Safeguarding climate-resilient mangroves requires a small increase in the global protected area

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Buelow, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5693704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Climate change and anthropogenic activities threaten biodiversity and ecosystem services. Climate-smart conservation plans address these challenges by focusing protection in climate-resilient areas. However, integrating climate change in the design of conservation plans is often deemed too expensive, as it may require larger networks or protecting more costly sites. Using mangroves as a case study, we evaluated the efficiency of protecting mangroves in climate-smart versus climate-naïve reserve networks. We found that climate-smart conservation plans could provide sizable benefits for relatively small increases in protected area. Moreover, transboundary plans, involving cooperation among countries, require less area and protect more climate-resilient mangroves than nation-by-nation plans. Implementing these strategies would improve the current network of protected areas for mangroves, which currently has poor climate resilience. These findings could also be applied in other ecosystems. Earth and environmental sciences/Ecology/Conservation biology Earth and environmental sciences/Climate sciences/Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The ongoing loss of biodiversity threatens the provision of critical ecosystem goods and services 1 . In response, conservation scientists have developed area-based management tools, including protected areas, to safeguard species and ecosystems from anthropogenic activities 2 . Although conservation planning is used extensively to design protected areas, its application has frequently overlooked climate change 3 . In a rapidly changing world, climate-smart conservation planning, where climate risks are explicitly considered in the design of conservation plans, can produce networks more resilient to future climate change and anthropogenic activities. Climate-smart planning supports adaptation and mitigation actions, while promoting sustainable use and conservation 4 . Despite the call from the Kunming-Montreal Global Biodiversity Framework to incorporate climate change in conservation planning (Target 8) 5 , most plans remain climate-naïve, neglecting impacts of climate change. This is probably because of perceptions that climate-smart approaches are more complex, uncertain, data-intensive, and result in substantially more expensive conservation plans requiring larger networks or protecting more costly sites 6 . Mangroves are vital coastal habitats that provide diverse ecosystem services, such as supporting fisheries, carbon sequestration and coastal protection 7 . However, these benefits are increasingly threatened by climate change and other anthropogenic activities 8 – 10 . Mangroves are vulnerable to sea-level rise, especially in areas with low sediment supply and where natural or human barriers restrict their landward expansion 11 . Further, intensifying drought and more frequent and severe cyclones can cause mangrove degradation and dieback 8 , 12 . The combination of climate change and the conversion of mangrove habitats for aquaculture, agriculture, and urban development, is projected to accelerate mangrove loss. This loss could negatively affect livelihoods 7 and release large quantities of CO 2 9 . Protecting mangroves not only conserves biodiversity and supports coastal livelihoods but is an effective climate-mitigation strategy, as mangroves store substantial amounts of carbon in their biomass and soil 9 . Recent studies on mangroves and climate change developed models evaluating the global effects of sea-level rise 11 , predicted carbon emissions by 2100 due to the loss of mangrove forests caused by anthropogenic activities and climate change 9 , and projected future distributions of mangroves under climate change 13 . Despite projections of severe climate change impacts on mangroves, spatial prioritisations (i.e., the formal quantitative design of protected areas in conservation planning), have neglected climate change 14 . However, ecosystem services are likely to be lost in areas most impacted by climate change 7 . Since there is limited ability to retroactively make established protected areas more resilient to climate change, there is considerable opportunity to design climate-smart protected areas placed to maximise climate resilience 15 . Most studies designing climate-smart protected area networks have focused on climate refugia—areas projected to be climatically stable 16 . A challenging aspect of mangrove conservation is that drivers of change in mangrove cover, which can cause both losses and gains, could act differently on the landward and seaward edges of mangroves. For instance, while sea-level rise is a driver of mangrove loss on the seaward edge, it can also be a driver of mangrove gain on the landward edge through inland migration where it is not impeded by natural or anthropogenic structures 11 . Here, we prioritise areas with higher climate resilience identified using an ecological network model 17 . We quantified climate resilience on a scale from 0 (least resilient) to 100 (most resilient), with the least and most resilient corresponding to the highest and lowest probability of loss respectively, from the model. We used this model to compare a climate-smart and climate-naïve protected area network in terms of differences in their total area and climate resilience but meeting identical biodiversity conservation objectives. These objectives were to select a predefined percentage of the area occupied by each mangrove species in different geomorphic landscape types (here called geomorphic species—see Methods). We answer four key questions: (1) How much larger is a climate-smart protected area network than a climate-naïve one? (2) When targeting areas more resilient to climate change, is a global prioritisation more efficient in terms of area than a country-scale one? (3) How different are climate-smart prioritisations of the landward and seaward edges of mangroves, given their differing responses to climate change? (4) How climate-smart is the existing protected area network for mangroves? Results Climate-smart mangrove conservation requires only a small increase in protected area The baseline global-scale climate-naïve prioritisation (Fig. 1 a), which identified a potential network of protected areas for mangrove plant species conservation by selecting the smallest area needed to meet all specified conservation targets, required 39.7% of the total mangrove area (Fig. 1 c; higher areal protection targets were set for species with small distributions—see Methods). This network had a mean climate resilience of 38.9 (calculated as average of landward and seaward edges resilience; Fig. 1 d). The climate-naïve prioritisation selected the highest percentages of mangroves in east Asia, Melanesia and South America. By contrast, the global-scale climate-smart prioritisation (Fig. 1 b), which used a climate-smart threshold of 0.3 (i.e., selecting 30% of the most climate-resilient areas of the overall distribution of each geomorphic species that would need to be selected to reach its species-specific conservation target—see Methods), achieved a mean climate resilience of 44.1. Thus, the global-scale climate-smart prioritisation had a 13.3% increase in mean climate resilience compared with the global-scale climate-naïve prioritisation (Fig. 1 d). This climate-smart prioritisation required only a relatively moderate increase in area, covering 42.6% of the total mangrove area, a 7.3% increase compared to the climate-naïve prioritisation (Fig. 1 c). This expansion favoured the selection in countries with more climate-resilient mangrove areas, such as the Republic of Congo, Venezuela and New Caledonia (Fig. 1 b). A global-scale protected area network is smaller but less representative than the sum of country-scale networks As conservation measures are generally implemented at national or local scales, we evaluated a country-scale prioritisation—i.e., a prioritisation that meet conservation targets independently in each country (sovereign dependencies and overseas territories were included as separate countries; Fig. 2 a, Supp. Figure 1 a-b). As expected, the climate resilience for the country-scale prioritisation was considerably greater for the climate-smart (40.2; Fig. 2 d) than the climate-naïve prioritisation (37.0; Supp. Figure 1 d). Further, compared with the global-scale prioritisations, the country-scale was less efficient in terms of total area, both for the climate-naïve (it included 48.6% of the total mangrove area; 22.4% larger; Supp. Figure 1 c) and the climate-smart prioritisations (50.5% of the total mangrove area; 18.5% larger; Fig. 2 c). However, in the global-scale analyses, mangroves were not selected in all countries: 46 out of 122 countries in the climate-naïve prioritisation, and 54 countries in the climate-smart prioritisation (Fig. 1 c). Further, mangroves in the African East Coast were not selected in either global-scale prioritisation (purple bins in Fig. 1 a-b). By contrast, country-scale prioritisations had more similar percentages of mangrove areas selected across countries, evident from the lower standard deviation of the proportion of mangrove area selected by country (0.23 for country-scale climate-smart vs 0.33 global-scale climate-smart). Higher increases in resilience compared to the required additional area We next examined how increasingly strict objectives for protecting climate resilient mangroves would impact the additional mangrove area needed for protection. We ran climate-smart prioritisations that placed an incrementally greater emphasis on selecting climate-resilient areas within the distribution for each geomorphic species (i.e., climate-smart thresholds were increased from 0.05 to 1 at steps of 0.05—see Methods). We found that the increase in resilience relative to the area protected was proportionally greater for climate-smart thresholds ≤ 0.9 in the global-scale and ≤ 0.95 in the country-scale prioritisation (Fig. 3 ). Overall, for the same increase in area, the increase in resilience benefits was greater in the prioritisations that were less climate-smart than the more climate-smart prioritisations (logarithmic curves in Fig. 3 ). This arises because the less climate-smart prioritisations (i.e., lower climate-smart thresholds) already include the most climate-resilient mangroves. As a result, stricter climate-smart