Explore the vast range of titles available.

Analysis of three wild-caught bumblebee species shows that family lineage survival and persistence is significantly increased between successive colony cycle stages with the proportion of high-value foraging habitat near the natal colony. Queen bee conservation Agricultural intensification is a major cause of the global decline in insect pollinators. In this UK-based field experiment, Claire Carvell and colleagues show that bumblebee colonies located close to high-value foraging habitats, including spring floral resources, are more likely to produce daughter queens that survive winter hibernation and emerge in the spring to start a new colony. Their findings add to the evidence that conservation interventions targeted at the landscape level have a positive effect on wild pollinators in agricultural settings. Insect pollinators such as bumblebees ( Bombus spp.) are in global decline 1 , 2 . A major cause of this decline is habitat loss due to agricultural intensification 3 . A range of global and national initiatives aimed at restoring pollinator habitats and populations have been developed 4 , 5 . However, the success of these initiatives depends critically upon understanding how landscape change affects key population-level parameters, such as survival between lifecycle stages 6 , in target species. This knowledge is lacking for bumblebees, because of the difficulty of systematically finding and monitoring colonies in the wild. We used a combination of habitat manipulation, land-use and habitat surveys, molecular genetics 7 and demographic and spatial modelling to analyse between-year survival of family lineages in field populations of three bumblebee species. Here we show that the survival of family lineages from the summer worker to the spring queen stage in the following year increases significantly with the proportion of high-value foraging habitat, including spring floral resources, within 250–1,000 m of the natal colony. This provides evidence for a positive impact of habitat quality on survival and persistence between successive colony cycle stages in bumblebee populations. These findings also support the idea that conservation interventions that increase floral resources at a landscape scale and throughout the season have positive effects on wild pollinators in agricultural landscapes.

Bumble bees (Bombus spp.) are important pollinators of both crops and wildflowers. Their contribution to this essential ecosystem service has been threatened over recent decades by changes in land use, which have led to declines in their populations. In order to design effective conservation measures, it is important to understand the effects of variation in landscape composition and structure on the foraging activities of worker bumble bees. This is because the viability of individual colonies is likely to be affected by the trade‐off between the energetic costs of foraging over greater distances and the potential gains from access to additional resources. We used field surveys, molecular genetics, and fine resolution remote sensing to estimate the locations of wild bumble bee nests and to infer foraging distances across a 20‐km² agricultural landscape in southern England, UK. We investigated five species, including the rare B. ruderatus and ecologically similar but widespread B. hortorum. We compared worker foraging distances between species and examined how variation in landscape composition and structure affected foraging distances at the colony level. Mean worker foraging distances differed significantly between species. Bombus terrestris, B. lapidarius, and B. ruderatus exhibited significantly greater mean foraging distances (551, 536, and 501 m, respectively) than B. hortorum and B. pascuorum (336 and 272 m, respectively). There was wide variation in worker foraging distances between colonies of the same species, which was in turn strongly influenced by the amount and spatial configuration of available foraging habitats. Shorter foraging distances were found for colonies where the local landscape had high coverage and low fragmentation of seminatural vegetation, including managed agri‐environmental field margins. The strength of relationships between different landscape variables and foraging distance varied between species, for example the strongest relationship for B. ruderatus being with floral cover of preferred forage plants. Our findings suggest that management of landscape composition and configuration has the potential to reduce foraging distances across a range of bumble bee species. There is thus potential for improvements in the design and implementation of landscape management options, such as agri‐environment schemes, aimed at providing foraging habitat for bumble bees and enhancing crop pollination services.

Carbon sequestration (C seq ) in soils and plant biomass is viewed as an important means of mitigating climate change. Recent global assessments have estimated considerable potential for terrestrial C seq , but generally lack sensitivity to climate warming, nutrient limitations and perspective on local land use. These are important factors since higher temperatures can accelerate the decomposition of soil organic matter, nutrient availability affects plant productivity, while land use pressures put broader constraints on terrestrial organic matter inputs and storage. Here, we explore the potential for C seq under changing land use, climate and nutrient conditions in a UK-based national scale case study. We apply an integrated terrestrial C–N–P cycle model with representative ranges of high-resolution climate and land use scenarios to estimate C seq potential across the UK. If realistic UK targets for grassland restoration and afforestation over the next 30 years are met, we estimate that an additional 120 TgC could be sequestered by 2100 (similar to current annual UK greenhouse gas emissions or roughly 7% of net emission cuts needed in meeting net zero), conditional on climate change of <2 °C. Conversely, we estimate that UK arable expansion would reduce terrestrial carbon storage by a similar magnitude. The most pessimistic climate trajectories are predicted to cause net losses in UK soil carbon storage under all land use scenarios. Warmer climates substantially reduce the potential total terrestrial carbon storage gains offered by afforestation and grassland restoration. We conclude that although concerted land use change could make an important moderate contribution to national level C seq for countries like the UK, soil C seq only provides a contribution if we are on a low emission pathway, and is therefore conditional on deep global cuts to emissions from fossil fuels, deforestation and soil degradation.

