation has been attached in Google My Maps. This is through setting different keyframes by storyboard camera path animation around the area of CNP. Stereo panorama has been applied to selection of woodland expansion scenarios (e.g. Broadleaved potential corridors, Conifer potential corridors), which is viewed with mobile technology and Virtual Reality (VR) equipment.The 3D model with simulation of woodland expansion was used at the event of 2019 Royal Highland Show and European Forest Institute Annual Conference 2019. Audience feedback suggested the enhancement of user interaction through VR has potential implications for the planning of future woodland to increase the effectiveness of their use and contribution to wider sustainable ecosystems.

Peatlands are an important potential sink or source of carbon and play a significant role in climate change regulation. Understanding peatland as 3D-landforms is as important as mapping their spatial extent. The main aim of this work was to estimate a 3D representation of peat properties and assess the associated spatial uncertainty, to provide baseline information for climate and land use change analyses. In this study a combination of 3D Generalized Additive Models and 3D geostatistics was applied to a raised basin bog using legacy data to map carbon content. The study presents a novel approach based on methods providing quantification of the spatial uncertainty and the possibility to model complex relationships. The approach fully exploits the 3D spatial relationships between the survey points while supported by environmental variables. The methods proved to be general and highly flexible. The results of this study showed that it is possible to model peat properties to obtain a detailed volumetric assessment of the peat, including carbon stocks from a limited set of legacy data. The estimates of spatial uncertainty are important when including the results in further environmental and climate-change models or for decision making to provide alternatives and prioritisation.

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m. The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim. Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible. The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.

We thank the authors for their thought-provoking comments on our paper. Most of the commentators agree that soil organic carbon (SOC) sequestration is important for improving the quality of soil, however they argue that we have overstated the potential of soil carbon sequestration. We welcome the comments and appreciate that the issue of SOC sequestration has always been somewhat factious (Schlesinger, 2000). We shall address the significance of the quantity “4 per mille”, reported sequestration rates, the limitation of carbon sequestration with time, and nutrient requirements. We clarify that our paper (Minasny et al., 2017) mainly deals with potentials for the 20 countries and regions, where SOC sequestration can also be seen as a way to improving the resilience of the soil to future climate change, that is, improving adaptation rather than mitigation. We believe that in some parts of the world where food security is threatened, the benefit of soil carbon management for adaptation should be stressed more than for mitigation. This is the reason why the 4 per mille initiative explicitly includes food security (Chabbi et al., 2017; Soussana et al., 2015). We need to add that the “4 per mille Soils for Food Security and Climate” initiative is just one of many national and global initiatives on SOC sequestration for mitigating climate change. The Intergovernmental Technical Panel on Soils (ITPS) of the Global Soil Partnership (GSP) discussed incorporating the topic of SOC in the IPCC Assessment Report (ARs), from AR6 onwards. The IPCC has also put a focus on soil in their upcoming special report “Climate Change and Land” (http://www.ipcc.ch/report/sr2/). The recent FAO Global Symposium (GSOC17) assembled experts engaged in FAO, GSP and its ITPS, IPCC, UNCCD-SPI and WMO activities to work together for the common goal of appropriate SOC management as part of overall sustainable soil management within the climate change mitigation and adaptation, sustainable development, Land Degradation Neutrality (LDN) and food security agendas (http://www.fao.org/about/meetings/soil-organic-carbon-symposium/en/). The Global Research Alliance on Agricultural Greenhouse Gases (GRA) focused on opportunities to reduce agricultural greenhouse gas emissions and increase soil carbon sequestration while still helping to meet food security objectives (http://globalresearchalliance.org/about/). The Common Agriculture Policy in the EU is currently being revised to include the potential use of SOC as an indicator. The 4 per mille initiative was launched at COP21, where the Paris Agreement was adopted, and one of the main aims of the Paris Agreement is to stop the planet from warming an additional two Celsius degrees. The two-degree target, although suggested by scientists through modelling work, was chosen more for political and pragmatic reasons whereby countries could agree on a target that they could work towards (Tollefson, 2015). And of course, there are many scientific critiques of this target (Knutti et al., 2016). Similarly, the 4 per mille initiative comes from a politically-driven aspiration, and our paper (Minasny et al., 2017) is a response to such an aspiration, to seek and outline possibilities based on current knowledge. The important concept is that soil and agriculture are part of the solution, and it is an interim and evidence-based solution that we can implement. Now we shall respond to each of the commentaries.