prioritisations can only expand the network by including less climate-resilient mangrove areas. Different opportunities for protecting landward and seaward edges We ran separate climate-smart prioritisations for landward and seaward edges to explore how differences in the severity and distribution of impacts of climate change influenced global priorities (Supp. Figure 2 ). The global-scale climate-smart prioritisations for landward and seaward edges showed a moderate spatial agreement (Cohen’s Kappa, 𝜘 = 0.38, indicating “fair” agreement between the prioritisations). However, many countries had markedly different places selected between the two prioritisations: 57 out of 90 selected countries ranged from “great disagreement” (𝜘 ≤ 0; e.g., Barbados, DR Congo and Quatar) to “none to slight agreement” (0 < 𝜘 ≤ 0.1; e.g., Bangladesh, China and Colombia; Fig. 4 ). By contrast, there was only a small difference in the total mangrove area selected (i.e., small circles in Fig. 4 ) between landward and seaward edges in many countries, such as Dominican Republic, French Guiana and Vietnam. However, there was a major difference in mangrove area selected in other countries (i.e., large circles in Fig. 4 ). For example, in Aruba, all mangroves were selected in the landward climate-smart prioritisation and no mangroves were selected in the seaward, with the opposite in Dominica. Poor climate-smart performance of existing protected areas compared to our climate-smart prioritisation The current network of protected areas covers 43.1% of mangroves, similar to the total area of our global-scale climate-smart prioritisation results. We found that the current network of protected areas overlapped 41.5% of the climate-smart selected areas. However, the current network of protected areas only met 56 of 258 geomorphic species conservation targets used in our prioritisations. For the geomorphic species whose targets were not met, the mean shortfall from the targets was equal to 20% of the distribution area for each. The current protected area network performs well at conserving mangroves resilient to climate change on the landward edge, as our proposed landward global-scale climate-smart prioritisation was, on average, only 5.1% more resilient to climate change (Fig. 5 a). By contrast, the current network performs poorly at protecting the seaward edge, as our proposed seaward global-scale climate-smart network was 82.5% more resilient to climate change than the current network of protected areas (Fig. 5 b). Discussion Effective conservation plans need to account for climate change and its future impact on species and ecosystems 3 – 5 . Our study demonstrates how incorporating the likely impacts of climate change into conservation plans can increase future ecosystem resilience, with negligible additional cost. We also found that transboundary plans are both cheaper and more climate-smart than nation-by-nation prioritisations. Further, our analysis revealed little agreement between climate-smart prioritisations of landward and seaward edges of mangroves because of the different stressors involved, which might also be the case for other coastal habitats 8 . Although our focus was on mangrove ecosystems, our approach of developing climate-smart conservation plans could be applied to other ecosystems if data on their extent, biodiversity, and climate resilience is available. Protected areas currently cover 43.1% of mangroves globally, meeting the 30% target (Target 3) defined by the 30x30 objective of the Kunming-Montreal Global Biodiversity Framework. However, the high percentage of mangroves in protected areas is misleading because it does not consider: (1) the global distribution of mangroves before extensive clearing and conversion 18 ; (2) uncertainties in tidal boundaries that influences whether mangroves are included within protected areas 19 ; or (3) the effectiveness of protected areas in reducing mangrove losses 20 . In fact, anthropogenic influences are a continuing (albeit decelerating) driver of mangrove loss globally 10 and considerable human-driven losses are occurring within protected areas 20 . In response, organisations are articulating more ambitious targets for mangrove conservation. For example, the Global Mangrove Alliance has called for mangrove protected area coverage to be doubled from 40–80% of their current distribution 21 . We found that the current protected area network does not protect the most climate-resilient mangroves, especially along their seaward edge. Moreover, the current network fails to achieve most of the area-based conservation targets for mangroves plant species we used for this analysis (see Methods), despite covering a similar amount of area to our climate-smart prioritisation. This could be related to the demanding area-based targets we set. While these targets, set following the approach of Rodrigues et al. 22 , are somewhat arbitrary, they are some of the most widely adopted in conservation planning (e.g., Hanson et al. 23 , Claes et al. 24 ). Nevertheless, our results highlight opportunities to expand the current protected area network to better represent mangrove diversity. Previous studies emphasise that transboundary cooperation can reduce conservation costs 25 – 28 , although they did not consider climate change. Our results show that designing climate-smart protected areas, planning at a global scale not only requires less area but also selects more climate-resilient areas compared to planning at a local scale. However, global-scale prioritisations have their limitations. For example, they do not ensure the representativity of species diversity in different areas or provide sufficient replication to reduce extinction risk 29 . Further, developing global-scale prioritisations could exacerbate the conservation burden for countries in the Global South 30 . While country-scale planning could solve some of these issues, it is more costly and could still miss other aspects of conservation. For example, the distribution of most species transcends borders, requiring transboundary management 31 . Moreover, climate-smart planning is challenging at a local-scale, given that climate model outputs are currently appropriate for use at global to regional scales 32 . There are likely to be advantages of multi-scale conservation planning, not only considering benefits of transboundary cooperation but also including the value of incorporating local-scale variability and replication. Developing large-scale prioritisations for mangroves and other systems helps identify priorities that could inform investments and actions but need to be adapted to local circumstances, allowing adaptive adjustment during implementation 33 . Ultimately, there is a need to strengthen international cooperation in conservation whilst downscaling global or regional efforts to effective local-level conservation actions. The probability of future net loss under climate change is typically higher for the seaward than the landward mangrove edge 17 , resulting in more opportunities to select climate-resilient mangroves on seaward edges. Considering the dissimilarity we found between landward and seaward prioritisations for individual countries, there is a need to develop strategies and actions to protect mangrove plant species specific to landward and seaward forest edges. For instance, landward migration can be facilitated by removing barriers, actively planting propagules as inundation increases, or improving ecological connectivity 17 . However, these actions may replace other land uses and could lead to declines in freshwater biodiversity 34 , 35 , making landward actions potentially less attractive than seaward ones. Setting aside areas to accommodate future landward migration through legal agreements could help maintain productivity for landholders while ensuring future mangrove migration 36 . Climate change could also cause species composition shifts 34 , 37 . For example, on the seaward edge, protected areas that now safeguard mangroves could be important for seagrass biodiversity in the future, shifting provisioning of ecosystem services 37 . Considering the response of coastal ecosystems to climate change, together with the development of land-sea management strategies (e.g., the ‘ridge-to-reef’ approach 38 ), could improve future conservation efforts. Customising prioritisations and conservation action on seaward and landward edges might also be valuable for other coastal ecosystems that, like mangroves, could expand or contract on different edges of their distributions (e.g. saltmarshes) 39 . Overall, developing strategies for conservation actions that consider the different effects of climate change on landward and seaward sides could improve the future conservation of mangroves. Our analysis has several caveats. First, the IUCN species distribution maps provide only broad distribution ranges. As in Dabalà et al. 40 , we improved these low-resolution species distributions by intersecting them with the latest high-resolution mangrove distributions from Global Mangrove Watch 41 . More accurate distribution maps would improve our results, but given the urgency of the threats, we need to proceed using available data. Second, we quantified the future resilience of mangroves to climate change using a relatively complex network model that requires capacity and time to develop. We selected this model as it evaluates the response of mangroves to interacting processes known to cause substantial mangrove loss or gain under climate change beyond just sea-level rise (e.g., intense storms, extreme rainfall and flooding), and allowed us to examine landward and seaward edges. However, simpler climate-smart approaches, such as using climate metrics, could be used instead 16 . Third, we used a single future emission scenario, SSP5-8.5, because this is the only scenario available with the model of mangrove climate change. Using the “worst-case” scenario aligns with our focus on the cost of including climate change in conservation, and more optimistic scenarios would result in lower costs. Moreover, using mid-term projections (years 2040–2060) as we did here, minimises the differences between emission scenarios compared to longer-term projections 32 . However, when developing on-the-ground conservation networks, we recommend using the full range of future scenarios 32 . Fourth, there are large uncertainties in climate projections, particularly for some variables such as future cyclone tracks. We used the most accurate data available wherever possible. Last, to simplify the analysis, we did not consider the