Spatial Conservation Prioritization Research Articles

Reconsidering priorities for forest conservation when considering the threats of mining and armed conflict

Many threats to biodiversity can be predicted and are well mapped but others are uncertain in their extent, impact on biodiversity, and ability for conservation efforts to address, making them more difficult to account for in spatial conservation planning efforts, and as a result, they are often ignored. Here, we use a spatial prioritisation analysis to evaluate the consequences of considering only relatively well-mapped threats to biodiversity and compare this with planning scenarios that also account for more uncertain threats (in this case mining and armed conflict) under different management strategies. We evaluate three management strategies to address these more uncertain threats: 1. to ignore them; 2. avoid them; or 3. specifically target actions towards them, first individually and then simultaneously to assess the impact of their inclusion in spatial prioritisations. We apply our approach to the eastern Democratic Republic of the Congo (DRC) and identify priority areas for conserving biodiversity and carbon sequestration services. We found that a strategy that avoids addressing threats of mining and armed conflict more often misses important opportunities for biodiversity conservation, compared to a strategy that targets action towards areas under threat (assuming a biodiversity benefit is possible). We found that considering mining and armed conflict threats to biodiversity independently rather than simultaneously results in 13 800–14 800 km2 and 15 700–25 100 km2 of potential missed conservation opportunities when undertaking threat-avoiding and threat-targeting management strategies, respectively. Our analysis emphasises the importance of considering all threats that can be mapped in spatial conservation prioritisation.

Improving biodiversity protection through artificial intelligence

Over a million species face extinction, highlighting the urgent need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people’s lives. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, conservation area prioritization through artificial intelligence (CAPTAIN), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects significantly more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, further improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resource-limited world.

Identifying optimal solutions between competing economic and conservation land use objectives for species that require widely distributed resources

Across the world, wildlife must coexist with humans in modified and increasingly fragmented landscapes but balancing the competing land use objectives of economic production and conservation is challenging. Multi-objective optimisation and spatial conservation prioritisation can inform land use planning but have not yet explicitly accounted for the way species access multiple resources at different locations in a landscape. Here, we demonstrate a novel approach for conservation prioritisation that accounts for the spatial distribution of different resources as well as a species' movements. This suite of tools and models identifies Pareto-optimal solutions to competing objectives of economic production on the one hand, with conserving a species’ food, drink and shelter requirements and movement corridors on the other. We demonstrate the broader functionality of these tools using a case study with competing objectives of clearing land for mining versus conservation of a vulnerable endemic species.

Dog in the matrix: Envisioning countrywide connectivity conservation for an endangered carnivore

Abstract Elevated rates of anthropogenic impacts on land‐use regimes have pushed terrestrial megafauna to the brink of extinction. Consequently, it is critical to adopt conservation approaches that safeguard individual populations while retaining connectivity among these populations. Conserving spatially structured populations of imperiled species at large scales is often complex; and the past decades have therefore seen a rise in spatial conservation prioritization exercises aimed at shaping landscape‐scale conservation programmes. We present a framework for informing nationwide connectivity conservation, linking ecological and administrative scales, to maximize relevance for management. We assessed connectivity of the endangered dhole Cuon alpinus among 155 potential source populations across India using a data‐driven approach combined with graph and circuit theory. We used clustering algorithms to identify ecologically meaningful conservation landscapes; within each landscape, we identified priority source populations based on their connectedness and quantified pixel‐specific habitat accessibility. We superimposed administrative boundaries on our findings to provide conservation recommendations at this management‐relevant scale. We first mapped potential dhole movement across India. Dhole populations fell within three primary clusters—Western and Eastern Ghats (WEG), Central Indian Landscape (CIL) and North‐East India (NEI)—of which NEI had the highest forest cover, most diffuse connectivity and lowest human density, while WEG had the highest protected area coverage and overall connectedness. Within each conservation landscape, we evaluated the relative importance of Protected Areas and accessibility to high‐quality patches. Parts of the Eastern Ghats had low habitat accessibility, yet high potential for dhole landscape connectivity. In 114 identified administrative units of priority for habitat restoration, we highlight those with low accessibility, that is, areas where restoration needs to be spatially targeted for maximum benefits. Synthesis and applications. We make recommendations for spatially informed habitat restoration to enhance dhole connectivity in India, highlighting the importance of improving matrix permeability where dhole movement is currently restricted. More broadly, the framework we present is useful across species and management contexts, as it combines spatial and administrative scales to make ecologically informed assessments of high relevance to management. Synergistically integrating species ecology, threats and administrative considerations in connectivity conservation plans can enhance success of species conservation programmes.