Assisted colonisation, one form of species translocation, has been proposed as a tool for helping species to track suitable conditions in a changing climate. There are considerable practical challenges associated with it, including predicting where to place translocated individuals. This problem may be particularly big for small and immobile species, where small-scale micro-environmental conditions de-couple them from environmental conditions as projected in large-scale climate models. To investigate this problem we developed a survey-based model to predict the occurrence of our target species, the fruticose terricolous arctic-alpine lichen, Flavocetraria nivalis, within the Cairngorm Mountains. We then undertook an experimental translocation of this species. A second model, using variables that were significant in the survey-based model, was only fair at predicting the initial pattern of survival at the recipient site. However, model fit of the translocation survival model improved over time as the distribution of surviving individuals more accurately reflected the distribution of suitable environmental conditions. In addition, model predictive power increased with the addition of data on micro-climatic conditions at recipient plots. Synthesis and applications. Our results demonstrate that, for species which respond strongly to local environmental conditions, are immobile and, to some extent, decoupled from larger-scale climates, it may be difficult to build a priori accurate predictive models of habitat suitability. In these cases, a combination of modelling and expert judgement, along with the movement of substantial numbers of transplants, may be the appropriate options for maximising the success of assisted colonisation. This article is protected by copyright. All rights reserved.

Reliable spatially explicit information about soil is important for global environmental challenges. Soil texture is one of the soil most important characteristics as it drives several physical, chemical, biological, and hydrological properties and processes. Despite the importance, there is scarcity of information on soil texture, especially at the resolution required for environmental modelling. Many recent efforts modelled soil texture with different approaches focussing on the spatial relationships with environmental covariates. This study aimed at i) modelling and map soil particle classes for Scotland at medium resolution (250 m), for topsoil and the whole profile, using an operational DSM approach following specifications from the GlobalSoilMap project; ii) assessing the spatial uncertainty of the modelling approach, and iii) evaluating the impact of spatial and modelling uncertainty on soil texture classification of the topsoil. An extension of the scorpan -kriging approach, i.e. hybrid geostatistical Generalized Additive Models (GAMs), combining GAM with Gaussian simulations was used on Additive-Log-Transformed soil particle classes. The R2 calculated with the validation dataset was between 0.55 and 0.60 and the RMSE values were below 13%. The set of covariates used in this study explained about 40% of the variance of the data. The significant covariates included morphological features, vegetation index and information about the phenological season. The results also showed a large percentage of the variability to be spatially structured. The assessment of the uncertainty on the soil texture classification showed variability and class shift. The resulting datasets can be used as input for further modelling in a number of areas, and they are also important for soil functions modelling in the context of provision of Ecosystem services. The accompanying uncertainty can be used to provide supporting information for land management choices.

The ecosystem services framework has now been embodied in policy and practice, creating the need for governance structures that allow science, policy and practice to come together and facilitate shared learning. We describe five years of progress in developing an Ecosystem Services Community of Practice in Scotland, which brings together over 600 individuals from diverse constituencies to share experiences and learn from each other. We consider the ‘community’ and ‘practice’ aspects to demonstrate the benefits of establishing an Ecosystem Services Community (ESCom). We also demonstrate how the journey involved in the creation and continuing evolution of ESCom has proved valuable to researchers, policy-makers, practitioners and students and as such has contributed to social learning. We reflect on challenges, given the voluntary nature, absence of formal institutional support and emergence of initiatives focusing on overlapping topics. Based on our experience, we provide ten recommendations to help future ecosystem services communities of practice.

Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation. Climate change and rapid land use change are degrading peatlands, liberating their stored carbon (C) into the atmosphere. To conserve peatlands and help in realising the Paris Agreement, we need to understand their extent, status, and C stocks. However, current peatland knowledge is vague—estimates of global peatland extent ranges from 1 to 4.6 million km2, and C stock estimates vary between 113 and 612 Pg (or billion tonne C). This uncertainty mostly stems from the coarse spatial scale of global soil maps. In addition, most global peatland estimates are based on rough country inventories and reports that use outdated data. This review shows that digital mapping using field observations combined with remotely-sensed images and statistical models is an avenue to more accurately map peatlands and decrease this knowledge gap. We describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping. We found that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing. Many studies have delineated peat extent using land cover from remote sensing, ecology, and environmental field studies, but rarely perform validation, and calculating the uncertainty of prediction is rare. This paper then reviews various proximal and remote sensing techniques that can be used to map peatlands. These include geophysical measurements (electromagnetic induction, resistivity measurement, and gamma radiometrics), radar sensing (SRTM, SAR), and optical images (Visible and Infrared). Peatland is better mapped when using more than one covariate, such as optical and radar products using nonlinear machine learning algorithms. The proliferation of satellite data available in an open-access format, availability of machine learning algorithms in an open-source computing environment, and high-performance computing facilities could enhance the way peatlands are mapped. Digital soil mapping allows us to map peat in a cost-effective, objective, and accurate manner. Securing peatlands for the future, and abating their contribution to atmospheric C levels, means digitally mapping them now.

Question We compared active and abandoned beech coppices in terms of: (1) structural features, (2) total, understorey and overstorey plant diversity across spatial scales, and (3) species richness of beech forest specialist species. Location Central Apennines, Italy. Methods We applied a multi-scale approach, working at fine scale (management unit) and at a coarser scale (forest patch). Two forest patches were selected according to management regimes (active coppicing, 1–40 yrs; abandoned coppicing, >40 yrs) in each of the two study areas (Bolognola and Ussita). Within each forest patch, 20 stands were sampled in 20 m × 20 m plots, for a total of 80 sampling units. Results We found that the height of standards and number of dominant trees were negatively correlated with the total number of species. At the plot scale, the number of beech specialist species was significantly higher in abandoned plots. At the forest patch scale, the number of species in Bolognola was markedly higher in the actively coppiced forest than in the abandoned one, while the opposite result was found at Ussita. Regarding the beech specialist species richness, the abandoned forest at Ussita showed higher richness than the coppiced forest, while in Bolognola we found the opposite. The managed forest hosted more overstorey species than the abandoned one in both areas. Surprisingly, at forest patch scale in Bolognola, the total beech specialist species richness was higher in the coppiced plots than in the abandoned ones. The species assemblages were more similar between patches having different management regimes within the same area, than between patches having the same management regime across different areas. Conclusions This material provides a novel contribution to the study of species diversity patterns in this forest system, suggesting the importance of a multiple scale approach in forest diversity studies. The beech specialist species can largely persist in a heterogeneous coppice landscape, where abandoned stands are mixed with stands under regular coppicing. The results can link current knowledge about beech coppice diversity on the landscape scale with that on the plot scale, and thus help guide new conservation planning.

This paper presents an approach to downscaling of climate models based on a combination of Generalised-additive-models and geostatistics. The paper aims at increasing the usefulness of Climate Models by creating data sets with a spatio-temporal resolution appropriate for applications of environmental models at the management scale. Simulations of climate change available from global and regional climate models require downscaling and bias correction for hydrological or ecological applications. The paper assess the effectiveness of the approach applied to monthly means of temperature and rainfall, to predict soil wetness conditions. The main focus was on reconstructing the spatial structure of the landscape. The climate model data were firstly downscaled using a geo-statistical approach combining generalised additive models (GAMs) with kriging, making use of the covariates to reproduce the spatial pattern. The downscaled climate model data were corrected for bias using interpolated meteorological ground station data (1961–1999). In general the downscaling approach provided lower RMSE and closer reproduction of the variogram structure. The use of the bias corrected downscaled data improved the results of the model used to predict soil wetness, increasing the validation metrics. The approach is completely implemented in open-source software, in particular GRASS-GIS and R.