opportunity cost related to the implementation and management of protected areas for mangroves. However, ecosystem services provided by mangrove conservation could mitigate these costs 40 . Further, we did not consider the cost of future land acquisition for protecting mangroves migrating landward, which could be explored in future analyses. Biodiversity plays a critical role in mitigating climate change through carbon sequestration, facilitating adaptation to climate change by safeguarding coastal settlements from storm surges, and providing other valuable ecosystem services including clean air, water and food 7 , 42 . These benefits are compromised by biodiversity loss, driven by anthropogenic activities and exacerbated by climate change 1 . Developing and implementing climate-smart conservation ensures the future sustainability of these benefits and safeguards biodiversity, including those depending upon critical habitats such as mangroves 4 . Our study demonstrates that a moderate increase in the extent of protected areas can result in a much more climate-smart protected area network. Our results can inform decision-makers, highlighting opportunities for establishing national and international conservation strategies to develop effective climate-smart conservation actions. Methods Study area The study area, mangrove biodiversity, and conservation features were prepared following a similar approach to Dabalà et al (2023) 40 , using updated databases and incorporating slight differences in methodology. Using the 2022 version of the Global Mangrove Watch (GMW) v3.1.4 dataset 41 ( https://www.globalmangrovewatch.org ) , the global distribution of mangroves was delineated as the study area. This area was subdivided into 7,559 hexagonal planning units, each with a spatial resolution of 631 km 2 (~ 0.25° alongshore) and defined under the Mollweide equal-area coordinate reference system (ESRI:54009). All spatial datasets were reprojected to match this resolution and coordinate system before analysis. Country boundary data was sourced from the rnaturalearth package 43 —separately including sovereign dependencies and overseas territories—to aggregate the results by continent and run a country-scale analysis. We performed all analyses using the R statistical computing environment version 4.4.0 44 . Mangrove biodiversity The spatial distribution of mangrove biodiversity came from two datasets. First, the geographic range data for the 65 most common mangrove plant species were obtained from the IUCN Red List of Threatened Species 45 (Supp. Table 1), and overlapped with the planning units. Non-intersecting planning units were assigned with species that intersected with the nearest planning unit. Second, mangrove biophysical typology data v3.0 from Worthington et al. 46 were included to account for geomorphic variability. These data categorise mangroves into geomorphic classes (i.e., deltaic, estuarine, lagoonal and open-coast mangroves) based on the influence of geomorphic and ecological processes on mangrove forest structure and associated taxa 46 . To refine biodiversity data, species ranges were further subdivided by biophysical typology classes, effectively creating separate conservation features (i.e., mangrove “geomorphic species” feature) as input data for the analysis. The mangrove area covered by each geomorphic species feature in a planning unit was calculated as the area covered by the specific biophysical typology used to define that geomorphic species. Conservation features To ensure mangrove plant species protection, the distribution of the different mangrove species was used to set minimum area requirements for each species within each mangrove biophysical typology. Area-weighted conservation targets were calculated for each species using a log 10 -interpolation, following Rodrigues et al. 22 , ranging from a maximum target of 100% for species with ranges smaller than 10,000 km 2 to a minimum target of 10% for species with ranges exceeding 250,000 km 2 . The target obtained for a mangrove species was used for each of its geomorphic subgroups. Climate change resilience Inclusion of information on resilience to climate change allowed prioritisation to maximise resilience. The probability of mangrove net gain/stability under future climate change estimated by Buelow et al. 17 was used as a metric for mangrove climate resilience. This value was derived from a simulation of a qualitative network model that predicts a binary outcome of either net gain/stability or net loss of the seaward and landward edges of mangroves in response to projected climatic and anthropogenic pressures under emission scenario SSP5-8.5 for the period 2040–2060. The probability of future net gain/stability of mangroves was estimated through simulations wherein the relative strength of the interactions between pressures and seaward/landward mangroves was randomly parameterised. A probability of 100% means that 100% of the network model simulations resulted in net gain/stability of mangroves and 0% resulted in loss. Conversely, a probability of 0% means that 0% of the network model simulations resulted in net gain/stability of mangroves and 100% resulted in loss. The probability of seaward and landward net gain/stability was estimated for each mangrove forest unit (n = 3983) of the mangrove biophysical typology map 46 , which were intersected with our 0.25° hexagonal planning units to obtain a climate resilience value for each planning unit. The analysis was run both using a mean value of climate resilience for seaward and landward edges, and separately using either the seaward or landward value. Existing protected areas Data on protected areas were obtained from the World Database on Protected Areas 47 and processed for analysis using the wdpar R package 48 . Only protected areas classified as “Designated”, “Inscribed” or “Established” were included, while United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves and point localities lacking spatial extent were excluded. The percentage of mangroves covered by protected areas in each planning unit was calculated by intersecting the biophysical typology map with the protected areas to calculate the area of each mangrove geomorphic species to be protected in each planning unit. The effectiveness of the existing protected area network was assessed by evaluating the number of conservation targets reached, its climate resilience, and its overlap with our resulting networks of climate-smart protected areas 47 . Spatial prioritisation Climate-smart networks for area-based management were identified using spatial prioritisations through the R package prioritizr 49 . All the prioritisations were generated using the minimum-set objective function, which aims to select areas that reach all the conservation targets at the minimum cost. They were solved using the Gurobi solver (v11.0.3) 50 , with an optimality gap of 0.01%. To ensure the same selection priority for each km 2 of mangrove area, the area of mangroves in each planning unit was used as the cost for the selection, with each prioritisation aiming to reach all conservation targets while minimising this cost. A climate-naïve prioritisation was generated without incorporating data on climate resilience. A second prioritisation was generated—for a mean value of climate resilience and also separately for a value of climate resilience of the seaward and landward mangrove edges—by incorporating data on climate resilience (i.e., making the designs climate-smart) using the climate-priority area method from Buenafe et al. 16 . This method splits the distribution of each geomorphic species into: (1) climate-priority areas, defined as the most climate-resilient areas within the geomorphic species’ distribution, and (2) the rest of the distribution. Climate-priority areas were identified as areas whose climate resilience exceeded a given percentile for each geomorphic species’ resilience values globally. Climate-priority areas were assigned a conservation target of 100%. Non-climate priority areas were then to reach the overall target for each of the geomorphic species (e.g. to select a total of 30% of the distribution of the species). To adjust the method to our species-specific conservation targets, the threshold used to select the climate-priority area of a geomorphic species was rescaled based on the conservation target of the geomorphic species. This meant that a threshold of 0.05 selects as a climate-priority area 5% of the overall distribution of the geomorphic species that would need to be selected to reach its conservation target. In a sensitivity analysis, we ran climate-smart prioritisations using incremental thresholds for the selection of the climate-priority areas (i.e., from 5–30% of the conservation target, with increments of 5%). The same climate-naïve and climate-smart prioritisations were then generated using geomorphic species of mangroves split by countries. This provides more freedom to the algorithm to select areas that are more resilient to climate change. Declarations Competing interests The authors declare no competing interests. Author contributions Conceptualisation, A.D., C.J.B, T.V., C.A.B., D.S.S., D.C.D., C.E.L., F.D., F.J. and A.J.R.; Methodology, A.D., C.J.B, T.V., C.A.B., D.S.S., D.C.D., and A.R.; Formal analysis, A.D. and J.E.; Writing – original draft, A.D.; Writing – review & editing, A.D., F.D.G., D.C.D., J.D.E., C.E.L., J.O.H., K.C.B., S.N., K.J.T.E, and A.J.R. Acknowledgements F.D.G. and K.J.T.E were supported by the Erasmus Mundus Joint Master Degree in Tropical Biodiversity and Ecosystems – TROPIMUNDO, which is funded by the European Commission. Code availability Pending acceptance, the code will be archived in a Zenodo digital repository. Code to run the analysis in this study is currently available at: https://github.com/AlviDab/Mangroves_ClimateChange Data availability Majority of the datasets used in our study are publicly available online. These include: (1) the global map of mangroves is available for download from the Global Mangrove Watch website ( https://www.globalmangrovewatch.org/ ); (2) IUCN distribution of mangrove species is available at: https://www.iucnredlist.org/resources/spatial-data-download ; (3) the global biophysical mangrove typology is available for download from a Zenodo repository ( https://zenodo.org/records/8340259 ); and (4) the data on the probability of future mangrove loss (i.e., mangrove climate-resilience) is available on Github: ( https://github.com/cabuelow/mangrove-network-models?tab=readme-ov-file#probabilistic-forecasts-of-the-direction-of-future-change-in-mangrove-extent-using-network-models ) The datasets generated during the current study are currently available at: https://github.com/AlviDab/Mangroves_ClimateChange . Pending acceptance, these datasets will be archived in a Zenodo digital repository. References Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. 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Sharing conservation burdens fairly. Conserv. Biol. 33, 554–560 (2019). Mason, N., Ward, M., Watson, J. E. M., Venter, O. & Runting, R. K. Global opportunities and challenges for transboundary conservation. Nat. Ecol. Evol. 4, 694–701 (2020). Schoeman, D. S. et al. Demystifying global climate models for use in the life sciences. Trends Ecol. Evol. 38, 843–858 (2023). Pressey, R. L., Mills, M., Weeks, R. & Day, J. C. The plan of the day: Managing the dynamic transition from regional conservation designs to local conservation actions. Biol. Conserv. 166, 155–169 (2013). Kelleway, J. J. et al. Review of the ecosystem service implications of mangrove encroachment into salt marshes. Glob. Change Biol. 23, 3967–3983 (2017). Rowland, P. I., Hagger, V. & Lovelock, C. E. Opportunities for blue carbon restoration projects in degraded agricultural land of the coastal zone in Queensland, Australia. Reg. Environ. Change 23, 42 (2023). Bell-James, J., Fitzsimons, J. A., Gillies, C. L., Shumway, N. & Lovelock, C. E. Rolling covenants to protect coastal ecosystems in the face of sea-level rise. Conserv. Sci. Pract. 4, e593 (2022). Twomey, A. J., Staples, T. L., Remmerswaal, A., Wuppukondur, A. & Lovelock, C. E. Mangrove ghost forests provide opportunities for seagrass. Front. Clim. 5, (2023). Wilmot, E. et al. Characterizing mauka-to-makai connections for aquatic ecosystem conservation on Maui, Hawaiʻi. Ecol. Inform. 70, 101704 (2022). Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B. & Cahoon, D. R. Responses of Coastal Wetlands to Rising Sea Level. Ecology 83, 2869–2877 (2002). Dabalà, A. et al. Priority areas to protect mangroves and maximise ecosystem services. Nat. Commun. 14, 5863 (2023). Bunting, P. et al. Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sens. 14, 3657 (2022). Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis . (Island Press, Washington, DC, 2005). Massicotte, P. & South, A. Rnaturalearth: World Map Data from Natural Earth . (2023). R Core Team. R: A Language and Environment for Statistical Computing . (R Foundation for Statistical Computing, Vienna, Austria, 2024). IUCN. The IUCN Red List of Threatened Species. Version 2022-12. https://www.iucnredlist.org/resources/spatial-data-download (2022). Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 10, 14652 (2020). UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA) . (UNEP-WCMC and IUCN, Cambridge, UK, 2023). Hanson, J. O. Wdpar: Interface to the World Database on Protected Areas . (2021). Hanson, J. O. et al. Systematic conservation prioritization with the prioritizr R package. Conserv. Biol. n/a, e14376. Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual. (2024). Additional Declarations There is NO Competing Interest. Supplementary Files DabalaetalMangrovesCCNatureCC2024Supplementarysubmission.docx Supplementary material Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5693704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":399937929,"identity":"00c2f2e5-2e6a-4d93-9247-656abeb72411","order_by":0,"name":"Alvise Dabalà","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYHACA2YGBgsGfjCbjXgtEgySDSRrMThArBb+BuaNnwtqJOSNbyRvYPhQdpiBf0YCfi0SB9iKpWcckzDcdiOtgHHGucMMEjcIaGE4wGMgzcMmwbjtRo4BM2/bYQYGQlrkD/AY/+b5J2G/eQZQy1+gFnlCWgwO8JhJ87ZJJG6QAGphBGoxIKTF8DBbmfXMPonkGWeeFRzsOZfOY3jmAX4tcsebN98u+GZj29+evPHBjzJrObnjBGxhYEZ2JJDgIaAeFRiQpHoUjIJRMApGDgAAQ9dAQKmay9EAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7567-1181","institution":"The University of Queensland","correspondingAuthor":true,"prefix":"","firstName":"Alvise","middleName":"","lastName":"Dabalà","suffix":""},{"id":399937930,"identity":"721ecb83-d08a-4ed5-bfd0-4f4cefb123f3","order_by":1,"name":"Christopher Brown","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Brown","suffix":""},{"id":399937931,"identity":"c8362e95-5653-42f9-b418-cbb187317693","order_by":2,"name":"Tom Van der Stocken","email":"","orcid":"https://orcid.org/0000-0002-1820-9123","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"Van der","lastName":"Stocken","suffix":""},{"id":399937932,"identity":"deef1698-8e35-4b17-9f6b-08905f8fd49b","order_by":3,"name":"Christina A. Buelow","email":"","orcid":"","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"A.","lastName":"Buelow","suffix":""},{"id":399937933,"identity":"be7c1f80-259e-4049-b69d-ea726aa9615b","order_by":4,"name":"David Schoeman","email":"","orcid":"https://orcid.org/0000-0003-1258-0885","institution":"University of the Sunshine Coast","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Schoeman","suffix":""},{"id":399937934,"identity":"ef5064bd-2f04-4788-87b1-6f22435e91f4","order_by":5,"name":"Daniel Dunn","email":"","orcid":"https://orcid.org/0000-0001-8932-0681","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Dunn","suffix":""},{"id":399937935,"identity":"4f3f2676-3fff-4095-ba21-a3354df62736","order_by":6,"name":"Catherine Lovelock","email":"","orcid":"https://orcid.org/0000-0002-2219-6855","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Lovelock","suffix":""},{"id":399937936,"identity":"25436813-50bb-4c44-aee6-e3087bc709f0","order_by":7,"name":"Farid DAHDOUH-GUEBAS","email":"","orcid":"https://orcid.org/0000-0002-5906-8996","institution":"Université Libre de Bruxelles - ULB / Vrije Universiteit Brussel - VUB","correspondingAuthor":false,"prefix":"","firstName":"Farid","middleName":"","lastName":"DAHDOUH-GUEBAS","suffix":""},{"id":399937937,"identity":"c192cdd3-b6a3-4173-bcd1-0af14f2240f0","order_by":8,"name":"Jason Flower","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Flower","suffix":""},{"id":399937938,"identity":"df32d9be-1cca-44a7-a16b-e5c348a20190","order_by":9,"name":"Sandra Neubert","email":"","orcid":"","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Neubert","suffix":""},{"id":399937939,"identity":"397431f5-9dcf-4dd6-accb-41e7c44a1580","order_by":10,"name":"Kristine Buenafe","email":"","orcid":"https://orcid.org/0000-0002-1643-5557","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Kristine","middleName":"","lastName":"Buenafe","suffix":""},{"id":399937940,"identity":"9e662b3d-d1aa-402c-9157-cea1094d50d3","order_by":11,"name":"Jason Everett","email":"","orcid":"https://orcid.org/0000-0002-6681-8054","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Everett","suffix":""},{"id":399937941,"identity":"ef4501e6-5abd-4a48-b62b-8fc7cc439e4f","order_by":12,"name":"Kris Jypson Esturas","email":"","orcid":"https://orcid.org/0009-0004-7222-8490","institution":"Université libre de Bruxelles","correspondingAuthor":false,"prefix":"","firstName":"Kris","middleName":"Jypson","lastName":"Esturas","suffix":""},{"id":399937942,"identity":"94f76e7c-e21a-491c-838f-8b59b732f14b","order_by":13,"name":"Anthony Richardson","email":"","orcid":"https://orcid.org/0000-0002-9289-7366","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Richardson","suffix":""}],"badges":[],"createdAt":"2024-12-22 12:30:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5693704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5693704/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-68877-4","type":"published","date":"2026-01-27T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74246620,"identity":"adcacba3-1d51-407e-935f-343eb77eaa8c","added_by":"auto","created_at":"2025-01-20 09:54:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":601539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA climate-smart prioritisation is more resilient than a climate-naïve one for a moderate increase in area. a,\u003c/strong\u003e Climate-naïve and \u003cstrong\u003eb, \u003c/strong\u003eclimate-smart global-scale spatial prioritisations. Note that the % selection is shown because the original 631 km\u003csup\u003e2\u003c/sup\u003e for each planning unit in the analysis was aggregated at a resolution of ~63,000 km\u003csup\u003e2\u003c/sup\u003e for visualisation. \u003cstrong\u003ec\u003c/strong\u003e, % mangrove area selected and \u003cstrong\u003ed\u003c/strong\u003e, area-weighted climate resilience of the mangrove areas selected in the prioritisations by country (each country is a dot). Both prioritisations used a climate-smart threshold of 0.3. The horizontal lines show \u003cstrong\u003ec\u003c/strong\u003e, the total % of mangrove area selected and \u003cstrong\u003ed\u003c/strong\u003e, the global average climate resilience.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/4e1f5b75c35ff4bbd742ae81.png"},{"id":74246637,"identity":"72cd2e2a-6d0f-4a2e-a1d8-d8977837e577","added_by":"auto","created_at":"2025-01-20 09:54:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":623561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA global-scale climate-smart prioritisation requires less area and is more resilient than a country-scale prioritisation. a\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eGlobal-scale and \u003cstrong\u003eb\u003c/strong\u003e, country-scale climate-smart spatial prioritisation. Note that the % selection is shown because the original 631 km\u003csup\u003e2\u003c/sup\u003e for each planning unit in the analysis was aggregated at a resolution of ~63,000 km\u003csup\u003e2\u003c/sup\u003e for visualisation. \u003cstrong\u003ec\u003c/strong\u003e, % mangrove area selected and \u003cstrong\u003ed\u003c/strong\u003e, area-weighted climate resilience of the mangrove areas selected in the prioritisations by country (each country is a triangle or a dot). Both prioritisations used a climate-smart threshold of 0.3. The horizontal lines show \u003cstrong\u003ec\u003c/strong\u003e, the total % of mangrove area selected and \u003cstrong\u003ed\u003c/strong\u003e, the global average climate resilience.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/0eabe34d2b810111883ed229.png"},{"id":74246617,"identity":"536301f5-b72a-4740-ad3e-5520f35ceb5a","added_by":"auto","created_at":"2025-01-20 09:54:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in effectiveness of global-scale vs country-scale climate-smart spatial prioritisation. \u003c/strong\u003ePercentage increase in area and resilience of the global-scale climate-smart prioritisation and of the country-scale climate-smart prioritisation from the respective baseline climate-naïve prioritisations for increasing climate-smart thresholds (0.05‒1 with 0.05 increases—see Methods). The increase in climate-smart thresholds is displayed by an increase in the opacity of the dots and triangles. A logarithmic curve was fit. We added a 1:1 dashed line to aid interpretation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/fc207d6711854d3feb682034.png"},{"id":74246623,"identity":"7786af6d-8600-4ca7-9ee0-fc0d1dba8e4c","added_by":"auto","created_at":"2025-01-20 09:54:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences between landward and seaward prioritisations by country. \u003c/strong\u003eEach circle represents a country and is positioned in the centroid of the mangroves in each country polygon. Circle colours represent the degree of agreement between the landward and seaward prioritisation calculated using Cohen’s kappa statistic. Circle size represents the difference in the percentage of mangrove area selected between the two prioritisations (i.e., larger circles represent greater difference in total mangrove area selected in the country). Countries in which mangroves were not selected in both prioritisations are not represented. Both landward and seaward prioritisations used a climate-smart threshold of 0.3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/f26f9b75bdfc166c382d81ab.png"},{"id":74246622,"identity":"603815ab-57cc-4cda-a2ca-104ad10dff7b","added_by":"auto","created_at":"2025-01-20 09:54:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":309955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtected area and associated resilience of landward and seaward networks. \u003c/strong\u003eKernel density plots reporting the area-weighted climate resilience of mangroves covered by protected areas, compared with those selected by our (\u003cstrong\u003ea\u003c/strong\u003e) landward and (\u003cstrong\u003eb\u003c/strong\u003e) seaward global-scale climate-smart prioritisations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/debc9e1f6a9a0e80d77e64eb.png"},{"id":103637099,"identity":"7533da90-1d6a-4603-ab7b-13a133ee9546","added_by":"auto","created_at":"2026-02-28 08:06:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2927844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/b9b12f12-f7ff-45a8-86a8-fcdbd587534c.pdf"},{"id":74246624,"identity":"d32386d2-fe6a-4055-9137-48f02f07c405","added_by":"auto","created_at":"2025-01-20 09:54:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":601690,"visible":true,"origin":"","legend":"Supplementary material","description":"","filename":"DabalaetalMangrovesCCNatureCC2024Supplementarysubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-5693704/v1/649471db535eab6b31c76c38.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Safeguarding climate-resilient mangroves requires a small increase in the global protected area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ongoing loss of biodiversity threatens the provision of critical ecosystem goods and services\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In response, conservation scientists have developed area-based management tools, including protected areas, to safeguard species and ecosystems from anthropogenic activities\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although conservation planning is used extensively to design protected areas, its application has frequently overlooked climate change\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In a rapidly changing world, climate-smart conservation planning, where climate risks are explicitly considered in the design of conservation plans, can produce networks more resilient to future climate change and anthropogenic activities. Climate-smart planning supports adaptation and mitigation actions, while promoting sustainable use and conservation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Despite the call from the Kunming-Montreal Global Biodiversity Framework to incorporate climate change in conservation planning (Target 8)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, most plans remain climate-na\u0026iuml;ve, neglecting impacts of climate change. This is probably because of perceptions that climate-smart approaches are more complex, uncertain, data-intensive, and result in substantially more expensive conservation plans requiring larger networks or protecting more costly sites\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMangroves are vital coastal habitats that provide diverse ecosystem services, such as supporting fisheries, carbon sequestration and coastal protection\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, these benefits are increasingly threatened by climate change and other anthropogenic activities\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Mangroves are vulnerable to sea-level rise, especially in areas with low sediment supply and where natural or human barriers restrict their landward expansion\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Further, intensifying drought and more frequent and severe cyclones can cause mangrove degradation and dieback\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The combination of climate change and the conversion of mangrove habitats for aquaculture, agriculture, and urban development, is projected to accelerate mangrove loss. This loss could negatively affect livelihoods\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and release large quantities of CO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e9\u003c/sup\u003e. Protecting mangroves not only conserves biodiversity and supports coastal livelihoods but is an effective climate-mitigation strategy, as mangroves store substantial amounts of carbon in their biomass and soil\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies on mangroves and climate change developed models evaluating the global effects of sea-level rise\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, predicted carbon emissions by 2100 due to the loss of mangrove forests caused by anthropogenic activities and climate change\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and projected future distributions of mangroves under climate change\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Despite projections of severe climate change impacts on mangroves, spatial prioritisations (i.e., the formal quantitative design of protected areas in conservation planning), have neglected climate change\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, ecosystem services are likely to be lost in areas most impacted by climate change\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Since there is limited ability to retroactively make established protected areas more resilient to climate change, there is considerable opportunity to design climate-smart protected areas placed to maximise climate resilience\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMost studies designing climate-smart protected area networks have focused on climate refugia\u0026mdash;areas projected to be climatically stable\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. A challenging aspect of mangrove conservation is that drivers of change in mangrove cover, which can cause both losses and gains, could act differently on the landward and seaward edges of mangroves. For instance, while sea-level rise is a driver of mangrove loss on the seaward edge, it can also be a driver of mangrove gain on the landward edge through inland migration where it is not impeded by natural or anthropogenic structures\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Here, we prioritise areas with higher climate resilience identified using an ecological network model\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. We quantified climate resilience on a scale from 0 (least resilient) to 100 (most resilient), with the least and most resilient corresponding to the highest and lowest probability of loss respectively, from the model. We used this model to compare a climate-smart and climate-na\u0026iuml;ve protected area network in terms of differences in their total area and climate resilience but meeting identical biodiversity conservation objectives. These objectives were to select a predefined percentage of the area occupied by each mangrove species in different geomorphic landscape types (here called geomorphic species\u0026mdash;see Methods). We answer four key questions: (1) How much larger is a climate-smart protected area network than a climate-na\u0026iuml;ve one? (2) When targeting areas more resilient to climate change, is a global prioritisation more efficient in terms of area than a country-scale one? (3) How different are climate-smart prioritisations of the landward and seaward edges of mangroves, given their differing responses to climate change? (4) How climate-smart is the existing protected area network for mangroves?\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClimate-smart mangrove conservation requires only a small increase in protected area\u003c/h2\u003e \u003cp\u003eThe baseline global-scale climate-na\u0026iuml;ve prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), which identified a potential network of protected areas for mangrove plant species conservation by selecting the smallest area needed to meet all specified conservation targets, required 39.7% of the total mangrove area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec; higher areal protection targets were set for species with small distributions\u0026mdash;see Methods). This network had a mean climate resilience of 38.9 (calculated as average of landward and seaward edges resilience; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The climate-na\u0026iuml;ve prioritisation selected the highest percentages of mangroves in east Asia, Melanesia and South America.