Solving conservation planning problems with integer linear programming

Systematic conservation prioritization with the prioritizr R package

Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land-use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision-making. We outlined how the prioritizr R package (https://prioritizr.net) can be used for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open-source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington state (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. We also conducted a benchmark analysis to examine the performance of different solvers. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species' distributions covered by protected areas. Although open source and commercial solvers were able to quickly solve large-scale conservation planning problems, commercial solvers were required for complex, large-scale problems.. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real-world decision-making.

Integrating forest restoration into land-use planning at large spatial scales

Forest restoration is being scaled up globally, carrying major expectations of environmental and societal benefits. Current discussions on ensuring the effectiveness of forest restoration are predominantly focused on the land under restoration per se. But this focus neglects the critical issue that land use and its drivers at larger spatial scales have strong implications for forest restoration outcomes, through the influence of landscape context and, importantly, potential off-site impacts of forest restoration that must be accounted for in measuring its effectiveness. To ensure intended restoration outcomes, it is crucial to integrate forest restoration into land-use planning at spatial scales large enough to account for - and address - these larger-scale influences, including the protection of existing native ecosystems. In this review, we highlight this thus-far neglected issue in conceptualizing forest restoration for the delivery of multiple desirable benefits regarding biodiversity and ecosystem services. We first make the case for the need to integrate forest restoration into large-scale land-use planning, by reviewing current evidence on the landscape-level influences and off-site impacts pertaining to forest restoration. We then discuss how science can guide the integration of forest restoration into large-scale land-use planning, by laying out key features of methodological frameworks required, reviewing the extent to which existing frameworks carry these features, and identifying methodological innovations needed to bridge the potential shortfall. Finally, we critically review the status of existing methods and data to identify future research efforts needed to advance these methodological innovations and, more broadly, the effective integration of forest restoration design into large-scale land-use planning.

Anticipating anthropogenic threats in acquiring new protected areas

Biodiversity continues to decline despite protected area expansion and global conservation commitments. Biodiversity losses occur in existing protected areas, yet common methods used to select protected areas ignore postimplementation threats that reduce effectiveness. We developed a conservation planning framework that considers the ongoing anthropogenic threats within protected areas when selecting sites and the value of planning for costly threat-mitigating activities (i.e., enforcement) at the time of siting decisions. We applied the framework to a set of landscapes that contained the range of possible correlations between species richness and threat. Accounting for threats and implementing enforcement activities increased benefits from protected areas without increasing budgets. Threat information was valuable in conserving more species per spending level even without enforcement, especially on landscapes with randomly distributed threats. Benefits from including threat information and enforcement were greatest when human threats peaked in areas of high species richness and were lowest where human threats were negatively associated with species richness. Because acquiring information on threats and using threat-mitigating activities are costly, our findings can guide decision-makers regarding the settings in which to pursue these planning steps.

Designing an optimized landscape restoration with spatially interdependent non-linear models

Brazilian Atlantic Forest is a biodiversity hotspot drastically fragmented due to different land use practices. Our understanding on the impacts of fragmentation and restoration practices on ecosystem functionality significantly increased during the last decades. However, it is unknown to our knowledge how a precision restoration approach, integrated with landscape metrics, will affect the decision-making process of forest restoration. Here, we applied Landscape Shape Index and Contagion metrics in a genetic algorithm for planning forest restoration in watersheds at the pixel level. We evaluated how such integration may configure the precision of restoration with scenarios related to landscape ecology metrics. The genetic algorithm worked toward optimizing the site, shape, and size of forest patches across the landscape according to the results obtained in applying the metrics. Our results, obtained by simulations of scenarios, support aggregation of forest restoration zones as expected, with priority restoration areas indicated where most of the aggregation of forest patches occurs. Our optimized solutions for the study area (Santa Maria do Rio Doce Watershed) predicted an important improvement of landscape metrics (LSI = 44 %; Contagion/LSI = 73 %). Largest shifts are suggested based on LSI (i.e., three larger fragments) and Contagion/LSI (i.e., only one well-connected fragment) optimizations. Our findings indicate that restoration in an extremely fragmented landscape will promote a shift toward more connected patches and with reduction of the surface:volume ratio. Our work explores the use of genetic algorithms to propose forest restoration based on landscape ecology metrics in a spatially explicit innovative approach. Our results indicate that LSI and Contagion:LSI ratio may affect the choice concerning precise location of restoration sites based on forest fragments scattered in the landscape and reinforce the usefulness of genetic algorithms to yield an optimized-driven solution for restoration initiatives.

