Global Priorities for Marine Biodiversity Conservation — TEI-tagged view

Elizabeth R. SeligpersName, Will R. TurnerpersName, Sebastian TroëngpersName, Bryan P. WallacepersName, Benjamin S. HalpernpersName, Kristin KaschnerpersName, Ben G. LascellespersName, Kent E. CarpenterpersName, Russell A. MittermeierpersName (2014date). “Global Priorities for Marine Biodiversity Conservation.” PLoS ONEtitle·j.

Object of study — the global spatial distribution of marine biodiversityterm priorities in relation to cumulative human impactsterm

Research question — where do high marine species richnessterm and endemismterm coincide with high or low human impactterm·var within and beyond national jurisdiction

Contribution — a global spatial prioritization workflow and map combining three biodiversity measures with two impact levels separately for EEZs and ABNJ

Full text — tagged

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Global Priorities for Marine Biodiversityterm Conservation Abstract In recent decades, many marine populations have experienced major declines in abundance, but we still know little about where management interventionsterm may help protect the highest levels of marine biodiversityterm. We used modeled spatial distribution data for nearly 12,500 species to quantify global patterns of species richnessterm and two measures of endemismterm. By combining these data with spatial information on cumulative human impactsterm, we identified priority areasterm where marine biodiversityterm is most and least impacted by human activities, both within Exclusive Economic Zonesterm (EEZs) and Areas Beyond National Jurisdictionterm (ABNJ). Our analyses highlighted places that are both accepted priorities for marine conservation like the Coral Triangle, as well as less well-known locations in the southwest Indian Ocean, western Pacific Ocean, Arctic and Antarctic Oceans, and within semi-enclosed seas like the Mediterranean and Baltic Seas. Within highly impacted priority areasterm, climateterm and fishingterm were the biggest stressors. Although new priorities may arise as we continue to improve marine species range datasets, results from this work are an essential first step in guiding limited resources to regions where investment could best sustain marine biodiversityterm.

Introduction Widespread impacts of human activities on the oceansterm [1] continue to cause declines in species diversity and abundanceterm [2] , [3] . As recognition of the benefits that healthy marine ecosystems provide to people increases [4] , [5] , protecting biodiversity and the essential ecosystem servicesterm it supports has become a priority for the scientific community, resource managers, and national and international policy agreements, including the Convention on Biological Diversity (CBD) [6] . Decreases in species richness or abundanceterm can threaten ecosystem servicesterm such as fisheries or nutrient cycling, and can reduce overall ecosystem stability and resilienceterm [7] , [8] . These declines have been documented for numerous marine ecosystems [9] , and can sometimes lead to major shifts in food web dynamics [10] – [12] . Many of these changes can be attributed to human impactsterm·var such as climate changeterm, overfishingterm, and pollutionterm [1] , [13] , [14] . However, limited capacity and financial support for conservation and management necessitate that resources be directed to regions where investment could best sustain areas of high marine biodiversityterm and their associated ecosystem servicesterm [15] , [16] .

Identification of priority areasterm such as ‘hotspotsterm’ [16] , high-biodiversity wilderness areas [17] , or other categorizations such as ecoregions [18] have been essential tools for conservation planning in terrestrial and marine ecosystems [15] . However, identifying spatially explicit areas of high biodiversityterm associated with either high or low human impactterm·var for marine ecosystems has never been done, despite their utility in terrestrial conservation [15] . We combined the most extensive global compilation of species distribution data currently available with high-resolution data on human impactsterm·var to identify emergent patterns as a key input to achieving global marine biodiversityterm conservation objectives.

Many biological and socioeconomic measures are potentially important for determining places of high conservation value, but relative levels of biodiversity and human impactterm·var are among important considerations for conservation prioritization efforts [19] – [21] . We focused our analyses on two fundamental metrics of biodiversityterm: species richnessterm and species endemismterm [19] , [20] , which are not necessarily spatially concordant [22] . These two metrics of diversity are thought to be important for different reasons. Dynamics can vary by ecosystem and species [23] , but marine communities with greater species richnessterm can have greater resilience to environmental stress than similar communities with lower species richnessterm [24] . Endemic or range-restricted species are generally considered to be at greater extinction risk due to localized human or natural disturbances [15] . Conserving places with high endemismterm is critical for preventing biodiversity loss and maintaining genetic variability [15] , [16] . Although previous efforts [25] have analyzed peaks in marine species richnessterm for select taxa, we used finer-scale data and included two metrics of endemismterm to create spatially explicit maps of diversity classified by degree of impact. Many conservation efforts require data at these finer scales for planning purposes [21] .

