Introduction

Ecosystems worldwide are experiencing profound ecological changes including biodiversity losses1 and community rearrangements (i.e., non-random species turnover)2, which are expected to worsen with climate change, even under moderate CO2 mitigation scenarios3. Non-random species turnover, which depends on the susceptibility of the organisms’ traits, can disrupt vital ecosystem processes such as trophic energy flow4 or habitat provisioning5, deeply affecting ecosystem functioning and resilience6. Understanding distinct and emerging species configurations and their contribution to key ecosystem functions is therefore needed to establish effective conservation and management strategies7,8.

Over the past four decades, tropical coral reefs, one of Earth’s most biodiverse ecosystems, have experienced global declines and shifts in species compositions that deeply affect their functioning and the ecosystem services provided2,9,10. A turnover from highly three-dimensional scleractinian corals such as Acroporidae to more robust corals (e.g., Poritidae), has been observed worldwide after acute disturbances, such as bleaching events or crown-of-thorns outbreaks2,11. Shifts in species compositions including decreases in scleractinians and increases in non-reef building species such as algae, sponges and octocorals are also becoming more frequent as a result of continuous anthropogenic and climate stressors12,13,14. Such compositional changes affect several core ecosystem processes (i.e., carbonate production, primary production, trophic interactions and reef replenishment) and pose new conservation challenges5,15,16.

Coral reefs are heterogeneous ecosystems, with highly varied biological communities that depend on both the local physical environment (e.g., reef topography, wave exposure) and larger biogeographic patterns17,18. Compositionally and functionally distinct ecosystems will likely respond differently to disturbances, which can then result in different species configurations, further hindering the study and prediction of coral reef trajectories and their effect on core ecosystem processes17,19. In this context, conservation approaches need to consider both coral reefs spatio-temporal heterogeneity (i.e., different species configurations) and their contribution towards core ecosystem processes20.

The study of species configurations (i.e., community biodiversity) has been traditionally studied as the relative abundance of different taxa, and as such, most studies assessing coral reef composition have mostly used taxonomic categories (often at family level or higher, especially for benthic organisms) to identify community changes21,22. Approaches using major taxonomic categories (e.g., hard coral, soft coral, algae, etc.), have the advantage of being easily implemented in global citizen science programs and have allowed identification of marked regime shifts, for example, from coral to algae-dominated communities22,23,24. However, the use of major benthic categories might overlook functionally important compositional changes25. Many studies have shown that different species contribute differently to ecosystem functioning, and therefore ecological research has seen a shift from taxonomic diversity to functional diversity studies26,27. In functional diversity analyses, organisms are classified according to their life traits or functions, which allows identifying community-level changes in mean community traits. In fact, the information provided by the functional structure of communities is nowadays considered as a key indicator of the ecological status and resilience of an ecosystem28. Trait-based approaches therefore offer new opportunities for a deeper mechanistic understanding on the role of biodiversity in maintaining multiple ecosystem processes and they allow identification of species with critical and vulnerable ecosystem functions29,30. Trait-based approaches have been successfully used to study changes in coral reef fish communities29,31 and in scleractinian coral assemblages26,32,33. However, whilst scleractinian corals are the key organisms of coral reefs, recent shifts towards assemblages dominated by alternate organisms highlight the need of expanding these approaches to include all types of benthic organisms.

Here, we studied coral reefs around Bangka and Bunaken islands (North Sulawesi, Indonesia), which are at the epicentre of marine biodiversity34 and display high spatial heterogeneity17,35. Site characteristics such as wave exposure, depth and local anthropogenic stressors such as pollution or fishing are strong determinants of communities’ compositions36,37,38. For example, in coral reefs, overfishing (loss of top down control) and eutrophication (loss of bottom up control) have been recurrently associated to coral-algal regimes shifts. Sedimentation and turbidity have also been observed to drive shifts towards regimes dominated by algae, sponges, or zoanthids39,40,41. Here, we studied reefs with different topographies and exposed to different anthropogenic pressures as a case study to explore how the use of different biodiversity approaches (major taxonomic categories, high taxonomic resolution categories and trait-based approaches) affects the detection of distinct community (benthic and fish) compositions. We also implemented the use of a trait-based approach to study the functional diversity of coral reef benthic assemblages, including all types of sessile organisms encountered (e.g., sponges, ascidians, soft corals, etc.). Furthermore, we also analysed how the determination of distinct benthic regimes (defined using a cluster analyses) change when commonly used groups such as scleractinian corals or reef fish are complemented with all sessile benthic organisms.

