Spatial analysis for site selection in marine aquaculture: An ecosystem approach applied to Baía Sul, Santa Catarina, Brazil

Aquaculture 489 (2018) 162–174

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Spatial analysis for site selection in marine aquaculture: An ecosystem approach applied to Baía Sul, Santa Catarina, Brazil

T

⁎

Luiz Fernando de Novaes Viannaa, , Jarbas Bonetti Filhob a Empresa de Pesquisa Agropecuária e extensão Rural de Santa Catarina – Epagri, Rodovia Admar Gonzaga, 1.347, Itacorubi, Caixa Postal 502, CEP 88034-901 Florianópolis, SC, Brazil b Departamento de Geociências, Centro de Filosofia e Ciências Humanas, Universidade Federal de Santa Catarina – UFSC, Campus Universitário - Trindade, CEP 88.010970 Florianópolis, SC, Brazil

A R T I C L E I N F O

A B S T R A C T

Keywords: Site selection Coastal zone management GIS Coastal zoning Social carrying capacity

The aim of this research was to propose and evaluate a methodological approach to integration and spatial data analysis in order to generate information towards a participatory site selection for bivalve marine aquaculture in the Baía Sul, Florianópolis, Santa Catarina, Brazil. For this purpose, the Baía Sul was investigated considering an ecosystem approach for aquaculture leading to an assessment of its potential for marine aquaculture. The planning of the aquaculture parks was made through a participatory process to incorporate both environmental carrying capacity and social carrying capacity. Experts and modellers developed a GIS model to assess the potential for marine aquaculture in Baía Sul. Continuous (unclassified) maps were used to provide spatial information about the variation of the potential for marine aquaculture in the Baía Sul. The maps were used to plan 53 aquaculture parks over the Baía Sul. The site selection of the parks was made in six public hearings attended by 403 stakeholders from 38 institutions representing different sectors with diverse interests in coastal zone. The results showed that although the Baía Sul is suitable for the growth of bivalve molluscs, some hydrodynamic characteristics and the influence of urbanization constitute a sanitary risk for the activity. Experts, modellers and stakeholders had a different perception about the importance of criteria in the aquaculture parks site selection. While the experts and modellers considered the environmental criteria as the most important aspect to locate the aquaculture parks, the stakeholders took into account mainly the logistics. The final result of the aquaculture parks location, approved by the Brazilian Ministry of Fisheries and Aquaculture (MPA), adopted the site selection by the stakeholders, providing aquaculture parks in areas with sanitary risk for the bivalve cultivation. The main advantage of the adopted assessment strategy was to identify the divergence between experts, modellers and the stakeholders and the distance that still exist between scientist and decision makers in Brazil. Statement of relevance: This is the first article about a participatory GIS for aquaculture in Brazil. The method was developed to be according to Ecological Approach to Aquaculture. The results highlight the importance of the participatory GIS in suitability study and site selection because the decision making process is different over the view of researchers, technicians and other social stakeholders.

1. Introduction Due to the rapid growth of aquaculture worldwide (FAO, 2014), the Food and Agriculture Organization of the United Nations (FAO) adopted the concept “Ecosystem Approach to Aquaculture” (EAA) in order to minimize environmental impacts and social and economic conflicts (Soto et al., 2008), through integrated coastal zone management (ICZM) policies (McCreary et al., 2001; Treby and Clark, 2004). The application of EAA includes four strategic carrying capacity analysis (Ross et al., 2013). The physical carrying capacity, based on the suitability of production, considers environmental factors and the ⁎

relationship to the farming system. The production carrying capacity, used to estimate the maximum production concerns the stocking density at which harvest are maximized. The ecological carrying capacity defines the amount of production that can be supported by the environment, taking into account all ecological processes and activities in a given area (e.g. a bay or an estuary). The social carrying capacity represents the stakeholders decision making process of aquaculture planning, considering all those tree carrying capacity levels, the social needs and conflicts for the use of coastal zone. For an EAA, the public participation in decision-making processes is necessary (AguilarManjarrez et al., 2010) and depends on the communication and

Corresponding author. E-mail addresses: [email protected] (L.F.d.N. Vianna), [email protected] (J.B. Filho).

https://doi.org/10.1016/j.aquaculture.2017.12.039 Received 1 February 2016; Received in revised form 22 August 2017; Accepted 22 December 2017 Available online 12 February 2018 0044-8486/ © 2018 Elsevier B.V. All rights reserved.

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other coasts dealing with aquaculture development. This replication could give a broader interest on the adoption of the proposed scheme and stimulate the reanalysis of historic marine data on GIS-based site selection projects.

integration between science and management (Byron et al., 2011). The need to generate territorial information for EAA reinforces the importance of using spatial analysis combined with multi-criteria analysis techniques (Kapetsky and Aguilar-Manjarrez, 2007; AguilarManjarrez et al., 2010; Meaden and Aguilar-Manjarrez, 2013; Ross et al., 2013). Geographic Information Systems (GIS) have been used since the 1990's for mapping, modelling and assessing the potential to select areas for marine aquaculture (Ross et al., 1993; Parker et al., 1998; Silva et al., 1999; Scott and Vianna, 2001; Pérez et al., 2003; Pérez et al., 2005; Beltrame and Bonetti, 2007; Radiarta et al., 2008; Silva et al., 2011, Micael et al., 2015). Maps resulting from GIS modelling are commonly used for the selection of sites for marine aquaculture while considering environmental, socio-economic and logistic criteria (Buitrago et al., 2005; Radiarta et al., 2008; Silva et al., 2011; Liu et al., 2013; Micael et al., 2015). Those maps are generated by modelling, in which the classes of descriptors (variables of interest), the layers of factors (transformed descriptors) and the layers of constraints are weighed and integrated through multi-criteria analysis (Aguilar-Manjarrez and Ross, 1995). The results are discrete suitability maps used to evaluate and discuss the spatial distribution of areas with different levels of suitability for production. The discretization of those maps in classes of suitability generates well-defined boundaries between classes. Discrete classification is not the best representation of suitability maps for a participatory decisionmaking process, considering the continuous nature of the natural factors (Couclelis, 1992; Couclelis, 1996; Couclelis, 2003), mainly in the aquatic environment. The discretization of maps in classes of suitability represents the interpretation of experts and modellers, with fewer or no participation of stakeholders. About this lack of communication between experts, modellers and stakeholders, Byron et al. (2011) proposes a framework to incorporate both environmental carrying capacity and social carrying capacity. This paper presents a methodological approach based on multi-criteria analysis (MCA), GIS and ICZM. MCA and GIS were used to build an environmental carrying capacity model for bivalve marine aquaculture in the Baía Sul, Florianópolis, Santa Catarina State, Brazil. The strategy was to try to overcome some site selection modelling limitations identified in most adopted frameworks: 1) The representation of potential areas for aquaculture with well-defined boundaries; 2) the modelling of spatial factors based exclusively on reclassification procedures; and 3) the lack in the analysis of the different perceptions of criteria importance among stakeholders involved in the participatory process. This methodological approach aimed to assist the participatory process of delimitation of areas intended for aquaculture parks. According to Brazilian law (Brasil, 2003) aquaculture parks are delimited areas in the aquatic environment comprising a set of individual aquaculture areas. The aquaculture parks should be planned by the Brazilian Ministry of Fisheries and Aquaculture (MPA) through a participatory process. After the approval of the aquaculture parks, the aquaculture areas can be bid to the individuals interested. The overall objective of this research is to propose and analyse the result of a methodological approach to plan the aquaculture parks in Brazil. For this, four specific objectives were defined: (i) to characterize the potential of the selected study area based on continuous maps of environmental, social, economic and logistical descriptors; (II) to evaluate the potential of the study area for marine aquaculture based on environmental, socioeconomic and logistic criteria; (III) to apply a participatory process to select sites to allocate aquaculture parks with participation of experts and stakeholders; and (IV) to analyse how the stakeholders used the continuous maps of criteria and the map of potential for bivalve aquaculture in the process of selecting the location of the aquaculture parks. Furthermore, the authors expect that the methodological approach to be proposed in this study could cause an impact beyond the Brazilian management needs and interests since it can be easily reproduced on

2. Study area The study was conducted in the state of Santa Catarina, southern coast of Brazil. The bivalve production of Santa Catarina state in 2014 was 21,553 t, mussels (17.853 t), oysters (3.670 t) and scallops (30 t) (Santos and Costa, 2014). The study area is the southern part of Florianópolis bay system, locally known as Baía Sul (central coordinate of the system: 27°42′S; 48°35′W) (Fig. 1). The Baía Sul has an area of 180.7 ha, 128.4 km perimeter. Baía Sul is a semi-enclosed water body, oriented towards the north-south direction and with an indented coast, forming several small coves and short beaches. The Baía Sul is subjected to a strong urban pressure due to the conurbation process of the municipalities of Florianópolis, São José and Palhoça and there are conflicts over the use and occupation of the coastal zone (SPG, 2010a). These municipalities account for 70% of the production of mussels and 94% of the production of oyster in the state of Santa Catarina (Santos and Costa, 2014). 3. Materials and methods The method adopted consists of four stages (Fig. 2): the first is descriptive, the second, analytical, the third involves the participatory decision-making process for aquaculture parks allocation, and the last stage is to evaluate how the stakeholders interpreted the results of the potential evaluation for allocate the aquaculture parks. The first three stages are present in various works related to site selection (Meaden, 1987; Ross et al., 1993; Parker et al., 1998; Scott and Vianna, 2001; Pérez et al., 2005; Buitrago et al., 2005; Beltrame and Bonetti, 2007; Kapetsky and Aguilar-Manjarrez, 2007; Silva et al., 2011; Micael et al., 2015), although their sequence may vary from author to author. In a different approach, in this article continuous raster maps and standardization equations were used rather than classification and weighting. Furthermore, the fourth stage was introduced to analyse how the stakeholders used the information generated by the experts and modellers in order to allocate aquaculture parks (Byron et al., 2011). 3.1. Selection of variables, descriptors, data sources and organization of GIS database Twenty-five variables were considered representative of the local suitability for the development of bivalve marine aquaculture. Their selection was obtained in a workshop comprising 18 experts in marine aquaculture, coastal management, biology, geography and oceanography, representing eight institutions, following a practice suggested in some site selection publications (Meaden, 1987; Scott and Vianna, 2001; Buitrago et al., 2005; Byron et al., 2011) and by FAO for EAA (Ross et al., 2013). This group of experts, coordinated by the authors of this article (modellers), was composed by researchers, aquaculture technicians of Brazilian Ministry of Fisheries and Aquaculture (MPA), technicians of Brazilian Coastal Zone Management Plan (GERCO) and bivalve producers. They were responsible for the determination of criteria, spatialization of descriptors, determination of factors, definition of standardization equations and for the AHP application. Table 1 summarizes how the variables were organized and processed. It shows the variable names, acronyms and the range of values in their respective units. It also contains the sources of data, the methods of generation of spatial layers and the abbreviations of descriptors. For factors, it shows the standardization equations for the scale 0–1, the acronyms and the criteria they belong to (environmental, socio-economic or logistic). Finally, it shows the constraints representing the legal criteria. 163

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Fig. 1. Location of the Baía Sul in the state of Santa Catarina, including major river basins, urban areas and mangroves.

analysis from pollution sources and results from numerical hydrodynamic models for tide. The experts also considered that the range in the values of physical-chemical descriptors were satisfactory for the physiological requirements of cultivated species, considering the historic production at Baía Sul (Santos and Costa, 2014) and the productive capacity (Rupp and Parsons, 2004; Rupp et al., 2005; Rupp et al., 2011a; Rupp et al., 2011b). Thereby, were selected only sanitary, hydrodynamic, sedimentological, socioeconomic and logistics descriptors for the potential assessment model. The potential was calculated on a continuous scale between 0 and 1, where 0 represents the worst condition and 1, the best condition. For each factor, a standardization equation was applied to rescale the descriptors values to the 0–1 scale. The spatial distribution of each factor was considerate to generate the equations. Factors with a “linear” spatial distribution were standardized by a linear equation (MTRC, ATRC, DMR, ODC, DDC, FI, PI, DCA, DAH, DB and EWF). Factors with a nonlinear spatial distribution were standardized by equations representing the cumulative frequency of the descriptor's values in each raster dataset (CST, TI, TOM, C/S, DEC). The equations of cumulative frequency of the descriptor's values were generated through the curve fitting toolbox on software Matlab. The definition and weighting of factors were performed with the aid of Expert Choice system, version 9.5, through the Analytic Hierarchy Process (AHP) (Saaty, 2001). The weights of the factors and criteria were obtained with a 0.02 consistency ratio for the environmental criteria, 0.01 for the logistic criteria, and 0.01 for evaluation of potential; 0.1 is the maximum recommended value for model validation through AHP. The model was implemented in ArcGIS 9.3 through map algebra, using the tool raster calculator. Within each criterion, ran as independent sub-models, a weighted sum of the factors was performed. The maps of the criteria were also weighted and summed, generating the map of potential assessment. The constraints were formulated in accordance with the current legislation and with the coastal zoning (SPG, 2010a, 2010b). Constraints included the legal provisions that prohibit marine aquaculture closer than 200 m from the beaches, 50 m from headlands and within environmental protected areas.

Spatial data from different sources were organized and processed to generate continuous surfaces of descriptors that represent the spatial distribution of variables needed to characterize and analyse the study area. Layers of the descriptors average wind fetch – AWF, critical shear stress - CST and extreme wind fetch - EWF were generated through the model Wind Fetch and Wave Model of the United States Geological Survey USGS (Rohweder et al., 2008) for ArcGIS 9.3. Data on average wind speed was used from the prevailing winds in the Baía Sul, in all quadrants, from 01/2000 to 12/2006, from weather station # 1501, National Institute of Meteorology (INMET). The layers of the descriptors meteorological tide residual currents MTRC and astronomical tide residual currents - ATRC, temperature - T, salinity – Sal, dissolved oxygen - DO, pH, turbidity - Tur, sediment textural index - TI, total organic matter - TOM, carbon/sulfur ratio - C/S and biodetrital carbonate - BC were obtained by kriging. The bathymetry layer - Bat - was generated through natural neighbour interpolation. All layers of distance descriptors were performed using Euclidean Distance. Layers of fluvial influence - FI and precipitation influence - PI were obtained by the weighted sum of the layers of residual currents (ATRC, MTRC) with layers of distance from the main rivers - DMR for fluvial influence determination; and distance from outfalls - DO and distance from drainage channels - DDC for precipitation influence determination (Fig. 3). The weights of the layers were assigned by the Analytic Hierarchy Process (AHP) (Saaty, 2001). 3.2. The potential assessment model The potential assessment model is presented at Fig. 3. The factors were selected in order to identify significant environmental differences at the area, regarding the bivalve molluscs culture. This variability of the Baía Sul was previously discussed by Rudorff et al. (2012) in a local scale, as well as to assess the health risk of production sites under the influence of pollution sources. In the absence of monitoring environmental contamination data (metals, pesticides, hydrocarbons, bacteria and viruses), the experts and modellers used, as proxies, distance 164

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Fig. 2. Flowchart showing the stages required to perform the characterization of the study area, the potential assessment for marine aquaculture, the selection of aquaculture parks and the evaluation of the whole process.

165

166 5

25.0–28.7 (°C) 29.0–33.4 (ppm) 6.6–11.2 (mg/L) 8.1–8.5 3.6–62.8 (NTU)

0.3–2.6

0–10.3 (%)

8.5–35.6

2.9–21.3 (%)

0–4790 (m)

Physiochemical Temperature Salinity Dissolved oxygen pH Turbidity

Sedimentological Sediment textural index

Total organic matter

Carbon/sulfur ratio

Biodetrical carbonate

Socioeconomic Distance from conflicting activities (fishing, sailing, anchorage, marina)

Logistic

5

0–1

MTRC. ATRC. ODC and DDC

4

5

5

5 5 5 5 5

3.4

3.4

0–1

MTRC. ATRC and DMR

4

4

0–4946 (m)

0–4772 (m)

Distance from outfalls

4

1

3

3

1.2

2

Data source

Distance from drainage channels

0–18,217 (m)

Sanitary Distance from the main rivers

0.008–0.149 (m/s)

Astronomical tide residuals currents

0–29.3 (m)

0.004–0.072 (m/s)

Meteorological tide residuals currents

Physiographic Bathymetry

0.02–1.01 (N/ m2)

0–8086 (m)

Hydrodynamic Average wind fetch

Critical shear stress

Data value range

Variable

Euclidian distance

Kriging

Kriging

Kriging

Kriging

Kriging Kriging Kriging Kriging Kriging

AHP

Euclidian distance AHP

Euclidian distance Euclidian distance

Natural neighbour

Kriging

USGS Wind Fetch and Wave Model USGS Wind Fetch and Wave Model Kriging

Raster dataset generation method

DCA

BC

C/S

TOM

TI

T Sal DO pH Tur

Fluvial Influence (FI) Precipitation influence (PI)

DDC

ODC

DMR

Bat

ATRC

MTRC

CST

AWF

Descriptor

(XDMR − min(XDMR) ) (max (XDMR) − min(XDMR ) )

(XMTCR − min(X ) MTCR) (max (X − min(X ) MTCR) MTCR ) (XATCR − min(X ) ATCR) (max (X − min(X ) ATCR) ATCR )

(XPI − min(XPI) ) (max (XPI) − min(XPI) )

ZDOA (0 − 1) =

(XDOA − min(X ) DOA) (max (X − min(X ) DOA) DOA )

⎜

⎟

ZC/S(0 − 1) = ⎜⎛0.7099 × exp ⎛X C × 0.01012⎞ ⎟⎞ + ⎝ S ⎠⎠ ⎝ (−31.45 × (exp(X C/S − 0.4394))

ZTI(0−1) = (−1.128 × exp (XTI × − 0.03979)) + ( − 1.704 × (exp(XTI − 1.673)) ZTOM(0−1) = (−1.56 × sin (0.1011 × XTOM) − 0.002885)) + (0.3077 × sin (0.4308 × XTOM) + 0.3508)) + (0.02723 × sin (1.536 × XTOM) + 1.689)) + (0.01629 × sin (1.953 × XTOM) + 1.603))

ZPI(0 − 1) =

(XDO − min(X ) ) DO (max (X ) − min(X ) ) DO DO (XDDC − min(X ) DDC) ZDDC(0 − 1) = (maxDDC − minDDC ) (XFI − min(XFI) ) ZFI(0 − 1) = (max (XFI) − min(XFI) )

ZDO(0 − 1) =

ZDMR(0 − 1) =

ZATCR(0 − 1) =

ZMTCR(0 − 1) =

ZCST(0−1) = − 2.915 × (XCST)−0.05401 + 3.875

Standardization matrix: Z(0–1) is the standardized raster dataset; X is the descriptor raster dataset; MIN X is the minimum value of descriptor; MAX X is the maximum value of the descriptor

Environmental

C/S

(continued on next page)

Socioeconomic

Environmental

TOM

DCA

Environmental

TI

Environmental

PI

Environmental

Criteria

Environmental

Constraint

FI

CST

Factor

Table 1 Variables, data value range, data source, method of generation of raster data, descriptors, standardization equation, factors, constraints and criteria used in the characterization and assessment of the potential of the Baía Sul for allocation of aquaculture parks.

L.F.d.N. Vianna, J.B. Filho

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n/a n/a n/a 4 4 6 n/a n/a n/a Legal Protected areas Normative Ibama 105/2006 Marine zoning

1: DHN (Diretoria de Hidrografia e Navegação) bathymetric data ASCII files of nautical charts #1902, 1904, 1905, 1906 and 1910; 2: INMET (Instituto Nacional de Meteorologia) meteorological station 1501 (27°36′9.1″S; 48°37′13.3″W); 3: Prudêncio (2003); 4: SEAP (2008); 5: Silva (2002); 6: SPG (2010b); n/a: not applicable.

EWF (XEWF − min(XEWF ) ) (max (XEWF ) − min(EWF ) )

EWF USGS Wind Fetch and Wave Model 2 Extreme wind fetch

0–0.7 (areas/Ha) 0–15,388 (m) Density of existing crops

4

DB 0–4373 (m) Distance from beaches

4

ZEWF (0 − 1) =

DEC ZDCA(0−1) = (0.9423 × exp (XDCA × 0.09287)) + ( − 0.2406 × (exp(XDCA − 12.11)) DEC

ZDB (0 − 1) =

(max (XDB ) − min(XDB ) )

DB

PA NI105 MZ

Legal Legal Legal

Logistic

Logistic

Logistic

Logistic DAH

(XDRA − min(X ) DRA) (max (X − min(X ) DRA) DRA ) (XDB − min(XDB ) )

ZDRA (0 − 1) = DAH

Euclidian distance Euclidian distance Density 0–5590 (m) Distance from access to highways

4

Descriptor Data value range Variable

Table 1 (continued)

Data source

Raster dataset generation method

Standardization matrix: Z(0–1) is the standardized raster dataset; X is the descriptor raster dataset; MIN X is the minimum value of descriptor; MAX X is the maximum value of the descriptor

Factor

Constraint

Criteria

L.F.d.N. Vianna, J.B. Filho

The final map resulted from overlay between constraints map and the potential assessment map. The maps of criteria and the final map were plotted in A0 size in a scale of 1:50.000 and exported as a google map layers to be used over satellite images for the planning of aquaculture parks in public hearings. 3.3. The participatory site selection of aquaculture parks The site selection of aquaculture parks was conducted in a participatory way, over six public hearings, organized by MPA, held in the three municipalities covered by the study area (Florianópolis, São José and Palhoça). Two public hearings for each municipality were necessary to plan, discuss and approve the aquaculture parks distribution. All the six public hearings were attended by 403 stakeholders from 38 institutions representing different sectors with different interests in coastal zone. Those included 3 universities, 12 governmental agencies, 15 representatives from associations of fisherman and aquaculture and 8 non-governmental organizations (NGO). This process was detailed by Vianna et al., 2012. In each public hearing, the MPA members presented the results of the modelling and organized small groups separated by region of interest to design the aquaculture parks over the paper maps or using the Google Earth. The results were analysed by the MPA technicians and after approved, published. 3.4. Assessment of interpretation of stakeholders in the site selection process A statistical analysis of the potential assessment model was performed, as well as of their environmental, socioeconomic and logistic criteria, for the aquaculture parks. Through the ArcGIS Zonal Statistics tool, the average potential value was calculated for each criterion and for the final map within the aquaculture parks. The parks were than classified into five potential classes (< 0.2; 0.2–0.4; 0.4–0.6; 0.6–0.8; > 0.8), equally distributed considering the range 0–1 as worst to best condition, and spatially represented. To identify the most representative criterion on the site selection of each park, a cluster analysis was performed using the Ward method and Chebychev distance metric, using the statistical package “R”. The higher value of each criterion in each park indicates the most important criterion considered by stakeholders to select the site. From the cluster result, an analysis of variance (ANOVA) and the Tukey test were applied to assess which criteria were the most significant in differentiating the groups. 4. Results and discussion 4.1. Characterization of spatial domain Fig. 4 shows the spatial distribution of environmental, socio-economic and logistical descriptors. The Baía Sul is a shallow bay, with an average depth of 2.7 m, reaching up to 29.3 m (Fig. 4 - Bat). The bay is semi-enclosed and receives almost no interference from ocean waves, only from waves locally generated by the wind and from tides. The winds from south quadrant have been identified as the strongest, with an average speed of 4.95 ms−1, while the north quadrant winds were the most frequent, occurring in 26.14% of time. The waves generated by the wind influences the substrate in the shallower sectors, where the highest values for CST were found (Fig. 4 CST). The local currents are predominantly caused by the astronomical tidal regime (Bonetti et al., 2007). Studies about the tides in Baía Sul identified an anti-node of stationary tides in the north-central area of the bay, or barotropic convergence zone (Martins et al., 1997; Prudêncio, 2003). In this barotropic convergence zone the water exchange with the ocean through the currents generated by the astronomical tide is reduced (Fig. 4 - ATRC), however the effect of prevailing winds, especially northern and north-eastern winds, partially offsets this reduction (Fig. 4 - MTRC). 167

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Fig. 3. Schematic representation of the conceptual model for evaluation of marine aquaculture potential of the Baía Sul, the descriptors that constitute the factors and weights of factors and criteria obtained by the Analytic Hierarchy Process (AHP).

In our analysis, the objective of the experts and modellers was not to use the distance from main rivers, outfalls and drainage channels as factor or constrains. The distance maps were used as descriptors to generate the continuous maps of pluvial/fluvial influence criteria (Fig. 3). The best way to evaluate the influence of those inputs are hydrodynamic modelling and Lagrangian discharges, however, it was not available when the project was executed. To solve that, the distance from inputs was integrated to the tidal residuals obtained from Prudêncio (2003). For the fluvial influence (FI), the distance received a higher score than the tidal residuals. On the other hand, for the pluvial influence, the distance from outfalls and drainage channels received a lower score than the tidal residuals. The authors are aware that FI and PI factors are not simply the Euclidian distance from the freshwater sources and, as explained, they were assessed also considering the influence of tidal residuals to minimize a misinterpretation. This kind of simplification may imply in a situation where the standardization approach could suggest sectors of maximum value of suitability in places that may not be the more suitable. However, even recognizing this limitation, the experts and modellers considered this approach feasible because the Baía Sul is a semi-enclosed bay with a mean distance from discharges lower than 2 km. Regarding physicochemical descriptors (temperature, salinity, dissolved oxygen, pH and turbidity), the analyses indicated that the entire bay has good conditions for the development of bivalve marine aquaculture (Fig. 4 - T, Sal, DO, pH, Tur). The range of values of the physicochemical descriptors are in accordance with the functional curves of the oysters, scallops and mussels, demonstrating that those bivalves can develop well in Baía Sul (Suplicy et al., 2003; Rupp and Parsons, 2004; Rupp et al., 2005; Resgalla Jr. and Brasil, 2007; Rupp et al., 2011a; Rupp et al., 2011b). About sedimentological descriptors, no references were found presenting optimal values for the cultivated species, however significant changes in environmental conditions have been identified within the substrate (Fig. 4 - TI, TOM, C/S). This agrees with previous investigations from Bonetti et al. (2007) and Rudorff et al. (2012). Higher values for TI, TOM and C/S were found in the north of the Baía Sul, suggesting that this sector is under greater influence of urban wastes and weaker hydrodynamics of the system. Although the values do not represent extreme conditions (Bonetti et al., 2007), it can be expected that the sanitary quality for marine aquaculture in the northern bay is lower.

In the north of Baía Sul, both meteorological and astronomical residual tidal currents presented values higher than 0.05 ms−1 (Fig. 4 – MTRC, ATRC). Despite higher values of currents, these do not reflect in effective renewal of water, since this area has no direct connection with the ocean (Bonetti et al., 2007). As the Baía Sul is a semi-enclosed bay, almost all its shoreline perimeter is under the influence of pluvial/fluvial inputs and the mean Euclidian distance is 1975 m (min 0 m, max 4946 m) (Fig. 4 - ODC, DDC). 104 outfalls and 69 drainage channels were identified over the shoreline perimeter. In urban areas outfalls (61) predominated, while drainage channels were found in greater numbers in less urbanized sectors (49). The higher concentration of outfalls and drainage channels are located on the north of the Baía Sul, over a 20 km of shoreline perimeter, where it was observed 40 inputs of this nature, with an average of one sewage every 500 m along the shoreline. The extent of the continental freshwater influence in bays and estuaries depends on the volume of coastal water inputs and local hydrodynamics. The environmental factors under the influence of continental freshwater dilution (salinity, organic pollution, heavy metals, pesticides, etc.) also depend on their concentrations at source (Hearn, 2008). Hydrodynamic studies have already simulated the tides, winds, waves and dispersion of pollutants in the South Bay (Martins et al., 1997; Prudêncio, 2003; Garbossa et al., 2014). Some recent studies have also evaluated the biological contamination of the coastal basins (Garbossa et al., 2017) and the contamination of water, sediments and molluscs by heavy metals and pesticides (Souza et al., 2016) in the South Bay. Garbossa et al. (2017) demonstrated the correlation between population density, basin areas and thermotolerant coliform concentration. Souza et al. (2016) concluded that there is no contamination by pesticides and that concentrations of heavy metals in the Baía Sul are in compliance with Brazilian sanitary legislation. However, there is no data to establish the extent of the influence of continental freshwater in the South Bay and its consequence in relation to environmental factors. To define the influence distance of freshwater inputs on semi-enclosed systems some authors used distance analysis of freshwater inputs to create constraint areas, without clearly describing how distance values were defined (Radiarta et al., 2008; Liu et al., 2013). Torres and Andrade (2010) defined the constrains areas from a 1 km radius from the river mouths, however this radius was arbitrarily defined by the modellers. 168

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Fig. 4. Spatial distribution of descriptors - (1) Hydrodynamic: Average wind fetch (AWF), critical shear stress (CST), meteorological tide residual currents (MTRC), astronomical tide residual currents (ATRC); (2) physiographic: bathymetry (Bat); (3) health-related: Distance from main rivers (DMR), distance from outfalls and drainage channels (ODC), distance from drainage canals (DDC); (4) physical-chemical: Temperature (T), salinity (Sal), dissolved oxygen (DO), pH; turbidity (Tur), (5) sedimentological: Textural index (TI), total organic matter (TOM), carbon/sulfur ratio (C/S), biodetrital carbonate (BC), (6) socioeconomic: Distance from conflicting activities (DCA) (anchorages, marinas, fishing areas and navigation canals); (7) logistics: Distance from access to highways (DAH), distance from beaches (DB), density of existing crops (DEC), extreme winds fetch (EWF).

fishing or presence of marinas (Fig. 4 - DCA). The beaches and the road system are accessible from anywhere in the bay, being possible to access a beach or a road in 25 min with a small boat (Fig. 4 - DAH, DB). Moreover, because of their very indented coastline and surrounding coves, there are many areas protected from extreme winds (Fig. 4 EWF). In the Baía Sul there are 103.15 ha of marine aquaculture areas already installed (SEAP, 2008; Santos and Costa, 2014) being the highest densities are in the communities of Tapera, Ribeirão da Ilha and Caieira (in Florianópolis) and Enseada do Brito (in Palhoça) (Figs. 1, 4 -

The higher values for biodetrital carbonates (Fig. 4 - BC) were found in areas of greatest hydrodynamics (Fig. 4 – ATRC) or those associated with higher densities of aquaculture crops (Fig. 4 – DEC). Its origin is biogenic, from the remains of oyster and mussel shells. Because these organisms are essentially marine, the largest natural biodetrital carbonate concentrations occur in the areas under greatest oceanic influence and associated with coarser sediments (Vilas et al., 2005). In socioeconomic terms, there may be conflicts with other activities around the vicinity of the bay, with the exception of the least populated areas. Conflicts may exist with respect to navigation, anchorage sites, 169

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of Palhoça the potential is lower, from 0.3 to 0.5, because of the worst environmental conditions on the north-western shore of the bay (Fig. 5 – ENV) and conflicts with other activities at the southwest (Fig. 5 SOEC). The best conditions (potential > 0.8) were observed in the south of Ribeirão da Ilha on the island side and in the far south of the Baía Sul. Environmentally, the Baía Sul has an increasing gradient of potential in the North-South direction. The areas closer to the shoreline in the northern bay had the lowest potential (< 0.4). Considering the oceanographic and hydrodynamic characteristics, those areas are under influence of the barotropic convergence zone (Fig. 4 – MTRC, ATRC), submitted to fluvial and pluvial inputs (Fig. 4 – DMR, ODC, DDC) and with the higher values of critical shear stress (Fig. 4 - CST). Those factors may jeopardize the culture of organisms that do not resist to seasonal variations in salinity or do not adapt to high levels of turbidity and suspended particulate matter. Also, the shoreline at the north of Baía Sul is adjacent to urban centres and receives domestic effluents from outfalls and drainage channels (Fig. 4 – ODC, DDC) compromising the environmental sanitary quality for bivalve cultivation. On the other hand, lower values of C/S and higher concentrations of muddy substrate and organic matter (Fig. 4 - C/S, TI, TOM) indicate the presence of nutrients which can support the growth of filter feeding organisms. At the northern Baía Sul an area on the border with Baía Norte was modelled with high environmental potential (Figs. 1, 5 – ENV). This high environmental potential is a limitation of the model, because the shoreline of the northern bay is highly urbanized, is under the influence of urban inputs (Fig. 4 – ODC, DDC) and close to the barotropic convergence zone. This area was classified as having high environmental potential due to its bathymetric and hydrodynamic characteristics, deep and with fast currents (Fig. 4 – Bat, MTRC, ATRC). However, high levels of TI, TOM and low values of C/S observed at the site are indication of organic deposition and low exchange of sea water. The socioeconomic potential represented the possible conflicts between marine aquaculture and other activities in the coastal zone. According to this criterion, three major areas with the potential higher than 0.5 were identified (Fig. 5 - SOEC). Those areas coincide with little urbanized locations or with the presence of preserved coastal ecosystems (Fig. 1). The logistic potential was determined by the facility of access to highways and beaches, the density of existing aquaculture farms and the protection against extreme winds. The logistic criterion showed an inverse spatial distribution comparing to environmental and socioeconomic criteria and the areas nearer the shoreline were those with the highest potential (Fig. 5 - LOG). The high potential of logistic criterion close to the coast is because the Baía Sul is elongated in the north-south direction and the mean distance to the shoreline is 3.56Km (Fig. 4 – DAH, DB). Moreover, it has a coastline protected from extreme winds (Fig. 4 – DAH, DB, EWF).

Fig. 5. Results for potential assessment (POT) and for environmental (ENV), socioeconomic (SOCE) and logistics (LOG) criteria.

DEC). It is important to highlight that the identification of suitable sites, in this work, did not consider stocking density or ecological carrying capacities. This type of study, which could be assessed by other methods such as dynamic models, is indispensable for the effective determination of production limits. This is because oysters and mussels are organic extractors, so the presence of one farm will have a potential effect on the performance of neighbouring farms, due to food depletion and disease propagation. Although such approach would extrapolate the scope of the present paper they should be performed aiming a more comprehensive management of the area. The growth of productivity of marine aquaculture in the Baía Sul (Santos and Costa, 2014) and the characteristics evaluated through the above-mentioned descriptors indicate that the study area is suitable for the activity. However, the Baía Sul is composed by different sub-environments (Bonetti et al., 2007; Rudorff et al., 2012), and this gives a potential variability for marine aquaculture performance. The proximity to urban centres and the inputs from coastal watersheds (Fig. 1) are the main aspects that contribute to environmental issues and challenges for the development of bivalve aquaculture. Those influences are responsible for the variability of potential, considering use conflicts, logistic and environmental risks.

4.3. Analysis of use of the criteria and potential maps by the stakeholders in the aquaculture parks site selection process The use of continuous maps of descriptors, factors and potential evaluation for the aquaculture parks site selection process, did not induce the stakeholders to select areas pre-classified by the experts and modellers. The MPA used those continuous maps to inform the stakeholders about the spatial variability of the potential of the Baía Sul for marine aquaculture and allowed a collective interpretation. This approach permitted to assess the aquaculture park sites considering the environmental, logistic and socioeconomics potential without pre-defined rigid limits.

4.2. Potential evaluation for bivalve aquaculture Fig. 5 shows the variability of potential for bivalve aquaculture in the Baía Sul. Higher values of potential for marine bivalve aquaculture (> 0.5) in areas near the coast were found in the southern Baía Sul (Fig. 5 – POT). The current producing sectors of Ribeirão da Ilha and Caieira are located in areas with potential higher than 0.5 (Figs. 1, 4 – DEC, 5 - POT). Those areas are logistically well attended and have good environmental quality, however in the southern of Caieira there are conflicts with other activities. Otherwise, in the producing communities

4.3.1. Assessment of aquaculture parks potential Fifty-three parks (822.8 ha) with an average area of 15.5 ha were selected by the stakeholders and approved by the Ministry of Fisheries and Aquaculture. The smallest park measured 0.23 ha and the largest, 170

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Fig. 6. Spatial distribution of aquaculture parks representing the potential assessment and the cluster analysis according to the environmental, socioeconomic and logistic criteria.

However, taking into account the behaviour of the second group, a difference in the values for logistical criterion was observed and the cutoff point of the dendrogram was reduced for 1.5, dividing the second group into two subgroups. The criterion of highest average value among all aquaculture parks was the logistic one (0.63), followed by the environmental (0.37) and socioeconomic criteria (0:33). This represented, on average, that the parks were selected considering, in descending order of importance, the logistical factors (47%), the environmental factors (28%) and the socioeconomic factors (25%). The environmental model without sanitary primary data did not convince the stakeholders to discard the northern Baía Sul as an aquaculture parks site. The stakeholders and the Ministry of Fisheries and Aquaculture considered all the Baía Sul as suitable for aquaculture, despite warnings of the experts about the risk of sanitary conditions on the northern Baía Sul. Moreover, the logistic conditions near the shoreline were the most important criterion used by the stakeholders to design the aquaculture parks. The result of the analysis of variance indicated the environmental and socioeconomic criteria as the most significant in separating the groups (p > 1.25e−09; p > 0.00452). Tukey test (Fig. 8) showed that group 1 (374,8 ha) differed from the others mainly due to the environmental criteria, while group 3, due to socioeconomic criteria. Groups 2 and 4 (349.1 ha) were separated by a negligible difference in the logistic criteria (p > 0.8), however this difference was considered by the authors. Fig. 9 shows the distributions of values for the criteria into groups. Regarding the environment, Group 1 showed the higher values. For the socio-economic criteria, Group 3 was the one most influenced. As for Logistics, there is little difference between the groups, except for Group 2, which despite having a negligible mean difference compared to the others, has more area with values of logistical potential below 0.5. The aquaculture parks of Group 1 showed higher influence from logistical and environmental criteria. The average value of the logistics potential in these parks was 0.65; the environmental was 0.49, and the socioeconomic was 0.14, the lowest for this criterion between groups. In Group 2, the influence of the criteria was more proportional. Mean values for the logistics, socioeconomic and environmental criteria were 0.51, 0.42 and 0.36, respectively. Group 3 showed the highest influence of socio-economic and logistical criteria (0.81 and 0.70), while the environment criterion was 0.32. Finally, Group 4 had a higher influence of the logistical criteria compared to the others (0.63). It was also the one with the lowest average value of environmental potential (0.2), despite a high variance. The group 1 represents the aquaculture parks with best environmental and logistical conditions and the group 3 had less conflicts with

107.7 ha. The selection sought to rearrange the current mosaic of existing bivalve aquaculture structures and propose new areas for expansion of the activity. The rearrangement accounted for 60% of the aquaculture parks area, while the remaining 40% was allocated to expansion of the area occupied in 2005 (Oliveira Neto, 2005). Fig. 6 shows the spatial distribution of aquaculture parks in accordance with the potential, constraints and also the result of the cluster analysis, considering the environmental, socioeconomic and logistic criteria. Regarding the potential, 66.64% (584.39 ha) of the area of the parks was allocated in places where average potential was above 0.41 (Fig. 6 – Aquaculture parks potential). The parks with average potential below 0.4 were located in the central northern Baía Sul, in the community of Tapera and near to the border of the metropolitan areas of Palhoça, São José and Florianópolis. Those areas were considered risk areas by the experts. Although physicochemical variables (Table 1) and the history of production indicated good suitability for the growth of bivalve molluscs (Suplicy et al., 2003; Rupp and Parsons, 2004; Rupp et al., 2005; Resgalla Jr. and Brasil, 2007; Rupp et al., 2011a; Rupp et al., 2011b), the experts considered the urbanization process as a sanitary risk factor for health in the northern Baía Sul. Nevertheless, the stakeholders selected those areas for aquaculture parks (Fig. 6) and the MPA approved them. The absence of monitoring data of water and bivalve quality during the modelling of potential evaluation for aquaculture makes difficult to confirm the sanitary situation of the Baía Sul. The hydrodynamic, sanitary and sedimentological factors adopted by experts, indicated the need of care about the sanitary condition, considering the barotropic convergence zone and the proximity to urban zones. According to Souza et al. (2016), the level of pesticides and heavy metals in the Baía Sul are still below the maximum tolerated levels for the water bodies and foodstuffs specified in the Brazilian legislation. However, a regression model to predict the concentrations of faecal indicator organisms showed a significant correlation between urban demographic density and the contamination of seawater and bivalve molluscs in the Baía Sul (De Souza et al., 2018). The contamination of sites nearby the urban areas at the northern Baía Sul has been monitored by the Environmental Agency of Santa Catarina State (FATMA, 2016) and results indicate levels of coliforms higher than the specified in the Brazilian legislation. 4.3.2. Assessment of the representativeness of the criteria in the aquaculture parks site selection process Regarding to the representativeness of the criteria in the aquaculture parks site selection process, four distinct groups of aquaculture parks were identified (Fig. 6 – Aquaculture parks cluster). In accordance with the dendrogram (Fig. 7), and assuming a cut-off point around linkage distance 2, three distinct groups could be considered. 171

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Fig. 7. Dendrogram for cluster analysis of aquaculture parks according to the environmental, socioeconomic and logistic criteria.

the scientists was used partially by the stakeholders. Besides, as member of the stakeholders, the government agreed with the decision.

other activities in the coastal zone. According to the experts, groups 2 and 4 are on the environmentally risk areas. Despite the favourable conditions for the growth of bivalves, the areas where the aquaculture parks of groups 2 and 4 were sited are under influence of urban sewage and with lower exchange of seawater. However, the aquaculture parks of groups 2 and 4 were selected considering its logistic conditions. The main advantage of the adopted assessment strategy was to permit identify the divergence between experts and the stakeholders and the distance that still exist between scientist and decision makers in Brazil. Following a framework to incorporate both environmental carrying capacity and social carrying capacity, as proposed by Byron et al. (2011) without a governance structure as the Working Group on Aquaculture Regulations (WGAR) or similar, is daring. The final decision of the government approves the aquaculture parks in areas with sanitary risk. This approval indicates that the information generated by

5. Conclusion The descriptors selected to characterize the area suggested that the Baía Sul is suitable for marine aquaculture activities, however the spatial variability of the descriptors indicated differences of potential along the bay. The continuous maps of the characterization and the potential assessment did not influence the stakeholders to project the aquaculture parks inside predefined areas with boundaries. The continuous maps provided useful information for the process of site selection to implement the aquaculture parks. The public hearings legitimized the aquaculture park site selection process within the prerogative proposed by the Ecosystem Approach to Aquaculture.

Fig. 8. Bar plot of Aquaculture Parks ANOVA and Tukey's test for environmental, socioeconomic and logistic criteria.

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Fig. 9. Box plots of aquaculture parks grouped according to the environmental, socioeconomic and logistic criteria. Group 1 (purple); Group 2 (red); Group 3 (brown); Group 4 (blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Nevertheless, there were differences of interpretation between the experts and modellers, who developed the study on potential, the stakeholders that defined the location of the parks and the Ministry of Fisheries and Aquaculture, that legalized them. While experts and modellers considered the environmental criteria and sanitary risks as the most important for the decision-making, the stakeholders based it mainly on the logistics. Thus, some aquaculture parks were allocated in areas with low environmental potential with agreement of Ministry of Fisheries and Aquaculture. This decision may, in the near future, jeopardize the use of those parks for aquaculture activities. It was not possible to compare the continuous maps approach with the pre-classified maps approach within the objectives of this paper. However, we could verify how the continuous map strategy helped to identify the divergence between experts and modellers and the stakeholders, and to show the distance that still exists between scientists and decision makers in Brazil.

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