There are two broad approaches to identifying places that may be important because of the endemic species they harbor. One approach, hereafter ‘range rarityterm’, identifies the greatest concentrations of relatively rare or range-restricted species. This approach has been used previously to identify conservation priority areasterm in terrestrial and marine ecosystems [15] , [20] , [26] – [28] . The other approach is to identify those places that have species with restricted ranges, independent of the number of species present. Because this approach divides range rarityterm values by species richnessterm, we hereafter refer to it as ‘proportional range rarityterm’. By including both metrics of endemismterm, we can provide complementary insights into the places that may be important for protecting endemic species. We identified locations where species richnessterm and these two metrics of endemismterm peaked across taxonomic groups and coupled these results with a high resolution model of estimated cumulative human impactsterm [1] to generate a spatially explicit roadmap for prioritizing particular places and types of impacts for marine conservation action.

Methods Species and human impactterm·var data We used modeled species distribution dataterm for 12,497 species from several sources [29] – [31] to get the greatest taxonomic coverage as possible. The overall species database covered more than 21 phyla from 966 families ( Table S1 ). For ∼90% of the species in our analyses, range maps were derived from AquaMapsterm [30] , an online species distribution modeling tool that produces standardized, digital range maps of aquatic species ( www.aquamapsterm.org ) ( Table S1 ). Although modeled species distribution databases can contain inaccuracies [32] , they represent the most comprehensive and highest resolution biodiversity distribution data available [30] for this purpose. AquaMapsterm maps are based on an environmental niche envelope modelterm and species-specific habitat usage derived from occurrence records available through the Global Biodiversity Information Facility ( www.gbif.org ), supplemented with expert knowledge and additional information obtained through online species databases such as FishBase ( www.fishbase.org ) and SeaLifeBase ( www.Sealifebase.org ). These maps predict relative probabilities of species occurrenceterm (ranging from 0.00–1.00) at a resolution of 0.5 degree latitude-by-longitude cells. For this analysis, we applied a probability thresholdterm·var value of 0.00 or greater for a cell to be considered within a species' distribution, which is the least conservative estimate of a species range, but is most comparable to the polygon maps of maximum range extents used for other species mapping exercises (e.g., those of Birdlife International and the International Union for the Conservation of Nature - Global Marine Species Assessment) [29] , [33] . We describe sensitivity analysesterm and results of this assumption below.

Across all species distributions, we calculated species richnessterm and both metrics of endemismterm within a global grid of 2,591.4 km 2 icosahedral Snyder equal area hexagonsterm ( n = 197,316). We assigned species to hexagons based on the overlap between the raster grid (i.e. species from AquaMapsterm) or the polygon data (i.e. corals and seabirds) and the hexagon grid. In other words, if a species range overlapped at all with a hexagon, it was scored as present. However, in cases where only a portion of the hexagon was considered to be part of a species range, we weighted values for richness, range rarityterm, or proportional range rarityterm in that cell by the percentage of the hexagon that the species range occupied. No minimum range area was set for inclusion as it sometimes is for terrestrial systems to avoid biasing results towards very restricted range species. In the datasets we used, species ranges were not restricted to such a small area that they would bias results. We defined species richnessterm as the number of species within each hexagonterm ( Fig. S1A ). We quantified endemismterm using two approaches. Range rarityterm [34] was defined as: where for each species i of N species per hexagon, is the total range area for that species i including all areas inside and outside of the hexagon and is the fraction of the hexagon that overlaps with the species range ( Fig. S1B ; i.e., w = 1 if the species range covered the whole hexagon). Range rarityterm reflects both the number of species and the size of their ranges, which is a common way to delineate priorities based on endemismterm because it quantifies the number of relatively restricted range species within a cell [20] , [26] . To calculate proportional range rarityterm , we used the same formulation of range rarityterm, but then divided the values by richnessterm to remove its confounding effect ( Fig. S1C ). For analytical purposes, range rarityterm values were then multiplied by 100,000 and proportional range rarityterm values were multiplied by 1,000 to create integer datasets.

For all metrics of biodiversityterm (richness, rarity, and proportional range rarityterm), we used a standard area-based measure for identifying places with the greatest diversity. For each metric, we identified the 5% of grid cellsterm within the total area of exclusive economic zonesterm (EEZs) that had the highest values (equating to 8,107,940 km 2 of global EEZ area) and, separately, the 5% of grid cellsterm in areas beyond national jurisdictionterm (ABNJ) that had the highest values (equating to 11,490,800 km 2 of global ABNJ area). For example, for species richnessterm in EEZs, we selected those cells with the highest richness values that summed to 5% of the total global EEZ area. The use of an area thresholdterm rather than a value threshold meant that each diversity metric contributed an equal area to the assignment of priorities. We selected a 5% threshold because it has been used previously in terrestrial analyses [21] and was specific enough to ensure that we could separate very high diversity areas within the coastal zone, but broad enough to enable the identification of multiple candidate areas in different regions.