Biodiversity conservation adaptation to climate change: Protecting the actors or the stage

To be able to protect biodiversity in coming decades, conservation strategies need to consider what sites will be important for species not just today but also in the future. Different methods have been proposed to identify places that will be important for species in the future. Two of the most frequently used methods, ecological niche modeling and climate resilience, have distinct aims. The former focuses on identifying the suitable environmental conditions for species, thus protecting the "actor," namely, the species, whereas the latter seeks to safeguard the "stage," or the landscape in which species occur. We used the two methods to identify climate refugia for 258 forest vertebrates under short- and long-term climatic changes in a biodiversity hotspot, the Appalachian ecoregion of the United States. We also evaluated the spatial congruence of the two approaches for a possible conservation application, that of protecting 30% of the Appalachian region, in line with recent national and international policy recommendations. We detected weak positive correlations between resilience scores and baseline vertebrate richness, estimated with ecological niche models for historical (baseline) climatic conditions. The correlations were stronger for amphibians and mammals than for birds and reptiles. Under climate change scenarios, the correlations between estimated vertebrate richness and resilience were also weakly positive; a positive correlation was detected only for amphibians. Locations with estimated future gain of suitable climatic conditions for vertebrates showed low correlation with resilience. Overall, our results indicate that climate resilience and ecological niche modeling approaches capture different characteristics of projected distributional changes of Appalachian vertebrates. A climate resilience (the stage) approach could be more effective in safeguarding species with low dispersal abilities, whereas an ecological niche modeling (the actor) approach could be more suitable for species with long-distance dispersal capacity because they may be more broadly impacted by climate and less sensitive to geophysical features captured by a climate resilience approach.

Operations research applicability in spatial conservation planning

A large fraction of the current environmental crisis derives from the large rates of human-driven biodiversity loss. Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are needed to use such a massive amount of data in integrative schemes addressing a large variety of concerns. This study provides an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. We discuss the progress of OR models and methods in spatial conservation planning and how those models may be optimized through sophisticated algorithms and computational tools. Moreover, we anticipate possible panoramas of modern spatial conservation studies supported by OR and we explore possible avenues for the design of optimized interdisciplinary collaborative platforms in the era of Big Data, through consortia where distinct players with different motivations and services meet. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation, planners navigate around multiple socioecological/environmental equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence under complex environments.

Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap

The biodiversity and climate change crises have led countries-including Canada-to commit to protect more land and inland waters and to stabilize greenhouse gas concentrations. Canada is also obligated to recover populations of at-risk species, including boreal caribou. Canada has the opportunity to expand its protected areas network to protect hotspots of high value for biodiversity and climate mitigation. However, co-occurrence of hotspots is rare. Here we ask: is it possible to expand the network to simultaneously protect areas important for boreal caribou, other species at risk, climate refugia, and carbon stores? We used linear programming to prioritize areas for protection based on these conservation objectives, and assessed how prioritization for multiple, competing objectives affected the outcome for each individual objective. Our multi-objective approach produced reasonably strong representation of value across objectives. Although trade-offs were required, the multi-objective outcome was almost always better than when we ignored one objective to maximize value for another, highlighting the risk of assuming that a plan based on one objective will also result in strong outcomes for others. Multi-objective optimization approaches could be used to plan for protected areas networks that address biodiversity and climate change objectives, even when hotspots do not co-occur.

