Estuarine, Coastal and Shelf Science 226 (2019) 106265

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Estuarine, Coastal and Shelf Science

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Multi-year changes of a benthic community in the mid-intertidal rocky shore T of a eutrophic tropical bay (Guanabara Bay, RJ – Brazil) ∗ Camila A. Pugaa, , Arthur S.S. Torresa, Paulo Cesar Paivab, Yocie Yoneshigue-Valentinc, Andrea O.R. Junqueiraa a Departamento de Biologia Marinha, Laboratório de Bentos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil b Departamento de Zoologia, Laboratório de Polychaeta, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil c Departamento de Botânica, Laboratório de Botânica Marinha, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

ARTICLE INFO ABSTRACT

Keywords: Intertidal zone of rocky shores is influenced by a wide variety of abiotic and biotic drivers. These variables can Rocky shore affect the species abundance at different temporal scales. In the present study our goal was toevaluatethe Benthic community community changes over the years using different temporal scales (fortnightly, monthly, seasonal and annual) asa Temporal variation tool to understand the effects of biotic (i.e. competition or facilitation) and abiotic drivers (i.e. water temperature, Bioinvasion air temperature, tidal regime, rainfall and water quality) in abundance pattern of dominant species (Crassostrea Guanabara bay rhizophorae, Saccostrea cuccullata, Tetraclita stalactifera and Amphibalanus amphitrite). The samples were seasonally Monitoring carried out in the mid-intertidal zone in the Boa Viagem Beach in Guanabara Bay over 7 years (2010 July - 2017 June). From June 2015 to June 2017 fortnightly samplings were made in addition to seasonal sampling. The inter- annual scale was the main scale in which the differences in the percent cover of the benthic community wasnoted (pseudo-F = 15.96, p = 0.0001). Hierarchical ANOVA indicated that for the dominant species only the annual scale was significant while to the cryptogenic macroalgae Ulva spp. only the seasonal scale. Among the environ- mental variables selected, dbRDA indicated 6 of them that were significantly relevant to percent cover variation of all species explaining 86,61% of data variation (p < 0.05). While C. rhizophorae e S. cucullata and T. stalactifera exhibited similar response to the relevant environmental variables, A. amphitrite exhibited opposite response to these variables. The species responded to abiotic drivers in different temporal scales. Water temperature was avery important variable, but its effect in the population dynamics of these long-lived species needs long time scales(60 days) to manifest responses at detectable levels as well as the biotic interactions, such as competition, and the effects of bioinvasion. The niche overlap observed among S. cucullata and C. rhizophorae with A. amphitrite was highly significantly (p < 0.001) (i.e.,negative SES). Conversely for the oyster species the observed niche overlapis greater than the niche overlap expected by chance (i.e., SES positive). Environmentally constrained null model approach showed a significant (p = 0.004) relationship only between A. amphitrite and S. cucullata (C-score obs = 1200; C-score exp = 716.6), indicating a negative association. During this long and continuous monitoring, we verified that each environmental variable affects the same species at distinct temporal scales, affectingalso some biotic interactions (S. cucullata × A. amphitrite) and consequently the community structure.

1. Introduction indicators because they are composed of sessile organisms, most of them filter-feeders. The intertidal zone of rocky shores is influenced by Rocky shore benthic communities are one of the most productive a wide variety of abiotic and biotic drivers such as tidal regime, in- marine environments since a great part of its biomass is represented by creasing air exposure, wave action, anthropogenic impacts, changes in macroalgae and microphytobenthos which have great importance to temperature and in the frequency of storms, predation, competition and the primary productivity of this area (Mann, 1973). This ecosystem is introduction of exotic species (Thompson et al., 2002; Murray et al., home to many economically important groups, such as bivalves 2006). These factors and the interaction among them are responsible (Davenport and Wong, 1992; Douillet and Langdon, 1994). These for the non-homogeneous patterns of population distribution in the benthic communities are also considered quite suitable biological intertidal zone (Murray et al., 2006; Zamprogno et al., 2012). All these

∗ Corresponding author. E-mail address: [email protected] (C.A. Puga). https://doi.org/10.1016/j.ecss.2019.106265 Received 31 August 2018; Received in revised form 29 January 2019; Accepted 23 June 2019 Available online 28 June 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved. C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265 different processes can affect the species distribution at different tem- Crassostrea gigas (Thunberg, 1793) and the indigenous Saccostrea glo- poral scales (Benedetti-Cecchi et al., 2000). Thus, only through long merata (Gould, 1850) increased with a large initial population, regardless and continuous monitoring that it is possible to identify patterns of of the species identity (Hedge and Johnston, 2014). This result could species distribution and how the interplay of abiotic and biotic drivers indicate a facilitation process, i.e., an interaction that can expand the affects them (Benedetti-Cecchi et al., 2000; Junqueira et al., 2000; realized niche of the facilitated species through of ameliorating abiotic Zamprogno et al., 2012). stresses (Bulleri et al., 2016). This interaction is very common among The importance of long-term studies to verify biotic responses was benthic organisms and it is well established in the literature. Most of highlighted by Hampton and Schindler (2006), since these take more time them are ecosystem engineers which can increase habitat complexity and to manifest responses at detectable levels. Mazzuco et al. (2015) state that heterogeneity (Sousa et al., 2009; Pereyra et al., 2017). the temporal replication over time series is a valuable tool for investigating Many researchers tried to evaluate the temporal changes of benthic relationship between ecological processes and environmental variables. In communities (Teixeira et al., 1987; Pagola-Carte and Saiz-Salinas, 2000; this context, the temporal replication in different time scales decreases the Taouil and Yoneshigue-Valentin, 2002; Zamprogno et al., 2012), but just a “background noise” in addition to facilitating the distinction among nat- small part of these studies does it in a continuous way. There are two very ural responses from those caused by anthropic activities or climate common types of temporal studies: those that are single or infrequent changes (e.g. El niño) (Underwood and Chapman, 2005). Environmental (‘one-off') or those that are frequent during a small period of time, butthat conditions are also important drivers in the pattern of distribution of the are not repeated in a yearly basis (‘snapshot’) (Hawkins and Hartnoll, species, since they influence animal behavior and most of the physiolo- 1983; Franke and Gutow, 2004). Both of them are only useful to identify gical characteristics which may cause responses in distinct intensity and major changes in community dynamics, following dramatic changes, such time lag. According to the level of benthic species resistance or sensibility as hot summers or cold winters. However, this type of sampling is in- to abiotic variables, it is possible to infer the environmental conditions. efficient to detect more subtle changes or delays in species response tothe Temperature can impact the performance and survival of marine organ- environmental conditions. In other words, the lack of temporal replication isms (Zamprogno et al., 2012). In marine bivalves this environmental may hinder the distinction between natural variation from those resulting condition can influence most ecological, biological and physiological as- from a particular event (Murray et al., 2006). pects (Cáceres-Puig et al., 2007). While for oyster species such as Saccos- Although, monitoring studies in rocky shore benthic communities trea cuccullata (Born, 1778) the low temperature decreases the embryonic have been given more importance, there still exists a huge knowledge development, for the barnacle species Tetraclita stalactifera (Lamarck, gap in this sort of studies (Franke and Gutow, 2004; Nicoletti et al., 1818) it may increase the density of larvae (Kalyanasundaram and 2007; Jung et al., 2017). A wide problem in several long-term studies is Ramamoorthi, 1986; Skinner and Coutinho, 2002). Other environmental the lack of continuous monitoring. Most of the surveys that are titled as conditions can cause different biotic responses, for instance, Ulva lactuca long-term studies just compare two short-term samplings or even mere Linnaeus, and Amphibalanus amphitrite (Darwin, 1854) are known as im- one-off sampling over different periods at the same place(Taouil and portant indicators of eutrophication, since they have more resistance to Yoneshigue-Valentin, 2002; Oliveira and Qi, 2003; Franke and Gutow, deal with high levels of pollution (Shalla et al., 1995; Fletcher, 1996; 2004; Nicoletti et al., 2007). This type of comparison may be quite Calcagno et al., 1998). On the other hand, the barnacle Tetraclita sta- complicated, because much information can be lost at large time in- lactifera is sensitive to high eutrophication and low salinity levels (Oliveira, tervals (Dye, 1998). Because of this, no conclusive evidence of the 1947; Lacombe and Monteiro, 1974). Therefore, monitoring programs are trends in the community dynamics may be identified. Considering a very useful tool to understand the effects of the environmental condi- benthic communities, it can be more complex, because long-term fac- tions in the biotic interactions and in the pattern of distribution and tors influence much more benthic organisms than pelagic ones(Franke abundance of intertidal organisms. and Gutow, 2004). Furthermore, the environmental variables and biotic Tropical marine ecosystems are characterized by minor seasonal interactions may be best observed in different time scales, since some changes or aseasonality (Heinrich, 1962; Sournia, 1969; Blackburn biotic responses may take a long time to reach detectable levels et al., 1970, Steven and Glombitza, 1972). However, some studies on (Hampton and Schindler, 2006). Several ecological processes such as tropical Hong Kong rocky shores verified seasonal trends, but these succession, bottom-up process, responses to changes in the environment studies are focused on algae species and on herbivores guild (Williams, and bioinvasion need multiscale samples to be understood (Benedetti- 1993; Kaehler and Williams, 1996; Macusi, 2010). This trend is likely Cecchi et al., 2000; Hampton and Schindler, 2006; Nicoletti et al., 2007; related to the algae species short life span and its effect on their con- Mazzuco et al., 2015). Monitoring programs are crucial to evaluate the sumers. Moreover, this region is characterized by monsoon tropical trends and understand the environment as a whole. climate which is marked by a cool and dry season during winter and a In Brazil, long term monitoring programs, created in 1996, aimed at hot and wet season during summer. In southeastern Brazil, despite the improving the knowledge of the Brazilian ecosystem and tried to predict occurrence of wet and dry periods, there is no well-marked seasons and the environmental shifts. The monitoring program in Guanabara Bay, strong annual variations. In tropical seas, the primary production does which started in 2010, aimed to study this ecosystem and its responses to not exhibit marked changes. The temperature and the nutrient con- the environmental changes either of anthropogenic or natural origin. centrations are strongly related to this. The primary production in This bay is a very impacted ecosystem due to heavy eutrophication tropical areas is not light-limited, because the sun does not change caused by the discharge of domestic, industrial and hospital sewage. much in height above the horizon as in higher latitudes (Nybakken, Guanabara Bay is the second largest bay on the Brazilian coastal and it is 1993). Conversely, the primary production can be nutrient-limited due surrounded by almost 74% of the population of Rio de Janeiro and to the low nutrient content found in the tropical seas. However, in the 18.000 industries (Fundação Cide – Anuário Estatístico, 2013). All this neritic zone it does not happen since it is influenced by the input of untreated sewage has been harming the human population and the entire nutrients from the continent. ecosystem. Several environmental recovery programs have been started There are several examples of exotic species on the rocky shores. The in Guanabara Bay over the last 20 years, but until now none of them mussel Isognomon bicolor (Adams, 1845) is known as an invasive species seems to achieve their goals (Soares-Gomes et al., 2016). found in the state of Rio de Janeiro (Brazil) and it is responsible for Our goal is to evaluate the community changes over the years using overtaking the area occupied by the mussel Perna perna (Linnaeus, 1758) different temporal scales (fortnightly, monthly, seasonal and annual) as (Henriques and Casarini, 2009; Breves-Ramos et al., 2010). However, the a tool to understand the effects of biotic (i.e. competition or facilitation) effect of non-indigenous species on the natives is highly variable andmay and abiotic drivers (i.e. water temperature, air temperature, tidal re- not be predominantly negative (Sánchez et al., 2005; Bulleri et al., 2008). gime, water quality and rainfall) in the abundance pattern of the In south-east Australia, the survival rate of the non-indigenous dominant species in the mid-intertidal. In this study, our first