To assess the degree of human impactterm·var on high biodiversityterm areas, we used estimated spatially explicit cumulative impact dataterm ( Fig. S1D ) from HalpernpersName et al. 2008 [1] . This model includes 17 different drivers of changeterm within marine ecosystems, weighted by the sensitivity of the ecosystems present in a grid cell to each of these drivers [1] . The native resolution of these data is 1 km 2 . We again used an area-based approach, this time with a 10% area thresholdterm, to identify the highest and lowest impact areas within EEZs and ABNJ. We defined areas of high impactterm·def as the 10% of total EEZ area having the highest impact valuesterm or, separately, the 10% of total ABNJ area having the highest impact values. Similarly, we defined low impact areasterm·def as the 10% of EEZ (or ABNJ) area having the lowest impact valuesterm. We included both high impactterm and low impact areasterm·def because alternative conservation approaches advocate ‘reactive’ protection of critical, yet highly impacted places [15] , [20] as well as more ‘proactive’ protection of important wilderness areas.

We used a 10% area thresholdterm for impact values for two reasons: 1) preliminary analyses indicated a 5% area thresholdterm to be too restrictive in terms of the area that would have potential overlap with the top 5% of EEZ or ABNJ area by richness, range rarityterm, or proportional range rarityterm, and thus was not informative, and 2) management interventionsterm can address a broader degree of impact levels rather than just the very most or least impacted. For some analyses, we grouped the 17 drivers of ecological change into four broad categoriesterm: climateterm (temperature, acidification and ultraviolet radiation), fishingterm (pelagic low bycatch, pelagic high bycatch, demersal low bycatch, demersal high bycatch, demersal habitat modifying, and artisanal fishingterm), land-basedterm (nonpoint inorganic, nonpoint organic, nutrient, and direct human), and ocean-based pollutionterm (benthic structures, shipping, ocean-based pollutionterm, and species invasions), following HalpernpersName et al. 2008 [1] .

Spatial concordance of biodiversity and estimated human impactsterm·var We developed 6 classifications of marine spatial priorities based on our classifications of 3 different measures of biodiversity (richness, range rarityterm, and proportional range rarityterm) at 2 different levels of human impactterm·var (low and high; Fig. 1 ). To determine the spatial overlapterm between impact and diversity, we first downscaled the species data to match the resolution of the human impactterm·var data (1 km 2 ). Although the native resolution of the species data is considerably coarser than the impact data, we wanted to preserve important spatial variability within the human impactterm·var data for our analyses. For example, many fishingterm, nutrient and sedimentation impacts are concentrated within narrow continental shelf areas [1] . We chose not to upscale the human impactterm·var data, as doing so would have artificially spread values from narrow and highly impacted shelf areas to less-impacted waters nearby. We did not use any smoothing algorithm in the downscaling process so patterns in the data remained true to the coarseness of the data involved.

Analytical process for identifying priority areasterm according to their biodiversity and impact levels. This illustration shows the process for identifying priority areasterm, which was done separately for EEZ and ABNJ areas. For each metric of diversity—richness, range rarityterm and proportional range rarityterm—we identified the top values within 5% of total EEZ or ABNJ area (red). We also identified the top (yellow) and bottom (blue) 10% of EEZ and ABNJ area by impact. Priority areasterm were then identified by the area of overlap between each biodiversity metric and areas of high impactterm·def (orange) or low impactterm (purple).

We then determined where grid cells with the highest richness, range rarityterm, or proportional range rarityterm values overlapped with the highest or lowest impact values. This analysis resulted in 6 grids that designated areas of high biodiversityterm and high or low impactterm within EEZ areas and 6 separate, additional grids for ABNJ areas ( Fig. 1 ). To identify concentrations of high biodiversityterm and high or low impactterm, we converted the 12 raster grids (6 each for EEZ and ABNJ) to points and calculated 3×3 point density functionsterm with ArcGIS at 0.25 degree. Running the point density functionterm allowed us to identify high-density clusters of candidate priorities and connect areas in the interstices into contiguous areas to create more broadly defined priority areasterm. For each of the 12 grids, we included high-density areas that amounted to 1% of total EEZ or 1% of total ABNJ area. If none of the 6 grids overlapped, priority areasterm would equal exactly 6% of total EEZ or ABNJ area. Because the different grids do overlap, the actual percentage of EEZ or ABNJ area identified is approximately 5% of EEZ or ABNJ area, respectively. We aimed for an approximate target of 5% of EEZ or ABNJ area because these areas were specific enough to identify areas of very high biodiversityterm and either low or high impactterm. A 5% target also leaves room to achieve CBD targets [6] based on other important criteria related to socioeconomic, governance, or other biodiversity considerations. Current CBD targets are to conserve 10% of coastal and marine areas by 2020. To explore the outcome of an alternative threshold, we also calculated where diversity was greatest using a 10% area thresholdterm for the biodiversity metrics and using the same the impact thresholds ( Fig. S3 ).

Validations and sensitivity analysesterm Nearly every species range distribution dataset, including those based on point data, has to be modeled in some respect to recreate distributions. Even for marine mammals, which are one of the most heavily sampled taxa, point occurrence records are currently only available for <60% of known marine mammals, and 70% of all available sighting records come from continental shelf waters of the Northern Hemisphere, according to the Ocean Biogeographic Information System [35] . In order to validate the general diversity patterns in our dataset, we conducted a linear regression between our normalized species richnessterm values and the normalized species richnessterm values of Tittensor et al. 2010 [25] . The Tittensor et al. 2010 dataset focuses on a somewhat different suite of species and uses different procedures for generating species range maps from point observations [25] .

In the marine realm, where there are many wide-ranging and cosmopolitan species, patterns of biodiversity derived from maximum range mapsterm like those used here may overestimate presence on continental shelf areas. These presence/absence range maps implicitly assume homogeneous species occurrence and thus can overestimate the relative importance of shelf and slope habitatterm for truly oceanic species. Therefore, we conducted sensitivity analysesterm to examine how priorities for richness, range rarityterm, and proportional range rarityterm would change if more conservative presence thresholds of probabilities >0.40 or >0.80 were used rather than the ≥0.00 threshold used in the main analyses. A higher probability of occurrence restricts species presence to species-specific areas of high environmental suitability, which corresponds to what may be considered the core range for most species. This analysis was only possible for the data from Aquamapsterm [30] and did not include birds or corals because they did not include information on probability of occurrence.

Although our data were the most comprehensive available, they represent a relatively small fraction of overall known marine biodiversityterm [36] , [37] , which is poorly documented for many taxa. Current species distribution databases are particularly biased towards ray-finned fishes (Actinopterygii) and other vertebrates. Not surprisingly, the taxonomic makeup of species in our analysis was not proportional to the number of species within these taxonomic groups overall. Therefore, we explored the effects of weighting the number of species in each taxonomic group by their estimated proportional representationterm of all species within that group, excluding Actinopterygii. This approach was designed to reduce the relative importance of some classes that are poorly represented in the dataset used here because the species represented in these classes may not be reflective of their overall taxonomic group patterns (i.e. Echinodermata, Arthopoda, Mollusca) ( Table S2 ). We then compared the top 5% by richness using this weighted approach to the equal weighting approach used in the main analyses.

To explore taxon-specific drivers of patterns in priority areaterm identification, we conducted an additional set of analyses where we repeated our priority setting methodology for eight taxonomic groups separately (Arthropoda, Ascidiacea, Aves, Cnidaria, Echinodermata, Elasmobranchii, Mammalia, and Mollusca) to create taxon-specific priority areasterm. We calculated priorities for these taxa only for richness because many taxa had relatively low variation in endemismterm values. We then compared taxon-specific results to the cross-taxa priorities in our main analysis.

Results and Discussion Human impactsterm·var can disproportionately affect areas of high biodiversityterm [25] so spatially quantifying the degree of human impactterm·var in these areas is a key component of conservation prioritization efforts [16] , [20] , [21] . We used a global model of estimated human impactsterm·var ( Fig. 1D ) [1] to understand how human activities may be affecting marine biodiversityterm within Exclusive Economic Zonesterm (EEZs) and Areas Beyond National Jurisdictionterm (ABNJ). Priority areasterm for conservation were characterized by high richness, range rarityterm, or proportional range rarityterm and relatively high or low levels of human impactterm·var. Our results highlighted not only places that are accepted priorities for marine conservation, like the Coral Triangle, but also less well-known places like the southwest Indian Ocean, western Pacific Ocean, semi-enclosed seas like the Mediterranean, Baltic and Black Seas and along eastern boundary current systems and the Antarctic and Arctic Oceans in ABNJ.

Biodiversity patterns Overall, biodiversity peaks within EEZs and ABNJ ( Fig. 2A–B ) generally followed well-documented patterns [20] , [25] , [38] , [39] , but we also identified places of high biodiversityterm that are less often considered, especially for range rarityterm and proportional range rarityterm, which have not been explored previously at global scales across so many taxa ( Fig. 2A ). To test the accuracy of the modeled species range data that we used for our analysis, we looked at the relationship between normalized richness values in our database with those in a different global database of species distributions that uses a somewhat different suite of species and a coarser resolution [25] and found a relatively strong correlation (R 2 = 0.5812; p-value<2.2×10 −16 ).

Spatial patterns for (A) species richnessterm, (B) range rarityterm, and (C) proportional range rarityterm and (D) cumulative human impactsterm within EEZs and ABNJ. The highest values for all diversity measures within 5% of EEZ or ANBJ area are also shown. Due to scale, not all values may be visible. EEZ boundaries are shown in white.

Peaks in marine species richnessterm and endemismterm generally occur in the tropics, although temperate areas were also identified, particularly for proportional range rarityterm ( Fig. 2C ). We found high species richnessterm values (range of top 5% = 1618–5099 species) in the EEZ waters of several Southeast Asian nations in the region known as the Coral Triangle. We also found higher richness in less well-known places in the Indian Ocean along the coasts of Madagascar and the Chagos, Maldives and Lakshadweep archipelagos. In contrast, higher range rarityterm values within EEZs (range of top 5% = 301–680) were most prevalent in the Coral Triangle, the Bahamas, and along the Pacific Central American coast ( Fig. 2B ). High proportional range rarityterm values (range of top 5% = 376–500) were found along Arctic and Antarctic coasts and within semi-enclosed seas such as the Mediterranean, Baltic, and Black Seas ( Fig. 2C ). There was only a 2% overlap between the two measures of endemismterm within EEZs, suggesting that they are largely complementary.

High values for all three metrics of biodiversityterm were found along coastal and shelf areas so the separation of EEZs from ABNJ not only reflected governance differences, but also facilitated identification of high biodiversityterm areas within ABNJ, where values are typically lower than in EEZs. Within ABNJ, richness peaked more strongly (range of top 5% = 424–4222) along eastern boundary currents dominated by the Canary and Benguela Current systems and more weakly off western boundary currents like the Gulf Stream and Kuroshio Current systems ( Fig. 2A ; Fig. S1 ). Peaks in range rarityterm (range of top 5% = 10–322) were concentrated along mid-oceanic ridge systems ( Fig. 2B ). For proportional range rarityterm, peaks were found on the Labrador and Newfoundland Basins, the Rockall Rise off the coasts of the United Kingdom and Ireland, and in the Arctic and Antarctic Oceans (range of top 5% = 48–3000; Fig. 1C ). In ABNJ, there was a 12% overlap between the two measures of endemismterm.

Although our analyses highlighted many areas that have been previously identified as endemismterm hotspotsterm for corals [20] and fish [40] , we may have failed to identify small pockets of high endemismterm – for example, those known to exist around the Marquesas and Mascarenes islands [40] – because they occur at a resolution finer than our species range data. In addition, failures in identifying peaks in richness, range rarityterm, and proportional range rarityterm may have been affected by a lack of knowledge in some regions. Additional sampling may enable more places of high diversity to be identified, particularly in places where our knowledge of marine biodiversityterm is still growing [41] . Our estimates of diversity peaks may differ from more region-specific analyses [42] because of the inclusion of a different suite of species that includes many temperate species. Finally, we lacked data for many benthic and demersal off-shelf species so we were likely not able to identify places of high endemismterm in ocean trenches, canyons, or other deep-sea formations, so patterns within ABNJ in particular may change as more data for other taxa are incorporated.

Priority areasterm The designation of priority areasterm was driven both by spatial patterns in diversity and human impactterm·var. When we considered the degree of human impactterm·var on these places of high biodiversityterm, several large areas emerged as priority areasterm for marine conservation ( Table 1 ; Table S3 ). Areas of high biodiversityterm – high impactterm were identified around India, South Africa, Sri Lanka, Fiji, southeastern Australia, the South China Sea, the Mediterranean Sea, the Baltic Sea, and the coasts of Southeast Asia among others ( Fig. 3A–C ; Table S3 ). High biodiversity-low impact areas were identified within the Pacific EEZs of Mexico, Colombia, and Honduras as well as the Bahamas, the Galapagos (Ecuador), Madagascar, Mozambique, West Papua (Indonesia), Papua New Guinea, the north and west coasts of Australia, southern Kalimantan (Indonesia), the Solomon Islands, and in the Arctic and Antarctic Oceans ( Fig. 3A–C ). Some countries did not have any priority areasterm within their EEZs ( Table S3 ) either because impact levels were relatively moderate or because they lacked high levels of one of the three biodiversity metrics used in the analyses.

Priority areasterm for marine biodiversityterm conservation for ( A ) species richnessterm, ( B ) range rarityterm, and ( C ) proportional range rarityterm within EEZs and ABNJ. Orange areas denote priority areasterm with high human impactsterm·var and green denotes areas with low human impactsterm·var. Total area of priorities is 7,233,550 km 2 within EEZs and 9,894,560 km 2 within ABNJ ( Tables S5 , S6 ).

Top 20 EEZ regions by total priority areaterm (km 2 ).

Australia's EEZ included the largest absolute spatial extent of priority areasterm, most of which were high biodiversityterm – low impactterm. Other EEZ regions that had relatively extensive high biodiversity-low impact areas included those of Indonesia, Antarctica, Russia, Canada, Papua New Guinea, Brazil, and the Bahamas ( Table 1 ). These places represent opportunities for proactive conservation efforts to protect marine resources before they become highly impacted or to ensure that effective management is maintained where it already exists such as in Australia's Great Barrier Reef Marine Protected Areaterm. In contrast, countries like the Philippines, Japan, and China, which had large areas of highly impacted priority areasterm, should be considered urgent priorities for conservation intervention ( Table 1 ). Within EEZs, priority areasterm for richness and range rarityterm overlapped by 21% ( Table S5 ). These areas of overlap may be particularly important for conservation efforts because they could represent areas that have high ecosystem complexity and high irreplaceability because of the levels of species richnessterm and endemismterm present.

Within ABNJ, high impactterm priority areasterm for species richnessterm were found primarily in the sub-tropics near the mid-Atlantic ridge and off the coasts of Japan and West Africa in the Canary Current system ( Fig. 3A ; Table S4 ). Low impactterm priority areasterm were found off of the coasts of southwestern Africa in the Benguela Current system, northwestern South America, and within the Arctic and Antarctic Oceans. Although high priorities for richness concentrated in the tropical latitudes, high priority areasterm for both measures of endemismterm were found in sub-tropical, temperate, and polar latitudes. When defined as discrete priority areasterm, between 55–65% of ABNJ priority areasterm also contained seamountsterm depending on the biodiversity metric used to define priority areasterm [43] . The overlap of many priority areasterm with seamountterm locations likely reflects known higher biodiversity associated with seamountsterm [44] . The relatively low degree of spatial concordance for priority areasterm for richness and either range rarityterm or proportional range rarityterm in ABNJ (11%) means that conservation measures aimed at preserving priority areasterm with high richness will not necessarily encompass places with high endemismterm ( Table S6 ). Even though our priorities cover relatively little area relative to the overall size of ABNJ, interventions targeted at these areas (e.g. marine protected areasterm, fisheries management, etc.) can be considered as one component of broader conservation efforts, which could also include monitoring and regulating trade of threatened species. Recent research suggests that marine protected areasterm can be useful for rebuilding populations of even wide-ranging species like Atlantic cod [45] .

Although we summarize priority areaterm extent here by EEZ or ABNJ ( Table 1 , Table S3 , Table S4 ), there was often considerable variability within these regions. For example, within Indonesia, priority areasterm were identified for West Papua and Kalimantan, but not Sulawesi. In this case and many others, impact levels varied within EEZs so places that had more moderate levels of impact were not designated as priority areasterm. These “moderate” impact but high biodiversityterm areas also have conservation value. Even though it was not the focus of this analysis, our methodology could be adapted to categorize high diversity places that fall into different categories of impact for conservation planning purposes.

To ensure that our priority areaterm results were robust to changing the probability of occurrence thresholds, we examined the degree of overlap between the ≥0.0 threshold we used in the main analysis and two additional thresholds. As with previous studies focusing on marine mammals [35] , we found that estimates of species richnessterm were relatively robust to changing the probability of occurrence thresholds. At a >0.4 probability thresholdterm·var, the top richness values within 5% of total EEZ area had an overlap of 93% with the locations identified at a ≥0.0 threshold, although overlap declined to 66% at a higher 0.8 probability thresholdterm·var ( Fig. S2 ). Range rarityterm and proportional range rarityterm values were less robust to changing the probability thresholdterm·var, likely because increasing the probability thresholdterm caused species ranges to contract idiosyncraticallyterm, which had a more direct impact on metrics of endemismterm ( Fig. S2 ). Increasing probability thresholdsterm·var would have identified more priority areasterm for richness and range rarityterm in the Caribbean and off the west coast of Africa ( Fig. S2 ). For proportional range rarityterm, higher probability of occurrence thresholds values resulted in more priority areasterm along the east and west coasts of the United States, while reducing areas in the eastern Mediterranean and Baltic Seas. Many of these areas had values that were close to the top 5% of biodiversity within EEZs and would have been included if broader area thresholdsterm were used ( Fig. S3 ). A 10% threshold would also have increased the degree of overlap between priority areasterm for richness, range rarityterm, and proportional range rarityterm.

Taxonomic considerations Because our analyses used modeled species range data for a subset of known marine species, we conducted a series of validations and sensitivity analysesterm that suggested that our approach was relatively robust to different taxonomic weightingterm and species composition. The overall percent overlap between the highest values within 5% of EEZ area for richness with the equal species weighting and the proportional representationterm weighting was 70% ( Fig. S5 ). The proportional weighting approach, which attempted to correct for taxonomic sampling biases, tended to identify larger areas within the Coral Triangle and Western Pacific regions whereas the equal weighting approach extended priority areasterm along the coasts of China, Japan, northern Australia, and eastern Africa ( Fig. S5 ). The differing spatial patterns may be a result of the increased focus of the proportional weighting on a more limited set of taxa, thereby extending core areas for those taxa. Although priorities could shift with the addition of more species and classes, the priorities identified in our analysis seem relatively robust.

Overall marine conservation priorities were determined by cross-taxonomic relationships, which were dominated by ray-finned fishes (Actinopterygii; 8013 out of 12,497 species; Table S1 ). However, global priorities captured taxon-specific prioritiesterm relatively well for many taxa ( Fig. S4 ; Table S7 ). When we compared taxon-specific richness priorities to the global cross-taxa priorities ( Fig. S4 ), we found relatively high overlap (>60% overlap for 5 out of 8 taxa; Table S7 ). The most divergent taxa from global patterns were Mammalia and Aves, both of which peak in richness in high latitudes ( Figs. S4F, S4H ; Table S7 ). Global marine priority areasterm also had less overlap for Cnidaria (∼41%), but more than 89% overlap for Arthropoda and 93% for Mollusca (Fig.S4; Table S7 ), although the proportional representationterm of species within these taxonomic groups in our analyses was relatively low ( Table S2 ).

Because taxa with known latitudinal counter-gradient patterns in richness like Aves and Mammalia had little overlap with global priorities ( Table S7 ), additional areas will be needed to conserve these taxa [35] , [46] . No single prioritization scheme will be optimal for all taxa so taxon-specific management goals will be better identified through focal analyses on specific taxa [26] , [35] , [47] . Global priority areasterm best serve the broadest set of taxa, but will need to be supplemented by additional areas that capture places that are important for specific taxa.

Impacts within priority areasterm To determine which types of conservation interventions may be most effective, we also assessed which impacts were driving the designation of ‘highly impacted’ in our identified priority areasterm. The 17 different human impactterm·var layers we used fall broadly into four general categories: climateterm, fishingterm, land-basedterm pollutionterm, and ocean-based pollutionterm. Management interventionsterm for each of these kinds of impacts may be quite different. For example, climateterm impacts will be most effectively managed through policy interventions to reduce the human activities causing climate changeterm, whereas fishingterm impacts may be managed more locally through a combination of marine protected areasterm and traditional catch or effort controls. Land-basedterm impacts can be managed through interventions like watershed management or upstream protected areas.

The distribution and intensity of impacts within highly impacted priority areasterm revealed several interesting patterns. The strong rightward skew of climateterm impacts (red line) in plots of the level of impact as a percentage of all impacts illustrate that they were the most intense type of impact in highly impacted priority areasterm, although these patterns were more pronounced in ABNJ than EEZs ( Figs. 4A–F ). Interestingly, impact patterns were relatively consistent in ABNJ despite the priorities covering distinctly different geographic areas ( Figs. 4B, 4D, 4F ). Within highly impacted priority areasterm in EEZs, fishingterm impacts were also widespread, while ocean-based pollutionterm and land-basedterm impacts generally had more localized effects ( Figs. 4A, 4C ). Patterns for proportional range rarityterm ( Fig. 4E ) differed from these overall patterns with climateterm impacts still having the most intense impacts, but with fishingterm impacts (green line) more skewed to the left, indicating less intensity in these priority areasterm than in those for richness ( Fig. 4A ) or range rarityterm ( Fig. 4B ). These results may reflect better management of fishingterm pressures in these areas or less intense fishingterm pressures. In both EEZs and ABNJ, areas in the Northern Hemisphere were generally more impacted than the Southern Hemisphere ( Fig. 3 ). These differences may be due in part to greater climateterm impacts [48] and a longer history and greater intensity of fisheries exploitation in the Northern Hemisphere [49] . Although the impact data are a proxy of cumulative impacts on marine ecosystems, effects of a particular impact can vary according to species, scale, and location. Managing impacts on biodiversity will require adopting strategies to ameliorate the impacts of individual stressors on particular species or ecosystems [50] . Nonetheless, our results emphasize the critical importance of redoubling efforts towards developing policies and actions that promote sustainable fisheries management and reduce the human activities responsible for climate changeterm. Although implementing climateterm policy has been challenging, a combination of management tools is still important to pursue because evidence suggests marine protected areasterm alone will not be able to mitigate climate changeterm impacts [51] .

Intensity and extent of climateterm (red), fishingterm (green), ocean-based (blue) pollutionterm, and land-basedterm impacts (orange) as a percentage of total impacts within highly impacted priority areasterm. Patterns are shown for priority areasterm that were designated based on ( A ) richness within EEZs ( B ) and ABNJ, ( C ) range rarityterm within EEZ and ( D ) ABNJ, and ( E ) proportional range rarityterm within EEZs and ( F ) ABNJ. Zero values are not shown.

Conclusions Our results can serve as a foundation for informing a wide range of policy and management objectives aimed at protecting marine biodiversityterm. Areas identified here can serve as initial components of a portfolio approach as countries move towards fulfilling Convention on Biological Diversity Aichi Target 11 for 10% of the ocean to be protected by 2020 [6] . In addition, our results can be compared with the current extent of protection to determine where gaps remain and where biodiversity may be particularly vulnerable to human impactsterm·var. Areas identified here can also be used in future analyses to determine where places with high biodiversityterm may also be providing important ecosystem servicesterm.

There are many other factors besides biodiversity and impact that should be assessed to meet CBD or other management targets. We specifically focused on the identifying priority areasterm that amounted to approximately 5% of EEZ area as stepping stones to a broader 10% goal ( Fig. S3 ). We do not suggest that areas highlighted here should be used exclusively to fulfill a 10% CBD target. For example, we did not try to achieve “representationterm” of all biogeographic provinces and realms with our priority areasterm. Many of these additional areas would also be important for biodiversity conservation, particularly at more local scales. However, our approach could be adapted to be applied to smaller areas within an EEZ or biogeographic region to identify additional priorities.

