Beyond physical control: Macrofauna community diversity across sandy beaches and its relationship with secondary production

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Estuarine, Coastal and Shelf Science 277 (2022) 108083

Available online 23 September 2022

Beyond physical control: Macrofauna community diversity across sandy beaches and its relationship with secondary production

Iv´an F. Rodil

^a^,^*

, Mariano Lastra

^b

aInstituto Universitario de Investigaciones Marinas (INMAR), Universidad de C´adiz, 11510, Puerto Real, C´adiz, Spain

bCentro de Investigaci´on Marina (CIM-UVIGO), Universidad de Vigo, 36310, Vigo, Spain

A R T I C L E I N F O Keywords:

Ecosystem functioning Secondary production Shannon diversity Species richness Sea surface temperature β-diversity

A B S T R A C T

Our understanding of the response of macrofauna diversity patterns to the variability of sandy beaches across spatial scales is limited. Defining relationships between diversity and ecosystem productivity is key to understanding the ecological consequences of the current global rates of biodiversity loss. Here, we conducted a study across a large spatial gradient of 39 sandy beaches involving a wide range of environmental conditions and macrobenthic diversity to (1) explore macrofauna diversity patterns (2) estimate secondary production and (3) quantify how much of the variability in beach secondary production can be explained by macrofauna diversity.

Beach macrofauna showed a clear increase in α-diversity across a beach geographic gradient linked to oceanographic conditions. Partitioning of β-diversity implied the replacement of some species by others between beaches (i.e. spatial turnover) instead of a process of species loss (or gain) from beach to beach (i.e. nestedness).

Variance partitioning analyses revealed that environmental and oceanographic variables (i.e., sea surface temperature, beach size, slope, and exposure rate), but also macrofauna diversity (i.e., species richness and Shannon index), largely determine beach secondary production. We showed that an increase in macrofauna diversity enhances beach secondary production, promoting energy transfer across trophic levels. The positive exponential relationship between macrofauna diversity and secondary production supports the idea that macrofauna plays an essential role in maintaining beaches as productive coastal ecosystems. Consequently, macrofauna diversity loss due to the ongoing shoreline recession and coastal occupation, exacerbated by climate change might cause exponential reductions in beach secondary production, which would affect the functioning of these sea-land interface areas.

1. Introduction

Sandy beaches dominate the world’s open coastlines and provide significant socio-economic benefits, such as coastal protection, nutrient cycling, and contribution to the health of fisheries (Defeo et al., 2009;

Harris and Defeo, 2022). Simultaneously, sandy beaches constitute one of the most heavily developed and populated coastal zones in the world.

Cascading interactions between sea-level rise linked to climate change and coastal occupation are resulting in dramatic changes in the stability of shorelines on a global scale (Vousdoukas et al., 2020; Barnard et al., 2021), which raises serious concerns about the detrimental effects these alterations have, not only on socio-economic structure but also on the diversity of sandy coastlines (Barnard et al., 2021). Sandy beaches are home to a unique and well-adapted diversity that provides irreplaceable ecosystem functions (Barboza and Defeo, 2015; Schooler et al., 2017).

Specifically, beach macrobenthos is a diversified community that has been the major focus of beach ecology studies over the past decades, and clear evidence of macrofauna community variability due to physical and nutritional variables has been documented at different spatio-temporal scales (Defeo and McLachlan, 2005; Ortega-Cisneros et al., 2011;

Rodil et al., 2012; Barboza and Defeo, 2015). The global scale of the diversity loss of sandy coastlines calls for research studies to establish how diversity matters across spatial scales and how it can respond to change.

The macrofauna community of sandy beaches is physically controlled by environmental variables, and it is well known that beach morphodynamics (i.e., the interaction between waves, tides, and sediment type) primarily structures the beach macrofauna community (Rodil and Lastra, 2004; Defeo and McLachlan, 2005; Barboza et al., 2012). However, few studies have simultaneously evaluated the effects

* Corresponding author.

E-mail address: [email protected] (I.F. Rodil).

Contents lists available at ScienceDirect

Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier.com/locate/ecss

https://doi.org/10.1016/j.ecss.2022.108083

Received 5 April 2022; Received in revised form 15 September 2022; Accepted 18 September 2022

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of abiotic and biotic factors on community structure (Ortega-Cisneros et al., 2011; Rodil et al., 2012) even though there is strong evidence to support the idea that biotic interactions play a significant role in structuring the macrofaunal communities of exposed sandy beaches (Defeo et al., 1997; Dugan et al., 2004). The analysis of faunal community diversity associated with any marine or terrestrial system is valuable because it allows for testing different hypotheses regarding the processes governing species distribution and maintaining diversity (Baselga, 2010). Furthermore, the effects of diversity on ecosystem functioning can only be fully understood by including biogeographic perspectives (Hooper et al., 2005). Hence, knowledge of macrofauna diversity patterns across broad spatial scales is important for better management decisions and conservation practices in marine habitats (Airoldi et al., 2008).

Secondary production by benthic invertebrates has been successfully used to evaluate ecosystem processes, assess the impact of environmental change, and understand function-diversity relationships in different coastal habitats (Wong et al., 2011; Dolbeth et al., 2012; Rodil et al., 2020). Several experimental studies have acknowledged that a more diverse consumer assemblage can increase community biomass production (Naeem et al., 2000; Duffy et al., 2003; Gamfeldt et al., 2005), suggesting that consumer diversity can be a strong driver of productivity (Duffy et al., 2017). However, the majority of the marine studies have been limited to small-scale or controlled experimental conditions (but see Rodil et al., 2022), and little is known about the effect of faunal diversity on secondary production in natural coastal ecosystems. Beach macrofauna process large quantities of organic material, recycle nutrients, and constitute a fundamental food resource for higher trophic levels such as fish and shorebirds, playing a key role in the transfer of energy within coastal food webs (Dugan et al., 2003;

Petracco et al., 2003; Schlacher et al., 2017). However, little is known about secondary production in sandy beach ecosystems, and no studies have examined the potential role of macrofauna community diversity on beach secondary production.

Here, we examined differences in macrofauna diversity patterns and secondary production across 39 exposed sandy beaches over a large temperate geographic gradient influenced by a wide range of abiotic factors and oceanographic conditions related to primary productivity.

Researching a large spatial scale, as in the present study, is relevant when it comes to analysing the composition of beach macrofauna communities since it provides an ideal natural system to explore diversity patterns (α- and β-diversity) and examine the relationship between macrofauna community diversity and secondary production. We aim to (1) explore the macrofauna diversity patterns across a large geographic extent of exposed sandy beaches, (2) estimate macrofauna secondary production in exposed sandy beaches and quantify how much of the variability in secondary production can be explained by macrofauna diversity after controlling a set of environmental variables, and (3) determine the potential relationship between macrofauna community diversity and ecosystem secondary production. We also compared the secondary production between sandy beaches and selected data of seafloor habitats obtained from the literature to contextualize the importance of beach secondary production across a range of different coastal habitats.

2. Material and methods

2.1. Study sites, sampling procedure, and data compilation

We compiled a large data set (see Lastra et al., 2006; Rodil et al., 2012) that included detailed information on the structure of the macrofauna community and the main environmental variables (Supple- mentary data, Tables S1–S4) from 39 sandy beaches that cover a large geographic extent along the northern margin of Spain (Fig. 1). This coast is affected by a seasonal oceanographic event (i.e., cold, nutrient-rich upwelling seawater), which stimulates primary production (chlor- ophyll-a) during the spring and summer seasons (Rodil et al., 2012). Sea surface temperature is strongly related (p < 0.001) to the geographic beach positioning (Fig. 1). Hence, the coldest nutrient-rich upwelling seawater was found on most western beaches.

