Search

Assessing habitat loss effects on biodiversity is a major focus of ecological research. The relationship between habitat amount and biodiversity, postulated in the habitat amount hypothesis, is usually assessed at one point in time, which does not account for habitat loss as a temporal process. We examined habitat amount effects at two time periods, 1930s and 2010s, using plant data from three semi-natural habitats: calcareous grassland, heathland and broadleaved woodland, across Dorset, southern England. Woodlands, which changed little in area over the time period, showed minimal effects of habitat amount on species occurrence in both time periods. For grassland and heathland, which had undergone severe losses over the study period, we found the expected positive relationship in the 2010s, but the relationship was negative for these habitats in the 1930s. We explored possible reasons for this result. Total perimeter-to-area ratio (TPAR) showed positive effects in the 1930s for grassland and heathland, suggesting effects of habitat configuration, specifically edge. However, TPAR was highly correlated with habitat amount so this finding is speculative. One possible explanation for the relationships with habitat amount, and the change between the two periods could be the quality of the surrounding matrix. In the 1930s, the landscape was less intensified and was dominated by semi-natural habitats, whereas by the 2010s much had been converted to arable and intensive grasslands. We speculate that species could likely utilise the matrix to a greater degree in the 1930s compared with the 2010s when the matrix was more hostile, thereby decreasing the importance of habitat amount in the 1930s compared with the 2010s. These findings have important implications for conservation, as they show the importance of context (i.e. matrix quality) in determining the relationship between habitat amount and biodiversity.

The academic literature on trade-offs in ecosystem management has paid relatively little attention to justice and poverty reduction objectives. The aim of this paper is to highlight the multiple dimensions of trade-offs in ecosystem services management for poverty alleviation, and to support decision-makers in planning for the almost inevitable trade-offs arising from environmental interventions. The paper brings together different dimensions or lenses through which to analyse trade-offs in ecosystem management for poverty alleviation in a low-income country context. Following a literature review of trade-off decisions, the paper introduces the Balance Sheets Approach to structure trade-off analysis and appraise decisions. We apply the Balance Sheets Approach to analyse five case studies set in very different social-ecological systems where trade-offs were pertinent and undermined poverty alleviation. We show how the combination of ‘positive’ approaches, often used at strategic level, with ‘value’ approaches which analyse multiple values, multi-scale governance, power and capacity, is necessary to analyse complex trade-offs. Based on the case studies we identify four lessons for future trade-off analysis in the context of ecosystem management for poverty alleviation in low-income settings.

Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM data set at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km resolution).

1. The stress-gradient hypothesis (SGH) provides a conceptual framework for explaining howenvironmental context determines the nature of biotic interactions. It may be also useful for predicting geographic variability in the effect of management interventions on biological invasions. We aimed to test hypotheses consistent with the SGH to explain context dependency in bamboo invasion of secondary forests in Japan, and establish a predictive understanding of forest management impacts on invasion.2. We use a priori physiological knowledge of invasive giant bamboo, Phyllostachys bambusoides, to generate hypotheses consistent with the SGH. We modelled variation in giant bamboo occupancy within 810 secondary forest plots across the broad environmental gradients of Japan using a national vegetation database.3. Consistent with the SGH, we find that the effect of tree canopy cover on bamboo occupancy depends on interactions between solar radiation and mean annual temperature. In cool regions with high solar radiation – stressful conditions for bamboo – shade cast by dense canopies facilitates invasion. However, in warmer regions that are more benign, dense canopies tend to inhibit spread via competition for light, space and other resources.4. We used our findings to characterise geographic variability in the effect of forest thinning, a widespread management intervention used to enhance forest biodiversity, on the risk of bamboo spread into secondary forests in Japan. Thinning forest canopies to increase understorey light radiation should limit bamboo spread in cooler regions, while tree planting to increase canopy shade should limit bamboo spread in warmer regions. We provide evidence that the SGH can inform practical recommendations for invasive species control.

Biosphere reserves (BR) balance biodiversity protection and sustainable use through different management restrictions in three zones: core areas, buffer zones, and transition areas. Information about the links between zoning and ecosystem services (ES) is lacking, particularly in terms of the relative roles of natural contributions (ecosystem properties and functions) and anthropogenic contributions (human inputs such as technology and infrastructure) in coproducing ES. This study aimed to: (1) analyse how coproduction of four ES (crop production, grazing, timber production, recreation) differs across the three zones of BRs; and (2) understand which predictors (zoning, natural and anthropogenic contributions, other environmental characteristics) best explain ES provision within BRs. To do this, we collected spatial data on 137 terrestrial BRs in the European Union and on 16 indicators of ES coproduction. We used non-parametric pairwise Wilcoxon rank sum tests to calculate differences in indicators between zones. We used model selection and multiple linear regression to identify predictors of ES provision patterns. Anthropogenic contributions showed most differences between zones, with contributions generally increasing from buffer zones to transition areas. Natural contributions did not, on average, differ between zones, however, for recreation and crop production they decreased from buffer zones to transition areas. ES provision differed between zones only for crop production and grazing, which increased from buffer zones to transition areas. Regression analysis showed that natural contributions are the best predictors of ES provision for all four services. Our results indicate that zoning of BRs has implications for ES coproduction. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. AB, JP and JF were (partly) funded through the Boost Fund of the Klaus Tschira Foundation (KT08, BIOSHARE) granted to MS. AB and MFL gratefully acknowledge the support of iDiv funded by the German Research Foundation (DFG-FZT 118, 202548816). We thank Mick Wu for statistical support. We thank Berta Martín-López for participation in the workshop and helpful comments on an earlier draft of this paper. We thank UNESCO-MAB and UNEP-WCMC for their useful advice on zoning data collection. We are grateful to all national agencies, organisations and biosphere reserve administrations, who helped through providing zoning data, in particular, the Spanish Organismo Autónomo Parques Nacionales and the German Federal Agency for Nature Conservation (BfN). We thank Carlo Rega (JRC) for providing data. This work was supported by the Klaus Tschira Stiftung [KT08, BIOSHARE]; German Research Foundation [DFG-FZT 118, 202548816]. AB, JP and JF were (partly) funded through the Boost Fund of the Klaus Tschira Foundation (KT08, BIOSHARE) granted to MS. AB and MFL gratefully acknowledge the support of iDiv funded by the German Research Foundation (DFG-FZT 118, 202548816). We thank Mick Wu for statistical support. We thank Berta Mart?n-L?pez for participation in the workshop and helpful comments on an earlier draft of this paper. We thank UNESCO-MAB and UNEP-WCMC for their useful advice on zoning data collection. We are grateful to all national agencies, organisations and biosphere reserve administrations, who helped through providing zoning data, in particular, the Spanish Organismo Aut?nomo Parques Nacionales and the German Federal Agency for Nature Conservation (BfN). We thank Carlo Rega (JRC) for providing data.

Summary The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992–2015). Deviations from models of cropland extent in 1992 (“frontierness”) can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models., Graphical Abstract, Highlights • The current (1992) global extent of cropland can be explained with existing data • Recent expansion (1992–2015) of cropland is not well explained with existing data • Deviations from the 1992 model of extent improve models of cropland expansion, Science for Society Cropland area is increasing globally to satisfy the growing population and consumption rates. Cropland expansion often comes at the expense of forests, which are critical for conserving biodiversity and mitigating against climate change. Therefore, it is essential to know where cropland expansion is likely to occur in the future in order to design policies that prevent expansion in areas that are most likely to conflict with forest conservation. However, predicting where expansion is most likely is difficult as few data are available on key predictors related to governance. Here, we devise a novel, two-stage method for predicting expansion of cropland. First, available data are used to model where cropland existed in 1992. We then use maps that show where the first model fails to explain cropland in 1992 to help predict expansion of cropland between 1992 and 2015. We show that this approach is an improvement over simply using existing data to predict recent expansion of cropland., Predicting where cropland is likely to expand globally has important implications for climate change and biodiversity. However, doing so is challenging because of a lack of data on key drivers of current expansion in so-called frontier areas (e.g., governance). Here, we show that using deviations from a model of cropland extent in 1992 built on all available data improves models of recent (1992–2015) cropland expansion over and above using the best available existing data.

Understanding the impact of changes in temperature and precipitation on crop yields is a vital step in developing policy and management options to feed the world. As most existing studies are limited to a few staple crops, we implemented global statistical models to examine the influence of weather and management practices on the yields of 18 crops, accounting for 70% of crop production by area and 65% by calorific intake. Focusing on the impact of temperature, we found considerable heterogeneity in the responses of yields across crops and countries. Irrigation was found to alleviate negative implications from temperature increases. Countries where increasing temperature causes the most negative impacts are typically the most food insecure, with the lowest calorific food supply and average crop yield. International action must be coordinated to raise yields in these countries through improvement and modernization of agricultural practices to counteract future adverse impacts of climate change. The yield variability of 18 crops, representing 70% of global crop area and two-thirds of global calorific intake, is assessed here at the country level for the impacts of weather (temperature and precipitation) and farm management practices (pesticide and fertilizer application, and irrigation).

Bioenergy with Carbon Capture and Storage (BECCS) features heavily in the energy scenarios designed to meet the Paris Agreement targets, but the models used to generate these scenarios do not address environmental and social implications of BECCS at the regional scale. We integrate ecosystem service values into a land-use optimization tool to determine the favourability of six potential UK locations for a 500 MW BECCS power plant operating on local biomass resources. Annually, each BECCS plant requires 2.33 Mt of biomass and generates 2.99 Mt CO 2 of negative emissions and 3.72 TWh of electricity. We make three important discoveries: (a) the impacts of BECCS on ecosystem services are spatially discrete, with the most favourable locations for UK BECCS identified at Drax and Easington, where net annual welfare values (from the basket of ecosystems services quantified) of £39 and £25 million were generated, respectively, with notably lower annual welfare values at Barrow (−£6 million) and Thames (£2 million); (b) larger BECCS deployment beyond 500 MW reduces net social welfare values, with a 1 GW BECCS plant at Drax generating a net annual welfare value of £19 million (a 50% decline compared with the 500 MW deployment), and a welfare loss at all other sites; (c) BECCS can be deployed to generate net welfare gains, but trade-offs and co-benefits between ecosystem services are highly site and context specific, and these landscape-scale, site-specific impacts should be central to future BECCS policy developments. For the United Kingdom, meeting the Paris Agreement targets through reliance on BECCS requires over 1 GW at each of the six locations considered here and is likely, therefore, to result in a significant welfare loss. This implies that an increased number of smaller BECCS deployments will be needed to ensure a win–win for energy, negative emissions and ecosystem services.

Soil moisture (SM) varies widely in space and time. This variability influences agriculture, land-atmosphere interactions and triggers hazards, such as flooding, landslides, droughts, and wildfires. Yet, current observations are limited to a few regional in situ measurement networks or coarse-scale satellite retrievals (9–36-km resolution). As a result, besides site-specific studies, little is known on how SM varies locally (1–100-m resolution). Consequently, quantifying the impact of this variability remains a critical and long-standing challenge in hydrology. This presentation introduces SMAP-HydroBlocks – a novel 30-m resolution SM dataset (2015–2019) that combines hyper-resolution land surface modeling, satellite, and in-situ observations over the United States. Using this data, we reveal the striking variability of local-scale SM across the United States. By mapping the SM spatial variability and its persistence across spatial scales, we show the complex interplay between the landscape and hydroclimate and how this variability is highly scale-dependent. Results show that up to 80% of SM spatial variability information is lost at the 1-km scale, with further losses expected at the scale of current monitoring systems (5–25-km). This high degree of SM variability has a critical influence on freshwater and land ecosystem dynamics. By mapping its spatial variability locally, we provide a stepping-stone towards understanding SM-dependent hydrological, biogeochemical, and ecological processes at local (and so far unresolved) scales.

Geodiversity is under threat from both anthropogenic activities and environmental change which therefore requires active management in the form of geoconservation to minimise future damage. As research on the role of geodiversity on ecosystem service (ES) provision has been limited, there is a need to improve our understanding of which aspects are most important to providing ES to better inform approaches to its conservation. Here, we focus on the cultural ES of hiking in Wales, UK. Harnessing big data from the social media website Flickr, we used the locations of geotagged images of hiking and a range of spatial layers representing geodiversity, biodiversity and anthropogenic predictor variables in habitat suitability models. To gain a deeper understanding of the role of geodiversity in driving the distribution of this cultural service, we estimated the strength and nature of the relationship of each geodiversity, biodiversity and anthropogenic indicator with hiking. Our models show that three geodiversity (distance from coast, range in slope and range in elevation) and two anthropogenic (distance from greenspace access point and distance from road) variables were the most important drivers of hiking. Furthermore, we assessed the content of the images to understand which features of geodiversity people interact with while hiking. We found that people generally take images of geomorphological and hydrological features, such as mountains and lakes. Through understanding the geodiversity, biodiversity and anthropogenic drivers of hiking in Wales, as well as identifying the geodiversity features people interact with while hiking, this analysis can help to inform future geoconservation methods by focusing efforts on these important features.

Protected areas and renewable energy generation are critical tools to combat biodiversity loss and climate change, respectively. Over the coming decades, expansion of the protected area network to meet conservation objectives will be occurring alongside rapid deployment of renewable energy infrastructure to meet climate targets, driving potential conflict for a finite land resource. Renewable energy infrastructure can have negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. Here, we assess current and projected overlaps of wind and solar photovoltaic installations and important conservation areas across nine global regions using spatially explicit wind and solar data and methods for predicting future renewable expansion. We show similar levels of co-occurrence as previous studies but demonstrate that once area is accounted for, previous concerns about overlaps in the Northern Hemisphere may be largely unfounded, although they are high in some biodiverse countries (e.g., Brazil). Future projections of overlap between the two land uses presented here are generally dependent on priority threshold and region and suggest the risk of future conflict can be low. We use the best available data on protected area degradation to corroborate this level of risk. Together, our findings indicate that while conflicts between renewables and protected areas inevitably do occur, renewables represent an important option for decarbonization of the energy sector that would not significantly affect area-based conservation targets if deployed with appropriate policy and regulatory controls.

Over the last decade many ecosystem service (ES) models have been developed to inform sustainable land and water use planning. However, uncertainty in the predictions of any single model in any specific situation can undermine their utility for decision-making. One solution is creating ensemble predictions, which potentially increase accuracy, but how best to create ES ensembles to reduce uncertainty is unknown and untested. Using ten models for carbon storage and nine for water supply, we tested a series of ensemble approaches against measured validation data in the UK. Ensembles had at minimum a 5–17% higher accuracy than a randomly selected individual model and, in general, ensembles weighted for among model consensus provided better predictions than unweighted ensembles. To support robust decision-making for sustainable development and reducing uncertainty around these decisions, our analysis suggests various ensemble methods should be applied depending on data quality, for example if validation data are available.

Social media sites have been gaining traction as a source of novel data for environmental research, particularly for cultural ecosystem service (CES) assessments. However, Reddit, a discussion-based site, has yet to establish itself as an important source of data for CES research, possibly due to researchers not being aware of its potential applications or because Reddit posts lack georeferencing information. Here, we demonstrate how researchers can search Reddit for CES datasets related to recreation and how specific pages on Reddit may provide data for other CES such as aesthetics. Using named-entity recognition, we developed an automated method of geocoding the approximate location of where images in Reddit posts were taken. Furthermore, we compare posts from Reddit and Flickr for a range of recreational activities and compare the content and textual metadata of images relating to hiking. Though there is potential for Reddit data to be used in spatial analysis, we highlight the limitations associated with georeferencing posts. We recommend that data from Reddit is best suited to assessing general trends in CES, either for a given service or place. By demonstrating the value of big data from Reddit we hope to encourage its inclusion in future CES and environmental research.

Following on from this morning’s session, this dialogue session will examine in more detail the benefits and potential risks of implementing nature-based solutions, covering both land and marine-based approaches.

Most decarbonization scenarios of energy systems necessitate more than 500 Mha of land converted to non-food bioenergy crops to provide both energy substitutes for fossil fuels and negative emissions through bioenergy with carbon capture and storage (BECCS). Understanding the environmental and societal impact of this significant land-use change (LUC) is important in determining where and how bioenergy crops should be deployed, and the trade-offs and co-benefits to the environment and society. Here, we use two systematic reviews and a meta-analysis to assess the existing literature on impacts that are likely to have an important effect on public perceptions of the acceptability of such land use change: biodiversity and amenity value. We focus on the impact of LUC to non-food bioenergy crops on agricultural landscapes, where large-scale bioenergy planting may be required. Our meta-analysis finds strong benefits for biodiversity overall (up 75 % ± 13 %), with particular benefits for bird abundance (+ 81 % ± 32 %), bird species richness (+ 100 % ± 31 %), arthropod abundance (+ 52 % ± 36 %), microbial biomass (+ 77 % ± 24 %), and plant species richness (+ 25 % ± 22 %), when land moves out of either arable crops or grassland to bioenergy production. Conversions from arable land to energy trees led to particularly strong benefits, providing an insight into how future LUC to bioenergy crops could support biodiversity. There were inadequate data to complete a meta-analysis on the effects of bioenergy crops on landscape amenity value, and few generalizable conclusions from a systematic review of the literature, however, findings highlight the importance of landscape context and planting strategies in determining amenity values. Our findings demonstrate improved farm-scale biodiversity on agricultural land with bioenergy crops, but also limited knowledge concerning public response to this land use change which could prove crucial to the effective deployment of bioenergy crops for BECCS.

Whilst dedicated bioenergy crops with non-food uses are currently sparsely deployed across the world, most future energy pathways necessitate a sizeable scale-up of 100-500 million ha of land converted to these crops to provide both energy substitutes for fossil fuels and negative emissions through bioenergy with carbon capture and storage (BECCS). In the face of expected bioenergy expansion, understanding the environmental and societal impact of this land-use change is important in determining where and how bioenergy crops should be deployed, and the trade-offs and co-benefits to the environment and society. Here we review the existing literature on two difficult to measure impacts which could prove critical to the future wide-scale acceptability of global bioenergy cropping in the temperate environment: biodiversity and amenity value. We focus on agricultural landscapes, since this is where large-scale bioenergy planting may be required. A meta-analysis of 42 studies on the biodiversity impacts of land-use change from either arable and grassland to bioenergy crops found strong benefits for bird abundance (+ 109 % ± 24 %), bird species richness (+ 100 % ± 31 %), arthropod abundance (+ 299 % ± 76 %), microbial biomass (+ 77 % ± 24 %), and plant species richness (+ 25 % ± 22 %) and a non-significant upward trend in earthworm abundance. Land-use change from arable land led to particularly strong benefits, providing an insight into how future land-use change to bioenergy crops could support biodiversity. Evidence concerning the impact of bioenergy crops on landscape amenity value highlighted the importance of landscape context, planting strategies, and landowner motivations in determining amenity values, with few generalizable conclusions. In this first meta-analysis to quanitfy the impacts of land-use change to bioenergy on on biodiversity and amenity, we have demonsrated improved farm-scale biodiversity on agricultural land but also demonstrated the lack of knowledge concerning public response to bioenergy crops which could prove crucial to the political feasibility of bioenergy policies such as BECCS.