The minimum land area requiring conservation attention to safeguard biodiversity

Ambitious conservation efforts are needed to stop the global biodiversity crisis. In this study, we estimate the minimum land area to secure important biodiversity areas, ecologically intact areas, and optimal locations for representation of species ranges and ecoregions. We discover that at least 64 million square kilometers (44% of terrestrial area) would require conservation attention (ranging from protected areas to land-use policies) to meet this goal. More than 1.8 billion people live on these lands, so responses that promote autonomy, self-determination, equity, and sustainable management for safeguarding biodiversity are essential. Spatially explicit land-use scenarios suggest that 1.3 million square kilometers of this land is at risk of being converted for intensive human land uses by 2030, which requires immediate attention. However, a sevenfold difference exists between the amount of habitat converted in optimistic and pessimistic land-use scenarios, highlighting an opportunity to avert this crisis. Appropriate targets in the Post-2020 Global Biodiversity Framework to encourage conservation of the identified land would contribute substantially to safeguarding biodiversity.

Trade-offs Between the Value of Ecosystem Services and Connectivity Among Protected Areas in the Upper Chattahoochee Watershed

The Upper Chattahoochee Watershed supplies most of the drinking water to the Atlanta Metropolitan Area, a region with one of the fastest urban growth rates in the United States. Smart conservation planning is necessary to conciliate urban development and the provision of critical ecosystem services (ESs) such as water quality, carbon storage, and wildlife habitat. We employed optimization models to compare the value of the ESs provided by alternative allocations of land parcels for conservation. We adopted boundary penalties to determine the trade-offs of choosing higher connectivity among parcels regarding economic values provided by carbon storage, wildlife habitat, and water quality. We used InVEST models to quantify and map ESs and value transfer to assign economic values to them. We set low and high ESs economic value bounds and discounted their values to perpetuity using 3% and 7% discount rates. Our results indicate that incorporating boundary penalties results in solutions with larger, fewer, and more connected parcels but yields lower economic benefits than unconstrained models. However, these differences are relatively small (between 2.6% and 7.3% loss in economic value). Additional transaction costs of purchasing more parcels and improving ecological networks provided by larger forest patches might justify the selection of solutions with higher connectivity. Decision-makers can use the developed models for estimating the economic cost of selecting connected parcels for conservation purposes at the landscape level.

Redefining and mapping global irreplaceability

Irreplaceability is a concept used to describe how close a site is to being essential for achieving conservation targets. Current methods for measuring irreplaceability are based on representative combinations of sites, giving them an extrinsic nature and exponential computational requirements. Surrogate measures based on efficiency (complementarity) are often used as alternatives, but they were never intended for this purpose and do not measure irreplaceability. Current approaches used to estimate irreplaceability have key limitations. Some of these are a result of the tools used, but some are due to the nature of the current definition of irreplaceability. For irreplaceability to be stable and useful for conservation purposes and to resolve limitations, irreplaceability measures should adhere to five axioms; baseline coherence, monotonic responsiveness, proportional responsiveness, intrinsic stability, and bounded outputs. We designed a robust method for measuring a site's proximity to irreplaceability that adheres to these requirements and used it to develop the first systematic global map of irreplaceability based on data for terrestrial vertebrates (n = 29,837 species, >1 million grid cells). At least 3.5% of land surface was highly irreplaceable, and 47.6% of highly irreplaceable cells were contained in 12 countries. More generous thresholds of irreplaceability flag greater portions of land surface that would still be realistic to protect under current global objectives. Irreplaceable sites should form a critical component of any global conservation plan and should be part of the UN Convention on Biological Diversity's post2020 Global Biodiversity Framework strategy, forming part of the 30% protection by 2030 target that is gaining support. The reliable identification of irreplaceable sites will be crucial to halting extinctions.