Due to rising demand for both food and environmental services, agriculture is increasingly required to deliver multiple outcomes. Characterising differences, across agricultural landscapes, via the identification of broad archetypal groupings, is an important step in exploring spatial patterns in the capacity of land to deliver these potentially competing functions. Creating characterisations at multiple levels, for landscape and farm management, can allow policy-makers and land managers to harmonise delivery of ecosystem services at different intervention scales. This can identify ways to increase the complementarity of public goods and the sustainability of farmed landscapes. We used data-driven machine learning to create landscape and agricultural management archetypes (1 km resolution) at three levels, defined by opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. By using a non-nested hierarchy, we identified which types of management are restricted to certain landscape settings, and which are applicable across multiple landscape contexts. Understanding variation within and between agricultural landscapes and farming practices has implications for planning environmental sustainability and food security. It can also aid understanding of the scale at which interventions could be most effective, from incentivising changes in farmer behaviour to policy drivers of large-scale land use change.

ContextHistorical maps of land use/land cover (LULC) enable detection of landscape changes, and help to assess drivers and potential future trajectories. However, historical maps are often limited in their spatial and temporal coverage. There is a need to develop and test methods to improve re-construction of historical landscape change.ObjectivesTo implement a modelling method to accurately identify key land use changes over a rural landscape at multiple time points.MethodsWe used existing LULC maps at two time points for 1930 and 2015, along with a habitat time-series dataset, to construct two new, modelled LULC maps for Dorset in 1950 and 1980 to produce a four-step time-series. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Scenario Generator tool to model new LULC maps.ResultsThe modelled 1950 and 1980 LULC maps were cross-validated against habitat survey data and demonstrated a high level of accuracy (87% and 84%, respectively) and low levels of model uncertainty. The LULC time-series revealed the timing of LULC changes in detail, with the greatest losses in neutral and calcareous grassland having occurred by 1950, the period when arable land expanded the most, whilst the expansion in agriculturally-improved grassland was greatest over the period 1950–1980.ConclusionsWe show that the modelling approach is a viable methodology for re-constructing historical landscapes. The time-series output can be useful for assessing patterns and changes in the landscape, such as fragmentation and ecosystem service delivery, which is important for informing future land management and conservation strategies.

ContextMaximising insect pollination of mass-flowering crops is a widely-discussed approach to sustainable agriculture. Management actions can target landscape-scale semi-natural habitat, cropping patterns or field-scale features, but little is known about their relative effectiveness.ObjectiveTo test how landscape composition (area of mass-flowering crops and semi-natural habitat) and field-scale habitat (margins and hedges) affect pollinator species richness, abundance, and pollen deposition within crop fields.MethodsWe surveyed all flower visitors (Diptera, Coleoptera and Hymenoptera) in oilseed rape fields and related them to landscape composition and field features. Flower visitors were classified as bees, non-bee pollinators and brassica specialists. Total pollen deposition by individual taxa was estimated using single visit pollen deposition on stigmas combined with insect abundance.ResultsThe area of mass-flowering crop had a negative effect on the species richness and abundance of bees in fields, but not other flower visitors. The area of semi-natural habitat in the surrounding landscape had a positive effect on bees, but was not as important as the area of mass-flowering crop. Taxonomic richness and abundance varied significantly between years for non-bee pollinators. Greater cover of mass-flowering crops surrounding fields had a negative effect on pollen deposition, but only when non-bee pollinator numbers were reduced.ConclusionsManagement choices that result in landscape homogenisation, such as large areas of mass-flowering crops, may reduce pollination services by reducing the numbers of bees visiting fields. Non-bee insect pollinators may buffer these landscape effects on pollen deposition, and management to support their populations should be considered.