Grasslands cover ca. 7% (2,100,000 km2) of the African continent. They provide a wide range of ecosystem services (e.g., forage, water, recreational spaces, carbon sequestration), and host large wildlife communities. Despite their importance, African grasslands are reported to be suffering from degradation and, perhaps more worryingly, have received little consideration within international policies (e.g., United Nations Sustainable Development Goals). A key issue at present is widespread woody plant encroachment (WPE), which it is shifting African grassland from a grassy- to a (less palatable) woody-dominated biome. However, the way climatic (e.g., precipitation, soil moisture) and non-climatic disturbances (e.g., fire, population density) affect WPE is still poorly understood, particularly at large spatiotemporal scales. Here we identified grasslands in sub-Saharan Africa according to the ESA Climate Change Initiative (CCI) land cover product and use vegetation optical depth (VOD) from passive microwave observations as a proxy for woody vegetation change between 1992 and 2011. We then use independent climatic (precipitation and soil moisture) and non-climatic (burn intensity, population change) data to assess how both spatiotemporal variations and interactions between climatic and non-climatic drivers controlled rates of VOD increase during 1992-2011. We consider not only annual precipitation, soil moisture, fire, and population data, but also integrated and lagged precipitation data (both up to five years ahead of VOD) in these models. Preliminary results reveal a large overall increase in woody vegetation in sub-Saharan Africa grasslands as well as considerable spatiotemporal variation in VOD change that is not due to climatic factors alone.

Protected areas and renewable energy generation are key tools to combat biodiversity loss and climate change respectively. Over the coming decades, very large-scale expansion of renewable energy infrastructure will be needed to meet climate change targets, while simultaneously large-scale expansion of the protected area network to meet conservation objectives is planned. However, renewable energy infrastructure has negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. However, data limitations mean that the degree of likely future conflict of these two key land management objectives has not been fully assessed. Here, we address this gap by examining current and projected future overlaps of wind and solar photovoltaic installations and important conservation areas globally using new spatially explicit wind and solar photovoltaic data, and new methods for predicting future renewable expansion. We show similar levels of co-occurrence of important conservation areas and wind and solar installations as previous studies but also show that once area is accounted for previous concerns about overlaps in Northern Hemisphere may be largely unfounded, though are high in some high-biodiversity countries (e.g. Brazil). Future projections of overlap between the two land uses are generally lower than previously predicted using new data, with regional correlation coefficients peaking at -0.3418 and 0.2053, suggesting a low risk of future conflict. Our results show that the current and future overlap of the two land uses may not be as severe as previously suggested. This is important, as global efforts to decarbonise energy systems are central to mitigating against climate change and against the strong negative impacts of projected climate change on biodiversity.

Machine learning (ML) expands traditional data analysis and presents a range of opportunities in ecosystem service (ES) research, offering rapid processing of ‘big data’ and enabling significant advances in data description and predictive modelling. Descriptive ML techniques group data with little or no prior domain specific assumptions; they can generate hypotheses and automatically sort data prior to other analyses. Predictive ML techniques allow for the predictive modelling of highly non-linear systems where casual mechanisms are poorly understood, as is often the case for ES. We conducted a review to explore how ML is used in ES research and to identify and quantify trends in the different ML approaches that are used. We reviewed 308 peer-reviewed publications and identified that ES studies implemented machine learning techniques in data description (64%; n = 308) and predictive modelling (44%), with some papers containing both categories. Classification and Regression Trees were the most popular techniques (60%), but unsupervised learning techniques were also used for descriptive tasks such as clustering to group or split data without prior assumptions (19%). Whilst there are examples of ES publications that apply ML with rigour, many studies do not have robust or repeatable methods. Some studies fail to report model settings (43%) or software used (28%), and many studies do not report carrying out any form of model hyperparameter tuning (67%) or test model generalisability (59%). Whilst studies use ML to analyse very large and complex datasets, ES research is generally not taking full advantage of the capacity of ML to model big data (1138 medium number of data points; 13 median quantity of variables). There is great further opportunity to utilise ML in ES research, to make better use of big data and to develop detailed modelling of spatial-temporal dynamics that meet stakeholder demands.