The Aichi 2020 Targets, under the Convention on Biological Diversity (CBD), aim to halt the loss of biodiversity by 2020, in order to ensure that ecosystems continue to provide essential services. Here we apply a social–ecological systems analysis to provide insight into the diverse system interactions that pose impediments to delivery of the Aichi Targets. We applied an analytical framework of pair-wise exchanges along six axes between the social, economic, environmental and political loci of the global social–ecological system. The analysis identified that many impediments result from partial decoupling in the system through phenomena including delayed feedbacks and insufficient information flows. It suggests 15 of the Aichi Targets are unlikely to be delivered; 3 are likely to be delivered in part; and 2 in full. We considered how interventions at leverage points may overcome the impediments, and compared these to actions included within the Implementation Decision for the Aichi Targets, to find gaps. These new leverage points to fill identified gaps involve many aspects of system re-coupling: co-production of knowledge and more equitable food systems governance (environmental–social axis); support for social change movements (social–political axis); an appropriate financial target for biodiversity conservation investment, with a clear means of implementation such as a currency transaction tax (economic– political axis); and co-governance of natural resources (environmental–political axis). The recently released Global Biodiversity Outlook 4 shows that 18 of the 20 Aichi Targets are tracking in accordance with our analysis; and that current efforts are unlikely to result in an improvement in the base state of biodiversity by 2020, confirming some of our results. We argue that attention to the interactions within, and the partial decoupling of, the global social–ecological system provides new insights, and is worthy of further attention both for delivery of the Aichi Targets and for guiding longer term actions for the conservation of biodiversity

Habitat associations of farmland birds are well studied, yet few have considered relationships between species distribution and soil properties. Charadriiform waders (shorebirds) depend upon penetrable soils, rich in invertebrate prey. Many species, such as the Northern Lapwing Vanellus vanellus, have undergone severe declines across Europe, despite being targeted by agri-environment measures. This study assessed whether there were additive effects of soil variables (depth, pH and organic matter content) in explaining Lapwing distribution, after controlling for known habitat relationships, at 89 farmland sites across Scotland. The addition of these soil variables and their association with elevation improved model fit by 55%, in comparison with models containing only previously established habitat relationships. Lapwing density was greatest at sites at higher elevation, but only those with less peaty and less acidic soil. Lapwing distribution is being constrained between intensively managed lowland farmland with favourable soil conditions and upland sites where lower management intensity favours Lapwings but edaphic conditions limit their distribution. Trials of soil amendments such as liming are needed on higher elevation grassland sites to test whether they could contribute to conservation management for breeding Lapwings and other species of conservation concern that depend upon soil-dwelling invertebrates in grassland soils, such as Eurasian Curlew Numenius arquata, Common Starling Sturnus vulgaris and Ring Ouzel Turdus torquatus. Results from such trials could support improvement and targeting of agri-environment schemes and other conservation measures in upland grassland systems.

Entregamos el primer analisis de patrones de actividad exhibidos por morfos moteados y melanicos de la vulnerable guina Leopardus guigna, en un paisaje de bosque del sur de Chile. Camaras trampas mostraron que las guinas estuvieron mayoritariamente activas en la noche (64% de fotos). Las guinas melanicas fueron mas nocturnas (82% de fotos) que las moteadas mas comunes (57%). Las guinas moteadas estuvieron mas activas en noches nubosas y oscuras. Nuestro estudio provee nuevos resultados sobre la plasticidad conductual de la guina en diferentes condiciones ambientales, y la ventaja relativa de cada morfo para persistir en un paisaje cada vez mas dominado por el ser humano.

Context Habitat networks are often advocated as an effective measure for adaptation to climate change, while intensification of land use is a possible response to threats to food security.

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making.