The management effectiveness of protected areas in Kenya

Merely designating new and/or expanding existing protected areas (PAs) does not guarantee the protection of critical ecosystems and species. The management of PAs must be effective to sustain meaningful conservational outcomes. We inferred the management effectiveness of PAs in Kenya based on the representation of ranges and distribution of multiple diversity dimensions of terrestrial mammals and their association with governance and designation types. We hypothesized that different governance types underlie variable management efficacies, such that stricter-managed PAs have better habitats that attract more wildlife, translating to higher species diversity compared to less strictly-managed PAs, especially for focal species groups (large carnivores, large herbivores, and endangered species). The results showed nearly all terrestrial mammals in Kenya represented in at least one PA. However, the relative proportion of represented ranges were low, and analysis of spatial conservation prioritization showed significant expansion beyond current PAs needed to achieve a one third coverage of focal species’ ranges in a best-solution reserve system. Differences in PA governance and designation types were not systematically associated with diversity variances, and while there were more unique species in state-managed PAs than in privately-managed ones, averaged diversity coefficients were comparable between categories. Diversity variances explained by PA size and status year were low in a combined species pool but increased in focal species groups. These findings suggest that success in terrestrial mammal conservation in PAs in Kenya require clearly and formally streamlined definition, performance feedback, and collaboration terms between state-managed and privately-managed PAs.

Spatial conservation prioritization for the Amur tiger in Northeast China

AbstractThe Amur tiger (Panthera tigris altaica) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large‐ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least‐cost paths to model corridors among tiger occurrence locations. We found that core habitats for Amur tigers are distributed in three mountain areas, namely, eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling‐Dalongling. We found significant protection gaps as existing protected areas only cover less than one‐fourth of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up‐to‐date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale.

Prioritizing restoration areas to conserve multiple sagebrush-associated wildlife species

Strategic restoration of altered habitat is one method for addressing worldwide biodiversity declines. Within the sagebrush steppe of western North America, habitat degradation has been linked to declines in many species, making restoration a priority for managers; however, limited funding, spatiotemporal variation in restoration success, and the need to manage for diverse wildlife species make decision-making regarding restoration actions challenging. To address the challenge of spatial conservation prioritization, we developed the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET). This decision support tool utilizes the prioritizr package in program R and an integer linear programming algorithm to select parcels representing both high biodiversity value and high probability of restoration success. We tested PReSET on a sagebrush steppe system within southwestern Wyoming using distributional data for six species with diverse life histories and a spatial layer of predicted sagebrush recovery times to identify restoration targets at both broad and local scales. While the broad-scale portion of our tool outputs can inform policy, the local-scale results can be applied directly to on-the-ground restoration. We identified restoration priority areas with greater precision than existing spatial prioritizations and incorporated range differences among species. We noted tradeoffs, including that restoring for habitat connectivity may require restoration actions in areas with lower probability of success. Future applications of PReSET will draw from emerging datasets, including spatially-varying economic costs of restoration, animal movement data, and additional species, to further improve our ability to target effective sagebrush restoration.

Promoting equity in the use of algorithms for high-seas conservation

Promoting equity in the use of algorithms for high-seas conservation

The benefits of heterogeneity in spatial prioritisation within coral reef environments

Coral reefs are highly vulnerable habitats, threatened by climate change and local anthropogenic impacts. Management is imperative, and spatial prioritisation apportions the area of interest to inform investments of scarce conservation resources. Spatially delineated planning units used to make management decisions are typically large enough to contain significant natural variabilities, but the ecological significance of such variance is seldom considered in planning decisions. On coral reefs, the patchiness of habitat quality within planning units matters both ecologically and functionally. Here, we show that considering within-planning unit variance in spatial prioritisation influences the location and design of reserve networks. Studying Timor-Leste, we statistically model the average and variance in coral cover. We compare conservation priority areas for scenarios informed by coral cover and variance to a baseline scenario with the spatial prioritisation software Marxan. To further explain these differences, and to show the value of including coral variance as a metric in spatial prioritisation, we created a reserve quality score. We show that the similarity between reserve networks was only 57% for protection, and 44% for restoration objectives. For both objectives, the inclusion of cover variance improves the conservation benefit of management. This project has shown a novel way to target areas for restoration. These results demonstrate that not only is mean coral cover (and, by extension, reef condition) a key criterion in selecting marine conservation actions, but its variance must be considered in spatial conservation prioritisation to improve both the efficiency and benefit of management actions within marine reserve networks.

Spatial Conservation Prioritization Considering Development Impacts and Habitat Suitability of Endangered Species

Spatial Conservation Prioritization Considering Development Impacts and Habitat Suitability of Endangered Species

Spatial conservation prioritization considering socioeconomic costs and degradation conditions in the Southwest China Biodiversity Hotspot

SummaryThere have been calls for the expansion of protected areas (PAs) to tackle the ongoing biodiversity loss, yet it is unclear where future PAs might help to protect biodiversity in degraded landscapes under the conservation planning principles of complementarity, connectivity and cost-effectiveness. Our conservation goal is to increase the PA network coverage to up to 30% of the landscape of the Zhangjiang River Basin for target species in the karst area of southwest China, a global biodiversity hotspot.Zonation 4GUIwas used to evaluate the adequacy of current PAs and to strategically expand PAs while maximizing the coverage of target species and considering ecological integrity and socioeconomic activities. The results show that significant habitat degradation has occurred across 77.9% of the basin. The current PAs cover 6.3% of the site and represent 8.7% of the total distribution of key species. With regards to the threshold of protection of 30% of the area, protecting an additional 27.2% of the site under an ecological integrity prioritization scenario and a scenario of the socioeconomic costs involved in iteration would cover 93.5% and 80.4% of the ranges of the key species, respectively. Our results can be used to inform the upcoming actions associated with karst area conservation-related policies.

Using remotely sensed indicators of primary productivity to improve prioritization of conservation areas for top predators

The need to incorporate habitat dynamics into spatial conservation prioritization is increasingly recognized but often limited by the lack of available temporal data. To overcome this limitation, model-assisted monitoring has been advocated and developed over the last decade. However, predictive modelling often relies on static habitat features or interpolated climate data, failing to consider other relevant ecological processes such as primary productivity and its dynamics. The importance of primary productivity relies on its key role in determining the availability of food resources via trophic cascades. This study aims to assess how the spatiotemporal variation in the habitat quality for top predators (using raptors as case study) can be affected by temporal fluctuations in primary productivity, and whether its incorporation into habitat modelling improves predictions in space and time. Through model predictions we then incorporate interannual habitat-quality dynamics into spatial conservation planning to identify priority areas for raptor conservation based on a new set of dynamic priority-area indices. Using raptor occurrence data from two surveys (2001 and 2014), we found high model performance and transferability for all species (AUC2001 = 0.89 ± 0.10 and Boyce’s Index2001 of 0.78 ± 0.20; AUC2014 = 0.89 ± 0.12 and Boyce’s Index2014 of 0.87 ± 0.06). Annual mean and minimum values of EVI (indicators of productivity and phenology, respectively) and elevation were the most important predictor variables for most raptor species. However, the standard deviation of EVI (an indicator of seasonality) was also found to be an important predictor for migratory species breeding in shrublands and croplands. Interannual model predictions showed a large interannual variation in habitat availability across species, partially associated with regional land-use changes. Interannual habitat-quality dynamics was translated into marked changes in the priority areas for raptor conservation —areas mostly associated with deciduous forest and water environments (rivers and dams) in our study area. Our results shed light on the role of intra- and interannual dynamics of primary productivity in driving habitat quality for top predators. They also confirm the potential of remotely sensed ecosystem functioning attributes to inform about habitat-quality dynamics in a cost-effective, standardized and repeatable way. Incorporating interannual dynamics of primary productivity in habitat modelling supports raptor conservation prioritization by informing about adequacy, stability and legacy of raptor conservation areas.

Overprediction of species distribution models in conservation planning: A still neglected issue with strong effects

Species distribution models (SDM) are increasingly used in conservation planning to identify priority areas for the establishment of protected areas. Nevertheless, the quality of SDM varies widely and may compromise the effectiveness of protected areas. Here we reviewed whether SDM overprediction is considered in spatial conservation prioritization exercises and evaluated how model overprediction influences the effectiveness and the spatial arrangement of priority areas. To do so, we carried out a systematic review to analyze how researchers have handled SDM overprediction when identifying priority areas for conservation. To show how spatial conservation prioritization outcomes are affected by SDM overprediction, we used SDM of native palm at three geographic scales (Neotropics, Amazon ecoregion, and Ecuadorian Amazon). We found that only 10% of the evaluated manuscripts accounted for model overprediction. Our spatial conservation prioritization based on SDM with overprediction conferred high priority rank values in a region where species do not occur, underestimated the efficiency of selected priority areas, and over or underestimated the efficiency of current protected areas. Such effects were lower at smaller geographic extents. Our findings highlight the importance of improving future spatial conservation prioritization studies through the correction of SDM overprediction, resulting in the detection of more adequate areas for species conservation, especially at broader extents.

Incorporating putatively neutral and adaptive genomic data into marine conservation planning.

The availability of genomic data for an increasing number of species makes it possible to incorporate evolutionary processes into conservation plans. Recent studies show how genetic data can inform spatial conservation prioritization (SCP), but they focus on metrics of diversity and distinctness derived primarily from neutral genetic data sets. Identifying adaptive genetic markers can provide important information regarding the capacity for populations to adapt to environmental change. Yet, the effect of including metrics based on adaptive genomic data into SCP in comparison to more widely used neutral genetic metrics has not been explored. We used existing genomic data on a commercially exploited species, the giant California sea cucumber (Parastichopus californicus), to perform SCP for the coastal region of British Columbia (BC), Canada. Using a RAD-seq data set for 717 P. californicus individuals across 24 sampling locations, we identified putatively adaptive (i.e., candidate) single nucleotide polymorphisms (SNPs) based on genotype-environment associations with seafloor temperature. We calculated various metrics for both neutral and candidate SNPs and compared SCP outcomes with independent metrics and combinations of metrics. Priority areas varied depending on whether neutral or candidate SNPs were used and on the specific metric used. For example, targeting sites with a high frequency of warm-temperature-associated alleles to support persistence under future warming prioritized areas in the southern coastal region. In contrast, targeting sites with high expected heterozygosity at candidate loci to support persistence under future environmental uncertainty prioritized areas in the north. When combining metrics, all scenarios generated intermediate solutions, protecting sites that span latitudinal and thermal gradients. Our results demonstrate that distinguishing between neutral and adaptive markers can affect conservation solutions and emphasize the importance of defining objectives when choosing among various genomic metrics for SCP.

Spatial prioritization for biodiversity conservation in a megadiverse country

Abstract Mexico is a biologically megadiverse country, but its biodiversity is endangered due to high deforestation rates. Impacts of land-use/cover-change and climate change are unevenly distributed, which hinders the execution of conservation practices. Consequently, an adequate spatial conservation prioritization is crucial to minimize the negative impacts on biodiversity. Global and national efforts to prioritize conservation show that >45 % of Mexico should be protected. This study develops an applicable spatial conservation prioritization to minimize impacts on biodiversity, under three scenarios. They integrate exposure to land-use/cover-change and climate change scenarios, adaptive capacity to deal with the exposure, and the distribution of endemic species on risk of extinction. Our results show that by 2050 between 11.6 %, 13.9 % and 16.1 % of Mexico would reach score ≥50 in vulnerability (VI), under the optimistic, BAU, and the worst-case scenarios, respectively. By 2070, these figures would rise to 11.9 %, 14.8 % and 18.4 %. Amphibians are the most threatened vertebrates with 62.2 % of endemic species being critically endangered or endangered, while 39.2 %, 11.8 %, and 8.5 % of endemic mammals, birds and reptiles are endangered or critically endangered. The distribution of these amphibians accounts for 3.3 % of the country’s area, while mammals, birds, and reptiles represent 9.9 %, 16.2 %, and 28.7 % of Mexico. Moreover, seven municipalities (0.39 % of the country) represent 30 % of the most vulnerable areas (VI = 70). This study offers relevant information at the levels of municipality and species to help decision-makers prioritize national efforts for the conservation of ecosystems and biodiversity under land-use/cover and climate change. This study is replicable in other regions which aim to adapt decision-making and land management for biodiversity conservation.

Integrating ecosystem services within spatial biodiversity conservation prioritization in the Alps

As anthropogenic degradation of biodiversity and ecosystems increases, so does the potential threat to the supply of ecosystem services, a key contribution of nature to people. Biodiversity has often been used in spatial conservation planning and has been regarded as one among multiple services delivered by ecosystems. Hence, biodiversity conservation planning should be integrated in a framework of prioritizing services in order to inform decision-making. Here, we propose a prioritization approach based on scenarios maximising both the provision of ecosystem services and the conservation of biodiversity hotspots. Different weighting scenarios for the α-diversity in four taxonomic groups and 10 mapped ecosystem services were used to simulate varying priorities of policymakers in a mountain region. Our results illustrate how increasing priorities to ecosystem services can be disadvantageous to biodiversity. Moreover, the analysis to identify priority areas that best compromise the conservation of α-diversity and ecosystem services are predominantly not located within the current protected area network. Our analyses stress the need for an appropriate weighting of biodiversity within decision making that seek to integrate multiple ecosystem services. Our study paves the way toward further integration of multiple biodiversity groups and components, ecosystem services and various socio-economic scenarios, ultimately fuelling the development of more informed, evidence-based spatial planning decisions for conservation.

Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

BackgroundSpatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion.ResultsFocal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt’s protected areas for conserving fauna was low.ConclusionsFor SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

Seascape metrics, spatio-temporal change, and intensity of use for the spatial conservation prioritization of a Caribbean marine protected area