The social media website Flickr contains a wealth of spatial and temporal metadata, which can play an important role in environmental research including cultural ecosystem service and ecological assessments. However, the uptake of Flickr is potentially limited by issues with accessibility to the Flickr Application Planning Interface (API), which limits results and restricts searches. Here, we introduce photosearcher, an R package aimed at overcoming these challenges. We provide examples of how photosearcher can be used as an accessible and reproducible method of accessing large spatio-temporal datasets from the Flickr API.

The past 35 years has seen an accumulation of empirical evidence suggesting a positive association between greenspace and mental health. Existing reviews of evidence are narrow in scope, and do not adequately represent the broad range of disciplines working in this field. This study is the first systematic map of studies investigating greenspace effects on mental health. A total of 6059 papers were screened for their relevance, 276 of which met inclusion criteria for the systematic map. The map revealed several methodological limitations hindering the practical applications of research findings to public health. Critically, the majority of studies used cross-sectional mental health data which makes causal inference about greenspace effects challenging. There are also few studies on the micro-features that make up greenspaces (i.e., their “quality”), with most focussing only on “quantity” effects on mental health. Moreover, few studies adopted a multi-scale approach, meaning there is little evidence about at which spatial scale(s) the relationship exists. A geographic gap in study location was also identified, with the majority of studies clustered in European countries and the USA. Future research should account for both human and ecological perspectives of “quality” using objective and repeatable measures, and consider the potential of scale-dependent greenspace effects to ensure that management of greenspace is compatible with wider scale biodiversity targets. To establish the greenspace and metal health relationship across a life course, studies should make better use of longitudinal data, as this enables stronger inferences to be made than more commonly used cross-sectional data.

Ecologically distinct species - species with distinct trait combinations - are not directly prioritized in current conservation frameworks. The consequence of this blind spot means species with the most distinct ecological strategies might be lost. Here, we quantify ecological distinctiveness, based on six traits, for 10,960 bird and 5,278 mammal species, summarizing species-level ecological irreplaceability. We find that threatened birds and mammals are, on average, more ecologically distinct. Specific examples of ecologically distinct and highly threatened species are Great Indian Bustard, Amsterdam Albatross, Asian Elephant and Sumatran Rhinoceros. These species have potentially irreplaceable ecological roles and their loss could undermine the integrity of ecological processes and functions. Yet, we also identify ecologically distinct widespread generalists, such as Lesser Black-backed Gull and Wild Boar. These generalist species have distinct ecological strategies that allow them to thrive across multiple environments. Thus, we suggest that high ecological distinctiveness is associated with either high extinction risk or successful hyper-generalism. We also find that ecologically distinct species are generally charismatic (using a previous measure of public perceptions of charisma). We thus highlight a conservation opportunity: capitalizing on public preferences for charismatic species could provide support for the conservation of the most ecologically distinct birds and mammals. Overall, our prioritization framework supports the conservation of species with irreplaceable ecological strategies, complementing existing frameworks that target extinction risk and evolutionary distinctiveness.

ContextFunctional responses to landscape heterogeneity are context-dependent, hampering the transferability of landscape-scale conservation initiatives. Japan provides a unique opportunity to test for regional modification of landscape effects due to its broad temperature gradient, coincident with a gradient of historical disturbance intensity.ObjectivesTo quantify and understand how regional contexts modify forest bird community responses to landscape heterogeneity across Japan.MethodsWe characterised the functional trait composition and diversity of breeding bird communities from 297 forest sites, and applied a cross-scale analytical framework to explain regional variation in community responses.ResultsThe effects of landscape diversity, coincident with forest loss, varied in strength and even direction across the temperature gradient. Cool regions of Japan with highly forested, homogeneous landscapes supported bird communities dominated by forest specialists: those with narrow habitat breadths and insectivorous diets. Warmer regions comprised communities dominated by generalists with wider habitat breadths, even in contiguous, highly forested landscapes. Heterogeneous landscapes selected for generalists, and only promoted functional trait diversity in cool regions where both specialists and generalists can be supplied by a diverse regional pool.ConclusionsOur results provide evidence that regional variation in trait responses to landscape heterogeneity—driven by past environmental filtering and broad-scale climates—leads to differential community responses across Japan. Future research that seeks a nuanced understanding of the regional modification of landscape variables will better serve to inform and target real-world conservation efforts.