Though the importance of coppicing for the conservation of forest biodiversity is acknowledged, little is known about flora diversity and how it may be affected by the perceptions, constraints and regulations governing how loggers choose to exploit forest resources. Building on previous research on coppiced forests in the central Italian Apennines, unstructured and structured social surveys were performed along with structural and compositional vegetation sampling to determine how the ecology of the understory flora, where most of the biodiversity is found, may be affected by direct and indirect interactions between formal rules, namely Marche Region prescriptions, and informal rules applied in coppicing management. We identified economic/logistical and ecological variables that influence change in understory plant communities, particularly focusing on specialist forest species, and we explored how commercial, private and group property (Comunanza Agraria) loggers are influenced by these variables in deciding whether a forest is suitable for coppicing. Our insights on the relationships between the human and the ecological dimensions of the coppicing system suggest that the users prefer specific forest conditions because they facilitate efficient harvesting. These conditions also happen to be compatible with the life cycle of the specialist forest species in the ground flora. The stand attributes considered to be indicators of productivity drive decision making almost independently from (but largely compatibly with) formal rules. This is true across the three groups of loggers. These attributes seem to act as determinants of the conservation of the functional diversity of understory plant species, at plot and forest patch scale. We conclude that economically viable coppicing can occur after the stumps have been allowed to regenerate and grow beyond a certain diameter. This enables understory conditions favorable to forest specialist plants to re-establish and makes the present coppicing regime compatible with conservation of the diversity of these species in the forest landscape.

International audience; The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha−1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year−1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

This chapter reports on an exercise in replicating the analysis of outputs from 20,000 runs of a social simulation of biodiversity incentivisation (FEARLUS-SPOMM) as part of the MIRACLE project. Typically, replication refers to reconstructing the model used to generate the output from the description thereof, but for larger-scale studies, the output analysis itself may be difficult to replicate even when given the original output files. Tools for analysing simulation output data do not facilitate keeping records of what can be a lengthy and complicated process. We provide an outline design for a tool to address this issue, and make some recommendations based on the experience with this exercise.

A spatial hidden Markov model (SHMM) is introduced to analyse the distribution of a species on an atlas, taking into account that false observations and false non-detections of the species can occur during the survey, blurring the true map of presence and absence of the species. The reconstruction of the true map is tackled as the restoration of a degraded pixel image, where the true map is an autologistic model, hidden behind the observed map, whose normalizing constant is efficiently computed by simulating an auxiliary map. The distribution of the species is explained under the Bayesian paradigm and Markov chain Monte Carlo (MCMC) algorithms are developed. We are interested in the spatial distribution of the bird species Greywing Francolin in the south of Africa. Many climatic and land-use explanatory variables are also available: they are included in the SHMM and a subset of them is selected by the mutation operators within the MCMC algorithm.

The variation of soil properties down a profile is usually considered continuous. The aim of this study was to develop and test a methodology to model the continuous vertical and lateral distributions of SOC stocks in Scottish soils making explicit the modelling and spatial uncertainty of the results. A comparison with regression kriging and other depth function methods is provided to show that better performances can be achieved taking into account non-linear relationships between covariates and soil properties. The analysis was run for the whole of Scotland. The carbon stocks were calculated for each point, i.e. each horizon in each available profile. The stock value at each cell for each of the considered depth layers was defined using a hybrid GAM-geostatistical 3D model, combining: 1) the fitting of a GAM to estimate the trend of the variable, using a 3D smoother with related covariates; and 2) kriging or Gaussian simulations of GAM residuals as spatial component to account for local details. The use of GAM makes the approach flexible, because it is able to deal with both linear and non-linear relationships between soil properties and the considered covariates. The results confirmed that MODIS data are a useful source of information for DSM especially at national scale. When comparing the proposed approach with similar methods such as regression kriging, the results showed better agreement with the data in the validation set with a global R2 of 0.60. The median values obtained are comparable with the values reported from previous studies on stocks in Scotland using different methods. The uncertainty is large indicating a wide range of credible values for each pixel.