Narrowly distributed taxa are disproportionately informative for conservation planning

Biological atlas data can be used as inputs into conservation decision-making, yet atlases are sometimes infrequently updated, which can be problematic when the distribution of species is changing rapidly. Despite this, we have a poor understanding of strategies for efficiently updating biological atlas data. Using atlases of the distributions of 1630 threatened plant taxa, we quantitatively compared the informativeness of narrowly distributed and widespread taxa in identifying areas that meet taxon-specific conservation targets, and also measured the cost-efficiency of meeting those targets. We also explored the underlying mechanisms of the informativeness of narrowly distributed taxa. Overall, narrowly distributed taxa are far more informative than widespread taxa for identifying areas that efficiently meet conservation targets, while their informativeness for identifying cost-efficient areas varied depending on the type of conservation target. Narrowly distributed taxa are informative mainly because their distributions disproportionately capture areas that are either relatively taxon rich or taxon poor, and because of larger number of taxa captured with given number of records. Where resources for updating biological data are limited, a focus on areas supporting many narrowly distributed taxa could benefit conservation planning.

Areas of global importance for conserving terrestrial biodiversity, carbon and water

To meet the ambitious objectives of biodiversity and climate conventions, the international community requires clarity on how these objectives can be operationalized spatially and how multiple targets can be pursued concurrently. To support goal setting and the implementation of international strategies and action plans, spatial guidance is needed to identify which land areas have the potential to generate the greatest synergies between conserving biodiversity and nature's contributions to people. Here we present results from a joint optimization that minimizes the number of threatened species, maximizes carbon retention and water quality regulation, and ranks terrestrial conservation priorities globally. We found that selecting the top-ranked 30% and 50% of terrestrial land area would conserve respectively 60.7% and 85.3% of the estimated total carbon stock and 66% and 89.8% of all clean water, in addition to meeting conservation targets for 57.9% and 79% of all species considered. Our data and prioritization further suggest that adequately conserving all species considered (vertebrates and plants) would require giving conservation attention to ~70% of the terrestrial land surface. If priority was given to biodiversity only, managing 30% of optimally located land area for conservation may be sufficient to meet conservation targets for 81.3% of the terrestrial plant and vertebrate species considered. Our results provide a global assessment of where land could be optimally managed for conservation. We discuss how such a spatial prioritization framework can support the implementation of the biodiversity and climate conventions.

A framework for collaborative wolverine connectivity conservation

Maintaining connectivity between high-elevation public lands is important for wolverines and other species of conservation concern. This work represents the first effort to prioritize wolverine connectivity under future climate conditions using a systematic conservation planning framework. We optimized 10, 15, 20, and 50% of habitat features for wolverines using integer linear programming. We identified 369 privately owned areas in the 10% solution, 572 in the 15% solution, 822 in the 20% solution, and 3,996 in the 50% solution where voluntary landowner easements would improve the long-term landscape functionality for wolverine connectivity. The median estimated easements ranged from $8,762 to $12,220 across the four solutions (total costs $14,874,371 to $196,346,714). Overall, this effort demonstrates the utility of optimization problems for conserving connectivity, provides a proactive tool to engage potential collaborators, identifies easements that will likely protect various subalpine species, and offers a framework for the conservation of additional species.

Optimal allocation of law enforcement patrol effort to mitigate poaching activities

Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade-off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger-based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1-km² sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US$1,244,800. We evaluated the trade-off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade-off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US$684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.

Expanding protected areas to encompass the conservation of the endangered common dolphin (Delphinus delphis) in the Alboran Sea

The Natura 2000 network is the centerpiece of the European Union conservation strategy to safeguard priority species and habitats. The question of whether other co-occurring species of conservation concern may also benefit from this network, however, remains largely unknown. Here, we used a systematic approach (MARXAN) for i) evaluating if the current Natura 2000 network in the Alboran Sea (western Mediterranean Sea), initially proposed to protect the common bottlenose dolphin (Tursiops truncatus) and priority habitats, is also spatially protecting the endangered common dolphin (Delphinus delphis), and ii) identifying additional marine areas that should be protected to reach adequate conservation targets for the common dolphin. While the current Natura 2000 network encompass ca. 22% of predicted abundances for common dolphins, this percentage might be enhanced by protecting coastal areas nearby the Strait of Gibraltar. However, dolphins and fisheries largely overlap spatially nearby the coastline, and only segregate in offshore areas that represent the marginal distribution of the species. Thus, conservation decision-makers must achieve a trade-off between cetacean conservation and fisheries by combining an area-based approach (i.e., new protected areas close to the Strait of Gibraltar) together with a basin-wide threat-based approach (e.g., regulation of fisheries).