2 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265 hypothesis is that the annual changes will be stronger than seasonal ones, despite the occurrence of wet and dry seasons. It is because of the weak seasonality in the tropics and the long-life cycles of species in the mid-intertidal zone. Our second hypothesis is that each abiotic driver affects species abundance in different temporal scales.

2. Material and methods

2.1. Study site

Guanabara Bay is the second largest coastal bay on the Brazilian coast and it is located in southeastern Brazil, in a region of tropical climate (Sournia, 1969; IBGE, 1977). This semi-sheltered bay has a perimeter of nearly 130 km and a surface area of 384 km2, excluding the islands (Amador, 1997). The second largest urban area of Brazil and an industrial park are located in the surroundings of this estuarine ecosystem. These are responsible for the discharge of raw or partially treated effluent. This situation contributes to the eutrophic profile of this bay. Based on hydro-biological characteristics, such as the nutrients and dissolved oxygen concentrations as well as phytoplankton density, Mayr et al. (1989) proposed to divide the Guanabara Bay into five sections according to levels of water quality. In this ecosystem four benthic biotopes are found: mangroves, sandy beaches, muddy- and sandy bottoms and rocky shores. The study was developed on a semi- exposed vertical rocky shore located in Boa Viagem Beach, state of Rio de Janeiro (22°24 ′- 22°57′S, 42°33′- 43°19′W) near the entrance of the bay (Fig. 1). This smooth bedrock is located in the second-best water quality area (Mayr et al., 1989) and can be classified as moderately steep with a low topographical heterogeneity.

2.2. Temporal variation in the mid-intertidal benthic community structure Fig. 1. Sampling site Boa Viagem Beach situated in southeast Brazilian coast- line (modified from Moraes and Lavrado, 2017). Samples were taken in the mid-intertidal zone on a seasonal scale, nearly quarterly, during 7 years (2010 July - 2017 June). Furthermore, PERMANOVA nested was performed for the last two years with a four- from June 2015 to June 2017 additional fortnightly samplings were factor nested design (fortnight in month, month in season, season in also performed. The 7 years sampling were: Year 1 (2010 July – 2011 year). SIMPER analysis was conducted to evaluate the species that June); Year 2 (2011 July – 2012 June); Year 3 (2012 July – 2013 June); contributed to the differences observed in the temporal scales. Year 4 (2013 July – 2014 June); Year 5 (2014 July – 2015 June); Year 6 Five taxa that were considered more representative of the changes (2015 July – 2016 June) and Year 7 (2016 July – 2017 June). Five observed on rocky shore during these seven years were selected. They photoquadrat images were randomly collected throughout a 10-m were: Ulva spp. (cryptogenic macroalgae), Crassostrea rhizophorae horizontal line-transect in the mid-intertidal zone. Photographic sam- (Guilding, 1828) (indigenous oyster species); Saccostrea cuccullata (in- plings (non-destructive sampling) were taken using a digital camera troduced oyster species); Tetraclita stalactifera (indigenous barnacle (Canon PowerShot D20 at 12.1 megapixels) attached to a polyvinyl species) and Amphibalanus amphitrite (barnacle species introduced long 2 chloride (PVC) quadrat frame (900 cm ), which was held perpendicu- time ago). Hierarchical analysis of variance (ANOVA hierarchical) was larly to the target area at a fixed distance of 60 cm. At the laboratory the conducted using R software in order to evaluate which temporal scale photos were analyzed in the program Coral Point Count with Excel (inter-annual or intra-annual) was more important to the shifts verified extensions (CPCe) 4.1 (Kohler and Gill, 2006), which randomizes 50 in these five-species percent cover. points in order to identify the organisms and estimate the species per- cent cover in each quadrat. All the species names were based on the accepted names found in the World Register of Marine Species 2.3. Environmental effects (WoRMS, 2018)(http://www.marinespecies.org). The temporal structure of the benthic community was compared Environmental factors likely to affect species distribution were se- through multivariate analysis using Primer 6 (Clarke and Warwick, lected from three databases (Table 1). For the frequency of low tides 2001). For these analyses we excluded the first year of sampling, be- (tides less than or equal to 0.2 – FRT02) and the frequency of high tides cause it was not possible to sample during this year's autumn. In order (tides greater than or equal to 0.3 – FRT03), just the tides that have to understand the community structure, nMDS analysis (with the Bray- occurred during 11:00 a.m. and 1:00 p.m. were considered. In our as- Curtis similarity index) was performed on percent cover of each species sessment this is the most critical period of the day for rocky shores previously transformed (logit transformation, as suggested by Warton species (period of the day in which desiccation is the main problem of and Hui, 2011). Permutational multivariate analysis of variance (PER- these species, due to the higher air temperatures). The criterion to se- MANOVA; Anderson, 2001) was used to test the null hypothesis of no lect the low and high tides was based on the rocky shore exposure levels temporal differences among assemblages according to a two-factor during the periods of low tides (with air exposure) and high tides design (year – inter-annual, season – intra-annual) based on Bray–Curtis (immersion). In this case, the meteorological tide was not considered. dissimilarities through 9999 random permutations of residuals under These different abiotic variables can cause effects on the community an unrestricted permutation of raw data using sums of squares type III in distinct temporal scales. Therefore, all these 12 variables were as- (partial). Factors were fixed and nested (seasons in years) with 4levels sessed for different time lags from the date of each sampling. The for season (winter, spring, summer and autumn) and six levels for years. temporal scales selected were: seven, fifteen, thirty, sixty and ninety

