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1. Landscape decisions are multi-faceted. Framing landscape decision-making as a governance process that requires a collective approach can encourage key stakeholders to come together to co-inform a discussion about their priorities and what constitutes good governance, leading to more holistic landscape decisions. 2. In this paper, we recognise that a suite of complementary and multi-dimensional approaches are in practice used to inform and evaluate land use decisions. We have called these approaches ‘lenses’ because they each provide a different perspective on the same problem. The four lenses are (i) power and market gain, (ii) ecosystem services, (iii) place-based identity and (iv) ecocentric. Each brings a different set of evidence and viewpoints (narrative, qualitative and experiential, as well as quantitative metrics such as monetary) to the decision-making process and can potentially reveal problems and solutions that others do not. 3. Considering all lenses together allows dialogue to take place which can reveal the true complexities of landscape decision-making and can facilitate more effective and more holistic decisions. Employing the lenses requires governance structures that give equal weight to all lenses, enable dialogue and coexistence between top down and bottom-up approaches, and permit adaptation to local and granular place-specifics rather than developing “one-size-fits-all” solutions. 4. We propose that formalising the process of balancing all the lenses requires public participation, and that a lens approach should be used to support landscape decisions alongside a checklist that facilitates transparency in the conversation, showing how all evidence has been considered and critically assessed.

Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models (“the capacity gap”) or knowledge of the accuracy of available models (“the certainty gap”), especially in the world’s poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.

A billion rural people live near tropical forests. Urban populations need them for water, energy and timber. Global society benefits from climate regulation and knowledge embodied in tropical biodiversity. Ecosystem service valuations can incentivise conservation, but determining costs and benefits across multiple stakeholders and interacting services is complex and rarely attempted. We report on a 10-year study, unprecedented in detail and scope, to determine the monetary value implications of conserving forests and woodlands in Tanzania’s Eastern Arc Mountains. Across plausible ranges of carbon price, agricultural yield and discount rate, conservation delivers net global benefits (+US$8.2B present value, 20-year central estimate). Crucially, however, net outcomes diverge widely across stakeholder groups. International stakeholders gain most from conservation (+US$10.1B), while local-rural communities bear substantial net costs (-US$1.9B), with greater inequities for more biologically important forests. Other Tanzanian stakeholders experience conflicting incentives: tourism, drinking water and climate regulation encourage conservation (+US$72M); logging, fuelwood and management costs encourage depletion (-US$148M). Substantial global investment in disaggregating and mitigating local costs (e.g., through boosting smallholder yields) is essential to equitably balance conservation and development objectives.

Sub-Saharan Africa is facing an expected doubling of human population and tripling of food demand over the next quarter century, posing a range of severe environmental, political, and socio-economic challenges. In some cases, key Sustainable Development Goals (SDGs) are in direct conflict, raising difficult policy and funding decisions, particularly in relation to trade-offs between food production, social inequality, and ecosystem health. In this study, we used a horizon-scanning approach to identify 100 practical or research-focused questions that, if answered, would have the greatest positive impact on addressing these trade-offs and ensuring future productivity and resilience of food-production systems across sub-Saharan Africa. Through direct canvassing of opinions, we obtained 1339 questions from 331 experts based in 55 countries. We then used online voting and participatory workshops to produce a final list of 100 questions divided into 12 thematic sections spanning topics from gender inequality to technological adoption and climate change. Using data on the background of respondents, we show that perspectives and priorities can vary, but they are largely consistent across different professional and geographical contexts. We hope these questions provide a template for establishing new research directions and prioritising funding decisions in sub-Saharan Africa.

The Nature Futures Framework (NFF) is a heuristic tool for co-creating positive futures for nature and people. It seeks to open up a diversity of futures through mainly three value perspectives on nature – Nature for Nature, Nature for Society, and Nature as Culture. This paper describes how the NFF can be applied in modelling to support decision-making. First, we describe key considerations for the NFF in developing qualitative and quantitative scenarios: i) multiple value perspectives on nature as a state space where pathways improving nature toward a frontier can be represented, ii) mutually reinforcing key feedbacks of social-ecological systems that are important for nature conservation and human wellbeing, iii) indicators of multiple knowledge systems describing the evolution of complex social-ecological dynamics. We then present three approaches to modelling Nature Futures scenarios in the review, screening, and design phases of policy processes. This paper seeks to facilitate the integration of relational values of nature in models and strengthen modelled linkages across biodiversity, nature's contributions to people, and quality of life.

Artisanal and small-scale mining (ASM) provides a vitally important livelihood for millions of people in many low- and middle-income countries. ASM can result in habitat clearance, increased hunting pressure, pollution, and sedimentation of waterways. Consequently, where mineral and biological wealth coincide, there are trade-offs. Here, we combine geological data with four datasets capturing conservation priorities, to evaluate where, and to what extent, mining may impact biodiversity, and to explore opportunities for both to co-exist. We use Madagascar as a case study: a biodiversity hotspot rich in economically important minerals where artisanal gem mining has conflicted with biodiversity conservation. We identify areas of Madagascar most likely to host primary deposits of gems and find that 11%–14% of the most important area for biodiversity on the island could host primary gem deposits. However, we also identify 7 million hectares (80%) of potentially prospective land which is outside of these areas. Establishing decentralised, community-managed zones for licensed ASM in such areas could help to incentivise formalisation and minimise social and environmental trade-offs. Our mapping approach could be applied in other countries to encourage the establishment of designated zones for ASM in places where mining does not conflict with conservation.

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the f

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.