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable fishery. The purpose of this paper is to perform a systematic review of the literature on the smart fishery and to identify upcoming themes for future research on the sustainable fishery in the Age of AI. The findings of the review reveal that scholarly attention in AI-inspired fishery literature focuses mostly on automation of fishery resources monitoring, mainly detection, identification, and classification. Some papers list marine health and primary production which are vital dimensions for Large Marine Ecosystems to recycle nutrients to sustain anticipated production levels. Very few reviewed articles refer to assessing individual needs, particularly fishers, from AI deployment in fisheries and policy response from governments. We call for future AI for sustainable fishery studies on how fishers perceive AI needs, and how governments possess a tangible strategy or depth of understanding on the regulation of AI concerning smart fishery systems and research on resilience-enhancing policies to promote the value and potentials of the AI-inspired smart fishery in different locations.

An accessible optimisation method for barrier removal planning in stream networks

Barriers associated to human infrastructure are a widespread impact in freshwater ecosystems worldwide, disrupting connectivity along river networks and key processes. Restoration of connectivity has risen in the last decade, with thousands of dams, weirs and culverts removed. Spatial optimisation methods can help inform decision on what barriers to remove to maximise gain in connectivity under limited budgets. However, current optimisation approaches rely on programming skills that are not easily accessible to stakeholders, which restrict the use of these methods. We demonstrate how Marxan, a publicly available tool, can be used to prioritise the allocation of barrier removal projects. We mapped the distribution of >900 barriers in the Tagus River (Iberian Peninsula) and 29 freshwater fish species with different movement abilities and needs. We assessed the passability of each barrier by all species and relative removal cost. We then identified priority barriers for removal to increase connectivity of populations of all species simultaneously. We tested two alternative scenarios: i) locking out barriers assesses as non-removable for their high strategic value or removal cost and ii) making all barriers available for removal. We found that connectivity recovery targets could be achieved by removing a small proportion of barriers, and avoiding large infrastructure. However, for some species, large recovery targets could only be achieved by removing some of these large infrastructures at high increases in cost. We also found some spatial differences in the recovery value of particular barriers for improving upstream and downstream connectivity. Our study demonstrates how to use a robust optimisation approach in an accessible tool, to address the complexity of prioritisation exercises commonly faced by stakeholders when deciding where to invest in barrier removal projects. This will improve decision-making for river connectivity restoration through a transparent, reproducible, and better-informed approach than traditional opportunistic or ranking-based approaches.

Freshwater conservation planning in the far north of Ontario, Canada: identifying priority watersheds for the conservation of fish biodiversity in an intact boreal landscape

Freshwater ecosystems show more biodiversity loss than terrestrial or marine systems. We present a systematic conservation planning analysis in the Arctic Ocean drainage basin in Ontario, Canada, to identify key watersheds for the conservation of 30 native freshwater fish, including four focal species: lake sturgeon, lake whitefish, brook trout, and walleye. We created species distribution models for 30 native fish species and accounted for anthropogenic impacts. We used the “prioritizr” package in R to select watersheds that maximize species targets, minimize impacts, and meet area-based targets based on the Convention on Biological Diversity commitment to protect 17% of terrestrial and freshwater areas by 2020 and the proposed target to protect 30% by 2030. We found that, on average, 17.4% and 29.8% of predicted species distributions were represented for each of the 30 species in the 17% and 30% area-based solutions, respectively. The outcomes were more efficient when we prioritized for individual species, particularly brook trout, where 24% and 36% of its predicted distribution was represented in the 17% and 30% solutions, respectively. Future conservation planning should consider climate change, culturally significant species and areas, and the importance of First Nations as guardians and stewards of the land in northern Ontario.