Results

Identifying community patterns

We studied the benthic (scleractinian corals and all benthic organisms) and fish community composition of nine coral reefs in North Sulawesi (Indonesia, Supplementary Table 1) by using major categories, categories at the highest taxonomic resolution possible and functional entities (FEs, defined using trait-base approaches) (Supplementary Tables 2, 3). The analysis of high taxonomic data and FEs showed marked differences between the benthos (all benthic organisms) and fish communities of the sites studied, allowing the identification of five significantly different regimes (i.e., sites with similar compositions identified using a cluster analysis) (Figs. 1, 2, Supplementary Figure 1). We identified three distinct regimes in Bangka, corresponding to the three sites studied (Ba1, Ba2 and Ba3) and two regimes in Bunaken, which grouped the deep sites (BuD regime containing Bu1, Bu2, Bu3 and Bu4) and the shallow sites (BuS regime, containing Bu5 and Bu6) (Figs. 1, 2, Supplementary Figure 1).

Figure 1
figure 1

Map of the sites monitored around Bangka and Bunaken Islands (North Sulawesi, Indonesia). Colours indicate the different regimes identified. This map was created using QGIS software (QGIS.org, 2021. QGIS Geographic Information System. QGIS Association. http://www.qgis.org).

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Figure 2
figure 2

Analysis of the benthos [only scleractinian corals (a–c) and all benthic organisms (d–f)] and fish (g–i) community similarities (nMDS based on Euclidean distances) between the different sites samples using three levels of ecological information: major categories, highest taxonomic resolution and functional entities (FE). Community regimes of sites grou** together are highlighted in bold and italics (Ba1, Ba2, Ba3, BuD and BuS).

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Regardless of the type of community studied (i.e., only scleractinian corals, all benthic organisms, or fish) the use of major categories failed to identify most of the community differences highlighted by the use of higher resolution data (i.e., high taxonomic resolution or FEs) (Fig. 2). Whereas it was to be expected that major categories would result in a lower regime identification (i.e., fewer input variables therefore fewer resulting clusters), here we show that the identified regimes using major categories are less consistent through the different datasets (corals, benthos, fish). For example, the analysis of coral major categories (i.e., classified upon their morphology), only allowed the clear identification of BuD regime, whereas all the other Bunaken and Bangka sites were distributed into three mixed clusters (Fig. 2a, Supplementary Figure 1). Similarly, the analysis of the coral reef benthos using major categories resulted in the identification of four clusters, two of which contained transects from BuD and Ba3 respectively, whereas the two remaining mixed transects from Ba2, Ba1 and BuS. Finally, the fish family analysis only allowed the identification of a significantly different regime in Ba2, which was not identified in the other datasets and grouped together the other Bangka (Ba1 and Ba3) and Bunaken sites into two other clusters (Figs. 1, 2 Supplementary Figure 1).

The results also show that the study of all benthic organisms allows much better detection of different community regimes than just the study of scleractinian coral communities. For example, the sites from Ba2 grouped together with the shallow Bunaken sites (BuS) when using only scleractinian corals, but were clearly distinguished when all the benthic organisms were considered (Fig. 2, Supplementary Figure 1).

Benthic community structure

Our dataset was composed of highly heterogeneous reefs, with each of the regimes detected using high resolution taxonomic data and trait-based analyses dominated by different benthic organisms: scleractinian hard corals (BuS), blue coral Heliopora coerulea (Ba2), xenid soft corals (Ba1), colonial ascidians (Ba3) and sponges (BuD) (Fig. 3a,b).

Figure 3
figure 3

Benthic composition (based on major categories) of the different community regimes identified (a) and cover (%) of the most abundant (≥ 5% at least in one regime) benthic taxonomic categories identified (b). Highlighted circles represent the dominant benthic organism in each of the communities.