At each beach, one sediment core (0.05 m², 15 cm deep) was collected in the low tide period (spring tides) at 10 equally spaced levels, from above the drift line to the swash zone, along 6 randomly chosen transects (n = 60). Samples were sieved (1 mm) and the macrofauna was sorted, identified, and counted. The number of individuals (per m⁻²) and the ash-free dry mass (AFDM; mg m⁻²) were calculated as the

Fig. 1. Map of the Spanish northern shoreline showing the location of the 39 sandy beaches (A), plot showing the significant relationship of the mean sea surface temperature anomaly (standardized as zero mean and unit variance) and the geographic beach extent (B), and picture of a typical sandy beach (C). Beach order follows a geographic longitude (West to East) gradient (Table 1): 1: Rostro; 2: Area Longa; 3: Carnota; 4: Louro; 5: Traba; 6: Corrubedo; 7: Lanzada; 8: Xu˜no; 9:

América; 10: Seiruga; 11: Rodas; 12: Baldaio; 13: Barrañán; 14: Doniños; 15: Frouxeira; 16: Bares; 17: San Román; 18: Viveiro; 19: Llás; 20: San Cosme; 21:

Pe˜narronda; 22: Otur; 23: San Pedro; 24: Xag´o; 25: Xivares; 26: Espasa; 27: Vega; 28: Toranda; 29: Andrín; 30: Oyambre; 31: Liencres; 32: Langre; 33: Berria; 34:

Laredo; 35: Salvaje; 36: Bakio; 37: Laga; 38: Zarautz; 39: Hendaya.

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average value of the six transects. Samples for the determination of sedimentary and morphodynamic characteristics were collected at each across-shore level along 3 transects (n = 30). Different abiotic variables were measured across all beaches (Table S1), including beach characteristics (length, width, and slope), sedimentary variables (water con- tent, mean grain size, and shear strength), wave characteristics (height and period), and beach morphodynamic variables (relative tide range, exposure rate, and Dean’s index). The beach slope was determined by Emery’s profiling technique (Emery, 1961). Mean grain size (MGS) was calculated using a Coulter LS 200 laser diffraction particle size analyser (mm). Shear strength (SS) explored the sediment compacting force using a Pilcon shear vane tester (kilopascals). Relative tide range (RTR) indicated the importance of tides versus waves in controlling morphodynamics (RTR = TR/Hb, where TR is spring tide range and Hb is breaker height, both in m). Dean’s index (Ω) was used to characterize the beach morphodynamic type (Ω = (Hb/Ws) * Tb, where Ws is sand fall velocity (m s⁻¹) and Tb is wave period (s)), and the 20-point exposure rating system was used to estimate the wave exposure rate at each beach (McLachlan and Brown, 2006). Sampling was performed at the end of the summer (i.e., September and October from 1995 to 1999) on all beaches (Fig. 1), thus reducing biotic and abiotic variability linked to the seasonal cycle, and interannual beach variability was non-significant (see Lastra et al., 2006).

Mean and maximum sea surface temperature and chlorophyll-a es- timates (standardized as zero mean and unit variance) were obtained from Bio-Oracle (http://www.oracle.ugent.be/) by averaging estimates from monthly climatology over seven consecutive years. These estimates were used as proxies of seasonality and temporal variation in food and nutrient supply due to oceanographic conditions (Rodil et al., 2012;

Tyberghein et al., 2012).

2.2. Macrofauna community diversity metrics

We used a combination of diversity metrics to describe the complexity of the macrofauna communities concerning the number and abundance of the main taxa across beaches (i.e., α-diversity). Species richness (SR), defined as the mean number of taxa per beach (i.e., all species, rare or common, count equally), and species diversity measured with the Shannon-Wiener log_ebased index (H^′) were calculated. We used beta diversity (β) as a multiple-site similarity measurement, inde- pendent of patterns of richness to examine the variation in macrofaunal species composition across beaches, taking into account the identities of all species (Baselga, 2010). We decomposed β-diversity into nestedness-resultant (βnes) and Simpson pairwise (βsim) dissimilarities.