Ocean planning for species on the move provides substantial benefits and requires few trade-offs

Societies increasingly use multisector ocean planning as a tool to mitigate conflicts over space in the sea, but such plans can be highly sensitive to species redistribution driven by climate change or other factors. A key uncertainty is whether planning ahead for future species redistributions imposes high opportunity costs and sharp trade-offs against current ocean plans. Here, we use more than 10,000 projections for marine animals around North America to test the impact of climate-driven species redistributions on the ability of ocean plans to meet their goals. We show that planning for redistributions can substantially reduce exposure to risks from climate change with little additional area set aside and with few trade-offs against current ocean plan effectiveness. Networks of management areas are a key strategy. While climate change will severely disrupt many human activities, we find a strong benefit to proactively planning for long-term ocean change.

Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems

The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programing (EILP) solvers. Using a case study in BC, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12–30% cheaper than plans using SA, due to EILP’s ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1,071 times faster than the SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.

Global conservation of species' niches

Environmental change is rapidly accelerating, and many species will need to adapt to survive¹. Ensuring that protected areas cover populations across a broad range of environmental conditions could safeguard the processes that lead to such adaptations^1-3. However, international conservation policies have largely neglected these considerations when setting targets for the expansion of protected areas⁴. Here we show that-of 19,937 vertebrate species globally^5-8-the representation of environmental conditions across their habitats in protected areas (hereafter, niche representation) is inadequate for 4,836 (93.1%) amphibian, 8,653 (89.5%) bird and 4,608 (90.9%) terrestrial mammal species. Expanding existing protected areas to cover these gaps would encompass 33.8% of the total land surface-exceeding the current target of 17% that has been adopted by governments. Priority locations for expanding the system of protected areas to improve niche representation occur in global biodiversity hotspots⁹, including Colombia, Papua New Guinea, South Africa and southwest China, as well as across most of the major land masses of the Earth. Conversely, we also show that planning for the expansion of protected areas without explicitly considering environmental conditions would marginally reduce the land area required to 30.7%, but that this would lead to inadequate niche representation for 7,798 (39.1%) species. As the governments of the world prepare to renegotiate global conservation targets, policymakers have the opportunity to help to maintain the adaptive potential of species by considering niche representation within protected areas^1,2.

The role of artificial intelligence in achieving the Sustainable Development Goals

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

Artificial intelligence (AI) is becoming more and more common in people’s lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.

Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization

Decision-support tools are commonly used to maximize return on investments (ROI) in conservation. We evaluated how the relative value of information on biodiversity features and land cost varied with data structure and variability, attributes of focal species and conservation targets, and habitat suitability thresholds for contrasting bird communities in the Pacific Northwest of North America. Specifically, we used spatial distribution maps for 20 bird species, land values, and an integer linear programming model to prioritize land units (1 km²) that met conservation targets at the lowest estimated cost (hereafter ‘efficiency’). Across scenarios, the relative value of biodiversity data increased with conservation targets, as higher thresholds for suitable habitat were applied, and when focal species occurred disproportionately on land of high assessed value. Incorporating land cost generally improved planning efficiency, but at diminishing rates as spatial variance in biodiversity features relative to land cost increased. Our results offer a precise, empirical demonstration of how spatially-optimized planning solutions are influenced by spatial variation in underlying feature layers. We also provide guidance to planners seeking to maximize efficiency in data acquisition and resolve potential trade-offs when setting targets and thresholds in financially-constrained, spatial planning efforts aimed at maximizing ROI in biodiversity conservation.

Reserve design to optimize functional connectivity and animal density

Ecological distance-based spatial capture-recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.

