DOI: 10.5061/dryad.xsj3tx9d1
title: A blueprint for securing Brazil's marine biodiversity and supporting the achievement of global conservation goals

description: Aim: As a step towards providing support for an ecological approach to
 strengthening marine protected areas (MPAs) and meeting international
 commitments, this study combines cumulative impact assessment and
 conservation planning approach to undertake a large-scale spatial
 prioritisation. Location: Exclusive Economic Zone (EEZ) of Brazil,
 Southwest Atlantic Ocean Methods: We developed a prioritisation approach
 to protecting different habitat types, threatened species ranges, and
 ecological connectivity, while also mitigating the impacts of multiple
 threats on biodiversity. When identifying priorities for conservation, we
 accounted for the co-occurrence of 24 human threats and the distribution
 of 161 marine habitats and 143 threatened species, as well as their
 associated vulnerabilities. Additionally, we compared our conservation
 priorities with MPAs proposed by local stakeholders. Results: We show that
 impacts to habitats and species are widespread and identify hotspots of
 cumulative impacts on inshore and offshore areas. Industrial fisheries,
 climate change, and land-based activities were the most severe threats to
 biodiversity. The highest priorities were mostly found towards the coast
 due to the high cumulative impacts found in nearshore areas. As expected,
 our systematic approach showed a better performance on selecting priority
 sites when compared to the MPAs proposed by local stakeholders without a
 typical conservation planning exercise, increasing the existing coverage
 of MPAs by only 7.9%. However, we found that proposed MPAs still provide
 some opportunities to protect areas facing high levels of threats. Main
 conclusions: The study presents a blueprint of how to embrace a
 comprehensive ecological approach when identifying strategic priorities
 for conservation. We advocate protecting these crucial areas from
 degradation in emerging conservation efforts is key to maintain their
 biodiversity value.
description: <strong>Habitat Data</strong><br> We We
 conceptualised a GIS-based habitat map (also often labelled ‘ecosystems’)
 at a national scale by assembling data from peer-reviewed literature,
 publicly-available and unpublished datasets, governmental and
 nongovernmental reports, and performed complementary GIS analysis. We
 developed a hierarchical classification scheme that accomplished a fine
 delineation of benthic habitats throughout Exclusive Economic Zone (EEZ)
 of Brazil and resulted in 161 distinct and non-overlapping classes of
 habitat types. Recognising the spatial structure of the marine
 environment, we developed separate pelagic (N=11) and benthic habitats
 (N=150) to account for different types and resolution of available data.
 The benthic habitats were delineated following a nested hierarchical
 classification scheme as a result of specific combinations of ecoregions
 (sensu Spalding et al. 2007), depth zones, seascape units (delineation of
 the seabed into hard, soft, or mixed substrate types), and within-habitat
 specificities (in places where more detailed data were available).
 Whenever possible, we obtained digital maps containing the extension of
 marine habitats (e.g., nearshore banks of coral reefs); however, some
 spatial data were manually digitised and inserted in the GIS (e.g.,
 seagrass meadows). We assigned unique code identifiers, names, and
 descriptions to the marine habitats (Table S1 of the paper). We used
 expert opinion within the authors to revise the draft habitats produced by
 the analytic steps described in Supporting Information of the paper. The
 level-1 habitats comprised eight ecoregions (1. Amazon; 2. Northeastern;
 3. Eastern; 4. Fernando de Noronha and Atoll das Rocas; 5. São Pedro and
 São Paulo Islands; 6. Trindade and Martin Vaz Islands; 7. Southeastern
 Brazil; and 8. Rio Grande), from costal to abyssal environments. The
 level-2 habitats provided a sub-delineation of the level-1 habitats
 following a depth-related differentiation in habitat distribution defined
 by geophysical constraints (Last et al., 2010), which resulted in six
 depth zones: coastal shelf (&lt; 25 m), mid shelf (25-75 m), outer
 shelf (75-200 m), upper slope (200-700 m), lower slope (700-2,000 m), and
 abyss (&gt;2,000 m). The level-3 habitats provided a further partition
 of the previous habitats and were based on mappable structures, which are
 of conservation interest, defined by habitat forming species or
 geomorphological structures, and assumed to be surrogates for distinctive
 biological assemblages. The seascape units were delimited by convenient
 boundaries and included: (i) beaches/sand dunes; (ii) Bryozoa reefs; (iii)
 canyons; (iv) cold-water reefs (based on the occurrence of deep-sea
 reef-constructing corals <i>Lophelia pertusa</i>,
 <i>Solenosmilia variabilis</i>, <i>Enallopsammia
 rostrate</i>, and <i>Madrepora oculata</i>); (v)
 estuaries; (vi) Halimeda reefs; (vii) kelps; (viii) mesophotic reefs; (ix)
 rhodolith beds; (x) seagrass meadows; (xi) shallow-water coral reefs;
 (xii) shallow-water rocky reefs; (xiii) mangroves; (xiv) seamounts; (xv)
 Bryozoan reefs ; and (xvi) submarine fan deltas. The pelagic habitats were
 classified based on a cluster analysis of ecological data that serve as
 surrogates for assemblages of pelagic species. For pelagic habitats, we
 acquired environmental parameters from open-access sources (Bio-ORACLE
 v2.0; (Assis et al., 2018) for the top surface layer of the ocean
 (2000-2014), including salinity (mean), sea surface temperature (mean,
 min, max), dissolved molecular oxygen (mean), chlorophyll a (mean), and
 nutrients (nitrate, phosphate, and silicate; mean). We downloaded the
 images into ArcGIS 10.1 (ESRI, 2011) and all raster datasets were
 projected to an Albers equal-area projection with metre measurement units.
 With every pixel in the study area characterized by all parameters, we
 used a cluster analysis to understand the distinctive habitats with within
 the study area. We used the hierarchical Iso Cluster Unsupervised
 Classification tool in ArcGIS to define pelagic habitats. We used default
 settings for all parameters.Descriptions of each physical environmental
 characteristics underpinning each pelagic habitat are summarized in Table
 S2.  
 <strong>Threatened species</strong>
 We obtained species distribution ranges and assessment of
 identified threats available for 143 animal species (invertebrates,
 fishes, mammals, turtles, and seabirds) listed under national legislation
 with a status of Critically Endangered, Endangered, or Vulnerable. Range
 maps for all species were obtained from shapefiles downloaded from the
 National Red List of Threatened Species spatial data repository (i.e., the
 national agency for biodiversity conservation, ICMBio), the literature,
 and the Aquamaps dataset (Kesner-Reyes, K. Kaschner, Kullander, Garilao,
 Barile, & Froese, 2016). The spatial data were processed by
 constraining them to the geographic distribution within the appropriate
 depth ranges for each species according to the text information in each
 species assessment. Following established practice, for wide-ranging
 (i.e., &gt; 20,000 km<sup>2</sup>) mammals, seabirds, and
 turtles, distribution maps corresponded to key areas for species
 conservation (breeding, foraging, calving or nursery areas) rather than
 encompassing large portions of habitats discontinuities (e.g., whale
 migration routes). However, spatial distribution data of breeding,
 foraging, or nursery grounds for fishes and invertebrates with large range
 extents were not available at the national scale; thus, the geographic
 distribution of the species within these groups was delimited using
 occurrence records and reported depth ranges. We checked for the quality
 of all distribution data for each species when more than one data provider
 was identified. We are confident this represents the best available
 database on the distribution of threatened marine species in
 Brazil.  