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Only two (Ba2 and BuS) out of the five regimes identified were dominated by reef-building species (i.e., hard corals, 28.9 ± 6.5% and 44.7 ± 15.6%, respectively). The reef at Ba2 was predominantly dominated by the blue coral H. coerulea (9.5 ± 3.7%), with lower covers of branching scleractinian corals (Acropora spp. 3.4 ± 3.5% and Porites spp. 2.1 ± 0.6%) and columnar coral Isopora palifera (2.4 ± 2.4%) (Fig. 3a,b). Ba2 also displayed important covers of encrusting coralline algae (8.5 ± 0.3%) and soft corals from the Alcyoniidae (mainly Sarcophyton spp. 5.0 ± 3.4% and Sinularia spp. 4.8 ± 2.1%) and Xeniidae (6.6 ± 2.3%) families. BuS reefs were dominated by branching scleractinian corals (Acropora spp. 13.7 ± 16.9% and Porites spp. 8.1 ± 6%), the columnar coral Isopora palifera (7.8 ± 5.9%) and massive Porites spp. (4.4 ± 4.0%). BuS sites also displayed large covers of encrusting coralline algae (11.7 ± 2.6%) and Alcyoniidae corals (mostly Sinularia spp. 6.3 ± 5.9%) (Fig. 3a,b).

Ba1 transects were dominated by soft corals from the Xeniidae family (23.5 ± 3.0%), followed by incrusting sponges (9.2 ± 3.6%), and presented low hard coral cover (7.9 ± 1.3%). Ba3 was dominated by sponges (15.2 ± 3.1%), mostly encrusting sponges (10.3 ± 1.3%), followed by ascidians (13.4 ± 4.4%, mostly encrusting colonial ascidians) and hard corals (12.9 ± 8.1%, mostly Pavona spp.). Sponges accounted for 49.4 ± 6.5% of the cover in BuD transects, with encrusting sponges being the most abundant organisms (21.2 ± 4.2%), followed by massive (13.4 ± 3.8%) and fleshy encrusting sponges (6.4 ± 1.5%). Hard coral cover was 24.7 ± 4.9%, with submassive Montipora ssp. (4.2 ± 2.9) and massive Porites spp. (3.1 ± 1.4%) being the most abundant genus (Fig. 3a,b).

Functional diversity analysis of benthos communities

We identified 99 high taxonomic resolution benthic categories that were classified into 64 FEs (Supplementary Table 2) for which their functional niche was displayed using a functional space built on four PCoA axis. Generally, species longevity, corallite maximum width (for scleractinian corals), flexibility and growth rate changed along the first axis (PC1) (Fig. 4a). Colony form was highly structured along the fourth axis (PC4) (Fig. 4a). 29 out of the 64 FEs contained calcified species contributing to reef accretion (e.g., hard corals, crustose coralline algae, foraminifera), with nine FEs also contributing to reef structural complexity (i.e., branching morphology). 25 FEs out of the 64 FEs identified contained fast-growing species, including some potentially proliferating species (e.g., cyanobacteria, macroalgae, encrusting sponges, encrusting ascidians).

Figure 4
figure 4

Benthic functional diversity of the different communities identified. (a) Distribution of functional entities (FEs) in the global benthic functional space, built using four PCoA axis (PC1 and PC2 left, PC3 and PC4 right) using twelve functional traits: colony formation, growth form, maximum colony size, longevity (L), growth rate (G), body flexibility (F), skeleton presence, reproductive strategy, sexual system, feeding strategy, presence of photosynthetic symbionts (PS) and corallite maximum width (CW, only for scleractinian corals). The numbers indicate the following functional entities: 1: massive hermaphrodite scleractinian corals, 2: branching hermaphrodite scleractinian corals, 3: branching gonochoric scleractinian corals, 4: Sinularia soft coral, 5: encrusting crustose coralline algae, 6: Xeniidae soft coral, 7: solitary ascidians, 8: macroalgae, 9: encrusting filter-feeders (sponges and ascidians). (b) Functional spaces of each of the communities analysed (coloured convex hull) superposed to the global functional space. (c) Functional diversity indices for each of the communities: relative taxonomic richness (Richness %), relative FE richness (FE %) and relative functional richness as % of filled global functional space (4D richness).

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The three Bangka sites displayed the lowest taxonomic and FE richness (Srichness = 52–67%, FErichness = 63–78%), but still filled 80 (Ba1), 85 (Ba2) and 84% (Ba3) of the benthos functional space (Fig. 4b,c). Only Ba2 was characterised (e.g., community-weighted means of trait values, CWM) by reef-building species (branching long-lived calcified species with fast growth) (Table 1, Fig. 4b). Ba2 contained 24 out of the 29 FEs contributing to reef accretion and the 9 FEs also contributing to reef structural complexity (Fig. 4b). In contrast, Ba1 and Ba3 were characterised by fast-growing short-lived species with no contribution to reef accretion (i.e., no skeleton) (Table 1, Fig. 4b). Ba1 and Ba3 also displayed the lowest functional diversity of reef-building species, with 19 and 18 FEs contributing to reef accretion, respectively. Ba1 contained only five FEs contributing simultaneously to reef accretion and reef structural complexity, but the cover of these FEs was extremely low (< 3%). Ba3 contained only four branching reef-building FEs, with the FE containing Pavona spp. reaching 7% of the benthic cover. Ba3 also displayed the lowest cover in hermaphrodite broadcaster branching hard corals such as Acroporidae (Fig. 4b).