Optimizing the conservation of migratory species over their full annual cycle

Limited knowledge of the distribution, abundance, and habitat associations of migratory species hinders effective conservation actions. We use Neotropical migratory birds as a model group to compare approaches to prioritize land conservation needed to support ≥30% of the global abundances of 117 species. Specifically, we compare scenarios from spatial optimization models to achieve conservation targets by: 1) area requirements for conserving >30% abundance of each species for each week of the year independently vs. combined; 2) including vs. ignoring spatial clustering of species abundance; and 3) incorporating vs. avoiding human-dominated landscapes. Solutions integrating information across the year require 56% less area than those integrating weekly abundances, with additional reductions when shared-use landscapes are included. Although incorporating spatial population structure requires more area, geographical representation among priority sites improves substantially. These findings illustrate that globally-sourced citizen science data can elucidate key trade-offs among opportunity costs and spatiotemporal representation of conservation efforts.

Conservation decisions to protect land used by migratory birds rely on understanding species’ dynamic habitat associations. Here the authors identify conservation scenarios needed to maintain >30% of the abundances of 117 migratory birds across the Americas, considering spatial and temporal patterns of species abundance.

An adaptive method for identifying marine areas of high conservation priority

Identifying priority areas for biodiversity conservation is particularly challenging in the marine environment due to the open and dynamic nature of the ocean, the paucity of information on species distribution, and the necessary balance between marine biodiversity conservation and essential supporting services such as seafood provision. We used the Patagonian seabird breeding community as a case study to propose an integrated and adaptive method for delimiting key marine areas for conservation. Priority areas were defined through a free decision-support tool (Marxan) that included projected at-sea distributions of seabirds (approximately 2,225,000 individuals of 14 species); BirdLife Important Bird and Biodiversity Areas (IBAs) for pelagic bird species; and the economic costs of potential regulations in fishing practices. The proposed reserve network encompassed approximately 300,000 km² that was largely concentrated in northern and southern inshore and northern and central offshore regions. This reserve network exceeded the minimum threshold of 20% conservation of the abundance of each species proposed by the World Parks Congress. Based on marine currents in the study area, we further identified the 3 primary water masses that may influence areas of conservation priority through water inflow. Our reserve network may benefit from enhanced marine productivity in these highly connected areas, but they may be threatened by human impacts such as marine pollution. Our method of reserve network design is an important advance with respect to the more classical approaches based on criteria defined for one or a few species and may be particularly useful when information on spatial patterns is data deficient. Our approach also accommodates addition of new information on seabird distribution and population dynamics, human activities, and alterations in the marine environment.

Designing a Protected Area to Safeguard Imperiled Species from Urbanization

Reserve design is a process that can address ecological, social, and political factors to identify parcels of land needed to sustain wildlife populations and other natural resources. Acquisition of parcels for a large terrestrial reserve is difficult because it typically occurs over a long timeframe and thus invokes consideration of future conditions such as climate and urbanization changes. In central Florida, the U.S. government has authorized a new protected area, the Everglades Headwaters National Wildlife Refuge. The new refuge will host important threatened and endangered species and habitats, and will be located to allow for species adaptation from climate change impacts. For this study we combined habitat objectives defined by the U.S. Fish and Wildlife Service and projections from two urbanization models to provide guidance for Everglades Headwaters National Wildlife Refuge design. We used Marxan with Zones to find near-optimal solutions for protecting explicit amounts of five target habitats. We identified parcels for inclusion into the reserve design that the models allocated among two zones representing different methods of protection: fee-simple purchase (up to 20,234 ha authorized by the U.S. government), and conservation easement agreements (up to 40,469 ha authorized). As expected, for all scenarios we found an increase in costs as the proportion of fee-simple purchases was increased, reflecting the lesser cost of easements, but the number of parcels required for protection differed little among scenarios. The two urbanization models showed considerable agreement over which habitat patches they did not forecast to be developed, and some agreement over which parcels might be developed. The U.S. Fish and Wildlife Service may benefit from focusing on parcels that our analyses select frequently under both urban scenarios because these parcels are more likely to be in areas where there are fewer urbanization threats and a lower demand for land. The reserve designs we generated met U.S. Fish and Wildlife Service habitat goals within fee and easement zone restrictions, and we found reserve configurations that fell well below the mandated size limit.

Solving multi-objective optimization problems in conservation with the reference point method

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems.