The measurement of ecosystem development in Caribbean coral reefs through topological indices

Ecological Indicators 110 (2020) 105866

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The measurement of ecosystem development in Caribbean coral reefs through topological indices

T

⁎

Jimmy Argüelles-Jiméneza, , Jorge Christian Alva-Basurtob, Horacio Pérez-Españaa, Manuel J. Zetina-Rejónc, Jesús Ernesto Arias-Gonzálezd a

Instituto de Ciencias Marinas y Pesquerías, Universidad Veracruzana, Hidalgo 617, Col. Río Jamapa, C.P. 94290 Boca del Río, Veracruz, Mexico Parque Nacional Costa Occidental de Isla Mujeres, Punta Cancún y Punta Nizuc, Boulevard Kukulcán Km 4.8, Zona Hotelera, Municipio de Benito Juárez, C.P. 77500 Cancún, Quintana Roo, Mexico c Instituto Politécnico Nacional-Centro Interdisciplinario de Ciencias Marinas, Av. Instituto Politécnico Nacional S/N Col. Playa Palo de Sta. Rita, C.P. 23096 La Paz, BCS, Mexico d Laboratorio de Ecología de Ecosistemas de Arrecifes Coralinos, Departamento de Recursos del Mar, Centro de Investigaciones y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, C.P. 97310 Mérida, Yucatán, Mexico b

A R T I C LE I N FO

A B S T R A C T

Keywords: Development attributes Succession Development stages Ecosystem maturity Coral reefs Mexican Caribbean

The exploration of gradients of development stages of coral reef ecosystems is a subject poorly studied, especially when they exhibit multiple degrees of geomorphological or structural development or both. The objective of the present work was to study the gradient of functional and structural maturity of the Mexican Caribbean coral reefs (CM). Here we analyzed three geomorphological zones that cover a gradient of 400 km in length in order to obtain coral reefs with different geomorphologies. Thirteen reefs were selected, for which 12 ecosystem development attributes and five topological indices were analyzed. The development attributes of coral reefs were estimated from trophic models constructed using Ecopath with Ecosim (EwE), while the topological indices were calculated from the predator-prey matrix obtained from each EwE model. Through a partial redundancy analysis (RDA) seven of the 12 development attributes (ascendency, overhead, development capacity, net primary production, ascendency/development capacity, overhead/development capacity and richness of functional group) were selected due their low or null collinearity. Using the developmental attributes selected in a nonmetric multidimensional scaling (stress: 0.1) and analysis of similarities (r global: 0.828 and p: 0.001), we found a gradient of maturity that increases from north to south, i.e., northern coral reefs (e.g. Puerto Morelos) are less mature than southern coral reefs (e.g. Mahahual). On other hand, through a non-parametric ANOVA and a partial redundancy analysis (first axis: F-ratio = 62.054, p = 0.012; second axis: F-ratio = 1.591, p = 0.014; 100% of the total variance explained by the first two canonical axes) we detected that topological indices respond to development stages, in this way the control flow increases with the maturity while the intermediation, number of connections and number of interactions depredator-prey are inverse to maturity; therefore, topological indices can be used to describe development stages. The determination of a gradient of maturity in MC coral reefs should be considered in management and conservation policies, therefore different strategies must be implemented in ecosystems, because resilience and ecosystem response depend on them.

1. Introduction The modern coral reefs located in the Caribbean Sea began their development during the Holocene (Blanchon et al., 2002; Blanchon,

2011) and despite having originated in the same geological period, their geomorphological and structural development differs. This may be due to gradients of enviromental conditions (temperature, transparency, salinity, wave exposure, and hurricane impacts), which have a

Abbreviations: EwE, Ecoptah with Ecosim; NMDS, non-metric muldidimensional scaling; ANOSIM, analysis of similarities; SIMPER, percentage similarity analysis; RDA, analysis of canonical redundance; Pp/R, primary production/respiration; Pp/B, primary production /biomass; TST, total system throughput; B/TST, biomass/ total system throughput; NPP, net primary production; A, ascendency; DC, development capacity; O, overhead; A/DC, ascendency/development capacity; O/DC, overhead /development capacity; FCI, Finńs cycling index; S, functional diversity; Dc, degree centrality; Cc, closeness centrality; Bc, betweenness centrality; Dw, weighted degree ⁎ Corresponding author. E-mail addresses: [email protected] (J. Argüelles-Jiménez), [email protected] (J.E. Arias-González). https://doi.org/10.1016/j.ecolind.2019.105866 Received 21 July 2019; Received in revised form 22 October 2019; Accepted 24 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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direct effect on the fundamental biological processes (including metabolites and photosynthesis) that determine the development of these reefs. These gradients have been explained through the physicochemical provinces described for the Caribbean Sea (e.g. Chollet et al., 2012); in this way, a gradient of physicochemical regions with optimum, intermediate and suboptimal conditions for coral reefs exist in the Caribbean Sea. An example of optimal conditions is the region of the Inner Caribbean, with oligotrophic waters, far from large pluvial discharges and characterized by stable warm environments, high transparency, and low sedimentation. An example of reefs in the Inner Caribbean, are those distributed throughout the Mexican Caribbean (Chollet et al., 2012), and, for these reefs a decrease in geomorphological development has been described from north to south (i.e., different reef extension, structural complexity, type and number of habitats) (Gutiérrez-Carbonell et al., 1993, 1995; Arias-González, 1998; Núñez-Lara et al., 2005). Some authors have identified differences in the community structure of fish and corals in this area, as well as in the geomorphological complexity and functioning of their trophic networks (Núñez-Lara et al., 2005; Arias-González et al., 2011a; RodríguezZaragoza and Arias-González, 2015). Chávez and Hidalgo (1988) and Chávez et al., (2007) have suggested that variations in reef geomorphology can result in differences in the level of ecosystem maturity. Trophic structure is vital to understanding the structure and functioning of ecological systems (Pimm et al., 1991). In this sense, Odum (1969) argued that structural changes can occur in each ecosystem development stage, being more complex in mature ecosystems. However, Odum (1969) did not delve into the structural behavior of trophic networks, i.e., the role or importance of species and/or functional trophic groups in the dynamics and structural stability of the ecosystem. For this reason and considering that the attributes of the ecosystems provide a guide to understanding maturity gradients, the present study intends to answer the following questions: 1) is there a gradient in the level of ecosystem development of coral reefs in the Mexican Caribbean that can be explained by geomorphological development? 2) Does the structure of trophic networks contrast with the gradient of ecosystem development stages? In this last question, we explore the way in which topological indices characterize development stages. Our approach is to determine the ecosystem’s maturity through ecological networks analysis (ENA), using the ecological descriptors of energy flows described by Odum (1969) and Ulanowicz (1986).

Fig. 1. Study area. (A) Yucatan Peninsula. (B) North portion of the Mesoamerican Reef System and its three sectors. Thirteen reefs were selected for the study: Punta Nizuc (PN), Puerto Morelos (PMo), Punta Maroma (PMa), Akumal (Aku), Boca Paila (BP), Yuyum (Yy), Punta Allen (PA), Punta Herrero (PH), Tampalam (Ta), El Placer (EP), Mahahual (Ma), Xahuayxol (Xa) and Xcalak (Xc). (C) Division of the reefs of the Mexican Caribbean based on their geomorphology: Northen sector (NS), Central sector (CS), and Southern sector (SS). Reef complexity with top and side view: L = lagoon, C = Cresta, F = front, S = slope, T = terrace. Depth in meters. Modified from Núñez-Lara et al., (2005), and Rodríguez-Zaragoza and Arias-González (2015).

2. Materials and methods

located in the southern sector (Núñez-Lara et al., 2005; RodríguezZaragoza and Arias-González 2015). For a detailed description of the study area, see Arias-González (1998) and Arias-González et al. (2011a).

2.1. Study area The study area is located in the physicochemical province of the Inner Caribbean (Chollet et al., 2012) and is part of the northern section of the Mesoamerican Reef System, which has been considered one of the most biodiverse places in the Caribbean Sea (Arias-González et al., 2011a). Thirteen reefs, located between 18°00′ and 21° 00′ N (Fig. 1) were selected, which are part of a semicontinuous fringing reef that grows near and parallel to the coast, from Cancun to the border between Mexico and Belize (Arias-González et al., 2011a; Alba-Basurto and Arias-González, 2014; Rodríguez-Zaragoza and Arias-González, 2015). According to its geomorphological characteristics, current human developments and patterns of use and protection, the reefs can be grouped into three sectors (north, center and south). The northern sector includes Punta Nizuc (PN), Puerto Morelos (PMo), Punta Maroma (PMa), and Akumal (Aku) reefs. These reefs are characterized by two habitats (lagoon and forereef) with long discontinuous and continuous coral formations, separated by large expansions of sand. On the other hand, the reefs of the central and southern sector have four habitats (lagoon, forereef, slope and terrace), and have more developed structures with spur and groove systems. The Boca Paila (BP), Yuyum (Yy), Punta Allen (PA), and Tampalam (Ta) reefs are in the center while El Placer (EP), Mahahual (Ma), Xahuayxol (Xa), and Xcalak (Xc) are

2.2. Ecopath models Models analyzed were built using the Ecopath with Ecosim software (EwE v6.5; available at http://www.ecopath.org). The EwE approach is defined by a linear equation for each species or functional group (i) included in the model. Each equation represents an energy balance between production and energy costs. Therefore, the output of each group (i) can be expressed by the linear Eq. (1):

P Bi ∗ ⎛ ⎞ ∗ EEi − ⎝ B ⎠i

n

∑ Bj ∗ j=1

⎛ Q ⎞ ∗ DCji − Yi − Ei − BAi = 0 ⎝ B ⎠j

where for a group i , Bi is the biomass,

P Bi

(1)

is the production/biomass

ratio, EEi is the ecotrophic efficiency, Bj is the predator biomass j, Q is Bi the consumption/biomass relationship of the predator j , DCji is the fraction of i in the diet of j , Yi is the export value or fishing catch of group i, Ei is the net migration rate (emigration-immigration) and BAi is the rate of accumulation of biomass. These last two elements were not used in the present study due to the lack of data and assuming that the 2

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attributes were considered the predictor variables. The significance of the final model that validated the selected development attributes was evaluated through a Monte Carlo test with 9999 permutations. The development stages (groups) were established with the selected development attributes by means of non-metric multidimensional scaling (NMDS). To test if the groupings established through the NMDS differ significantly, a one-way analysis of similarity (ANOSIM) was applied, and, the classification of the developmental stages obtained from the NMDS was used as a factor. On the other hand, to evaluate the contribution of the development attributes to each grouping, a percentage similarity analysis (SIMPER) was applied to identify those variables that contributed to 95% of the cumulative contribution. As a measure to reduce variation of out-lier data, developmental attributes were normalized before performing the NMDS, ANOSIM and SIMPER. Both the NMDS and the ANOSIM were performed on a dissimilarity matrix estimated using the euclidean distance. The three analyses were carried out with the PRIMER-E v7 software (Clarke and Gorley, 2006).

net balance of migration and accumulation of biomass is insignificant. For each equation of each group i, input parameters include B, P/B, Q/B and EE as well as the composition of the diet (DCij ) of all consumers. At least three of the four basic parameters for each functional group of the model must be included, and the program can calculate the remaining value through the EE. The EE is the proportion of the production that is used within the system and must be less than or equal to 1; usually the EE is used to check the balance of the model. Fishing values may or may not be included, depending on the presence of a fishing fleet (Christensen et al., 2008). 2.3. Construction of the models Input data were collected from the beginning of the year 2000, and are based on fish counts and benthic records in the study area (NúñezLara et al., 2005; Arias-González et al., 2004; Acosta-González et al., 2013) using linear transects of 50 × 2 m carried out in 714 sites distributed among the 13 reefs. At each site 12 replicates were performed, and number and length of the fish was recorded. Fish length was converted into biomass using length-weight relationship for which the parameters were obtained from samplings performed in this area (Arias-González et al., 2004), and from electronic FishBase database (Froese and Pauly, 2019). Biomass of functional groups that were not fish were estimated by EwE. Both the aggregation of functional groups of similar ecological characteristics (Table 6) as well as diet values for non-fish groups were based on Opitz (1996). The basic parameters of production/biomass (P/ B) and consumption/biomass (Q/B) for the fish groups were obtained from FishBase (Froese and Pauly, 2019). The values of the diet matrix and P/B were adjusted gradually for each of the models until they were balanced, when all EE < 1. The fishing values were obtained from commercial catch data (in tonnes) was obtained from a goverment office dedicated to monitoring fishing in Mexico (Secretaria de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación: SAGARPA) in the state of Quintana Roo. For each model biomass data were standardized at t/km2 and evaluated with a prebalance (PREBAL; Links, 2010). This diagnosis provides a guide (Table 7) that was used to identify problems related to the structure of the model and quality of the data after the mass balance (Links, 2010).

2.4.2. Structural analysis In a trophic network, not all functional groups are equally relevant in terms of structural dynamics and stability, hence their importance can be quantified by analyzing the contribution of each group and as a whole; this can reveal general structural patterns (Abascal-Monroy et al., 2015). Considering this last idea, four topological indices (degree centrality, closeness centrality, betweenness and weighted degree) and one of substructures (modularity) were calculated using the consumption matrix estimated by EwE and transformed to log10 (AbascalMonroy et al., 2015; Zetina-Rejón et al., 2015). Each index was normalized to allow the comparison of results between models. Topological indices and their normalization were performed using the igraph library (Csardi, 2017) in R software (R Development Core Team, 2018). Topological indices are briefly described below: a) Degree centrality (Dc ), considers the number of connections or relationships of the functional groups (prey and predators), so that the functional groups with the greatest number of connections are obtained (Wasserman and Faust, 1994; Allesina et al., 2009). b) Closeness centrality (Cc ), measures the average of the inverse of the distance between a given functional group and the rest of the functional groups in the network; high values indicate a close relationship between the given group and the other groups in the trophic network (Wasserman and Faust, 1994). c) Betweenness centrality (Bc ), quantifies the frequency at which a functional group appears in the trophic routes between each pair of groups in the trophic network (Wasserman and Faust, 1994). In other words, it measures the importance of a functional group that appears frequently in trophic routes and that presents a position in the flow of matter from one group to another, or from one trophic level to another (Abarca-Arenas et al., 2007). d) Weighted degree (Dw ), is given by the sum of the flow of magnitudes between a functional group in terms of its position in the trophic network and in terms of the energy that flows through it (Horvath, 2011), i.e. it measures the control that a functional group has over the flow of energy among the rest of the functional groups. e) Modularity, unlike the topological indices, which are estimated for each functional group, this indicator measures the degree to which the trophic network is structured in subgroups (groupings). These subgroups are characterized because members that compose them have more intense trophic interactions with each other than with other groups in the trophic network (Stouffer and Bascompte, 2011; Zetina-Rejón et al., 2015). The modularity of each model was quantified by means of the algorithm “fast-greedy finding” (Newman and Girvan 2004), which is included in the igraph library (Csardi, 2017). This algorithm uses modularity as a function of maximization in an iterative way. Considering that modules are groups determined by the strength of their trophic interactions,

2.4. Theory/calculation 2.4.1. Ecosystem development stages For each of the 13 models, 12 ecosystem attributes were quantified. Odum (1969) and Ulanowicz (1986) proposed eleven attributes to describe the ecosystem development stages and one additional attribute was proposed by the authors of the present study (net primary production, see Table 1). Development attributes can be cataloged in two of the six categories proposed by Odum (1969): 1) community energetics, and 2) community structure. Although species richness is found in the category of community structure, we propose to use functional diversity (S) instead, because functional trophic groups have the potential to generate a better approach to describe process that occur within an ecosystem (Abarca-Arenas and Ulanowicz (1986)); even if two ecosystem have the same number of species, they will not necessarily have the same number of functional groups, so each ecosystem will tend to function differently. Table 1 briefly describes attributes of the ecosystems used. To establish the developmental stages of the Mexican Caribbean coral reefs, the development attributes that contributed to explaining the gradient of maturity and that did not present colinearity were selected (Table 8); this selection was carried out using forward selection in a partial redundancy analysis (Ter Braak, 2002). For this, the geomorphological gradient of the Mexican Caribbean (factored) was considered the response variable and the 12 normalized development 3

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Table 1 Development attributes used to assess development stages of the Mexican Caribbean coral reefs. References: Odum (1969)1, Ulanowicz (1986)2, Reaka-Kudla (1997)3, Chollet et al. (2012)4 and Robertson et al. (2014)5. Attribute

Description

Development stages

Maturity stage

Primary production/respiration (Pp/ R )1 Primary production /Biomass (Pp/ B ) 1 Total system throughput (TST ) 2 Biomass/ Total system throughput (B / TST )1 Net primary production (NPP )

Relationship between primary production and the respiration rate.

>1

∼1

Relationship between primary production and the total biomass.

Lower

Higher

Measurement of the metabolism of the system that describes its size and vigor. Relationship between the total biomass of the system with respect to the TST .

Lower Lower

Higher Higher

In mature ecosystems there is an increase in TST 1, which must be supported by various sources, such as net primary production. It is proposed that this measure is positively associated with the metabolism of the system and with energy cycling. Measurement of the size and maturity of the organization of flows in the ecosystem. Describes the full potential for the development of an ecosystem. The amount of DC that does not appear as an organized structure and that is available to be organized as the system develops. Relationship between A and DC that describes the degree of specialization. Relationship between O and DC that describes the resilience of the system. The fraction of the total transfers of the system that is cycled. Species diversity increases in succession processes and decreases in mature stages, with exception of forests1. Coral reefs have a great species diversity3 that are housed in the inert structures (e.g. CaCO3 in the reefs, wood in forests) that maintain their developed configurations. The greatest reef fish diversity coincides with the Inner Caribbean4,5, which is composed of physicochemical provinces where optimum values for the proper development of coral reefs are presented. Here it is proposed that number of functional groups behaves as species diversity tends to increase towards mature ecosystems, thus helping ecological redundancy and ecosystem stability.

Lower

Higher

Lower Lower Lower

Higher Higher Higher

Lower Higher Lower Lower

Higher Lower Higher Higher

Ascendency ( A )2 Development capacity (DC )2 Overhead (O )2

A/ DC 2 O/ DC 2 Finńs cycling index (FCI )1,2 Functional diversity (S )

these were used as a factor to determine whether the topological indices differ between reefs and stages of development; the modules were identified by their average trophic level (i.e. low, intermediate and high). Finally, to establish whether the topological indices differ between the development stages, a nonparametric ANOVA was applied using the Kruskal-Wallis test. The factor in each ANOVA performed was the classification of the development stages obtained from the NMDS and ANOSIM. As a complement to the Kruskal-Wallis test and to identify association trends between the topological indices, the average trophic level (number of modules per reef) and the development stages, a partial redundancy analysis (RDA) was carried out. For this, the matrix of topological indices were related to a factorial matrix (average trophic level of the modules), while the development stages were used as a supplementary variable. Prior to the RDA analysis, the topological indices were normalized, and their significance was tested using the Monte Carlo test with 9999 permutations. The nonparametric ANOVA was carried out in R (R Development Core Team, 2018) and the RDA was performed using the CANOCO software (Ter Braak, 2002).

Fig. 2. Nonmetric multidimentional scaling (NMDS) based on the Euclidean distance for seven development attrbutes: A , O , DC , NPP , A/ DC , O/ DC and S . The code of the reefs corresponds to Fig. 1; High_D: high development, Medium_D: intermediate development and Low_D: low development. Table 2 One-way comparisons through the ANOSIM between the development stages identified considering the development attributes.

3. Results 3.1. Ecosystem development stages Five (PP/B, B/TST, PP/R, TST and FCI) of 12 attributes of development ecosystem, were discarded because they presented high colinearity and had little explanatory power (Table 8), therefore the final model of the partial RDA with the nine remaining development attributes was significant (first axis: F-ratio = 5.319, p = 0.0397; second axis: F-ratio = 2.642, p = 0.0423; 100% of the total variance explained by the first two canonical axes). With the remaining attributes, the NMDS detected three groups of development stages, which coincide with the three geomorphological sectors of the Mexican Caribbean: 1) north: low development, 2) center: intermediate development and 3) south: high development (Fig. 2). ANOSIM results indicated that there are significant differences between the three groups (Table 2). The SIMPER at 95% indicates that although the order of importance of the attributes is different between the development stages, the same

Comparisons

R

P

Global Low vs medium Low vs High Medium vs High

0.828 0.606 0.994 0.979

0.0001 0.008 0.008 0.029

attributes appeared in all three groups. Attributes that contributed most to the formation of the groups were A/DC, O/DC and S, while the attributes O, DC, NPP and A contributed less (Table 3). A characteristic of the attributes NPP, A, O and DC is that they presented low internal variation and clearly distinguish the three stages of development, which is contrary to what the attributes A/DC, O/DC and S exhibit by partially distinguishing some of the stages of development and presenting greater internal variation (Fig. 3). 4

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Table 3 Percentage Similarity Analysis (SIMPER) with a 95% contribution for the development attributes in three ecosystemic development stages. Low development DA

Mean 1

A/DC O/DC1 S1 O2 DC2 NPP2 A2

22.43 77.57 37.4 24,030 30,981 2389 6951

Medium development Co 25.62 25.62 13.27 8.89 8.88 8.87 8.85

AC 25.6 51.2 64.5 73.4 82.3 91.2 100

DA 1

S O/DC1 A/DC1 O2 DC2 NPP2 A2

Hight development

Mean

Co

41 77.59 22.41 38,466 49,579 3813 11,114

22.26 18.34 18.34 10.32 10.29 10.26 10.19

AC 22.3 40.6 58.9 69.3 79.6 89.8 100

DA 1

S A/DC1 O/DC1 A2 NPP2 DC2 O2

Mean

Co

AC

41.3 22.51 77.5 23,302 8006 103,511 80,209

26.46 22.15 22.15 7.35 7.32 7.29 7.27

26.5 48.6 70.8 78.1 85.4 92.7 99.99

DA: Development attributes, Mean: average of each development attribute, Co: contribution (%), AC: accumulated contribution (%). The superscript 1 is for the attributes that partially distinguish development stages, while 2 is for those that distinguish them clearly based on the boxplots in Fig. 3.

Finally, the RDA corroborates the trends described at the development stage level and demonstrates the importance of the average trophic level analyzed through the modules (Fig. 5). In this way, the highest values of DW occur in reef ecosystems with greater ecosystemic development (except for Punta Nizuc), linked to the intermediate and high trophic levels. On the other hand, the highest values of Dc and Bc were associated with low and intermediate development stages and a high average trophic level (e.g. Punta Maroma, Yuyum reefs), while Cc presented a negative relationship with the ecosystems of greater ecosystemic development (e.g. Mahahual) and tended to be independent of the trophic level.

3.2. Structural analysis The Kruskal-Wallis test detected that the topological indices (Dc , Cc , Bc , WD ) discern the different stages of development (Fig. 4). One of the four topological indices, Cc , indicates that the interactions (depredadorprey) among functional groups tends to be greater for less developed ecosystems (H = 284.03, p < 0.0001). Contrary to Cc , WD , which determines the intensity of the connections in terms of energy flow, tends to increase in reefs of greater ecosystemic development (H = 9.69, p = 0.0079). On the other hand, Dc and Bc did not show significant differences between the three development stages (H = 0.86, p = 0.64 and H = 1.24, p = 0.49, respectively).

Fig. 3. Box plot of the attributes of three stages of development identified in the reefs of the Mexican Caribbean. NPP : net primary production, A : ascendency, O : overhead, DC : development capacity, A/ DC : ascendency/developmente capacity, O/ DC : overhead/development capacity and S : diversity of trophic groups. 5

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Fig. 4. Box plot of the topological indices in three stages of development identified in the reefs of the Mexican Caribbean. Dc = degree centrality, Cc = closeness centrality, Bc = betweenness centrality, WD = weighted degree. Different letters indicate significant differences.

4. Discussion 4.1. Ecosystem development stages of the Mexican Caribbean coral reefs Studies that analyze development attributes in Caribbean Sea coral reefs are scarce, especially those that use models built with similar criteria (e.g. Arias-González, 1998; Cobián et al., 2017). From an ecological point of view, the construction of models following the approach of Opitz (1996) can be considered appropriate since it is based on a good number of functional groups (and species that integrate them) which considers the size of the species, their metabolic rates and trophic similarities. In the definition of trophic groups such attributes have been considered of vital importance for the construction of ecological models, since they generate a better approximation to describe the processes that occur within an ecosystem (Abarca-Arenas and Ulanowicz, 2002; Pinnegard et al., 2005). In this context, the present study is the first to analyze development gradients for the coral reef ecosystems in the Caribbean Sea under the same ecological construction scheme. The statistical methods used RDA, NMDS, ANOSIM and SIMPER helped to address the maturity gradient, and showed that the geomorphological sectors of the Mexican Caribbean with differences in the community structure of fish and corals (e.g. Núñez-Lara et al., 2005; Arias-González et al., 2008; Arias-González et al., 2011a; RodríguezZaragoza and Arias-González, 2015) have different stages of development. In this way, as the geomorphological and/or structural complexity of the reefs increases towards the south of the Mexican Caribbean, the diversity (Arias-González et al., 2011a), functional diversity and maturity of the coral reefs also increase.

Fig. 5. Canonical Redundancy Analysis (RDA) between the topological indices, the average trophic level per reef and the ecosystem development stages of the Mexican Caribbean reefs (first axis: F-ratio = 62.054, p = 0.012; second axis: Fratio = 1.591, p = 0.014; 100% of the total variance explained by the first two canonical axes). The dark blue arrows indicate the topological indices: Dc = degree centrality, Cc = closeness centrality, Bc = betweenness centrality, WD = weighted degree. The sky-blue arrows indicate the average trophic level per reef (LTL: low trophic level, ITL: intermediate trophic level, HTL; high trophic level): Punta Nizuc (PN), Puerto Morelos (PMo), Punta Maroma (PMa), Akumal (Aku), Boca Paila (BP), Yuyum (Yy), Punta Allen (PA), Punta Herrero (PH), Tampalam (Ta), El Placer (EP), Mahahual (Ma), Xahuayxol (Xa) y Xcalak (Xc). The gray arrows denote the development stage (LD: low development, ID: intermediate development; HD: high development).

4.1.1. Comparison with existing evidence from literature Legendre (1993) and Legendre and Legendre (2003) suggested that 6

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Table 4 Comparisons of the ecosystem development attributes with studies conducted on Caribbean Sea reefs. Categories

DA

Low development

Medium development

High development

LDP

LGu

IDP

Ma

G&G

HDP

Ta

BP

ML

IV

CCo

G1

PP/R(∼1) PP/B(+) B/TST (+)

1.031 8.62 0.041

2.56 18.28 0.021

1.031 8.56 0.041

1.25 12.45 0.025

1 24.33 0.009

1.03 8.69 0.04

1.27 14.08 0.022

1.28 15.6 0.021

1.6 10.21 0.036

1.26 – 0.047

5.78 11.82 0.038

G2

TST(+) NPP(+) A(+) O(+) DC(+)

6,803 2,389 6,951 24,030 30,981

3,275 1,249 4,095 8,079 12,174

10,860 3,813 11,114 38,466 49,579

13,169 4152 18,802 37,812 56,615

14,332 3118 – – –

22,804 8,006 23,302 80,209 103,511

45,202 14,293 54,142 105,380 159,523

48,037 15,889 55,578 122,523 178,101

71,305 26,043 96,334 218,036 314,370

107,473 20,025 147,524 290,689 438,213

220,232 98,796 296,771 331,127 627,897

G3

A/DC(−) O/DC(−) FCI(−) S(+)

22.43 77.57 7.39 37.4

33.64 66.3 7.47 27

22.42 77.59 7.4 41

33 67 13.6 13

– – – 51

22.51 77.49 7.43 41.3

34 66 14.3 13

31 69 11.7 13

31 69 6.95 21

33.7 66.3 18.3 50

47 53 1.6 22

DA = Development attributes; G1 = Group one of DA, G2 = Group two of DA, G3 = Group three of DA; LDP = average of the DA for the reefs with a low development stage in the present study, LGu7 = La Guajaira (Críales-Hernández et al., 2006), IDP = average of the DA for the reefs with an intermediate development stage in the present study, Ma = Mahahual (Arias-González 1998; Arias-González et al., 2004), G&G = Grenada and Grenadines (Mohammed 2003), HDP = average of the DA for the reefs with the highest development stage in the present study, Ta = Tampalam - BP = Boca Paila (Arias-González 1998; Arias-González et al., 2004), ML = Media Luna - CCo = Cayos Cochinos (Cáceres et al., 2015), VI = Virgin Islands (Opitz 1996).

4.2. Topological indices and development stages

autocorrelation is a property of gradients, so, in order to increase the knowledge of coral reef ecosystem development, all 12 development attributes are analized irrespective of whether or not the attributes present collinearity. Through statistical analyses used, we distinguish three groups of development attributes: G1) those which have low explanatory power and high colinearity (PP / R , PP / B , B / TST ); G2) those that help to separate development stages (NPP , A , O , DC ), and G3) those that partially separate development stages ( A/ DC , O/ DC , S ) (Table 4). Since TST is colinear with A (Table 8), it is included in G2, although for the FCI the authors suggest including it in G3 because it could separate partially stages. On the other hand, we expected that G1 attributes help to discern gradients at a large geographic scale, as long as the same criterion of ecological construction is used and ecosystems with marked variations in their geomorphological development are included. Through the three groups of attributes and the development stages identified, comparisons can be made with other studies (Table 4) to denote trends in the degree of maturity. Considering the values of the G2 attributes, La Guajaira is considered a reef with low development, Grenada and Grenadinas with intermediate development, while Media Luna, Cayos Cochinos and Virgin Islands denote greater maturity. However, these comparisons should be considered with caution (except for the study carried out in the Virgin Islands), because construction criterion and number of functional groups differ, which affects the results of each study. By example, studies that have addressed development attributes for the Mexican Caribbean coral reefs (Arias-González 1998; Arias-González et al., 2004), describe an inverse trend, i.e., a lower ecosystem development stage for southern coral reefs such as Mahahual and a higher stage for the northern reefs, such as Boca Paila reef (Table 4). These contradictory results may be due to the construction criterion used and the smaller number of functional groups, which are close to the limit (n = 12) suggested by Louis-Félix and Sugihara (1997) to represent an ecosystem. Different studies (e.g. Abarca-Arenas and Ulanowicz 2002; Krause et al., 2003; Heymans et al., 2014) have pointed out that both the construction criterion and the number of functional groups influence the final values of the development descriptors (e.g. A , O and DC ) and the detectability of substructures such as modules (Krause et al., 2003). Therefore, it is necessary to standardize the criterion of ecological construction to give certainty to these comparisons, since incorrect conclusions could be made regarding the ecosystem development stages of the Caribbean Sea reefs.

Coral reef ecosystems contain a great diversity (Reaka-Kudla, 1997) that is reflected in the complexity of their trophic interactions. Although Odum (1969) suggests that structural complexity increases in mature ecosystems, the dynamics and structural stability in the process of functional development and maturity are unknown. Considering the information gaps, we use topological indices in trophic networks, because, they have different applications, such as identifying key ecological roles (e.g. Sole and Montoya, 2001; Bauer et al., 2010), as well as global temporal (Abascal-Monroy et al., 2015) or spatial patterns. In the spatial sense, topological indices helped to test the hypothesis that suggests the existence of differences in the structure of trophic networks due to a spatial geomorphological gradient, which is related to different ecosystem development stages. Likewise, through exploration of topological indices we find two trends. First trend indicates that Cc and DW indices can differentiate development stages, so, they are proposed as developmental attributes. In the second trend, Dc and Bc indices only denote differences when analyzed together with average trophic level of the modules and development stages, i.e., the response of these indices is masked by trophic level of their substructures (modules). Considering that modules are influenced by the strength of trophic interactions (Stouffer and Bascompte 2011; Zetina-Rejón et al., 2015), their application to describe the importance of trophic levels in the topology of networks helped to discern the patterns found. On the other hand, a greater number of modules in reefs considered mature or HD (Table 5) implies greater stability and persistence of their trophic networks, as suggested by Pimm (1979) and Stouffer and Bascompte (2011). 4.2.1. Comparison with existing evidence from literature Although trends described in Section 4.2 are clear, it is not possible to delve into other reef systems of the Caribbean Sea because there are no published studies integrating topological indices and development attributes. Studies that address topological indices and development attributes to a certain extent are those carried out in coastal lagoons (Table 5), ecosystems that according to various studies, such as Heymans et al., (2014) and those shown in Table 5, are inmature because they have low TST values. Despite the information gaps, the information analyzed in Table 5 helps to explain and theorize patterns. In relation to Cc and DW , these were found to describe inverse trends, i.e., Cc increases in ecosystems with low development while DW increases in reefs with high development. The Cc index indicates that in 7

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Table 5 Comparisons of development attributes and topological indices with studies carried out in coastal lagoons. Categories

DA

LDP

TLa-1980

TLa-1998

TLa-2011

BMa

IDP

HDP

G1

PP/R(∼1) PP/B(+) B/TST (+)

1.031 8.62 0.041

6.17 – 0.07

3.98 – 0.06

4.49 – 0.07

1.14 9.26 –

1.03 8.56 0.041

1.03 8.69 0.04

G2

TST(+) NPP(+) A(+) O(+) DC(+)

6,803 2,389 6,951 24,030 30,981

4,926 – 5,918 9,014 14,932

2,972 – 3,667 6,704 10,371

2,080 – 2,598 4,445 7,025

3,361 – – – –

10,860 3813 11,114 38,466 49,579

22,804 8006 23,302 80,209 103,511

G3

A/DC(−) O/DC(−) FCI(−) S(+)

22.43 77.57 7.39 37.4

39.63 60.37 3.04 40

35.36 64.64 4.32 40

36.98 63.28 3.65 40

22 – – 30

22.42 77.59 7.4 41

22.51 77.49 7.43 41.3

Topological indices

Cc Bc Dc DW Modules

All HTL HTL ITL 3

ITL – – LTL –

ITL – – LTL –

ITL – – LTL/ITL –

ITL ITL ITL – –

All HTL HTL ILT 3

All HTL HTL ITL/HTL 3.5

DA = Development attributes; G1 = Group one of DA, G2 = Group two of DA, G3 = Group three of DA; LDP = average of the DA for the reefs with a development stage in the present study, IDP = average of the DA for the reefs with an intermediate development stage in the present study, HDP = average of the DA for the reefs with the highest development stage in the present study; TLA = Terminos Lagoon (Abascal-Monroy et al., 2015), BMa = Bahía Magdalena (Cruz-Escalona et al., 2016); All = high values at all trophic levels, HTL = high trophic level, ITL = intermediate trophic level, LTL = low trophic level.

histories, adaptations and ranges of tolerance to long-term fluctuations) means that the food availability of predators is unaffected. Hypothetically, if there are coral reefs with even lower stages of development, such as those suggested by Chávez and Hidalgo (1988) and Chávez et al., (2007), in the Gulf of Mexico (which have evolved in eutrophic environments) (Salas-Pérez et al., 2015; Jordán-Garza et al., 2017), then it is possible that their flow control would be bottom-up (similar to that of coastal lagoons) (Abascal-Monroy et al., 2015) and the energy recycling would be lower, as suggested by Ulanowicz (1986). Under this argument, a gradient in flow control is perceived for different stages of development, being bottom-up in immature ecosystems, wasp-waist in ecosystems with intermediate development and top-down in mature ecosystems. To determine if this hypothesis is true, it is necessary to address reefs with different levels of geomorphological development, under different physicochemical conditions and under the same ecological construction scheme. On the other hand, the number of connections and the degree of intermediation was the same for the three development stages of the studied reefs. Despite this, the higher trophic levels were notable for presenting a greater number of connections and a position of great importance in trophic intermediation. This is contrary to that reported by Cruz-Escalona et al., (2016) in estuarine ecosystems with a low development stage such as Bahía Magdalena where trophic groups with a high Dc and Bc have intermediate trophic levels (Table 5). The Magdalena Bay trend is consistent for other estuarine systems; hence Scotti and Jordán (2010) suggested that greater emphasis should be placed on intermediate positions in conservation policies rather than final predators. However, the position and importance of the species and/or trophic groups may depend on the development stage of an ecosystem in such a way that the management policies must be directed towards the particular characteristics of each ecosystem. For example, in estuarine ecosystems it is important to protect intermediate trophic levels, while in reefs, such as those of the Mexican Caribbean, efforts should be focused on preventive actions of protection for species of higher trophic levels. Finally, bearing in mind the patterns obtained between topological indices and development stages, their use in the detection of developmental stages can be considered in conjunction with attributes proposed by Odum (1969) and Ulanowicz (1986). Therefore, Dc and Bc can be considered within the G1 attributes since they can indicate broad scales, Cc in G2 since it differentiates three development stages

reefs with low development there is easy access to other members of the trophic networks, which may be due to fewer low complexity habitats (Arias-González et al., 2011a), which generate a smaller number of spaces and structures for protection against predators; as a consequence, all trophic levels (based on the modules) have a high and similar access to the members of the networks. It has been shown that when the complexity and number of habitats increase, the vulnerability of prey to predators is reduced (Claro et al., 1990; Cobián et al., 2016, 2017), resulting in reduced access to members of the community network and a greater richness and abundance of fish, as occurs in the south of the Mexican Caribbean (Núñez-Lara et al., 2005; AriasGonzález et al., 2011a). This trend is not the same in coastal lagoons with low development (Table 5), possibly due to two factors: 1) different ecological processes: for example, high productivity results in the greatest access to members of the network through intermediate trophic levels that interact strongly with higher and lower trophic levels (Abascal-Monroy et al., 2015; Cruz-Escalona et al., 2016), and 2) the maturity scale; it is important to consider that the G1 attributes of the Mexican Caribbean tend to be similar in the three development stages of the reefs studied. Therefore, if these attributes are useful at a large development scale, then, theoretically, reefs can ocurr with even lower development stages than that described for the northern Mexican Caribbean, as conceptualized by Chávez and Hidalgo (1988) and Chávez et al., (2007). These would tend to have characteristics in common with less developed ecosystems, such as the coastal lagoons of Table 5. For ecosystems with low development, such as the Términos lagoon (Table 5), during three annual periods, the highest DW was present at the lower trophic levels, i.e. the control of the energy flow was bottomup (Abascal-Monroy et al., 2015) due to the large primary production characteristic of this type of ecosystem (Lara-Domínguez et al., 2011). In contrast, the reefs of the Caribbean Sea present a wasp-waist control, with the exception of several reefs with high development, which present a top-down control. The systems that generally exhibit a waspwaist control are those of upwelling regions, in which few species of intermediate trophic level are vital for their large biomasses (e.g. anchovies, sardines). However, long-term fluctuations can alter the balance of the system by affecting its abundance and therefore alter the food provision of those who prey on them (Cury et al., 2000). Despite having detected a wasp-waist control in the reefs of the Mexican Caribbean, this can favor their persistence and resilience, since the great diversity of prey at the intermediate trophic level (with different life 8

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Table 6 Functional groups for each Mexican Caribbean coral reefs. Functional groups

PN

PMo

PMa

Aku

BP

Yy

PA

PH

Ta

EP

Ma

Xa

Xc

Large sharkslrays, carnivorous Sharks/scombrids, carnivorous Large jacks, carnivorous lntermediate jacks, carnivorous lntermediate reef fish, carnivorous 1 Large-intermediate schooling fish, pelagic lntermediate reef fish, carnivorous 2 Kyphosidae, herbivorous lnterrnediate reef fish, herbivorous 1 lnterrnediate reef fish, herbivorous 2 Large reef fish, carnivorous Intermediate reef fish, carnivorous 3 Small reef fish, carnivorous 1 Small reef fish, carnivorous 2 Large groupers, carnivorous lntermediate reef fish, carnivorous 4 Small reef fish, omnivorous 1 Small reef fish, omnivorous 2 Small reef fish, omnivorous 3 Large Scaridae, herbivorous lntermediate Scaridae, herbivorous Small Scaridae, herbivorous Blenniidae, herbivorous Small Gobiidae, carnivorous Dolphins Seabirds Squids Seaturtles Octopuses Lobsters Crabs Shrimp/Stomatopod Small benthic arthropods Asteroids Echinoids Grastropods Chitons/Scaphopods Polychaete/Priapuloid/Ophiuroids Bivalves Sponges Corals/Sea anemones Zooplankton Decomp/Microfauna Phytoplankton Benthic producers Detritus