\u003c/p\u003e \u003cp\u003eBy contrast, the global-scale climate-smart prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which used a climate-smart threshold of 0.3 (i.e., selecting 30% of the most climate-resilient areas of the overall distribution of each geomorphic species that would need to be selected to reach its species-specific conservation target\u0026mdash;see Methods), achieved a mean climate resilience of 44.1. Thus, the global-scale climate-smart prioritisation had a 13.3% increase in mean climate resilience compared with the global-scale climate-na\u0026iuml;ve prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). This climate-smart prioritisation required only a relatively moderate increase in area, covering 42.6% of the total mangrove area, a 7.3% increase compared to the climate-na\u0026iuml;ve prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This expansion favoured the selection in countries with more climate-resilient mangrove areas, such as the Republic of Congo, Venezuela and New Caledonia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eA global-scale protected area network is smaller but less representative than the sum of country-scale networks\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs conservation measures are generally implemented at national or local scales, we evaluated a country-scale prioritisation\u0026mdash;i.e., a prioritisation that meet conservation targets independently in each country (sovereign dependencies and overseas territories were included as separate countries; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). As expected, the climate resilience for the country-scale prioritisation was considerably greater for the climate-smart (40.2; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) than the climate-na\u0026iuml;ve prioritisation (37.0; Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Further, compared with the global-scale prioritisations, the country-scale was less efficient in terms of total area, both for the climate-na\u0026iuml;ve (it included 48.6% of the total mangrove area; 22.4% larger; Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and the climate-smart prioritisations (50.5% of the total mangrove area; 18.5% larger; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eHowever, in the global-scale analyses, mangroves were not selected in all countries: 46 out of 122 countries in the climate-na\u0026iuml;ve prioritisation, and 54 countries in the climate-smart prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Further, mangroves in the African East Coast were not selected in either global-scale prioritisation (purple bins in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). By contrast, country-scale prioritisations had more similar percentages of mangrove areas selected across countries, evident from the lower standard deviation of the proportion of mangrove area selected by country (0.23 for country-scale climate-smart vs 0.33 global-scale climate-smart).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHigher increases in resilience compared to the required additional area\u003c/h3\u003e\n\u003cp\u003eWe next examined how increasingly strict objectives for protecting climate resilient mangroves would impact the additional mangrove area needed for protection. We ran climate-smart prioritisations that placed an incrementally greater emphasis on selecting climate-resilient areas within the distribution for each geomorphic species (i.e., climate-smart thresholds were increased from 0.05 to 1 at steps of 0.05\u0026mdash;see Methods). We found that the increase in resilience relative to the area protected was proportionally greater for climate-smart thresholds\u0026thinsp;\u0026le;\u0026thinsp;0.9 in the global-scale and \u0026le;\u0026thinsp;0.95 in the country-scale prioritisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, for the same increase in area, the increase in resilience benefits was greater in the prioritisations that were less climate-smart than the more climate-smart prioritisations (logarithmic curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This arises because the less climate-smart prioritisations (i.e., lower climate-smart thresholds) already include the most climate-resilient mangroves. As a result, stricter climate-smart prioritisations can only expand the network by including less climate-resilient mangrove areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDifferent opportunities for protecting landward and seaward edges\u003c/h3\u003e\n\u003cp\u003eWe ran separate climate-smart prioritisations for landward and seaward edges to explore how differences in the severity and distribution of impacts of climate change influenced global priorities (Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The global-scale climate-smart prioritisations for landward and seaward edges showed a moderate spatial agreement (Cohen\u0026rsquo;s Kappa, \u0026#120600; = 0.38, indicating \u0026ldquo;fair\u0026rdquo; agreement between the prioritisations). However, many countries had markedly different places selected between the two prioritisations: 57 out of 90 selected countries ranged from \u0026ldquo;great disagreement\u0026rdquo; (\u0026#120600; \u0026le; 0; e.g., Barbados, DR Congo and Quatar) to \u0026ldquo;none to slight agreement\u0026rdquo; (0 \u0026lt; \u0026#120600; \u0026le; 0.1; e.g., Bangladesh, China and Colombia; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). By contrast, there was only a small difference in the total mangrove area selected (i.e., small circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) between landward and seaward edges in many countries, such as Dominican Republic, French Guiana and Vietnam. However, there was a major difference in mangrove area selected in other countries (i.e., large circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, in Aruba, all mangroves were selected in the landward climate-smart prioritisation and no mangroves were selected in the seaward, with the opposite in Dominica.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePoor climate-smart performance of existing protected areas compared to our climate-smart prioritisation\u003c/h3\u003e\n\u003cp\u003eThe current network of protected areas covers 43.1% of mangroves, similar to the total area of our global-scale climate-smart prioritisation results. We found that the current network of protected areas overlapped 41.5% of the climate-smart selected areas. However, the current network of protected areas only met 56 of 258 geomorphic species conservation targets used in our prioritisations. For the geomorphic species whose targets were not met, the mean shortfall from the targets was equal to 20% of the distribution area for each. The current protected area network performs well at conserving mangroves resilient to climate change on the landward edge, as our proposed landward global-scale climate-smart prioritisation was, on average, only 5.1% more resilient to climate change (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). By contrast, the current network performs poorly at protecting the seaward edge, as our proposed seaward global-scale climate-smart network was 82.5% more resilient to climate change than the current network of protected areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEffective conservation plans need to account for climate change and its future impact on species and ecosystems\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Our study demonstrates how incorporating the likely impacts of climate change into conservation plans can increase future ecosystem resilience, with negligible additional cost. We also found that transboundary plans are both cheaper and more climate-smart than nation-by-nation prioritisations. Further, our analysis revealed little agreement between climate-smart prioritisations of landward and seaward edges of mangroves because of the different stressors involved, which might also be the case for other coastal habitats\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although our focus was on mangrove ecosystems, our approach of developing climate-smart conservation plans could be applied to other ecosystems if data on their extent, biodiversity, and climate resilience is available.\u003c/p\u003e \u003cp\u003eProtected areas currently cover 43.1% of mangroves globally, meeting the 30% target (Target 3) defined by the 30x30 objective of the Kunming-Montreal Global Biodiversity Framework. However, the high percentage of mangroves in protected areas is misleading because it does not consider: (1) the global distribution of mangroves before extensive clearing and conversion\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; (2) uncertainties in tidal boundaries that influences whether mangroves are included within protected areas\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; or (3) the effectiveness of protected areas in reducing mangrove losses\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In fact, anthropogenic influences are a continuing (albeit decelerating) driver of mangrove loss globally\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and considerable human-driven losses are occurring within protected areas\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In response, organisations are articulating more ambitious targets for mangrove conservation. For example, the Global Mangrove Alliance has called for mangrove protected area coverage to be doubled from 40–80% of their current distribution\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found that the current protected area network does not protect the most climate-resilient mangroves, especially along their seaward edge. Moreover, the current network fails to achieve most of the area-based conservation targets for mangroves plant species we used for this analysis (see Methods), despite covering a similar amount of area to our climate-smart prioritisation. This could be related to the demanding area-based targets we set. While these targets, set following the approach of Rodrigues et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, are somewhat arbitrary, they are some of the most widely adopted in conservation planning (e.g., Hanson et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, Claes et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e). Nevertheless, our results highlight opportunities to expand the current protected area network to better represent mangrove diversity.