AimThe diversity of birds and mammals is typically described in separate analyses, but species may play similar roles. Here, we develop a comparative trait framework for birds and mammals to provide a global quantification of the similarity of species roles (functional redundancy) and the breadth of roles across taxa (functional dispersion). We predict different contributions of birds and mammals to redundancy and dispersion, and unique geographical patterns of redundancy and dispersion by including both taxa.LocationGlobal.Time periodContemporary.Major taxa studiedBirds and mammals.MethodsWe systematically select, compile and impute the same six traits (i.e., a common currency of traits) across 15,485 bird and mammal species from multiple databases. We use these six traits to compute functional redundancy and functional dispersion for birds and mammals across all 825 terrestrial ecoregions. We then calculate the standardized effect size (SES) of these observed values compared with null expectations, based on a randomization of species composition (i.e., independent of differences in species richness).ResultsWe find that species‐rich regions, such as the Neotropics, have high functional redundancy coupled with low functional dispersion, characterizing a global trade‐off. Thus, in general, as species richness increases, the similarity in species functional roles also increases. We therefore suggest that different processes generate species richness/functional redundancy and functional dispersion, leading to a novel, and generally non‐tropical, distribution of hotspots of high functional dispersion across Madagascar, Eastern Asia and Western USA.Main conclusionsWe recommend consideration of both the similarity and the breadth of functional roles across species pools, including taxa that may play similar roles. We therefore suggest that functional redundancy, as a means of insurance, and functional dispersion, as an indicator of response diversity, should be evaluated further as conservation objectives.

The UK government has made formal commitments to reduce GHG emissions (e.g. under the Climate Change Act 2008 and subsequent amendments) and to protect/improve natural capital and the environment (e.g. as part of the 25 Year Environment Plan published in 2018). Meeting these objectives requires an integrated approach to two parallel challenges i) decarbonising the energy system and ii) better understanding and valuation of natural capital and ecosystem services. From an academic perspective this involves bringing together two substantial, but rather weakly connected bodies of research, while also acknowledging that this integration in a UK setting needs to recognise the international context (i.e. a whole systems perspective).The ADVENT project (ADdressing Valuation of Energy and Nature Together) has been funded by the UK National Environment Research Council to develop conceptual frameworks and modelling tools which ‘integrate the analysis of prospective UK energy pathways with considerations relating to the value of natural capital’. A methodology has been implemented to downscale the outputs of pathways from national energy system models and incorporate environmental impacts into the assessment of different options. This has required defining spatially-optimised distributions of investments in new energy infrastructure using a range of financial and welfare criteria. These distributions are then compared in terms of their construction, transport and land opportunity costs, as well as the implications for biodiversity, greenhouse gas emissions, recreation, visual amenity and water resources.This paper will present results from comparing different UK energy pathways through to 2050 in terms of the implications of electricity generation from three types of renewables (bioenergy, solar and onshore wind). The results illustrate that i) individual pathways can vary appreciably in their environmental impacts, ii) overall societal welfare can be enhanced by using spatial modelling to incorporate valuations of such impacts into implementation of pathways and iii) assessment outcomes can be sensitive to modelling assumptions (e.g. regarding the proportion of biomass feedstock from domestic or international sources). More broadly, the results demonstrate how important improvements can be achieved in the integration of environmental considerations into the assessment of future energy pathways at regional and national scales. The approach is now being further refined through the UK Energy Research Centre Phase 4 programme and ADVANCES Landscape Decisions project in the UK, as well as the five-country IRENES project funded by Interreg Europe.

Bioenergy has been identified as a key contributor to future energy scenarios consistent with the Paris Agreement targets, and is relied upon in scenarios both with and without bioenergy with carbon capture and storage, owing to the multiple ways in which bioenergy can substitute fossil fuels. Understanding the environmental and societal impacts of land-use change (LUC) to bioenergy crops is important in determining where and how they could be deployed, and the resulting trade-offs and co-benefits. We use systematic review and meta-analysis to assess the existing literature on two poorly understood impacts of this LUC that are likely to have an important effect on public acceptability: cultural ecosystem services and biodiversity. We focus on the impact of LUC to non-food bioenergy crops on agricultural landscapes, where large-scale bioenergy planting may be required. Our meta-analysis finds strong benefits for biodiversity overall (up 75% ± 13%), with particular benefits for bird abundance (+81% ± 32%), bird species richness (+100% ± 31%), arthropod abundance (+52% ± 36%), microbial biomass (+77% ± 24%), and plant species richness (+25% ± 22%), when land moves out of either arable crops or grassland to bioenergy production. Conversions from arable land to energy trees led to particularly strong benefits, providing an insight into how future LUC to non-food bioenergy crops could support biodiversity. There were inadequate data to complete a meta-analysis on the effects of non-food bioenergy crops on cultural ecosystem services, and few generalizable conclusions from a systematic review of the literature, however, findings highlight the importance of landscape context and planting strategies in determining impact. Our findings demonstrate improved farm-scale biodiversity on agricultural land with non-food bioenergy crops, but also limited knowledge concerning public response to this LUC, which could prove crucial to the successful expansion of bioenergy to meet the Paris targets.

Images and textual metadata from social media sites such as Flickr have been used to understand the drivers and distributions of cultural ecosystem services (CES). However, using all available data from social media sites may not provide an accurate representation of individual services. For example, an image of nature might be described negatively in the image’s description. Here, we present a novel approach to refining social media data to represent CES better, including filtering by keywords, photograph content and enriching the data by including a measure of the sentiment expressed in the textual metadata. We demonstrate that the distribution of an enriched dataset of Flickr images representing hiking in the USA can contribute to different results and conclusions than the full dataset. Furthermore, we classified the contents of these hiking images and, using latent semantic analysis, clustered the images into ten groups based on the similarity of their content. The groups provide rich information, such as the importance of geodiversity and biodiversity in supporting a positive hiking experience. The application of this method can help to enrich social media data for CES studies, allowing researchers to further untangle the complex socio-ecological interactions that drive CES distributions, benefits and values.

Multiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science.Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge.

Many decision-makers are looking to science to clarify how nature supports human well-being. Scientists' responses have typically focused on empirical models of the provision of ecosystem services (ES) and resulting decision-support tools. Although such tools have captured some of the complexities of ES, they can be difficult to adapt to new situations. Globally useful tools that predict the provision of multiple ES under different decision scenarios have proven challenging to develop. Questions from decision-makers and limitations of existing decision-support tools indicate three crucial research frontiers for incorporating cutting-edge ES science into decision-support tools: (1) understanding the complex dynamics of ES in space and time, (2) linking ES provision to human well-being, and (3) determining the potential for technology to substitute for or enhance ES. We explore these frontiers in-depth, explaining why each is important and how existing knowledge at their cutting edges can be incorporated to improve ES decision-making tools.

Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet’s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems., Measurement(s)geographic location • powerTechnology Type(s)digital curation • computational modeling techniqueFactor Type(s)landscape area • panel area • turbinesSample Characteristic - Environmentatmospheric wind • stellar radiation • power plantSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12063225

The fulfilment of the benefits resulting from services provided by nature requires an integrated framework that combines appropriate ecosystem service governance with spatially explicit models of service provision. Here, we propose using a social‐ecological network approach to develop a ‘landscape governance framework’ that identifies how different types of governance can act on supply, demand and flow of ecosystem services through changes in landscape structure and connections. Starting from undesirable situations where demand exceeds supply, we exemplify the application of this conceptual model considering hierarchical (e.g. creation of protected areas), market (e.g. payments for environmental services) and community‐based (e.g. enhancing links between stakeholders) governance approaches. We show how interventions associated with each of these approaches act in distinct ways to regulate different components of the service provision chain in heterogeneous landscapes. Filling such knowledge gaps can help identify appropriate governance interventions depending on factors that limit provision: restricted supply, demand or flow. The application of the landscape governance framework entails challenges related to availability of data and limited understanding of key underlying mechanisms. However, it opens important new research questions at the interface between governance and ecosystem services, with great potential as a tool for landscape management that aims to achieve ecosystem service sustainability. A free Plain Language Summary can be found within the Supporting Information of this article.

Holistic conservation of ecosystem services (ES) requires a greater understanding of how the interactions of biotic and abiotic aspects of nature provide them. Currently, geodiversity, the diversity of geology, geomorphology, sediments and soils and hydrology, as well as the services that they provide in isolation of interactions with biotic nature – geosystem services (GS) – are overlooked in ES literature and frameworks. Here, we provide a series of three nested frameworks which together help to provide clarity for both the theoretical role of geodiversity in service production as well as the basis for real-world management strategies. First, we present the ‘Geodiversity Flower’, a framework that can be operationalised to provide clarity in terminology to decision-makers. Second, we present the ‘Geo-Eco Services Framework’, which establishes the difference between ES and GS. The final framework presented is the ‘Geo-Eco Services Cascade Model’, which builds upon the widely used ES cascade model by demonstrating how geodiversity interacts with biotic nature to simultaneously provide ES and GS. Providing a holistic model that integrates both biotic and abiotic nature alongside ES and GS allows for a greater understanding of the roles of abiotic and biotic nature to services and their associated benefits and values to people.

Vulnerability to habitat fragmentation Habitat fragmentation caused by human activities has consequences for the distribution and movement of organisms. Betts et al. present a global analysis of how exposure to habitat fragmentation affects the composition of ecological communities (see the Perspective by Hargreaves). In a dataset consisting of 4489 animal species, regions that historically experienced little disturbance tended to harbor a higher proportion of species vulnerable to fragmentation. Species in more frequently disturbed regions were more resilient. High-latitude areas historically experienced more disturbance and harbor more resilient species, which suggests that extinction has removed fragmentation-sensitive species. Thus, conservation efforts to limit fragmentation are particularly important in the tropics. Science , this issue p. 1236 ; see also p. 1196