– – – 1 1 – 1 – 1 – 1 1 1 1 – 1 1 1 – 1 1 1 – – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– – – 1 1 – 1 1 1 – 1 1 1 1 – 1 1 1 – – 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– – – 1 1 – 1 1 1 – 1 1 1 1

– – – 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 – – 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– 1 – 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– 1 – 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– – – 1 1 – 1

1 – – 1 1 – 1 1 1 – 1 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– 1 1 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 – 1 – 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

– – – 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 – 1 – 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 – 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 – 1 1 1 – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 – 1 1 1 1 – 1 1 1 – – 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

The key to the reefs is shown in Fig. 1. Table 7 PREBAL analysis among the trophic level (TL) with three vital biological descriptors: P/B, Q/B and B. Coral reef

PN Pmo Pma Aku BP Yy PA PH Ta EP Ma Xa Xc

TL vs Log10 P/B

TL vs Log10 Q/B

TL vs Log10 B

n

r

p-value

n

r

p-value

n

r

p-value

33 33 34 36 38 39 36 34 37 38 38 36 40

−0.46 −0.46 −0.43 −46 −0.5 −0.49 −0.48 −0.52 −0.47 −0.49 −0.53 −0.45 −0.5

** ** ** ** ** ** ** ** ** ** ** ** **

31 31 33 33 36 35 34 30 36 36 37 34 38

−0.35 −0.36 −0.34 −0.3 −0.42 −0.4 −0.4 −0.41 −0.38 −0.41 −0.44 −0.37 −0.43

* * * * * * * * * * ** * **

36 37 38 39 39 42 39 35 41 37 42 39 43

−0.7 −0.71 −0.69 −0.7 −69 −0.6 −0.72 −0.73 −0.52 −0.6 −0.69 −0.67 −0.54

*** *** *** *** *** *** *** *** ** *** *** *** **

Table 8 Marginal effects (Lambda) obtained from the forward selection summary and inflation values (colinearity) obtained from the RDA. DA

Lamda

IF1

IF2

IF3

A/CD O/DC S TST* NPP A O DC FCI*** B/TST** PP/B** PP/R**

0.25 0.25 0.24 0.24 0.24 0.24 0.24 0.24 0.12 0.08 0.08 0.04

15.7 0 0 98975.7 0 0 97522.6 0 0 1795.7 1499.3 29

0 0 2.4 94420.6 0 94741.3 0 0 – – – –

5.3 0 3.3 – 8.9 0 0 0 – – – –

DA: development attributes, IF: Inflation factor and subscripts 1, 2, 3 correspond to the three RDA models made to obtain the lowest colinearity. *** Attribute with little explanatory power and low colinearity discarded in the first model; ** Attributes with little explanatory power and high colinearity that were discarded in the first model (IF1); * Attribute that due to its high colinearity was discarded in the second model (IF2).

The key to the reefs is shown in Fig. 1. Significance of the p-value: * for p < 0.05, ** for p < 0.01 and *** for p < 0.0001.

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described herein, and DW in G3, because it partially differentiates one of the three scales.

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5. Conclusions The present study is perhaps the first one that seek and provide evidence of the existence of a gradient of ecosystemic maturity linked to the geomorphological and/or structural development of the reefs of the Caribbean Sea through development attributes. In this way, we concluded that as the geomorphological development of the reefs increases towards the south of the Mexican Caribbean, so does their degree of maturity. The statistical analyzes used gave the guideline to identify three groups of development attributes, which have been considered here as G1, G2 and G3. Each one exhibits different information in relation to ecosystem development stage, so depending on the objective of the research it will be necessary to use one or all groups of attributes. Topological indices helped to establish that the results obtained through the topology of the trophic networks depend on the degree of development of the reef ecosystems, likewise, it is proposed that the topology of the system can be used as developmental attributes to establish development stages. The determination of development stages of coral reefs can help to formulate management plans and measures, so that more rational use of resources can be made in coral reef ecosystems with different degrees of maturity. CRediT authorship contribution statement Jimmy Argüelles-Jiménez: Conceptualization, Writing - review & editing, Formal analysis, Writing - original draft. Jorge Christian AlvaBasurto: Writing - review & editing, Data curation, Writing - original draft. Horacio Pérez-España: Conceptualization, Writing - review & editing, Writing - original draft. Manuel J. Zetina-Rejón: Writing review & editing, Formal analysis, Writing - original draft. Jesús Ernesto Arias-González: Writing - review & editing, Data curation, Writing - original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We want to thank Dr. Robert Ulanowicz, Dr. Fabian Rodríguez Zaragoza and the reviewers for their accurate observations in the review of this article as well as Centro de Investigación en Ciencias de Información Geoespacial of CINVESTAV-Mérida for their support to run trophic routines in their processors. Funding This study was supported by the Consejo Nacional de Ciencia y Tecnología de México (CONACyT) and the Secretaria del Medio Ambiente y Recursos Naturales (SEMARNAT) through the project “Diversity and functioning of the Mexican Caribbean coral reefs”. References Abarca-Arenas, L.G., Franco-López, J., Peterson, M.S., Brown-Peterson, N.J., ValeroPacheco, E., 2007. Sociometric analysis of the role of penaeids in the continental shelf food web off Veracruz, Mexico based on by-catch. Fish. Res. 87, 46–57. Abarca-Arenas, L.G., Ulanowicz, R.E., 2002. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296. Abascal-Monroy, I., Zetina-Rejón, M.J., Escobar-Toledo, F., López-Ibarra, G.A., Sosa-

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