3 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Table 1 expected C-score by chance (Standardized Effect Size – SES – positive) – The names and the codes of the environmental variables separated according to indicates a negative relationship (e.g. competition or predation). the database where they were acquired. From INMET database (INMET, 2017) In these two last analyses the first year of sampling was excluded. (http://www.inmet.gov.br/portal/index.php?r=bdmep/bdmep) were acquired All tests used in this section were performed using R software (R daily abiotic variables. From the data set of the systematic monitoring system of Development Core Team, www.r-project.org): the package Vegan was Boa Viagem Beach of INEA were provided two weekly abiotic variables: the used to test the Collinearity and to perform dbRDA analysis, the concentration of Enterococcus spp. in the water (which is used as a proxy of water package MuMIn was used for model selection approach, the package quality) and water temperature. From this data set we obtained the other INEA variables. On the Brazilian Navy website (www.mar.mil.br/dhn/chm/box-pre- EcoSimR was used for Null Model approach and the package ecospat visao-mare/tabuas/), we assessed the tide tables from 2010 until 2017 and then was used for Environmentally Constrained Null Model analysis. selected two more variables, frequency of low and high tides. 3. Results VARIABLES CODES DATABASE

Total Rainfall TR INMET*1 3.1. Temporal variation in the mid-intertidal benthic community structure Days Without Rain** DWR Maximum Air Temperature MXAT Benthic community over the years (Appendix C) was assessed by the Minimum Air Temperature MIAT Maximum Water Temperature MXWT INEA*2 nMDS analysis (Fig. 2). The differences on the inter-annual scale were Minimum Water Temperature MIWT detected by Permutational Multivariate Analysis of Variance (PERMA- Mean concentration of Enterococcus spp. in the MENT NOVA, pseudo-F = 15.96, df = 5, p = 0.0001), but no significant dif- water ferences were detected for the last two years (Years 6 and 7). On the Maximum concentration of Enterococcus spp. in MXENT intra-annual scale, only the seasonal one was significant (PERMAN- the water Minimum concentration of Enterococcus spp. in MINENT OVA, pseudo-F = 2.24, df = 18, p = 0.0001). Nonetheless, it did not the water reveal a strong pattern of seasonality, because there were only differ- Amplitude of concentration of Enterococcus spp. AENT ences between some seasons in some years (Appendix E). in the water SIMPER analysis of the transformed percent cover data showed the Frequency of low tides between 11:00 a.m. and FRT02 BRAZILIAN ∗ 1:00 p.m. (tides less than or equal to 0.2) NAVY 3 main taxa which contribute to the dissimilarity between years: Tetraclita Frequency of high tides between 11:00 a.m. and FRT03 stalactifera; Hildenbrandia rubra (Sommerfelt) Meneghini; Ulva spp.; 1:00 p.m. (tides greater than or equal to 0.3) Crassostrea rhizophorae; Saccostrea cuccullata; Amphibalanus amphitrite; dead Ostreidae; Ostreidae recruit; barnacle recruit and dead barnacle (the *1National Institute of Meteorology; *2State Environmental Institute; ∗3 last one only between year 6 and 7). Among these taxa, five were selected Brazilian Navy. to focus on: two species of barnacles – Tetraclita stalactifera (indigenous) and Amphibalanus amphitrite (introduced a long time ago), two species of days before the sampling day (in all the scales the sampling day was oysters – Crassostrea rhizophorae (indigenous) and Saccostrea cuccullata included). For instance, the code MXWT_30 is the maximum tempera- (introduced) and the cryptogenic macroalgae – Ulva spp. ture value that was recorded thirty days before the sampling day. For the other PERMANOVA nested analysis, considering only the last Collinearity among the abiotic variables was assessed by means of two sample years (Appendix D), significant differences were detected variance inflation factor (VIF < 4). Among the 60 variables initially between years 6 and 7 (Df = 1; MS = 4507.1; Pseudo-F = 7.88; considered, 15 were discarded. Thus, all the remaining 45 variables p < 0.001). On the intra-annual scale only the seasonal one was sig- were used in multiple regressions performed over each species percent nificant (Df = 6; MS = 949.51; Pseudo-F = 1.66; p < 0.05). Nonetheless, cover previously transformed (logit). Best model were selected using it did not reveal a strong pattern of seasonality since only the spring of the Akaike Information Criterion corrected to small samples (AICc) and the sixth year was significantly different (p < 0.05) from the autumn. Akaike information weights (AICc weights) (Anderson, 2008). SIMPER analysis of the transformed percent cover data showed that The environmental variables selected were then used in a distance- the main taxa that contribute to the dissimilarity between years 6 and 7 based redundancy analysis (dbRDA) with Bray-Curtis distance. This were: Tetraclita stalactifera, Hildenbrandia rubra, Ulva spp., Saccostrea analysis selects the environmental variables that best describe the cuccullata, dead Ostreidae, Ostreidae recruit and dead barnacle. species percent cover over the years through two variable matrices. The Hierarchical analysis of variance (ANOVA) indicated that for in- first matrix was represented by the remaining environmental variables vertebrates, overall, the annual scale was significant while to the and the second was represented by the five-species percent cover macroalgae Ulva spp the seasonal scale was significant. Among the in- (Appendix B). In this analysis all the seven years were used in order to vertebrates only A. amphitrite also revealed a significant difference increase the power of tests. among seasons (Table 2). The null model niche overlap approach, using the Pianka index, was Percent cover of Ulva spp, C. rhizophorae, S. cucullata, T. stalactifera used to evaluate if the observed niche overlap pattern is greater or less and A. amphitrite changes over these seven years (Figs. 3–5). Crassostrea than expected by chance (Gotelli and Graves, 1996). The significance of rhizophorae did not show any seasonal pattern, but it presented an in- the space niche overlap among four species were tested: Crassostrea crease on percent cover over the years as well as S. cucullata (Fig. 3). rhizophorae; Saccostrea cuccullata; Tetraclita stalactifera and Amphiba- However, percent cover of the latter has increased more from the fifth lanus amphitrite. From this analysis it was possible to assess which year on. In the summer of the last year it is possible to verify almost the species are significantly overlapping their niches (in this case space). same percent cover for both of them. An environmentally constrained null model was used to identify if Tetraclita stalactifera also did not present a seasonal pattern (Fig. 4), there are significant species interactions if the environmental conditions but an increase trend over the last two years with a high variability were not taken into account. In this analysis the percent cover data set among quadrats, which shows a spatial variability along the rocky was transformed to species occurrence. The frequency of all pair-wise shore. On the other hand, A. amphitrite showed a strong decrease over species co-occurrence was tested with a null-model constrained by en- the years, almost disappearing from the fifth year on (Fig. 4). This vironmental variables. The observed scores were then compared to the species also showed a seasonal variation, however no consistent sea- expected scores in order to assess possible biotic interaction. The strength sonality pattern was verified (Fig. 4). of the interactions was assessed by the C-score, which calculates the In general, Ulva spp increased during the winter and/or spring, in- number of sites where one of the species is present while the other dicating a seasonal variability (Fig. 5). Nevertheless, no any annual species is absent (Peres-Neto et al., 2001). C-scores greater than the pattern was detected.

4 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Fig. 2. Non-metric multidimensional scaling plot (n-MDS) of the mean of quadrats based on species percent cover data (logit transformation) recorded in each season (WIN – Winter; SPR – Spring; SUM – Summer; AUT – Autumn) during six years (2011 July - 2017 June).

Table 2 cover for all species. The first axis (dbRDA1) explained 59.13% ofthe Hierarchical Anova with the factor interannual (years) and the factor intra- total variance data while the second axis (dbRDA2) explained 27.48%. annual (season) for five species. All the six variables explained a significant amount (p < 0.05) of data Species Temporal Scale df MS F variation (86.61%). According to the dbRDA, the percent covers of C. rhizophorae, S. cucullata and T. stalactifera were negatively related to Ulva spp Seasonal 17 3.54 15.74 *** MITW_60 (Minimum Water Temperature), MINENT_15 (Minimum Crassostrea rhizophorae Annual 6 4.36 8.66 *** Enterococcus spp. concentration) and FRT02_60 (Low tide frequency) Saccostrea cuccullata Annual 6 6.45 14.96 *** Tetraclita stalactifera Annual 6 2.53 3.81 * and positively to MITA_60 (Minimum Air Temperature). It means that Amphibalanus amphitrite Seasonal 17 0.756 3.50 *** these species thrive in cooler waters (MITW), in warmer air tempera- Annual 6 6.82 9.01 *** ture (MITA) and that they are more sensitive to deal with high levels of pollution (MINENT) and long periods of air exposure (FRT02) during *p < 0.05, **p < 0.01, ***p < 0.001. the most critical period of the day (11:00 a.m. until 1:00 p.m.). Crassostrea rhizophorae was also negatively correlated with FRT03_7. 3.2. Environmental effects Conversely, A. amphitrite presented an opposite response, that is, it was negatively correlated with MITA_60 and positively correlated with The abiotic variables selected (VIF < 4) were used in a model se- MITW_60, MINENT_15 and FRT02_60. It means that this species thrives lection to evaluate the relationship among these variables and the in warmer waters (MITW), in cooler air temperature (MITA) and that percent cover of each of the five-species selected (Ulva spp, A. amphi- they are more resistant to deal with high levels of pollution (MINENT) trite, T. stalactifera, C. rhizophorae and S. cucullata). This analysis se- and long periods of air exposure (FRT02). Ulva spp was only positively lected the most relevant set of variables that explains percent cover of correlated with FRT03_7 and negatively correlated with MITA_60 each species over the years. Among these variables the dbRDA in- (Fig. 6). It means that this species thrives with a high frequency of dicated that six of them were more relevant for the variation in percent

Fig. 3. Percent cover (mean ± SE) per season (WIN – Winter; SPR – Spring; SUM – Summer; AUT – Autumn) of Crassostrea rhizophorae and Saccostrea cuccullata over the seven years (2010 July to 2017 June).

5 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Fig. 4. Percent cover (mean ± SE) per season (WIN – Winter; SPR – Spring; SUM – Summer; AUT – Autumn) of Amphibalanus amphitrite and Tetraclita stalactifera over the seven years (2010 July to 2017 June). immersion and in cooler air temperatures MXAT_60 effect was not as- sessed owing to its weak effect represented by vector length. Null model analysis of niche overlap showed significant overlap (p < 0.001) between S. cucullata (SAC) with A. amphitrite (AMA) and also between and C. rhizophorae (CRA) and the same barnacle (Table 3). It means that the number of samples over the years where they had overlapping percent cover, is less than the expected by chance (i.e., negative Standardized Effect Size (SES)). For T. stalactifera and C. rhi- zophorae null model niche overlap exhibited the same pattern while for T. stalactifera and S. cucullata no niche overlapping pattern was found (p > 0.05). Conversely for S. cucullata and C. rhizophorae, the null model exhibited a different pattern, in which the observed niche overlap is greater than the niche overlap expected by chance (i.e., SES positive) (Table 3). Environmentally constrained null model approach showed a sig- nificant (p = 0.004) relationship only between A. amphitrite and S. cu- cullata. The C-score observed (1,200) was greater than the C-score ex- Fig. 6. Distance-Based Redundancy Analysis (dbRDA) with Bray-Curtis distance pected (716.60), indicating a negative association between S. cucullata showing the relationship between the percent cover of five species and the environmental conditions. Species code: SAC - Saccostrea cuccullata; CRA - and A. amphitrite. Thus, suggesting despite environmental conditions, Crassostrea rhizophorae; TET - Tetraclita stalactifera; AMA - Amphibalanus am- biotic interactions are still important only for this pair of species. phitrite and ULV - Ulva spp. Environmental variables codes: FRT03_7 – high tide frequency seven days before the sampling day; FRT02_60 – low tide frequency 4. Discussion sixty days before the sampling day; MXTA_60 – maximum air temperature detected sixty days before the sampling day; MITA_60 – minimum air tem- 4.1. Temporal variation in the mid-intertidal benthic community structure perature detected sixty days before the sampling day; MITW_60 – minimum water temperature detected sixty days before the sampling day and MINENT_15 Studies with long temporal scales are essential to verify trends in the – minimum Enterococcus spp concentration detected fifteen days before the sampling day. biotic community distribution. Biotic responses to environmental shifts

Fig. 5. Percent cover (mean ± SE) per season (WIN – Winter; SPR – Spring; SUM – Summer; AUT – Autumn) of Ulva spp over the six years (2010 July to 2017 June).

6 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Table 3 studied years. While A. amphitrite (barnacle species introduced a long Null model approach used to assess the space niche overlap of the species-pair. time ago) has been disappearing from the rocky shore, S. cucullata (a The species are represented by model codes: AMA - A. amphitrite; TET - T. sta- recent introduced oyster species) has been increasing their percent lactifera; CRA - C. rhizophorae; SAC - S. cucullata. Standardized Effect Size (SES). cover. The indigenous oyster species Crassostrea rhizophorae has been Models Observed Index Mean of Simulated Index SES also increasing their percent cover over the study years.

AMA × TET 0.37 0.42 −1.00 4.2. Environmental effects AMA × CRA 0.36 0.54 −4.51*** AMA × SAC 0.11 0.37 −4.63*** TET × CRA 0.38 0.49 −2.75*** Saccostrea cuccullata is indigenous to the Indo-West Pacific and its TET × SAC 0.26 0.38 −1.24 distribution ranges from East Africa to the Pacific islands (Braley, 1982; CRA × SAC 0.65 0.43 4.79*** http://www.marinespecies.org). Since this area is a tropical zone like our study area, the similar environmental requirements as showed in dbRDA ***p < 0.001. for the non-indigenous S. cucullata and for indigenous C. rhizophorae are likely related to this. The significant niche overlap assessed in the null model approach is another point that reinforces this similar preference can be subtle (Hawkins and Hartnoll, 1983) or may exhibit long time between these two species. During the first years of study, S. cucullata lags (Hampton and Schindler, 2006). Therefore, long-term monitoring was almost absent, but from the fifth sample year some environmental programs have been increasingly valued, since they can provide a large conditions changed and S. cucullata exhibited an increase in their percent data set which is crucial to differentiate patterns from “background cover. According to dbRDA, when the minimum water temperature is noise” (Hawkins and Hartnoll, 1983; Franke and Gutow, 2004; lower (19°/18 °C) the S. cucullata and C. rhizophorae percent cover in- Hampton and Schindler, 2006). A very common problem faced by creases (negative correlation). The temperature is known as an important several studies is that different species respond to abiotic and biotic environmental variable which influences most of the aspects of ecology, drivers in different time scales. During the seven years of the present biology and physiology of the marine bivalve species (Cáceres-Puig et al., study, the inter-annual scale was the main scale in which the differ- 2007). For instance, S. cucullata, according to Kalyanasundaram and ences in the percent cover of the benthic community were noted. This is Ramamoorthi (1986) in their study in India verified in experiments of expected owing to the weak seasonality of the tropics and to the life spawning induction that the early embryonic development entirely cycle of the dominant species in the studied area. These species present ceases at 20 °C and 35 °C. Braley (1982), in his study in Sasa Bay in long-life cycles and, thus can take long time lags to manifest responses Guam, did not find a temperature effect (mean 28.7 °C ± 1.4 °C over14 to environmental shifts at detectable levels (Hampton and Schindler, months) on the gametogenesis regulation. For C. rhizophorae, Dos Santos 2006). Barnacles and oysters (dominant species on this rocky shore) and Nascimento (1985), in Salinas Margarida (BA), assessed in their la- may live approximately 5 and 8 years, respectively (Dye, 1989; boratory experiments of embryo development of this species no sig- Calcagno et al., 1998). Therefore, the effect of abiotic and biotic drivers nificant differences in the mean proportion of normal D-larvae at20°Cor in the population of these groups requires more time to be detected. 25 °C, but the results at 30 °C were significantly lower. In this study they However, we recorded that some years showed significant differ- did not test any temperature below 20 °C since this species occupies a ences among seasons, but it seems to be strongly related to the presence tropical zone. Moreover, Cáceres-Puig et al. (2007) tested the thermo- of green algae Ulva spp. in this zone. The mid-intertidal zone is char- tolerance of the juveniles of Crassostrea corteziensis in an experimental acterized by the absence of algae due to the long periods of air exposure study. They reported that growth and survival of juveniles were much caused by tide level influences. This pattern was also found by more affected by higher (above 32 °C) than lower temperatures (16°C), Underwood (1981), who verified a negative correlation among the since in the lower ones there was a significant reduction in the growth of distribution of sessile animals and high percent cover of algae. juveniles, but survival was not affected. Meanwhile we verified a great increase in the Ulva spp. percent cover However, Nascimento and Lunetta (1978) in their study in Jacuruna (55,78%) in this zone during the winter of the fourth sample year. It River (Baía de Todos os Santos, Bahia State, Brazil) about the C. rhi- seems to be strongly related to the frequency of high tides (equal or zophorae reproductive cycle, state that the reproductive cycles of bi- greater than 0.3 m) during the period of greatest air exposure (11:00 a.m. valves can be different not only among species but also within thesame to 1:00 p.m.) seven days before the sample day. During the winter and species, as long as they came from different places. This statement is the spring of the fourth year we could find greater frequency of high related to the influence of different environmental conditions that could tides. A downward pattern followed by a further upward shift was al- cause differences in reproductive cycles and reproductive periodicity of ready found for Ulva lactuca and Ilea fascia (O.F.Müller) Fries as a sy- bivalves (Nascimento and Lunetta, 1978; Braley, 1982). We believe the nonym of Petalonia fascia (O.F.Müller) Kuntze in New South Wales contradiction between our results and the literature is likely related to (Underwood, 1981). Generally, algae species have shorter life cycles and the point highlighted by these last authors. Most of the studies cited their responses to changes in the environmental conditions are faster were done in a laboratory where the conditions are very different from than those of long-lived species. However, even among algae species, the natural habitat. Nevertheless, in a recent growth study of S. cu- those that have longer life cycles (3–6 months) are not considered good cullata on Ascencion Island in the central-east Atlantic, reported an seasonal indicators (Lin et al., 2018). Thus, the response of short-lived accelerated growth in cooler winter months (Arkhipkin et al., 2017). species to the environmental shifts occurs on a smaller temporal scale This study corroborates the results we found, since the increase in the (seasonal). Therefore, the time scale of our samplings is efficient for the percent cover at lower temperature sixty days before the sample day main species found in this zone. Our results also reinforce the importance may be not only a recruitment effect, but also an effect of oyster's to acquire this sort of information about the species life cycle in that area growth. Our results confirm the influence of temperature in some life of interest before choosing the temporal scale. stage of these species and might be an indicator of the thermo-tolerance Many studies highlight the importance of long and continuous of these species. The high thermo-tolerance of these species has also sampling (Hawkins and Hartnoll, 1983; Likens, 1989; Franke and been established for aerial temperature exposure during the low tides Gutow, 2004; Hampton and Schindler, 2006; Nicoletti et al., 2007), (Littlewood, 1989; Davenport and Wong, 1992). In our study the since biotic responses to more complex changes, such as ecological minimum air temperature was positively correlated with the C. rhizo- succession and bioinvasion require longer study periods to be identi- phorae and S. cucullata percent cover. According to the literature both fied. In this study, two non-indigenous species, Amphibalanus amphitrite species have air-gaping as a survival strategy (Littlewood, 1989; and Saccostrea cuccullata, exhibited different patterns over the seven Davenport and Wong, 1992). The strategy of shell gaping is used for

7 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265 respiratory purposes, but it may promote an evaporative cooling and release of this barnacle species (thermal shock). The larval metamor- thereby ameliorating high air temperature. It likely gives an explana- phosis, from nauplius to cyprids, is known to occur between one week tion for the increase in percent cover found for these species from the to sixty days, depending on the environmental conditions (Lacombe and fifth year on. Another factor that could, at least initially, have enhanced Monteiro, 1972; Anil et al., 1995). It is in agreement with the time lag the percent cover of these oyster species is the facilitation relationship. recorded for minimum water temperature, which means that after the Although in this study no significant evidence of this biotic interaction larval release at lower temperature, the metamorphosis and further was found, it is already documented for oyster species. Some studies settlement just happen when the temperature conditions for the next with Crassostrea gigas verified that the presence of “neighbors” sur- larval stage have improved. rounding the oysters, regardless of the species identity, improved the The minimum concentration of Entereococcus spp. in the water fifteen survival, ameliorating the effect of harsher environmental conditions days before the sample day was also recorded as an environmental (Ruesink, 2007; Hedge and Johnston, 2014). variable which negatively affects the T. stalactifera percent cover. The barnacle A. amphitrite is known as a species that has been in- Lacombe and Monteiro (1974), ascribed the decrease in this species troduced a long time ago in this study area. As this species exhibits a distribution in Guanabara Bay to the pollution increase. Nevertheless, no cosmopolitan distribution, its original distribution was unknown for a pattern in the water quality could be verified, the dbRDA results indicate long time. More recent studies with fossil records identify its origin in the that when the minimum concentration of Enterococcus spp. is higher, the Pacific (Carlton et al., 2011). According to dbRDA, A. amphitrite ex- percent cover of this barnacle decreases. It means that this species is hibited a positive correlation with minimum water temperature sixty more sensitive to deal with high levels of pollution. This is another en- days before the sample day. It means that this species thrives in warmer vironmental requirement for which the response was similar to what was waters (MITW). Several studies have already recorded the negative effect observed for oysters. The null model recorded a significantly negative of low temperature (15 °C) in different stages of its larval development niche overlap between the indigenous oyster and this barnacle species (Anil et al., 1995; Anil and Kurian, 1996; Qiu and Qian, 1999; Anil et al., (i.e. the number of samples over the years where they had overlapping 2001). Among these effects the authors mention a decrease in larval percent cover, is less than the expected by chance). Over the years, this survival, frequency of molting, the percent of adults that are developing barnacle species seems to be getting more restricted to a small part of the ovaries and embryos (Qiu and Qian, 1999) and a longer naupliar de- rocky shore (unpublished results). In this part we could not find the velopment (Anil et al., 2001). These negative effects may increase the presence of these oyster species. There are many factors that may explain chance of larvae being preyed upon or carried to unfavorable habitats. how this species succeeded to increase this percent cover over the years. Thus, minimum water temperature (19 °C) seems to be the main factor Some of these are the low niche overlap, the higher spatial variance for the initial decrease in the A. amphitrite percent cover. We recorded the found in the last year and the similar environmental requirement of the lowest percent cover of this species (3,66) in the winter of the fifth oyster species and Tetraclita stalactifera. The survival and increase of this sample year together with the highest frequency verified for 19 °C tem- barnacle species may be related to this “refuge site”. peratures over the sixty days before the sample day (Appendix A). In the environmentally constrained null model approach we tested Another variable that was positively correlated with A. amphitrite the possible biotic interactions. An interesting result was a significant percent cover was the minimum concentration of Enterecoccus spp. in negative relationship (competition) between the non-indigenous oyster the water fifteen days before the sample day. Although no clear pattern species S. cucullata and the barnacle species, introduced a long time could be verified in this biotic variable, the knowledge about there- ago, A. amphitrite. Although, no study has tested the competitive power sistance to pollution of this barnacle has already been recorded of A. amphitrite, Junqueira et al. (2000) have suggested it. This species (Lacombe and Monteiro, 1974). This is one of the reasons why the is quite resistant to several environmental conditions that are un- larvae of this species are considered a model organism to ecotox- favorable to many species. When the environmental conditions become icological essays by the Italian regulatory authority UNICHIM (Piazza favorable for other species and they manage to increase their dis- et al., 2016). On the other hand, Miranda and Guzenski (1999) recorded tribution, A. amphitrite is displaced, being restricted by the preemption an opposite pattern to C. rhizophorae in their studies in larval oyster of substratum by other species. A similar pattern is observed on the culture, in which high temperatures may increase the presence of Paquetá rocky shore in this same bay, where pollution is one of the bacteria, causing larval death. This opposite response between C. rhi- unfavorable environmental conditions that limits the distribution of zophorae and A. amphitrite based on the water quality was also verified mid-intertidal species. In this area it is possible to find a higher percent in our dbRDA results. The null model approach indicated both oyster cover of A. amphitrite in some seasons in contrast to lower oyster per- species and A. amphitrite overlapping their niches less than it would cent cover (unpublished results). This great difference seems to be re- occur by chance. This result reinforces the different environmental re- lated to its resistance and its weak ability as a competitor. quirements recorded for them in the dbRDA and in literature. In this study we verified that each environmental variable affects Tetraclita stalactifera is an indigenous species of the study area and it the same species at different temporal scales such as, temperature af- can be found in the western Atlantic Ocean, Gulf of Mexico and eastern fecting the dominant species sixty days before sampling day and con- Pacific (Skinner et al., 2007). Our result showed that this species has centration of Enterococcus spp. in the water affecting with a fifteen days environmental requirements similar to those of the oyster species, delay. Moreover, we verified the importance of these variables in- which means that most of the environmental conditions that cause a directly affecting species interactions and consequently the community positive effect in oyster percent cover cause the same effect inthis structure as we found for S. cucullata e A. amphitrite. Tropical rocky barnacle species. Tetraclita stalactifera exhibited a negative correlation shores are submitted to stressful conditions due to the intense year- with minimum water temperature sixty days before the sample day. round heat experienced by the mid and high intertidal species (Garrity, Skinner and Coutinho (2002), recorded that low temperature affects the 1984). Rocky shore communities are generally space-limited which larval release due to thermal shock, since they recorded high densities intensified the biotic interactions affecting the species co-occurrence of nauplii during the summer in Arraial do Cabo in Rio de Janeiro State patterns, even though such pattern is not common in tropical commu- (upwelling period). On the other hand, they recorded that the highest nities (Menge and Lubchenco, 1981; Lubchenco et al., 1984). Patterns density of cyprids was more frequent at high temperatures. Thus, the of niche overlap differ among species. Only the two species of oysters temperature may affect the different larval stages of barnacles differ- showed a niche overlap greater than the expected by chance. This po- ently. From fifth sample year on we recorded an increase in the percent sitive association confirms a shared environmental requirement mainly cover of this barnacle species. It is very likely that, initially, highest related to temperature variables as shown by the dbRDA analysis. Al- frequency of minimum water temperature (19 °C) sixty days before the though a significant evidence of species interaction (i.e. facilitation) sample day observed in this period could have stimulate the larval was not found by a pairwise environmentally constrained null model,

8 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265 this interaction could initially enhanced the abundance of both species Acknowledgements as found for other species of oysters (Ruesink, 2007; Hedge and Johnston, 2014; Vozzo and Bishop, 2019). The introduced oyster S. This study is part of the ‘Long-Term Ecological Research of the cucullata and the barnacle A. amphitrite showed a niche overlap less Guanabara Bay (PELD-Guanabara) – Rocky shore Chapter’ funded by than the expected by chance which reflected distinct environmental ‘Conselho Nacional de Desenvolvimento Científico e Tecnológico – requirements. Nevertheless, a negative species interaction between CNPq’ (nº 403809/2012-6) and ‘Fundação Carlos Chagas Filho de these species was revealed by a pairwise environmentally constrained Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ’ (nº E-26/ null model which indicates that despite environmental conditions, 111584/2014). The authors are indebted to Leonardo Fidalgo from biotic interactions are still important for this pair of species. Distinguish ‘Instituto Estadual do Ambiente – INEA’ for provided some of the en- if the species distribution pattern is related to environmental require- vironmental variables, to Dr. Joel Campos de Paula for their assistance ments or it is a product of biotic interactions is a challenge (D'Amen with the photos sample, to Dr. Jean Louis Valentin (IB/UFRJ) for the et al., 2017). In the present study using a 7-year abiotic and biotic data coordination of the PELD, to Maria Isabel Figueiredo for the photo's was possible to provide valuable information about environmental ef- edition, to Benthos laboratory staff for the helping in the fieldwork and fects over rocky shores species, distinguishing these effects from effects to Thomas Sprengers for the English revision. CAP received a scholar- of species interactions. ship from ‘Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES’ and PCP a fellowship from CNPq.

Appendix

Appendix A. Frequency of the dbRDA variables (MIAT_60 = Minimum Air Temperature value recorded sixty days before the sampling day; MIWT_60 = Minimum Water Temperature value recorded sixty days before the sampling day; FRT02_60 = Frequency of low tides sixty days before the sampling day; MINENT_15 = Minimum Concentration of Enterococcus spp in the water fifteen days before the sampling day;; FRT03_7 = Frequency of high tides seven days before the sampling day) in each season (WIN – Winter, SPR – Spring, SUM – Summer, AUT – Autumn) over the seven years (1, 2, 3, 4, 5, 6, 7).

SEASON MIAT_60 MIWT_60 FRT02_60 MINENT_15 FRT03_7

WIN 1 1 3 8 – 0 SPR 1 1 1 0 – 1 SUM 1 1 1 0 1 1 WIN 2 1 5 10 1 0 SPR 2 1 1 2 1 0 SUM 2 1 2 0 1 0 AUT 2 1 3 4 1 0 WIN 3 1 2 7 1 0 SPR 3 1 2 0 1 0 SUM 3 1 2 0 1 0 AUT 3 1 8 3 1 0 WIN 4 1 1 5 1 3 SPR 4 1 1 0 1 2 SUM 4 3 2 0 1 0 AUT 4 1 2 1 1 0 WIN 5 1 4 6 1 0 SPR 5 1 1 0 1 1 SUM 5 1 1 0 1 0 AUT 5 1 3 1 1 0 WIN 6 1 1 6 1 0 SPR 6 1 2 0 1 0 SUM 6 1 1 0 1 0 AUT 6 1 4 3 1 0 WIN 7 1 2 5 2 0 SPR 7 1 1 0 1 0 SUM 7 1 1 0 2 2 AUT 7 1 3 3 1 0

Appendix B. The biotic and abiotic (MIAT_60 = Minimum Air Temperature value recorded sixty days before the sampling day; MXAT_60 = Maximum Air Temperature value recorded sixty days before the sampling day; MIWT_60 = Minimum Water Temperature value recorded sixty days before the sampling day; FRT03_7 = Frequency of high tides seven days before the sampling day; FRT02_60 = Frequency of low tides sixty days before the sampling day; MINENT_15 = Minimum Concentration of Enterococcus spp in the water fifteen days before the sampling day) matrices of dbRDA. Biotic matrix - mean of percent cover of the quadrats in each season over the seven years.

SEASON Ulva spp. A. amphitrite T. stalactifera C. rhizophorae S. cucullata

Winter 1 2,87 15,04 3,22 2,03 0,00 Spring 1 1,62 3,24 2,86 5,66 0,00 Summer 1 0,00 5,61 9,20 10,04 0,00 Winter 2 2,45 11,38 6,96 12,63 0,83 Spring 2 15,55 8,92 3,21 6,92 0,00 Summer 2 3,23 5,23 6,42 6,44 1,22 Autumn 2 0,00 18,13 4,95 7,28 1,63 Winter 3 0,00 13,87 12,87 8,17 1,23 Spring 3 0,00 8,58 9,74 8,65 1,19

9 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Summer 3 0,80 12,80 3,20 15,20 1,60 Autumn 3 0,83 8,13 8,30 8,45 2,43 Winter 4 55,78 7,43 0,43 2,65 2,13 Spring 4 19,61 4,32 2,54 10,90 0,00 Summer 4 0,89 11,74 1,24 13,44 0,00 Autumn 4 0,00 10,83 1,60 15,29 0,40

SEASON Ulva spp. A. amphitrite T. stalactifera C. rhizophorae S. cucullata

Winter 5 0,00 3,66 4,47 18,63 1,20 Spring 5 4,80 3,60 8,80 13,20 2,80 Summer 5 0,80 0,80 8,00 17,20 4,80 Autumn 5 0,00 3,51 7,00 25,67 7,52 Winter 6 0,00 2,00 7,60 20,00 8,80 Spring 6 0,80 0,40 7,24 24,69 6,04 Summer 6 0,40 0,00 9,10 28,04 3,61 Autumn 6 0,83 0,42 7,35 23,75 7,67 Winter 7 0,80 0,00 17,42 21,01 18,61 Spring 7 9,20 1,22 15,22 17,41 15,18 Summer 7 2,04 2,02 15,88 13,72 13,73 Autumn 7 2,82 0,81 8,42 23,64 13,23

SEASON MIAT_60 MXAT_60 MIWT_60 FRT03_7 FRT02_60 MINENT_15

Winter 1 15,70 34,90 20,00 0,00 8,00 32,48 Spring 1 17,10 39,90 20,00 1,00 0,00 32,48 Summer 1 20,90 40,00 22,00 1,00 0,00 31,00 Winter 2 14,60 33,00 20,00 0,00 10,00 10,00 Spring 2 15,00 37,50 20,00 0,00 2,00 10,00 Summer 2 19,70 39,40 21,00 0,00 0,00 10,00 Autumn 2 16,80 35,90 21,00 0,00 4,00 10,00 Winter 3 15,90 33,80 19,00 0,00 7,00 75,00 Spring 3 18,30 38,90 20,00 0,00 0,00 122,00 Summer 3 21,00 39,10 24,00 0,00 0,00 10,00 Autumn 3 17,60 35,60 22,00 0,00 3,00 61,00 Winter 4 13,60 33,70 20,00 3,00 5,00 31,00 Spring 4 17,50 40,10 19,00 2,00 0,00 20,00 Summer 4 21,80 38,10 22,00 0,00 0,00 74,00 Autumn 4 17,30 37,90 21,00 0,00 1,00 52,00

SEASON MIAT_60 MXAT_60 MIWT_60 FRT03_7 FRT02_60 MINENT_15

Winter 5 15,30 34,00 19,00 0,00 6,00 31,00 Spring 5 16,30 37,80 20,00 1,00 0,00 20,00 Summer 5 22,60 38,50 22,00 0,00 0,00 62,00 Autumn 5 18,30 34,20 20,00 0,00 1,00 30,00 Winter 6 17,30 32,80 18,00 0,00 6,00 10,00 Spring 6 19,00 39,30 19,00 0,00 0,00 41,00 Summer 6 20,70 38,40 22,00 0,00 0,00 10,00 Autumn 6 17,30 35,80 21,00 0,00 3,00 10,00 Winter 7 15,80 35,80 18,00 0,00 5,00 10,00 Spring 7 18,50 36,30 19,00 0,00 0,00 52,00 Summer 7 23,00 38,50 20,00 2,00 0,00 10,00 Autumn 7 17,96 36,89 19,00 0,00 3,00 10,00

Appendix C. Mean of percent cover of the quadrats in each season (WIN – Winter, SPR – Spring, SUM – Summer, AUT – Autumn) of all the species that were found from the second until the last sample year (2, 3, 4, 5, 6, 7).

TAXA WIN 2 SPR 2 SUM 2 AUT 2 WIN 3 SPR 3 SUM 3 AUT 3 WIN 4 SPR 4 SUM 4 AUT 4

Recruit Ostreidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Biofilme 36,47 44,19 50,90 38,76 36,80 28,92 35,60 32,75 20,36 30,40 42,09 34,38 Ulva spp. 2,45 15,55 3,23 0,00 0,00 0,00 0,80 0,83 55,78 19,61 0,89 0,00 Chondracanthus spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 2,57 0,00 0,00 0,00 Articulated Corallinaceae 0,40 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Hildenbrandia rubra 5,30 2,84 3,21 3,70 10,93 20,68 17,20 18,33 0,00 14,65 10,92 14,52 Amphibalanus amphitrite 11,38 8,92 5,23 18,13 13,87 8,58 12,80 8,13 7,43 4,32 11,74 10,83 Chthamalus bisinuatus 0,42 0,00 0,40 1,68 1,20 3,25 0,00 0,83 0,00 0,00 0,00 1,20 Dead Barnacle 19,45 13,89 16,91 17,38 11,30 11,14 12,40 9,80 6,04 13,29 9,66 12,11 Recruit Barnacle 0,40 0,00 0,00 0,00 1,60 5,43 0,00 5,60 1,73 2,10 7,55 8,86 Tetraclita stalactifera 6,96 3,21 6,42 4,95 12,87 9,74 3,20 8,30 0,43 2,54 1,24 1,60 Serpulidae 0,00 0,00 0,82 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Spionidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,43 0,00 Mytilaster solisianus 0,00 0,40 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Lottia subrugosa 0,00 0,00 0,00 0,85 0,00 0,00 0,00 0,40 0,00 0,00 0,00 0,00 Crassostrea rhizophorae 12,63 6,92 6,44 7,28 8,17 8,65 15,20 8,45 2,65 10,90 13,44 15,29 Isognomon bicolor 0,42 0,82 0,00 0,40 0,00 0,00 0,00 0,00 0,44 0,00 0,00 0,00 Dead Ostreidae 2,07 2,83 3,21 2,00 2,03 1,22 1,20 1,63 0,43 2,21 2,05 0,81 Perna perna 0,00 0,00 0,00 0,40 0,00 0,43 0,00 0,00 0,00 0,00 0,00 0,00

10 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Saccostrea cuccullata 0,83 0,00 1,22 1,63 1,23 1,19 1,60 2,43 2,13 0,00 0,00 0,40 Diadumene lineata 0,82 0,42 2,02 2,05 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Bugula neritina 0,00 0,00 0,00 0,00 0,00 0,80 0,00 2,50 0,00 0,00 0,00 0,00 Other Bryozoa 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Unidentified 2,00 1,60 0,80 1,20 1,60 2,80 0,00 1,60 7,60 6,80 4,40 0,80 Hydrozoa 0,00 0,00 0,00 0,80 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

TAXA WIN 5 SPR 5 SUM 5 AUT 5 WIN 6 SPR 6 SUM 6 AUT 6 WIN 7 SPR 7 SUM 7 AUT 7

Recruit Ostreidae 0,00 0,00 0,00 0,00 4,00 3,63 3,23 1,22 2,02 2,86 4,03 3,21 Biofilme 48,97 36,80 36,80 32,72 41,60 34,60 32,76 31,60 20,64 20,25 23,42 24,48 Ulva spp. 0,00 4,80 0,80 0,00 0,00 0,80 0,00 0,83 0,80 9,20 2,04 2,82 Chondracanthus spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Articulated Corallinaceae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Hildenbrandia rubra 7,24 19,20 19,20 7,52 1,60 2,41 2,01 6,49 0,40 2,02 2,02 4,85 Amphibalanus amphitrite 3,66 3,60 0,80 3,51 2,00 0,40 0,00 0,42 0,00 1,22 2,02 0,81 Chthamalus bisinuatus 0,00 1,60 2,40 2,50 0,40 1,21 2,02 0,80 2,00 0,40 0,40 1,20 Dead Barnacle 9,33 4,40 4,80 5,52 7,20 3,64 4,47 7,32 4,93 7,64 11,38 5,67 Recruit Barnacle 0,82 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,41 0,00 0,40 0,00 Tetraclita stalactifera 4,47 8,80 8,00 7,00 7,60 7,24 14,69 7,35 17,42 15,22 15,88 8,42 Serpulidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Spionidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Mytilaster solisianus 0,41 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,81 Lottia subrugosa 0,40 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,42 0,00 0,00 0,00 Crassostrea rhizophorae 18,63 13,20 17,20 25,67 20,00 24,69 22,66 23,75 21,01 17,41 13,72 23,64 Isognomon bicolor 0,41 0,40 0,00 0,50 0,40 0,40 0,00 0,00 0,83 0,40 0,40 0,00 Dead Ostreidae 4,47 4,00 4,80 7,54 5,60 13,35 9,31 10,11 9,69 6,58 8,51 10,85 Perna perna 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Saccostrea cuccullata 1,20 2,80 4,80 7,52 8,80 6,04 7,22 7,67 18,61 15,18 13,73 13,23 Diadumene lineata 0,00 0,40 0,40 0,00 0,80 0,40 1,23 2,44 0,82 0,40 1,62 0,00 Bugula neritina 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Other Bryozoa 0,00 0,00 0,00 0,00 0,00 0,80 0,00 0,00 0,00 0,00 0,41 0,00 Unidentified 1,20 0,00 0,00 0,50 0,00 0,80 1,20 1,20 1,20 1,20 1,20 0,40 Hydrozoa 0,00 0,00 0,00 0,00 0,00 0,00 0,40 0,00 0,00 1,23 0,00 0,00

Appendix C. Mean of percent cover of the quadrats in each fortnight (1, 2, 3, 4, 5, 6, 7) of each season (WIN – Winter, SPR – Spring, SUM – Summer, AUT – Autumn) of all the species that were found in the last two sample year (6 and 7).

TAXA WIN 6.1 WIN 6.2 SPR 6.1 SPR6.2 SPR 6.3 SPR 6.4 SPR 6.5 SPR 6.6 SUM 6.1 SUM 6.2 SUM 6.3 SUM 6.4

Recruit Ostreidae 4,00 3,70 2,45 2,48 2,80 1,21 2,40 3,63 3,28 0,81 2,80 2,41 Biofilme 41,60 37,49 34,38 32,20 38,80 32,12 32,40 34,60 26,93 26,87 29,32 24,91 Chaetomorpha antennina 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Ulva spp. 0,00 0,00 0,00 0,00 0,80 1,60 0,00 0,80 3,22 1,60 2,43 1,20 Chondracanthus spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Hildenbrandia rubra 1,60 1,76 4,04 6,08 2,80 3,61 4,80 2,41 2,83 5,20 5,22 7,67 Amphibalanus amphitrite 2,00 1,41 0,41 1,25 0,00 0,81 0,00 0,40 0,00 0,40 0,40 0,80 Chthamalus bisinuatus 0,00 0,73 2,82 0,82 0,80 0,40 0,40 1,21 1,23 0,41 3,61 0,80 Dead Barnacle 7,20 6,92 6,82 5,35 5,60 8,02 8,80 3,64 4,92 5,24 7,67 6,84 Recruit Barnacle 0,00 0,00 0,40 0,00 0,40 0,00 0,00 0,40 0,42 0,41 0,00 0,40 Tetraclita stalactifera 7,60 6,96 12,87 16,71 14,80 13,73 15,20 7,24 17,53 17,72 13,31 12,44 Phragmatopoma spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Serpulidae 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Mytilaster solisianus 0,00 0,00 0,40 0,00 0,40 0,00 0,40 0,00 0,00 0,00 0,00 0,00 Lottia subrugosa 0,00 0,18 0,00 0,00 0,00 0,40 0,00 0,40 0,00 0,00 0,00 0,40 Crassostrea rhizophorae 20,00 21,61 26,06 25,16 22,40 23,26 23,20 24,69 27,77 25,30 19,63 25,31 Isognomon bicolor 0,40 0,67 0,40 0,83 0,40 0,81 0,80 0,40 0,41 0,00 0,41 0,00 Dead Ostreidae 5,60 6,67 4,47 5,40 4,80 7,61 4,80 13,35 4,49 9,62 10,40 11,60 Perna perna 0,00 0,00 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,00 0,00 0,00 Saccostrea cuccullata 8,80 6,44 4,08 3,31 4,00 5,62 6,00 6,04 5,31 5,21 3,60 4,41 Diadumene lineata 0,80 0,72 0,40 0,42 0,40 0,81 0,40 0,40 1,22 1,22 1,21 0,40 Other Bryozoa 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,80 0,42 0,00 0,00 0,41 Unidentified 0,00 0,18 1,20 3,20 0,00 0,40 0,00 0,80 2,00 0,40 0,40 0,40 Hydrozoa 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Appendix D. Mean of percent cover of the quadrats in each fortnight (1, 2, 3, 4, 5, 6, 7) of each season (WIN – Winter, SPR – Spring, SUM – Summer, AUT – Autumn) of all the species that were found in the last two sample year (6 and 7) (continuation).