Although our analysis focused on areas that are severely impacted or relatively unimpacted, several other variables and levels of impact may be important for marine spatial planning processes [52] . For example, even though the southern areas of the Coral Triangle were not highlighted because they have more moderate levels of impact, they may still be important for conservation efforts that consider costs and benefits of implementation. In addition, our analysis was limited to examining current stressors to marine ecosystems. We were also not able to account for synergies or negative interactions, which may have affected the range of values in our analyses. However, these interactions should not have changed our priority delineations, which were based on relative rankings. A greater understanding of synergistic or other interaction types would increase the differences between priority areasterm and other areas in most cases. Nonetheless, our results could be useful inputs for future cost-benefit analyses, and can be paired with scale-appropriate data on specific management options, which were not available at a global scale.

Future priority-setting and planning exercises will also need to consider socio-economic variables, governance considerationsterm·var, presence of other uses or stakeholder interests, and other biological properties, some of which may be too fine-scale to be mapped regionally or globally. For example, highly productive areas for fisheries or highly productive ecosystems like salt marshes can be relatively low in species richnessterm and endemismterm [38] and may not have been highlighted by our analyses. Furthermore, our analyses likely underestimated diversity in open or deep ocean ecosystems, where data are limited. Accounting for complementarityterm among taxa may also identify additional priority areasterm. In addition, variation in species-specific densities may result in areas of high biodiversityterm actually representing relatively marginal habitat for many species, which would reduce their conservation value [53] . Local-scale processes or values may be better captured by existing prioritization frameworks such as Key Biodiversity Areas [54] and Ecologically or Biologically Significant Areas [55] .

Results from our analyses can help to guide global investment in biodiversity conservation, assist national and regional scale conservation prioritization exercises, and provide critical baselines for assessing the effectiveness of current and future management activities. Because healthy natural ecosystems are increasingly recognized as important for maintaining human well-being, identifying and conserving priority areasterm for marine biodiversityterm are critical steps towards preserving the biodiversity on which human populations depend.

Supporting Information Taxonomic groups of species in the analysis. These taxa were used because they had publicly available data on spatial distribution. Aves (337 species) are from Birdlife International and Cnidaria (920 species) are from the Global Marine Species Assessment. All other taxonomic groups came from the AquaMapsterm database [30] .

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Proportional weighting by taxa used for sensitivity analysis. All estimates of total taxonomic diversity are from Bouchet et al. [37] except for Aves (Birdlife International), Elasmobranchii (IUCN Shark Specialist Group) and Mammalia [46] .

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Total priority areaterm (km 2 ) within EEZs. Area estimates have been rounded to the nearest 10 km. EEZ boundaries may still be in dispute. Overlap refers to areas of overlap between richness, range rarityterm, or proportional range rarityterm in any combination. Countries not listed did not have priority areasterm identified by the global analysis because they lacked spatially concordant high levels of diversity and high impactterm or low impactterm.

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Total priority areaterm (km 2 ) within ABNJ by FAO regions. Area estimates have been rounded to the nearest 10 km. Overlap refers to areas of overlap between richness, range rarityterm or proportional range rarityterm in any combination.

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Area of priority areasterm (km 2 ) within EEZs by level of impact and type of priority. Area estimates have been rounded to the nearest 10 km.

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Area of priority areasterm (km 2 ) within ABNJ by level of impact and type of priority. Area estimates have been rounded to the nearest 10 km.

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Percent overlap between taxon-specific prioritiesterm and global cross-taxa priorities for species richnessterm.

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Continuous values for (A) richness, (B) range rarityterm, (C) proportional range rarityterm, and (D) cumulative impact values [ 1 ] for all ocean areas. For analytical purposes, range rarityterm values were multiplied by 100,000 and proportional range rarityterm values by 1,000 to create integer datasets.

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Changes in priority areasterm using different probability thresholdsterm·var for (A) richness, (B) range rarityterm, and (C) proportional range rarityterm. Priority areasterm differ from those in the main analysis because only data from Aquamapsterm were used for this analysis. Aquamapsterm is the only species range dataset that has probability of occurrence information. The biggest changes were in priority areasterm designated according to proportional range rarityterm. The Caribbean and off the western coast of Africa also had differences for richness and range rarityterm.

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Marine priorities within Exclusive Economic Zonesterm using 10% area thresholdterm for (A) richness, (B) range rarityterm, and (C) proportional range rarityterm.

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Taxa-specific priorities for (A) Arthropoda, (B) Ascidiacea, (C) Cnidaria, (D) Echinodermata, (E) Elasmobranchii, (F) Mammalia, (G) Mollusca, and (H) Aves. Orange areas are in places of high human impactterm·var and green areas are in places of low human impactterm·var.

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Comparison of equal weighting of species versus proportion weighting by representationterm within each taxonomic group for the top 5% of EEZ area by richness. Overlap between the two approaches is in light green, equal weighting is in dark blue, and proportional weighting is in yellow.

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