\u003c/p\u003e \u003cp\u003ePrevious studies emphasise that transboundary cooperation can reduce conservation costs\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, although they did not consider climate change. Our results show that designing climate-smart protected areas, planning at a global scale not only requires less area but also selects more climate-resilient areas compared to planning at a local scale. However, global-scale prioritisations have their limitations. For example, they do not ensure the representativity of species diversity in different areas or provide sufficient replication to reduce extinction risk\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Further, developing global-scale prioritisations could exacerbate the conservation burden for countries in the Global South\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. While country-scale planning could solve some of these issues, it is more costly and could still miss other aspects of conservation. For example, the distribution of most species transcends borders, requiring transboundary management\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Moreover, climate-smart planning is challenging at a local-scale, given that climate model outputs are currently appropriate for use at global to regional scales\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. There are likely to be advantages of multi-scale conservation planning, not only considering benefits of transboundary cooperation but also including the value of incorporating local-scale variability and replication. Developing large-scale prioritisations for mangroves and other systems helps identify priorities that could inform investments and actions but need to be adapted to local circumstances, allowing adaptive adjustment during implementation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Ultimately, there is a need to strengthen international cooperation in conservation whilst downscaling global or regional efforts to effective local-level conservation actions.\u003c/p\u003e \u003cp\u003eThe probability of future net loss under climate change is typically higher for the seaward than the landward mangrove edge\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, resulting in more opportunities to select climate-resilient mangroves on seaward edges. Considering the dissimilarity we found between landward and seaward prioritisations for individual countries, there is a need to develop strategies and actions to protect mangrove plant species specific to landward and seaward forest edges. For instance, landward migration can be facilitated by removing barriers, actively planting propagules as inundation increases, or improving ecological connectivity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, these actions may replace other land uses and could lead to declines in freshwater biodiversity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, making landward actions potentially less attractive than seaward ones. Setting aside areas to accommodate future landward migration through legal agreements could help maintain productivity for landholders while ensuring future mangrove migration\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Climate change could also cause species composition shifts\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. For example, on the seaward edge, protected areas that now safeguard mangroves could be important for seagrass biodiversity in the future, shifting provisioning of ecosystem services\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Considering the response of coastal ecosystems to climate change, together with the development of land-sea management strategies (e.g., the ‘ridge-to-reef’ approach\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e), could improve future conservation efforts. Customising prioritisations and conservation action on seaward and landward edges might also be valuable for other coastal ecosystems that, like mangroves, could expand or contract on different edges of their distributions (e.g. saltmarshes)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Overall, developing strategies for conservation actions that consider the different effects of climate change on landward and seaward sides could improve the future conservation of mangroves.\u003c/p\u003e \u003cp\u003eOur analysis has several caveats. First, the IUCN species distribution maps provide only broad distribution ranges. As in Dabalà et al.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, we improved these low-resolution species distributions by intersecting them with the latest high-resolution mangrove distributions from Global Mangrove Watch\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. More accurate distribution maps would improve our results, but given the urgency of the threats, we need to proceed using available data. Second, we quantified the future resilience of mangroves to climate change using a relatively complex network model that requires capacity and time to develop. We selected this model as it evaluates the response of mangroves to interacting processes known to cause substantial mangrove loss or gain under climate change beyond just sea-level rise (e.g., intense storms, extreme rainfall and flooding), and allowed us to examine landward and seaward edges. However, simpler climate-smart approaches, such as using climate metrics, could be used instead\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Third, we used a single future emission scenario, SSP5-8.5, because this is the only scenario available with the model of mangrove climate change. Using the “worst-case” scenario aligns with our focus on the cost of including climate change in conservation, and more optimistic scenarios would result in lower costs. Moreover, using mid-term projections (years 2040–2060) as we did here, minimises the differences between emission scenarios compared to longer-term projections\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, when developing on-the-ground conservation networks, we recommend using the full range of future scenarios\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Fourth, there are large uncertainties in climate projections, particularly for some variables such as future cyclone tracks. We used the most accurate data available wherever possible. Last, to simplify the analysis, we did not consider the opportunity cost related to the implementation and management of protected areas for mangroves. However, ecosystem services provided by mangrove conservation could mitigate these costs\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Further, we did not consider the cost of future land acquisition for protecting mangroves migrating landward, which could be explored in future analyses.\u003c/p\u003e \u003cp\u003eBiodiversity plays a critical role in mitigating climate change through carbon sequestration, facilitating adaptation to climate change by safeguarding coastal settlements from storm surges, and providing other valuable ecosystem services including clean air, water and food\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These benefits are compromised by biodiversity loss, driven by anthropogenic activities and exacerbated by climate change\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Developing and implementing climate-smart conservation ensures the future sustainability of these benefits and safeguards biodiversity, including those depending upon critical habitats such as mangroves\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Our study demonstrates that a moderate increase in the extent of protected areas can result in a much more climate-smart protected area network. Our results can inform decision-makers, highlighting opportunities for establishing national and international conservation strategies to develop effective climate-smart conservation actions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe study area, mangrove biodiversity, and conservation features were prepared following a similar approach to Dabalà et al (2023)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, using updated databases and incorporating slight differences in methodology. Using the 2022 version of the Global Mangrove Watch (GMW) v3.1.4 dataset\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalmangrovewatch.org\u003c/span\u003e\u003cspan address=\"https://www.globalmangrovewatch.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the global distribution of mangroves was delineated as the study area. This area was subdivided into 7,559 hexagonal planning units, each with a spatial resolution of 631 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (~ 0.25° alongshore) and defined under the Mollweide equal-area coordinate reference system (ESRI:54009). All spatial datasets were reprojected to match this resolution and coordinate system before analysis. Country boundary data was sourced from the \u003cem\u003ernaturalearth\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e—separately including sovereign dependencies and overseas territories—to aggregate the results by continent and run a country-scale analysis. We performed all analyses using the R statistical computing environment version 4.4.0\u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\u003ch3\u003eMangrove biodiversity\u003c/h3\u003e\u003cp\u003eThe spatial distribution of mangrove biodiversity came from two datasets. First, the geographic range data for the 65 most common mangrove plant species were obtained from the IUCN Red List of Threatened Species\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Supp. Table\u0026nbsp;1), and overlapped with the planning units. Non-intersecting planning units were assigned with species that intersected with the nearest planning unit. Second, mangrove biophysical typology data v3.0 from Worthington et al.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e were included to account for geomorphic variability. These data categorise mangroves into geomorphic classes (i.e., deltaic, estuarine, lagoonal and open-coast mangroves) based on the influence of geomorphic and ecological processes on mangrove forest structure and associated taxa\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. To refine biodiversity data, species ranges were further subdivided by biophysical typology classes, effectively creating separate conservation features (i.e., mangrove “geomorphic species” feature) as input data for the analysis. The mangrove area covered by each geomorphic species feature in a planning unit was calculated as the area covered by the specific biophysical typology used to define that geomorphic species.\u003c/p\u003e\u003ch2\u003eConservation features\u003c/h2\u003e\u003cp\u003eTo ensure mangrove plant species protection, the distribution of the different mangrove species was used to set minimum area requirements for each species within each mangrove biophysical typology. Area-weighted conservation targets were calculated for each species using a log\u003csub\u003e10\u003c/sub\u003e-interpolation, following Rodrigues et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, ranging from a maximum target of 100% for species with ranges smaller than 10,000 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to a minimum target of 10% for species with ranges exceeding 250,000 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The target obtained for a mangrove species was used for each of its geomorphic subgroups.\u003c/p\u003e\u003ch2\u003eClimate change resilience\u003c/h2\u003e\u003cp\u003eInclusion of information on resilience to climate change allowed prioritisation to maximise resilience. The probability of mangrove net gain/stability under future climate change estimated by Buelow et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was used as a metric for mangrove climate resilience. This value was derived from a simulation of a qualitative network model that predicts a binary outcome of either net gain/stability or net loss of the seaward and landward edges of mangroves in response to projected climatic and anthropogenic pressures under emission scenario SSP5-8.5 for the period 2040–2060. The probability of future net gain/stability of mangroves was estimated through simulations wherein the relative strength of the interactions between pressures and seaward/landward mangroves was randomly parameterised. A probability of 100% means that 100% of the network model simulations resulted in net gain/stability of mangroves and 0% resulted in loss. Conversely, a probability of 0% means that 0% of the network model simulations resulted in net gain/stability of mangroves and 100% resulted in loss. The probability of seaward and landward net gain/stability was estimated for each mangrove forest unit (n = 3983) of the mangrove biophysical typology map\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which were intersected with our 0.25° hexagonal planning units to obtain a climate resilience value for each planning unit. The analysis was run both using a mean value of climate resilience for seaward and landward edges, and separately using either the seaward or landward value.\u003c/p\u003e\u003ch2\u003eExisting protected areas\u003c/h2\u003e\u003cp\u003eData on protected areas were obtained from the World Database on Protected Areas\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and processed for analysis using the \u003cem\u003ewdpar\u003c/em\u003e R package\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Only protected areas classified as “Designated”, “Inscribed” or “Established” were included, while United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves and point localities lacking spatial extent were excluded. The percentage of mangroves covered by protected areas in each planning unit was calculated by intersecting the biophysical typology map with the protected areas to calculate the area of each mangrove geomorphic species to be protected in each planning unit. The effectiveness of the existing protected area network was assessed by evaluating the number of conservation targets reached, its climate resilience, and its overlap with our resulting networks of climate-smart protected areas\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eSpatial prioritisation\u003c/h2\u003e\u003cp\u003eClimate-smart networks for area-based management were identified using spatial prioritisations through the R package \u003cem\u003eprioritizr\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. All the prioritisations were generated using the minimum-set objective function, which aims to select areas that reach all the conservation targets at the minimum cost. They were solved using the Gurobi solver (v11.0.3)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, with an optimality gap of 0.01%. To ensure the same selection priority for each km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of mangrove area, the area of mangroves in each planning unit was used as the cost for the selection, with each prioritisation aiming to reach all conservation targets while minimising this cost. A climate-naïve prioritisation was generated without incorporating data on climate resilience. A second prioritisation was generated—for a mean value of climate resilience and also separately for a value of climate resilience of the seaward and landward mangrove edges—by incorporating data on climate resilience (i.e., making the designs climate-smart) using the climate-priority area method from Buenafe et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This method splits the distribution of each geomorphic species into: (1) climate-priority areas, defined as the most climate-resilient areas within the geomorphic species’ distribution, and (2) the rest of the distribution. Climate-priority areas were identified as areas whose climate resilience exceeded a given percentile for each geomorphic species’ resilience values globally. Climate-priority areas were assigned a conservation target of 100%. Non-climate priority areas were then to reach the overall target for each of the geomorphic species (e.g. to select a total of 30% of the distribution of the species). To adjust the method to our species-specific conservation targets, the threshold used to select the climate-priority area of a geomorphic species was rescaled based on the conservation target of the geomorphic species. This meant that a threshold of 0.05 selects as a climate-priority area 5% of the overall distribution of the geomorphic species that would need to be selected to reach its conservation target. In a sensitivity analysis, we ran climate-smart prioritisations using incremental thresholds for the selection of the climate-priority areas (i.e., from 5–30% of the conservation target, with increments of 5%). The same climate-naïve and climate-smart prioritisations were then generated using geomorphic species of mangroves split by countries. This provides more freedom to the algorithm to select areas that are more resilient to climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualisation, A.D., C.J.B, T.V., C.A.B., D.S.S., D.C.D., C.E.L., F.D., F.J. and A.J.R.; Methodology, A.D., C.J.B, T.V., C.A.B., D.S.S., D.C.D., and A.R.; Formal analysis, A.D. and J.E.; Writing \u0026ndash; original draft, A.D.; Writing \u0026ndash; review \u0026amp; editing, A.D., F.D.G., D.C.D., J.D.E., C.E.L., J.O.H., K.C.B., S.N., K.J.T.E, and A.J.R.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eF.D.G. and K.J.T.E were supported by the Erasmus Mundus Joint Master Degree in Tropical Biodiversity and Ecosystems \u0026ndash; TROPIMUNDO, which is funded by the European Commission.\u003c/p\u003e\n\u003ch3\u003eCode availability\u003c/h3\u003e\n\u003cp\u003ePending acceptance, the code will be archived in a Zenodo digital repository. Code to run the analysis in this study is currently available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AlviDab/Mangroves_ClimateChange\u003c/span\u003e\u003cspan address=\"https://github.com/AlviDab/Mangroves_ClimateChange\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eMajority of the datasets used in our study are publicly available online. These include: (1) the global map of mangroves is available for download from the Global Mangrove Watch website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalmangrovewatch.org/\u003c/span\u003e\u003cspan address=\"https://www.globalmangrovewatch.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); (2) IUCN distribution of mangrove species is available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/resources/spatial-data-download\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/resources/spatial-data-download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; (3) the global biophysical mangrove typology is available for download from a Zenodo repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/8340259\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/8340259\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); and (4) the data on the probability of future mangrove loss (i.e., mangrove climate-resilience) is available on Github: (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/cabuelow/mangrove-network-models?tab=readme-ov-file#probabilistic-forecasts-of-the-direction-of-future-change-in-mangrove-extent-using-network-models\u003c/span\u003e\u003cspan address=\"https://github.com/cabuelow/mangrove-network-models?tab=readme-ov-file#probabilistic-forecasts-of-the-direction-of-future-change-in-mangrove-extent-using-network-models\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe datasets generated during the current study are currently available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AlviDab/Mangroves_ClimateChange\u003c/span\u003e\u003cspan address=\"https://github.com/AlviDab/Mangroves_ClimateChange\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5693704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5693704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and anthropogenic activities threaten biodiversity and ecosystem services. Climate-smart conservation plans address these challenges by focusing protection in climate-resilient areas. However, integrating climate change in the design of conservation plans is often deemed too expensive, as it may require larger networks or protecting more costly sites. Using mangroves as a case study, we evaluated the efficiency of protecting mangroves in climate-smart versus climate-na\u0026iuml;ve reserve networks. We found that climate-smart conservation plans could provide sizable benefits for relatively small increases in protected area. Moreover, transboundary plans, involving cooperation among countries, require less area and protect more climate-resilient mangroves than nation-by-nation plans. Implementing these strategies would improve the current network of protected areas for mangroves, which currently has poor climate resilience. These findings could also be applied in other ecosystems.\u003c/p\u003e","manuscriptTitle":"Safeguarding climate-resilient mangroves requires a small increase in the global protected area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 09:54:17","doi":"10.21203/rs.3.rs-5693704/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fa56cc53-9fdd-45fe-9499-821d02f3be5e","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42620408,"name":"Earth and environmental sciences/Ecology/Conservation biology"},{"id":42620409,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2026-02-28T08:06:05+00:00","versionOfRecord":{"articleIdentity":"rs-5693704","link":"https://doi.org/10.1038/s41467-026-68877-4","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-01-27 05:00:00","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-01-20 09:54:17","video":"","vorDoi":"10.1038/s41467-026-68877-4","vorDoiUrl":"https://doi.org/10.1038/s41467-026-68877-4","workflowStages":[]},"version":"v1","identity":"rs-5693704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5693704","identity":"rs-5693704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}