ABSTRACT Most national assessments of climate change‐related risks to agriculture focus on the productivity of existing crops. However, one adaptation option is to switch to alternative crops better suited to changing local climates. Spatially explicit projections of relative climatic suitability across a wide range of crops can identify which ones might be viable alternatives. Parametrising process‐based models for multiple crops is complex, so there is value in using simpler approaches to ‘horizon scan’ to identify high‐level issues and target further research. We present a horizon scan approach based on EcoCrop data, producing mapped changes in suitability under +2°C and +4°C warming scenarios (above pre‐industrial), for over 160 crops across the United Kingdom. For the United Kingdom, climate change is likely to bring opportunities to diversify cropping systems. Many current and potential new crops show widespread increases in suitability under a +2°C warming scenario. However, under a +4°C scenario, several current crops (e.g. onions, strawberries, oats, wheat) begin to show declines in suitability in the region of the United Kingdom where most arable crops are currently grown. Whilst some new crops with increasing suitability may offer viable alternatives (e.g. soy, chickpea, grapes), the greatest average increases in suitability across crops occur outside the UK's current areas of greatest agricultural production. Realising these opportunities would thus be likely to require substantial changes to current farming systems and supply chains. By highlighting these opportunities and challenges, our approach provides potentially valuable information to farmers and national assessments. One potential option for agricultural adaptation to climate change is to identify alternative crops better suited to changing local climates. We present a national‐scale horizon‐scan of climatic suitability across over 160 crops for the UK. Climate change may bring opportunities for UK cropping systems. Many potential new crops show increases in suitability (e.g. soy, chickpea, grapes). However, several current crops (e.g. onions, strawberries, wheat) show declines, and the greatest average increases in suitability occur outside the UK's most arable regions.

Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper.

Volunteer-based plant monitoring in the UK has focused mainly on distribution mapping; there has been less emphasis on the collection of data on plant communities and habitats. Abundance data provide different insights into ecological pattern and allow for more powerful inference when considering environmental change. Abundance monitoring for other groups of organisms is well-established in the UK, e.g. for birds and butterflies, and conservation agencies have long desired comparable schemes for plants. We describe a new citizen science scheme for the UK (the 'National Plant Monitoring Scheme'; NPMS), with the primary aim of monitoring the abundance of plants at small scales. Scheme development emphasised volunteer flexibility through scheme co-creation and feedback, whilst retaining a rigorous approach to design. Sampling frameworks, target habitats and species, field methods and power are all described. We also evaluate several outcomes of the scheme design process, including: (i) landscape-context bias in the first two years of the scheme; (ii) the ability of different sets of indicator species to capture the main ecological gradients of UK vegetation; and, (iii) species richness bias in returns relative to a professional survey. Survey rates have been promising (over 60% of squares released have been surveyed), although upland squares are under-represented. Ecological gradients present in an ordination of an independent, unbiased, national survey were well-represented by NPMS indicator species, although further filtering to an entry-level set of easily identifiable species degraded signal in an ordination axis representing succession and disturbance. Comparison with another professional survey indicated that different biases might be present at different levels of participation within the scheme. Understanding the strengths and limitations of the NPMS will guide development, increase trust in outputs, and direct efforts for maintaining volunteer interest, as well as providing a set of ideas for other countries to experiment with.

Context Land-use change is a key driver of biodiversity loss. Models that accurately predict how biodiversity might be affected by land-use changes are urgently needed, to help avoid further negative impacts and inform landscape-scale restoration projects. To be effective, such models must balance model realism with computational tractability and must represent the different habitat and connectivity requirements of multiple species. Objectives We explored the extent to which process-based modelling might fulfil this role, examining feasibility for different taxa and potential for informing real-world decision-making. Methods We developed a family of process-based models (*4pop) that simulate landscape use by birds, bats, reptiles and amphibians, derived from the well-established poll4pop model (designed to simulate bee populations). Given landcover data, the models predict spatially-explicit relative abundance by simulating optimal home-range foraging, reproduction, dispersal of offspring and mortality. The models were co-developed by researchers, conservation NGOs and volunteer surveyors, parameterised using literature data and expert opinion, and validated against observational datasets collected across Great Britain. Results The models were able to simulate habitat specialists, generalists, and species requiring access to multiple habitats for different types of resources (e.g. breeding vs foraging). We identified model refinements required for some taxa and considerations for modelling further species/groups. Conclusions We suggest process-based models that integrate multiple forms of knowledge can assist biodiversity-inclusive decision-making by predicting habitat use throughout the year, expanding the range of species that can be modelled, and enabling decision-makers to better account for landscape context and habitat configuration effects on population persistence.