The death of native trees caused by non-native pathogens is a global problem. An assessment of the potential ecological and conservation impacts of any tree disease should identify: (1) ecosystem functions associated with the tree species; (2) which species use the tree and how; (3) the suitability of alternative tree species to replace the threatened tree species; and (4) potential management options to mitigate or reduce the impact of the disease. We assess the potential ecological impact of Hymenoscyphus pseudoalbidus (ash dieback) on Fraxinus excelsior in the UK. 953 species were identified as associated with F. excelsior trees: 12 birds, 28 mammals, 58 bryophytes, 68 fungi, 239 invertebrates, 548 lichens. Forty-four ‘obligate’ species were identified: 11 fungi, 29 invertebrates and 4 lichens; and 62 ‘highly associated’ species. Off-setting the loss of ash with ‘alternative tree species’ may be one ‘solution’ to the biodiversity threat. No single alternative tree species can act as host for all ash-associated species but Quercus robur / petraea can host 69%. In an assessment of ecosystem function, when compared to other European deciduous tree species, F. excelsior interacts with the environment in a unique way, particularly in relation to nutrient cycling. Exploration of different management scenarios in response to ash dieback indicated that management which did not remove infected F. excelsior trees was the best for ‘obligate’ and ‘highly associated’ species. The results highlight wide-ranging ecological implications of ash dieback of relevance to other invasive pests and pathogens that are threatening the integrity of other tree species and woodland ecosystems.

We have carried out an in-depth investigation into the effects of image compression on synthetic Probe for On-Board Autonomy – Vegetation (PROBA-V) scenes and Landsat-derived image tiles. The two image compression implementations used were the TER implementation and a bespoke implementation of the Consultative Committee for Space Data Systems (CCSDS) Blue Book standard, which are functionally identical but operate on different image architectures. This work included (1) the development of an approach for producing synthetic scenes that were appropriate in terms of structure and content, and (2) evaluation of the image compression approach on the two kinds of image in terms of their usefulness for land-cover mapping. The synthetic image (SI) generation approach has been rigorously tested and produces images that are statistically similar to real scenes, both compressed and uncompressed. The results of our work show that the effects of image compression vary significantly between bands and with different co...

Knowledge of soil properties with complete area coverage is needed for policy-making, land resource management, and monitoring environmental impacts. Remote sensing offers possibilities to support Digital Soil Mapping, especially in data-poor regions. The aim of this work was to test the potential of time-series of MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation and drought indices to provide relevant information to model topsoil properties in a Boreal-Atlantic region (Scotland) focussing on differentiation between soils with high and soils with low organic matter contents. For each of the three considered years, 345 MODIS data sets were included in the exploratory analysis; 15 data products for 23 dates (bi-weekly) per year. Terrain parameters derived from Shuttle Radar Topography Mission were also included. A methodology was implemented to exploit fully the high number of covariates, to identify the band, index or product that best correlates with the soil property of interest. In particular the proposed approach i. relies on freely globally available data-sets; ii. uses statistical criteria to select the combination of covariates providing the highest predictive capability, among the data considered and available; iii. deals with both continuous (using Generalized Additive Models, GAMs) and multinomial categorical (using Random Trees) types of variables; iv. takes into account fully the spatial autocorrelation of the data; v. provides estimates of the spatial uncertainty for each pixel; and vi. is computationally efficient when compared with methods such as forward stepwise. The models fitted show a fairly good agreement with existing data sets, presenting a consistent spatial pattern. The use of MODIS data as covariates increased the predictive capabilities of GAMs using only terrain parameters. The misclassification error for organic matter classes was between 25 and 35%. The assessment provided of the spatial uncertainty of the modelled values can be used in further modelling and in the assessment of consequences of climate-change and trade-off in land use changes. This approach can contribute to improving our understanding and modelling of soil processes and function over large, and relatively sparsely sampled, areas of the world.