TAXA SUM 6.5 SUM 6.6 AUT 6.1 AUT 6.2 AUT 6.3 AUT 6.4 AUT 6.5 AUT 6.6 AUT 6.7 WIN 7.1 WIN 7.2 WIN 7.3

Recruit Ostreidae 2,47 2,82 0,00 0,40 2,45 2,03 2,80 1,22 3,62 0,82 2,40 3,20 Biofilme 27,15 25,35 19,56 26,80 24,75 23,83 23,71 31,60 26,57 30,11 23,70 26,60 Chaetomorpha antennina 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Ulva spp. 1,22 0,40 3,27 0,40 0,00 1,60 0,00 0,83 0,00 1,60 1,60 0,82 Chondracanthus spp 0,00 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,00 0,00 0,00 0,00 Hildenbrandia rubra 5,29 8,32 7,32 6,40 8,12 4,01 8,04 6,49 2,01 4,41 2,41 1,22

11 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Amphibalanus amphitrite 0,40 0,00 1,22 0,00 1,22 1,61 1,60 0,42 1,62 0,80 0,80 0,41 Chthamalus bisinuatus 2,01 2,07 1,62 0,80 0,41 2,03 0,40 0,80 0,80 1,62 0,82 2,03 Dead Barnacle 3,66 7,00 4,89 5,20 2,86 7,66 5,27 7,32 7,62 4,80 6,47 4,07 Recruit Barnacle 0,00 0,00 0,42 0,00 0,00 0,00 0,00 0,00 0,40 0,00 0,00 0,00 Tetraclita stalactifera 14,14 9,10 22,40 14,80 23,18 17,73 16,54 7,35 12,40 13,22 20,15 17,08 Phragmatopoma spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Serpulidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Mytilaster solisianus 0,00 0,40 0,00 0,00 0,82 0,81 0,80 0,00 0,40 0,80 0,82 0,00 Lottia subrugosa 0,40 0,40 0,00 0,40 0,00 0,00 0,00 0,00 0,81 0,00 0,00 0,40 Crassostrea rhizophorae 26,52 28,04 23,49 28,40 19,13 22,15 20,81 23,75 18,23 22,52 18,83 24,11 Isognomon bicolor 0,00 0,81 0,42 2,00 0,00 0,00 0,40 0,00 1,22 0,40 0,40 0,81 Dead Ostreidae 9,77 8,85 8,51 8,00 9,76 8,47 8,03 10,11 12,16 12,48 9,22 6,02 Perna perna 0,00 0,00 0,00 0,40 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Saccostrea cuccullata 6,16 3,61 3,62 4,00 5,67 6,06 8,00 7,67 9,32 6,02 10,40 12,41 Diadumene lineata 0,82 2,83 2,48 1,60 1,22 1,22 1,61 2,44 2,42 0,41 2,00 0,82 Other Bryozoa 0,00 0,00 0,80 0,40 0,41 0,40 2,00 0,00 0,40 0,00 0,00 0,00 Unidentified 1,60 2,00 1,60 0,00 1,60 0,80 0,40 1,20 0,80 0,40 0,40 0,80 Hydrozoa 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

TAXA WIN 7.4 WIN 7.5 WIN 7.6 SPR 7.1 SPR 7.2 SPR 7.3 SPR 7.4 SUM 7.1 SUM 7.2 SUM 7.3 SUM 7.4 SUM 7.5

Recruit Ostreidae 1,20 2,06 2,02 2,81 3,23 2,80 2,86 3,27 3,61 0,82 3,65 1,20 Biofilme 26,00 31,21 20,64 35,00 24,24 31,57 20,25 16,87 24,20 26,18 31,53 27,60 Chaetomorpha antennina 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Ulva spp. 0,80 2,05 0,80 1,62 3,67 2,47 9,20 11,60 18,24 7,20 6,03 4,80 Chondracanthus spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Hildenbrandia rubra 3,60 1,24 0,40 0,41 2,02 0,81 2,02 2,80 0,40 2,00 1,63 2,00 Amphibalanus amphitrite 1,20 1,22 0,00 0,40 0,00 0,00 1,22 0,00 1,61 0,81 0,80 1,20 Chthamalus bisinuatus 2,40 1,22 2,00 1,21 0,81 1,62 0,40 0,40 0,40 0,80 0,40 1,60 Dead Barnacle 6,80 7,72 4,93 2,83 4,47 3,27 7,64 4,80 4,04 6,01 7,35 9,20 Recruit Barnacle 0,00 0,00 0,41 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Tetraclita stalactifera 15,60 16,72 17,42 15,38 17,37 28,97 15,22 16,42 15,80 14,02 15,97 19,20 Phragmatopoma spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,40 Serpulidae 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Mytilaster solisianus 0,40 0,82 0,00 0,00 0,41 0,41 0,00 0,40 0,41 0,00 0,00 0,40 Lottia subrugosa 0,00 1,62 0,42 0,00 0,00 0,00 0,00 0,80 0,40 0,40 0,00 0,00 Crassostrea rhizophorae 16,40 14,07 21,01 16,93 14,86 9,62 17,41 10,60 7,23 15,36 11,05 11,60 Isognomon bicolor 0,80 0,00 0,83 1,21 0,41 0,40 0,00 0,00 0,00 0,40 0,00 0,40 Dead Ostreidae 10,80 8,82 9,69 11,31 10,83 4,42 6,58 12,22 6,82 11,39 7,70 6,40 Perna perna 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,40 Saccostrea cuccullata 13,20 10,03 18,61 10,89 15,67 12,81 15,18 17,83 15,63 14,23 13,47 13,20 Diadumene lineata 0,80 1,21 0,82 0,00 2,02 0,00 0,40 0,80 0,82 0,40 0,42 0,40 Other Bryozoa 0,00 0,00 0,00 0,00 0,00 0,83 0,00 1,20 0,41 0,00 0,00 0,00 Unidentified 0,00 1,20 1,20 0,80 0,80 1,20 1,20 0,80 0,80 0,80 1,60 0,00 Hydrozoa 0,00 0,00 0,00 0,00 0,00 0,00 1,23 0,00 0,00 0,00 0,00 0,00

TAXA SUM 7.6 AUT 7.1 AUT 7.2 AUT 7.3 AUT 7.4 AUT 7.5 AUT 7.6

Recruit Ostreidae 4,03 1,62 2,01 2,80 4,05 2,85 3,21 Biofilme 23,42 18,46 19,08 20,13 18,50 21,66 24,48 Chaetomorpha antennina 0,00 0,40 0,00 0,00 0,00 0,00 0,00 Ulva spp. 2,04 4,40 4,87 7,65 9,20 13,61 2,82 Chondracanthus spp 0,00 0,00 0,42 0,00 0,00 0,00 0,00 Hildenbrandia rubra 2,02 3,22 3,21 2,00 1,60 1,20 4,85 Amphibalanus amphitrite 2,02 3,20 1,21 0,80 2,00 0,40 0,81 Chthamalus bisinuatus 0,40 0,40 0,40 1,23 0,80 0,40 1,20 Dead Barnacle 11,38 16,02 12,54 12,58 6,03 6,82 5,67 Recruit Barnacle 0,40 0,40 0,00 0,00 0,00 0,00 0,00 Tetraclita stalactifera 15,88 13,20 19,86 12,63 16,00 13,61 8,42 Phragmatopoma spp 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Serpulidae 0,00 0,00 0,42 0,00 0,00 0,00 0,00 Mytilaster solisianus 0,00 0,00 1,20 0,00 0,40 0,80 0,81 Lottia subrugosa 0,00 0,40 0,00 0,00 0,00 0,40 0,00 Crassostrea rhizophorae 13,72 12,11 10,07 14,87 15,43 11,28 23,64 Isognomon bicolor 0,40 0,40 0,80 1,22 0,00 1,21 0,00 Dead Ostreidae 8,51 11,28 10,96 10,08 9,65 11,26 10,85 Perna perna 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Saccostrea cuccullata 13,73 11,70 12,16 13,60 15,53 14,11 13,23 Diadumene lineata 1,62 2,80 0,00 0,40 0,80 0,00 0,00 Other Bryozoa 0,41 0,00 0,80 0,00 0,00 0,40 0,00 Unidentified 0,00 0,40 1,20 0,80 0,80 0,40 0,40 Hydrozoa 1,20 0,00 0,00 0,00 0,00 0,00 0,00

Appendix E. Results of pair-wise test of two-way permutational multivariate analysis of variance (PERMANOVA).

Groups Year 2 Year 3 Year 4 Year 5 Year 6 Year 7

Winter × Spring n.s. n.s. ** ** n.s. n.s. Winter × Summer n.s. n.s. ** ** n.s. n.s.

12 C.A. Puga, et al. Estuarine, Coastal and Shelf Science 226 (2019) 106265

Winter × Autumn n.s. n.s. ** n.s. n.s. n.s. Spring × Summer n.s. ** ** n.s. n.s. n.s. Spring × Autumn ** n.s. ** * n.s. n.s. Summer × Autumn n.s. n.s. n.s. n.s. n.s. n.s.

(n.s. – not significant) p > 0.05; *p < 0.05; **p < 0.01.

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