 <strong>Spatial data on
 threats</strong> Industrial fishing
 information consisted of vessel monitoring systems (VMS) data from 10
 fisheries and associated gear types that are monitored within Brazilian
 waters by government agencies (<i>Programa nacional de rastreamento
 das embarcações pesqueiras por satélite</i> - PREPS). We estimated
 the density of points (fishing operations) for each gear type separately
 over a period of three years (2015-2017). For this analysis, we obtained a
 total of 4,205,607 data points representing signals produced from 905
 vessels in fishing operations. We created a layer for each gear type by
 dividing the number of signals in each raster cell of 1
 km<sup>2</sup> by the total number of signals emitted by all
 vessels operating that specific gear type. This produced our map of
 relative threats for each gear type. For global warming, we used the 4-km
 Advanced Very High Resolution Radiometer (AVHRR) Version 5.0 sea surface
 temperature (SST) data produced by NOAA’s National Oceanographic Data
 Center spanning the time period (1985-2009) to calculate rate of warming
 using non-linear mixed effect models (package nlme in R) as described by
 (Magris, Heron, & Pressey, 2015). SST data were composited to
 monthly resolution (from weekly) for calculation of trends. Information on
 ultraviolet (UV) radiation, ocean acidification, shipping lanes, and
 invasive species were obtained from global, publicly available datasets
 (Halpern et al., 2012, 2008). The coastal development index was calculated
 as described by (Aubrecht et al., 2008), whose index was measured by
 distance from emission of night-time lights, a proxy for human settlement
 and urbanisation. Night-time satellite imagery was obtained by NOAA's
 National Geophysical Data Center (NGDC). We chose a 25 km radius as a
 reasonable distance from a source of high night light at which many key
 human impacts from coastal development (e.g. domestic housing and presence
 of engineering structures) might affect the marine
 environment. Information on mining (i.e. geographic
 areas of active or exploratory mines [N=564]) and oil/gas fields (i.e.
 geographic areas either licensed for exploration [N=2,242] or production
 [N=670]) were provided by the Brazilian Ministry of Mines and Energy.
 Ports information were obtained from data provided by the Brazilian
 Environmental Agency. We estimated port-derived pollution by digitizing
 the areas of direct influence of each port (N=64), which included port
 infrastructure areas, dredging and disposal sites. We
 evaluated the threats derived from land-based activities considering four
 main layers: i) sediment, ii) organic pollution, iii) fertilisers, and iv)
 pesticides. To determine the influence of sediment in coastal waters, we
 first identified a sediment settling rate in function of depth
 <i>x</i>. For that, we adapted the methods used by Doheny et
 al. (2013) and Halpern et al. (2008). First, we assumed a settling
 velocity of 0.0001 ms<sup>-1</sup> for silt and clay and that
 this rate decreases in waters less than 10 m deep: This helped us to map the maximum plume extent possible considering the settling rates of fine sediments (i.e. silt and clay). Second, we used the “Path Distance” tool in ArcMap 10.7(ESRI, 2011) to determine the sediment travel time within the coastal waters based on the river mouth point locations and surface currents from the eddy-resolving 1/12° global HYCOM (Hybrid Coordinate Ocean Model) (Mehra & Rivin, 2010). We acquired oceanographic model data using the Marine Geospatial Ecology Tools software package (Roberts, Best, Dunn, Treml, & Halpin, 2010). Third, we used the “Raster Calculator” tool in Arcmap 10.7 (ESRI, 2011) to identify the pixels where sediment is still in suspension (i.e. positive values) using the following equation: Finally, sediment loading within the plume was estimated using an exponential distance-decay function of 0.0001 in Arcmap 10.7 (ESRI, 2011). For organic pollution, we used data from the Brazilian National Water Agency (Agencia Nacional das Aguas, 2018) on the amount of diluted non-treated sewer discharge per coastal council. In this case, we assumed that most of the pollution comes from urban runoff and that some coastal councils release their sewer directly in the ocean. For that, we considered that the amount of diluted non-treated sewer multiplied by population size (IBGE, 2015) would spread within a buffer of 5 km from each council coastal zone. We used the Cost-Path Surface tool in Arcmap 10.7 (ESRI, 2011) with a decay function of 0.05 (i.e. assuming a fixed amount of the threat in the initial cell and then evenly distributed to the remaining cells within the buffer area). Data for the usage of fertilisers in different cropping systems were extracted from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística (IBGE)., 2015), which included the amount of fertilisers used per cultivated area (kg ha<sup>-1</sup>) and the total area cultivated for each cropping system in each Brazilian State. We followed a similar approach to create the layer for pesticides. In this case, we extracted information on the average usage per ha (litres ha<sup>-1</sup>) and area used for different cropping systems (ha) from (Pignati et al., 2017). In both cases, we aggregated the data by catchment (i.e. 32 coastal catchments) and distributed to pour points (i.e. 302 river mouths) for each catchment. Then, we estimated the spread of fertilisers and pesticides into coastal waters by using the Cost-Path Surface tool in Arcmap 10.7 (ESRI, 2011) considering a 5 km buffer from each pour point and a decay function of 0.05. After creating these four layers, we have standardised their values between 0 and 1 using the equation below: Mining sites, oil/gas fields, port locations and associated dredging and disposal sites were treated as binary data (present or absent in any 1 km<sup>2</sup> raster cell) for our analyses since there are no data on the relative severity of individual areas. Other threat layers were normalised to range between zero and one, where one reflected the maximum value across all raster cells throughout study region for each layer and zero indicated the minimum value of threat. This normalisation made it possible to compare all threat layers. <strong>Cumulative impact map</strong> Estimates of cumulative impact were calculated as described by Halpern et al. (2008) and consisted of three components: (i) spatial distribution of each marine habitat; (ii) spatial distribution of each threat; and (iii) sensitivity weights to convert each threat to its relative impact on each of the habitats. We summarised the occurrence of all threat layers within each planning unit containing varying extents of each habitat throughout their distribution. We incorporated the sensitivity  of each habitat to each threat (i.e., ‘vulnerability’) following the framework developed by Halpern, Selkoe, Micheli, & Kappel (2007), based on expert judgment. Where there was an overlap, we multiplied each threat layer with each habitat layer, and then multiplied each combination by the corresponding weight variable representing a relative vulnerability of that habitat to each overlapping threat. This means that the presence of a threat and a habitat in the same planning unit is not considered an impact unless the given habitat is known to be sensitive to that threat (Tables S3 and S4). We link the habitats and threats developed here to the ones presented by Halpern et al., 2007 to assign the weight values (Table S5).
description: Please refer to the original paper ( A blueprint for securing
 Brazil's marine biodiversity and supporting the achievement of global
 conservation goals - Diversity and Distributions) and the Supporting
 Information for a full description of each dataset. Anyone using this
 dataset should cite both the dataset and the paper as data
 providers. AM.shp: Marine benthic habitats
 for the Amazon ecoregion. Please refer to Table S1 for habitat
 codes. FN.shp:
 Marine benthic habitats for the Fernando de Noronha and Atoll das Rocas.
 Please refer to Table S1 for habitat codes. SPSP.shp:  Marine benthic
 habitats for the São Pedro and São Paulo Islands ecoregions. Please refer
 to Table S1 for habitat codes. NE_Br.shp: Marine benthic
 habitats for the Northeastern Brazil ecoregion. Please refer to Table S1
 for habitat codes. Eastern_Br.shp: Marine benthic
 habitats for the Eastern Brazil ecoregion. Please refer to Table S1 for
 habitat codes. TMV.shp: Marine benthic habitats
 for the Trindade and Martim Vaz Islands ecoregion. Please refer to Table
 S1 for habitat codes. SW.shp: Marine benthic habitats
 for the Southeastern Brazil ecoregion. Please refer to Table S1 for
 habitat codes. RG.shp: Marine benthic habitats
 for the Rio Grande ecoregion. Please refer to Table S1 for habitat
 codes.  PU_CumulativeThreat.shp: Spatial
 distribution of the cumulative impacts on marine habitats (N=161) and
 threatened species (N=143) across the Exclusive Economic Zone (EEZ) of
 Brazil. pu_ThreatenedSpecies.shp:
 Distribution of threatened species across the Exclusive Economic Zone
 (EEZ) of Brazil.