We report results from over 20,000 runs of a coupled agent-based model of land use change and species metacommunity model. We explored the effect of increasing government incentive to improve biodiversity, in the context of other influences on land manager decision-making: aspirations, input costs, and price variability. The experiments test the four kinds of policy varying along two dimensions: activity-versus-outcome-based incentive, and individual-versus-collective incentive. The results from the experiments using boundedly rational agents, and comparison with profit-maximisation reveal thresholds in incentive schemes, where a sharp increase in environmental benefit occurs for a small increase in incentive. Further, the context affects the level of incentive at which turning points occur, and the degree of effect. Variability in outcome can also change with incentive and context, and some evidence suggests that environmental benefits are not always monotone increasing functions of incentives. Intuitively, if the incentive signal is large enough, land managers will farm the subsidy; and if the subsidy does not exactly match desired landscape outcomes, deterioration in environmental benefits may occur for higher incentives. Our results, whilst they suggest that outcome-based incentives may be more robust than activity-based, also highlight the importance of context in determining the success of agri-environmental incentive schemes. As such, they lend theoretical support to schemes, such as the Scottish Rural Development Programme, that include a localised component. Highlights? We examine the effect of increasing agri-environmental incentives on biodiversity. ? Experiments are conducted using a coupled agent-based and metacommunity model. ? Results show a nonlinear relationship between incentive and biodiversity. ? Scenarios often show declining biodiversity at high incentives. ? Context-sensitivity supports schemes designed to include a local component.

Habitat loss and fragmentation are affecting populations of forest dwelling mammalian carnivores worldwide. In southern Chile, a biodiversity hotspot, anthropogenic activities have resulted in high loss of native forest cover. The guiña, or kodkod cat, Leopardus guigna is a small forest-dwelling felid with a narrow range in the temperate forest of southern Chile. The few existing studies of the species have suggested that it is almost exclusively restricted to large tracts of native forest. This paper reports a study in the temperate forest within a fragmented Andean piedmont landscape which demonstrates that smaller forest fragments in the farmland matrix are playing a key role in the persistence of the guiña. We estimated occupancy in both continuous native forest and remnant forest fragments and, with single-species/single-season models, evaluated the extent to which forest cover, habitat type and proximity to protected areas have a modulating effect on occupancy. A continuous survey during 2008–2009, in three seasons of 90–100 days each, accumulated 6,200 camera trap days and returned 47 photographs of guiña. Total detection in fragments was higher than in continuous forests, with detection confirmed in almost 70% of studied fragments. We found that probability of a site being occupied significantly increased with forest cover (adult/secondary forest, scrubland) and probability was low (< 0.2) in sites with < 50% of surrounding forest cover. Our study highlights the importance of remnant forest fragments in the mosaic of extensive agriculture for the spatial dynamics of a guiña population and hence for the future conservation of the species.

International audience; An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

Any model for digital soil mapping suffers from different types of errors, including interpolation errors, so it is important to quantify the uncertainty associated with the maps produced. The most common approach is some form of regression kriging (RK) or variation involving geostatistical simulation. Another way of assessing the spatial uncertainty lies in the Bayesian approach where the uncertainty in the results is described by the posterior density. The aim of this paper is to present an example of a Bayesian approach for uncertainty estimation when mapping the topsoil organic matter content in the Grampian region of Scotland (UK, about 12,100 km2). The chosen approach uses (Bayesian) latent Gaussian models fitted using integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) models approach for coping with spatial correlation (INLA_SPDE). For practical comparison purposes, the results of INLA_SPDE were compared with the results of an extension of the scorpan kriging approach, i.e., (1) combining generalized additive models (GAM) with Gaussian simulations and (2) traditional RK. The results were assessed using in-sample and out-of-sample measures and compared for distribution similarity, spatial structure reproduction, computational load, and uncertainty ranges. We conclude that the Bayesian framework using INLA offers a viable alternative to existing methods and an improvement over traditional RK.