Table 1 Community-weighted-mean values (CWM) for the different traits and benthic communities studied.
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BuD presented the highest taxonomic and FE richness (95%), however it presented a smaller functional richness (90%) than BuS (95%) (Fig. 4b,c). BuD sites were dominated by massive, long-lived, slow-growing filter-feeding species, such as barrel and massive sponges; whilst BuS was characterised by branching, calcified, long-lived, broadcaster species such as Acroporidae corals (Table 1, Fig. 4b). Both BuD and BuS contained most of the FEs contributing to reef accretion and reef structural complexity (Fig. 4b).

Out of the 64 FEs and 99 high-resolution taxonomic categories, only 29 FEs and 28 taxonomic categories were found in all sites (Supplementary Figure 2). BuD was the site with the highest number of unique FEs (9) and unique taxonomic categories (11), but none of the Bangka sites presented any unique benthic FEs or taxonomic categories (Supplementary Figure 2). Bunaken communities (BuD and BuS) presented 2 FEs that were absent from the sites studied at Bangka island. Deep sites (BuS, Ba1 and Ba3) presented two FEs that were absent in the shallow sites, whereas the shallow communities (BuS and Ba2) had one unique FE, the blue coral H. coerulea (Supplementary Figure 2).

Functional diversity analysis of fish communities

We identified 172 fish species that were classified into 97 FEs (Supplementary Table 3), for which their functional niche was displayed using a functional space built on four PCoA axis. Generally, gregariousness and vertical position changed along the first axis of the functional space (PC1) (Fig. 5a). The second axis (PC2) was characterised by differences between nocturnal and diurnal species, fish size and diet, showing a clear separation between planktivorous (e.g., damselfishes, fusiliers) and piscivorous fish (e.g., snappers, barracudas). Fish mobility was captured by both PC1 and PC2. PC3 showed a clear separation between nocturnal (left) and diurnal (right) species, and also captured fish mobility, with highly mobile fish species such as fusiliers or surgeonfishes at the right extreme of the functional space. Fish gregariousness also changed along PC4, but the pattern was not as clear as with PC1. Vertical position also changed with PC4, with highly substrate associated species such as parrotfishes or squirrelfishes at the bottom of the functional space (Fig. 5a).

Figure 5
figure 5

Fish functional diversity of the different communities identified. (a) Distribution of functional entities (FEs) in the global fish functional space, built using four PCoA axis (PC1 and PC2 left, PC3 and PC4 right) using six functional traits: body size, diet, period of activity, vertical position (V), gregariousness (G) and mobility (M). The numbers indicate the following functional entities: 1: Sphyraena quenie (Sphyraenidae), 2: big snappers (e.g., Macolor macularis, Lutjanidae), 3: damselfishes, 4: pelagic planktivores such as fusiliers (Caesionidae), 5: squirrelfishes (Sargocentron spp., Holocentridae), 6: soldierfishes (Myripristis spp., Holocentridae), 7: Melichthys vidua, 8: unicornfishes (Naso spp., Acanthuridae). (b) Functional spaces of each of the communities’ analysed (coloured convex hull) superposed to the global functional space (grey). The bubble sizes represent the FEs mean biomass at each of the regimes. (c) Functional diversity indices for each of the communities: relative taxonomic richness (Richness %), relative FE richness (FE %) and relative functional richness as % of filled global functional space (4D richness).

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All Bangka sites displayed the lowest taxonomic and FE richness (Srichness = 29–33%, FErichness = 40–45%). Ba1 and Ba3 also presented the lowest functional richness, filling only 37 and 39% of the functional space respectively. Ba2, however, exhibited the second highest functional richness, filling 59% of the functional space (Fig. 5b,c), which was related to the presence of few highly original FEs such as big highly mobile predators (i.e., barracudas, PC1) and the nocturnal, gregarious, highly-site attached omnivorous sweeper (Pempheris oualensis, PC3–PC4), which were uniquely found in Ba2 (Fig. 5b). Overall, all Bangka sites were characterised by the lack of browsers; although this trophic group was only represented by two FEs consisting of three Acanthuridae species (Data S2). Ba1 and Ba3 also displayed low functional diversity and biomass of grazers/detritivores. Ba1 and Ba2 lacked the presence of most big, high-trophic chain fish (i.e., piscivorous, macro-invertivore and omnivorous), excepting the barracudas in Ba2 (Fig. 5b).

Bunaken sites harboured the highest number of fish species and FEs, which were characterised by middle-size, diurnal planktonic species (Table 2, Fig. 5b,c). BuD, which harboured 50% of all species and 66% of all FEs, displayed the highest functional richness (68%). BuS, which hosted the largest taxonomic richness (58%) and FE richness (70%) only filled 47% of the functional space, which was related to the absence of nocturnal piscivorous species such as snappers or barracudas (Fig. 5b,c).

Table 2 Community-weighted-mean values (CWM) for the different traits and fish communities studied.
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Out of the 97 FEs and 172 species identified in all communities, only 19 FEs and 13 species were present in all sites (Supplementary Figure 2). None of the piscivorous FEs was shared between all sites, but, all sites presented unique piscivorous FEs. BuD was the community with the highest number of unique FEs (11), followed by BuS (9 FEs) which was however the site with the most unique number of species (26). The Bangka sites all displayed lower number of unique FEs and species ranging from 3–4 unique FEs and 9–12 unique species (Supplementary Figure 2). Bunaken communities had 9 FEs that were absent in the communities studied in Bangka island, which were mostly medium to highly mobile FEs such as fusiliers (Caesionidae) and large Acanthuridae (Fig. 5, Supplementary Figure 2). Shallow sites (BuS and Ba2) presented four unique FEs that were absent from deep sites (BuS, Ba1 and Ba3) (Supplementary Figure 2).

Discussion

As coral reef communities change and reorganise in response to increasing anthropogenic and climate disturbances, approaches that detect new species configurations and their contribution to key ecosystem processes are required42,43. Here, we selected reefs with different natural characteristics and exposed to anthropogenic factors, which we hypothesised would display different fish and/or benthic regimes, to compare the use of different biodiversity approaches. We show that the use of major categories (family level or above) in studying coral reef communities fails to identify distinct regimes. We also implement the use of a trait-based approach to study coral reef fish and benthic communities, and show its relevance in the study and detection of different communities.

The spatio-temporal study of coral reefs is key to predicting their trajectories and recovery potential after disturbances17. Within this context, global citizen science programs are vital for the temporal study of coral reefs, and contribute to community capacity building and education23. Such programs, however, rely often on the study of coral communities using major categories (at family level for fishes and at class level or higher for benthic organisms), which as we show here might mask the presence of distinct assemblages. In fact, our results showed that by using major categories we not only detected fewer regimes, but they were also less consistent across the different organisms studied (corals, benthos, fish), possibly suggesting lower ecological relevance or accuracy of the regimes identified. For example, the study of major benthic categories mixed Ba1 (dominated by fast-growing xeniids), with Ba2 (dominated by the hydrocoral H. coerulea) and BuS (dominated by branching fast growing scleractinian corals such as Acroporidae), which were clearly separated by using functional entities and high resolution taxonomic categories. Communities dominated by branching scleractinian corals (such as in BuS), are generally examples of healthy, highly complex coral reefs7,26, whilst communities dominated by xeniids (such as Ba1) tend to display much lower structural complexity and might be characteristic of degraded habitats44. Given that current global change scenario is resulting in unprecedented ecosystem degradation, temporal monitoring of ecosystems and the detection of community changes is of foremost importance21,45,46. However, the monitoring approaches might need to be readjusted or extended in order to provide higher taxonomic resolution surveys that capture the different emerging species configurations as previously suggested by Jouffray et al. (2015), Lam et al. (2017), and Donovan et al. (2018)21,45,46. Furthermore, our results also agree with Smith et al. (2016), which highlighted the importance of including non-coral benthic organisms in monitoring47.

Changes in coral reef communities and especially the decline of key reef building species contribute to the long-term functional erosion of coral reefs that could result in the loss of associated ecosystem services9,16. For example, decreases in structural complexity and the associated loss of habitat structure have been associated with a decline in fish biomass and therefore fisheries16. However, as shown recently in some Caribbean reefs, not all communities with low-coral cover might display compromised ecosystem functioning42, highlighting the need to understand the composition but also functioning of different coral reef communities. The use of trait-based approaches to gain insights into the role of biodiversity in ecosystem functioning has been successfully implemented to study and detect changes in fish and scleractinian coral communities26,29, but to date such approaches have not yet been implemented to study coral reef benthic changes beyond scleractinian corals. Identification of coral reef benthic organisms to species or even genus level is extremely challenging, especially from visual census or imagery48, which probably has restrained researchers from applying trait-based approaches to whole coral reef benthic communities. Many benthic organisms are highly understudied49, whilst others such as sponges or soft corals require advanced genetic tools or microscopic examination for their taxonomic classification50,51,52. Here, we show that even if visual identification of many coral reef benthic organisms to species level remains impossible, the classification of organisms at lower levels, which for some organisms may just be at the morphological level (i.e., sponges)53, still yields high quality data on which trait-based approaches can be applied. Within our trait-based approach, we used ordered categorical traits instead of continuous traits (e.g., growth ranges and broad lifespan ranges instead of specific values) in order to consider the inherent trait variability from categories that contain several species (e.g., Acropora spp.). The use of such an approach allowed us not only to delineate different community regimes that matched the ones identified using fish communities (at species level), but also to obtain insights into some of the functions that might be compromised in the different community regimes.

Our results show that coral reef assemblages around Bangka and Bunaken islands are highly heterogeneous as previously highlighted by Ponti et al.35. More importantly, we observed that out of five different community regimes detected, only two were dominated by reef-building species, one of which was dominated by the blue coral H. coerulea. Dominance of the blue coral in other Indo-Pacific reefs has been previously reported and has been attributed to high growth, high thermal tolerance and its capacity of inhibiting scleractinian coral larval recruitment54,55. Under the present scenario of climate change, communities dominated by H. coerulea might become increasingly common, but to date, there is little information if H. coerulea dominated communities might sustain similar ecosystem functions as scleractinian dominated reefs55. Here, we show that the community dominated by H. coerulea (Ba2) presented comparable benthic and fish functional diversity to the scleractinian-dominated regime (BuS). However, we would like to note that our surveys were one-time diurnal surveys and therefore a temporal data series is required to analyse the community temporal trajectories and temporal changes in functional diversity within sites. Such data would provide further insights on whether some of the observed regimes (i.e., dominated by Heliopora) have long-term negative functional impacts, or if they are just new regimes that can sustain key ecosystem functions (e.g., reef accretion, structural complexity).

The approach used also allowed us to identify two regimes that were dominated by potentially proliferating non-calcifying invertebrates displaying fast-growths and short lifespans. The proliferation of invertebrates able to overgrow live corals such as ascidians, sponges, or some soft corals such as the opportunistic xeniids has been previously linked to the degradation of environmental conditions56,57,58,82. The different clusters identified were considered hereafter as compositionally different community regimes and were used for the subsequent description of the communities using high taxonomic resolution data and functional analysis.

Functional space and functional indices

The functional richness was calculated as the volume within the multidimensional functional space enclosing all the FE in a specific community, where each species is placed according to their functional niche83,84. First, a species dissimilarity matrix was built using the Gower’s distance85. This distance was first implemented in functional diversity analyses by Pavoine et al. (2009) due to its capacity of dealing with different types of traits (continuous, ordinal and categorical), its efficiency in dealing with missing data as well as allowing inclusion of variable weights86. Nowadays, it is one of the most commonly used distances in functional diversity analysis, and is specially recommended to detect changes in marked different communities87. Then, a Principal Coordinates Analysis (PCoA) was performed using the previous dissimilarity matrix. In order to select the number of PCoA axis that would result in the best functional space, which needs to be congruent with the initial functional distance, we computed the mean squared deviations (mSD) of functional spaces with multiple axis (up to 10), in which lower mSD represents a higher quality of the functional space84. After examination of mSDs, we selected four axis to build both of our functional spaces (for benthos and fish), since adding a fifth axis only weakly increased the quality of the functional spaces (Supplementary Figure 3, Supplementary Figure 4). The functional space and the mSD values were computed using the R function quality_funct_space, developed by Maire et al.84.

The number of FE (FErichness) was calculated to explore the functional diversity. We also computed the community-weighted means of trait values (CMW) using the dbFD function from the FD R package88. The CMW provides information on functional composition by identifying the most common value traits in a specific community.