Melissa Chapman

Growing demands for food, energy, minerals and other natural resources are intensifying pressure on ecosystems. Meeting these demands while reducing biodiversity loss requires making decisions about which areas can simultaneously support sustainable development, conserve biodiversity and maintain or restore ecosystem services. Existing spatial optimization tools in planning contexts offer limited support to accommodate multiple objectives, such as maximizing agricultural production and biodiversity outcomes, because they rely on predefined formulations that lack the flexibility to incorporate objectives from multiple sectors and explicitly characterize trade‐offs and synergies among them. Here, we present a multi‐objective optimization framework for spatial planning. The framework is used to express multiple planning goals and requirements as optimization problems, identify solutions that simultaneously satisfy multiple objectives and explore trade‐offs between them. It provides a flexible structure for specifying objectives within and across sectors and supports multiple multi‐objective optimization approaches—weighted‐sum, hierarchical and reference point approaches. To promote accessibility, we implemented the framework as an update to the prioritizr R package. Using a multiple‐use case study in the European Union, we demonstrate how the framework supports the identification of solutions that balance trade‐offs between ecosystem restoration, agriculture and forestry production. Synthesis and applications. Our framework enables conservation scientists, planners and practitioners to identify spatial plans that achieve multiple objectives. It is designed to help explore trade‐offs across a broad range of domains, including terrestrial, marine and freshwater realms. By reducing technical barriers, the framework facilitates exploration of trade‐offs and identification of co‐benefits across diverse ecological, economic and social planning contexts.

Understanding how wealth and economic inequality shape global land-use change is both a long-standing and urgent challenge. While existing empirical evidence largely relies on either national-scale metrics of wealth and inequality or small-scale case studies, few analyses combine global scope with local-scale data. Here we do so, linking local wealth and local inequality to satellite-derived deforestation and its proximate drivers for 27,657 household survey clusters across 52 countries. We find that local wealth, rather than inequality, is the stronger predictor of forest loss, but the direction of this effect is not uniform: depending on the country, a one-standard-deviation increase in local wealth is associated with anywhere from a 19% decrease to a 36% increase in the expected deforestation rate, with no discernible effect in most countries. The share of tree cover loss due to permanent agriculture tends to be higher in wealthier clusters, while the share due to shifting cultivation tends to be higher in areas with lower wealth. We further identify clusters with high wealth, low inequality, and low deforestation, to understand the environmental and socioeconomic drivers of these "triple-win" clusters. Triple-win areas tend to cluster spatially, suggesting that district-level analysis is key to identifying policy levers and institutional structures that can reconcile human development with environmental protection.

How does stakeholder participation in natural resource management change when conservation rules are grounded in near real-time data? Recent technological advances have increased the feasibility of the ‘dynamic management’ of natural resources, which promises to align the spatiotemporal scales of management with ecological variability and resource use. Drawing on Kelty’s (2020) concept of ‘contributory autonomy,’ this article offers a critical comparison of how participation is conceived of in the more established context of static conservation areas and planning versus the emergent field of dynamic management. A systematic review of the dynamic ocean management literature reveals a varied, but shallow engagement with the topic of stakeholder participation in that context. Whereas static management regimes are governed by relatively intuitive and contestable maps, dynamic management is governed by models and data flows. Overall, the decision-making stakeholder of participatory mapping processes under static management is displaced by the stakeholder conceived as an ‘end-user’ of a dynamic management product and consultant in its design. Yet, these shifts also open up potential points of contestation, which may pattern the future theory and practice of participation in dynamic management: counterdata, countermodeling, and data chokepoints. Beyond the empirical focus on oceans, this article contributes to broader conversations about the political stakes of environmental data, and algorithmic and artificial intelligence-driven natural resource conservation by considering how possibilities for participation are foreclosed, enabled, and reconstituted by new spatiotemporal and technological conditions.

In June 2023, the United Nations adopted the High Seas Treaty. Two years later, signatories are poised to ratify this treaty and create a legal instrument to implement marine protected areas (MPAs) in our greatest global commons, ocean areas beyond national jurisdiction. Protection of the open and deep ocean is timely: we stand at the precipice of an industrial revolution in Earth's largest remaining wilderness. Deciding where to strategically implement high seas MPAs under this treaty requires robust biodiversity information, yet publicly accessible data is sparse, particularly at depth. There is now an opportunity for collective science action to support this collective policy action. Realising this opportunity necessitates swift solutions including (1) supporting and incentivising standardised public sharing of existing biodiversity data; (2) broadening the scope of participatory science to process ocean observations into biodiversity data; and (3) equitably implementing new data collection with research partners across our global community.

With over 2 billion observations of species from across the earth, the Global Biodiversity Information Facility (GBIF) has become an influential repository of information for not only ecologists, but also conservation scientists and policy makers. Yet biodiversity data reveal more than species distributions. They reflect colonial histories, economic inequities, and cultural differences. This article argues for moving beyond ideas of ‘spatial bias’ in conservation science to highlight the colonial legacies and contemporary social forces that shape biodiversity data. Drawing on interviews with the GBIF staff and data publishers as well as critical mapping of GBIF data patterns, we visualize and analyze how and why colonial patterns persist and shift in this global biodiversity data. To explain diverse trajectories of postcolonial production of data, we identify three social forces–colonial legacies of collecting, infrastructures of international development, and contemporary data cultures–that structure biodiversity observation distributions and data ownership and availability. Together, these findings demonstrate how, even in the age of open access, colonial and postcolonial practices and politics continue to shape what we know about the natural world.

The European Union is committed to achieving ambitious area-based conservation and restoration targets in the upcoming decade. However, there is concern that these targets risk conflicting with socioeconomic needs, particularly for food, timber and bioenergy production. Here we develop an integrated spatial planning approach to identify where restoration, conservation and production allocation could maximize benefits to species conservation and climate mitigation, while acknowledging future land demands of the bio-economy. We show that, while changing production demands risk driving further biodiversity loss by 2030, when these demands are met alongside strategic restoration measures, as outlined by the EU Nature Restoration Regulation, future landscapes could improve the conservation status of populations for more than 20% of species of conservation concern while also increasing terrestrial carbon stocks. Our analysis demonstrates how critical the Nature Restoration Regulation is to achieving biodiversity targets and how integrated planning can align biodiversity policy objectives with future socioeconomic demands.

The European Union is committed to achieving ambitious area-based conservation and restoration targets in the upcoming decade. However, there is concern that these targets risk conflicting with socio-economic needs, particularly for food, timber, and bioenergy production. Here, we develop an integrated spatial planning approach to identify where restoration, conservation, and production allocation could maximize benefits to species conservation and climate mitigation, while acknowledging future changing lands demands of the bioeconomy. We show that, while changing production demands risk driving further biodiversity loss by 2030, when these demands are met alongside strategic restoration measures, as outlined by the EU Nature Restoration Regulation (NRR), future landscapes could improve the conservation status of $>$20\% of species of conservation concern while also increasing terrestrial carbon stocks. Our analysis demonstrates how critical the NRR is to achieve biodiversity targets, and how integrated planning can align biodiversity policy objectives with future socio-economic demand.