An effective management tool for the spatial prioritization of the Cozumel Reefs National Park (CRNP), using a systematic approach, is an urgent need for the conservation of one of the most important Marine Protected Areas (MPAs) in Mexico. In this study, we integrated a model using seascape metrics, the spatio-temporal variation of substrate coverage, and frequency of reef use for SCUBA diving, to systematically identify priority areas for conservation within the CRNP. We identified seven habitat types: sand beds, reef, two habitats of macroalgae beds (high and low cover), and seagrass beds with three different densities, using a supervised classification on a SPOT 7 (2017) satellite image (overall accuracy of 84%, Kappa of 0.79). Matlab and Fragstats were used to calculate seascape metrics (habitat β-diversity, shape complexity and connectivity). A change detection analysis, comparing the SPOT 7 with an Ikonos image, was utilized to estimate seascape change between 2004 and 2017. Interviews with diving operators were also obtained, to calculate the frequency of visits to different reefs within the CRNP. A weighted overlay analysis, integrating the metrics mentioned above, was used to identify priority areas for conservation. We identified five areas, where management strategies need to be strengthened (e.g. limiting the number of visitors). The current subzonation of the protected area does not reflect the conservation priorities identified by our analysis. However, given that the CRNP management plan is in the process of being updated, the obtained results can help the CRNP managers improve the conservation efforts based on a systematic and robust approach, ensuring the design and implementation of cost-effective strategies for the conservation of the MPA.

Integrating fine‐scale seafloor mapping and spatial pattern metrics into marine conservation prioritization

Abstract Marine protected area (MPA) planning often relies on scientific principles that help ensure that an area selected for conservation will effectively protect biodiversity. Capturing ecological processes in MPA network planning has received increased attention in recent years. High‐resolution seafloor maps, which show patterns in seafloor bio‐physical characteristics, can support our understanding of ecological processes. In part, owing to a global lack of high‐resolution seafloor maps, studies that aim to integrate seascape spatial pattern and conservation prioritization often focus on shallow biogenic habitats with less attention paid to deeper benthic seascapes (benthoscapes) mapped using acoustic techniques. Acoustic seafloor mapping strategies yield the spatial information required to extend conservation prioritization research into these environments, making incorporating seafloor ecological processes into conservation prioritization increasingly achievable. Here, a new method is proposed and tested that combines benthoscape mapping, landscape ecology metrics and a conservation decision support tool to prioritize areas with structural and potential connectivity value in MPA placement. Using a case study in eastern Canada, benthoscape composition and configuration were quantified using spatial pattern metrics and integrated into Marxan. Results illustrate how large patches of seafloor habitat in close proximity to neighbouring patches can be preferentially selected when benthoscape configuration is considered. The flexibility of the method for including relevant spatial pattern metrics or species‐specific movement data is discussed to illustrate how benthic habitat maps can improve existing conservation planning methods and complement existing and future work to support marine biodiversity conservation.

Addressing distribution equity in spatial conservation prioritization for small-scale fisheries.

Spatial conservation prioritization is used worldwide for designing marine protected areas (MPA) that achieve set conservation objectives with minimal impacts to marine users. People involved in small-scale fisheries (SSF) may incur negative and disproportionate impacts from implementing MPAs, yet limited available data often restricts their representation in MPA planning. Using a Philippines case study, we focus here on the systematic design of a MPA network that aims to minimize and distribute costs equitably for SSF whilst achieving representation targets for biodiversity conservation. The objectives of the study are to: (1) document a participatory mapping approach for collecting SSF data for prioritization using the local knowledge of fishers; and (2) examine how the completeness and resolution of SSF data may affect prioritization outputs in terms of biodiversity representation, spatial efficiency, and distribution equity. In the data-poor region, we conducted participatory mapping workshops with fishers in 79 communities to collect data on the spatial distribution patterns of different SSF fisheries and communities, and employed remote sensing techniques to define coastal habitats, which were targeted for inclusion in MPAs. The datasets were integrated within the decision-support tool Marxan with Zones to develop three scenarios. The SSF data incorporated in each scenario varied based on their completeness (considered all fishing methods or only dominant methods) and resolution (fishing methods itemized by community or municipality). All scenarios derived MPA plans that met representation targets with similar area coverage. The outputs, however, varied in terms of distribution equity, measured by the distribution of opportunity costs (loss of fishing grounds) across different fisheries and communities. Scenarios that did not include minority fisheries or variations between communities, led to inequitable costs. These results highlight the need to incorporate detailed data on SSF at appropriate resolutions, and how this can be achieved through participatory approaches.