The Scottish Government wish to preserve the carbon stocks already stored or sequestered in both organic and mineral soils and see land-use change as one of the key drivers affecting storage of soil organic carbon (SOC). A key component to develop any strategy to maintain the existing carbon stocks is the quantification of these stocks both in terms of the carbon content and its spatial distribution. To date, two different methods that use the same existing legacy data have been used to quantify carbon stocks in Scotland: a traditional approach and a hybrid generalised additive model (GAM)—geostatistical 3D model. Each of the methods revealed differences in the spatial patterns of SOC stocks. Understanding these differences will enable the development of more robust and accurate models that can be used to assess changes in stocks due to changing land use. Here, we compare these methods for the Scottish mainland, Western Isles, and Orkney. The traditional approach was based on calculating average organic carbon values from a subset (6000) of around 40,000 observations stored within the Scottish Soil Database. The total SOC stock was then determined by multiplying the areal extent of each soil series/land-use combination by the calculated profile stock. The uncertainty was also quantified based on standard error of the measured carbon contents and the uncertainty in the bulk density pedotransfer functions. A hybrid GAM-geostatistical 3D model combined the fitting of a GAM using a 3D smoother with related covariates and the kriging or Gaussian simulations of the residuals to spatially account for local details. The uncertainty was also calculated and was found to be large, indicating a wide range of credible values for each pixel. The deviation from the median ranges was between 5 and 75 % for the interpolated values depending on location.

Time series of satellite data have an important role in the monitoring of regional and global ecosystem properties. Satellite images often present missing data due to atmospheric aerosol, clouds or other atmospheric conditions. Most methods proposed to minimise the effects of degradation and to restore signal values do not take into account the spatial and temporal correlation of the values in the pixels. The aim of this study was to propose and test a spatio-temporal interpolation method to reconstruct pixel values in MODIS data time series that are missing due to cloud cover or other image noise. The method presented and tested is an example of a hybrid Generalised Additive Model (GAM)-geostatistical space-time model, including the fitting of a smoother spatio-temporal trend and a spatial component to account for local details supported by information in covariates. The method is not limited by the type of noise or degradation of pixels values, latitude, vegetation dynamics and land uses. The application of cloud masks on the target image provided the data for a quantitative validation through the comparison between the modelled EVI values and those from the MODIS product. The method was able to restore data providing very good to adequate responses in series of simulations of missing data. The comparison of distributions showed good agreement and predictive capabilities. The spatio-temporal method always performed better and the use of kriged residuals was helpful for situations with high percentages of missing data. The spatial pattern and the local features were well preserved for cloud coverage ⩽20%. For higher percentages of missing data, the results were smoother with less local detail retained, but still showing the general spatial pattern of the variable. The method has proved to be flexible and able to provide reconstructed images reproducing spatial patterns and local features of the measured product, even with substantial amounts of missing pixels.

Landscape adaptation to climate change requires policies that facilitate species dispersal, to counteract the effects of fragmentation and allow tracking of a species’ ‘climatic niche’. Expanding existing ecological networks is often proposed as a measure to maintain functional connectivity for forest species in multi-functional landscapes. In the next decades, however, such networks will be threatened by climate change through its effects on land use change, as global drivers are likely to have an increasing influence on national land use policy. Evaluation of indirect effects of climate change, on habitat networks, mediated by land use change, is therefore needed. We used an approach integrating climate, soil properties, and landscape resistance to dispersal, the latter estimated using Circuit Theory, to evaluate the vulnerability to land use change of forest habitat networks in Scotland, given two scenarios of land use change. In Scotland a combination of high food prices and improved land capability for agriculture could lead to decreased landscape connectivity for woodland species, especially in the East and South, with potentially large trade-offs between agriculture and woodland connectivity in the case of loss of woodland on prime agricultural land. We suggest that planning of ecological networks needs to account for future land use change. Adaptation and mitigation strategies across multiple sectors should be reconciled. Woodland networks will benefit from minimising creation of new woodlands on future prime agricultural land, the protection of existing patches, and the creation of wide-scale dispersal pathways along climatic gradients, i.e. in the N–S and E–W directions.