The former implies a process of species loss, whereas the latter is a measure of the turnover or replacement of species between sites (Base- lga, 2010).

We examined the contribution of rare species (i.e., species with either a restricted range of distribution or low abundance) to beach communities. Species restricted to a single beach were classified as unique, and species found only on two beaches as duplicates (Ellingsen et al., 2007). Here, ‘range size’ is expressed as the number of beaches occupied by a species within the whole area, and species present at ≥20 of the 39 beach sites are referred to as the most widespread species.

2.3. Macrofauna secondary production

We estimated secondary production using the empirical model of Edgar (1990). The temperature range on which the model is based (5–30 ^◦C) is appropriate for our study, and the model is not based on data related to mean annual biomass (Wong, 2018):

P = 0.0049B^0.80T0.89

where P is the production of an individual macrobenthic animal (μg C day⁻¹), B is body mass (μg AFDM), and T is water temperature (^◦C). We

calculated body mass by dividing the total AFDM per taxon by the total abundance of that taxon (Wong, 2018). Daily secondary production for each taxon was estimated by multiplying the mean abundance per taxonomic group (m⁻²) and referred to as Ptaxon (mg C m⁻²d⁻¹) to be used for multivariate analyses (i.e., the productivity of the macrofauna assemblages). Then, the daily secondary production of the macrofauna community was estimated by summing the production of each taxonomic group and referred to as Ptotal.

We contextualized the importance of our estimates of beach secondary production by comparing different coastal environment studies (including bare sediments and habitats with large biogenic structures such as seagrass meadows or mussel reefs) using average estimates of secondary production (minimum-maximum values) and with a similar model approach.

2.4. Statistical analysis

We applied distance-based linear models (DistLM, stepwise selection procedure and R²criteria, 4999 permutations) to ascertain how much of the overall change (variance partitioning) in secondary production of the macrofauna assemblages (Ptaxon) could be explained by diversity indices (i.e., SR and Shannon-Hʹ), while statistically controlling the abiotic covariates on the beaches (i.e., length, width and slope, sedimentary variables, wave characteristics, and morphodynamic variables). The distribution of all the variables was examined (Draftsman plot) to assess skewness and correlations, and log (x+1) transformations were performed when needed (Table S1). Distance-based redundancy analysis (dbRDA) was performed to visualize the position of the beaches

by Ptaxon fitted to the significant predictor variables. The macrofauna

taxa that mostly contributed to the similarity in the productivity among beaches were identified using SIMPER analysis. Analyses were performed using PRIMER7 (Clarke and Gorley, 2015). Calculations for α-diversity indices were performed using the DIVERSE function.

We assessed differences in species assemblages across all beaches by examining total β-diversity (Sørensen-based multiple-site dissimilarity;

βsor), spatial turnover (βsim), and nestedness (βnes, which is calculated:

βnes = βsor – βsim) components compiling a presence/absence matrix for the species occurring on each beach. Analyses were carried out in R 4.0.4. (R Core R Development Core Team, 2021) using the functions within the “betapart” package (Baselga and Orme, 2012; Baselga et al., 2018).

We separately plotted sea surface temperature, diversity metrics (SR and H^′), and secondary production across the longitudinal geographic extent (from west to east) to establish potential trends linked to the oceanographic conditions of the coastal area. The normality (Shapiro test) and the constant variance test of the residuals were evaluated (SIGMAPLOT 14.0). We used generalized linear models to establish relationships between secondary production and diversity metrics (SR and H^′) based on the Gamma family (log-link) with a dispersion of 1 due to the exponential relationship. The normality (Shapiro test) and potential autocorrelation (Durbin-Watson test) of the residuals were evaluated.

Models were built using R 4.0.4 software (R Core R Development Core Team, 2021).

3. Results

3.1. Macrofauna community diversity across the geographic extent of sandy beaches

A total of 30385 macroinvertebrate individuals and 94 different taxa were collected (36 crustaceans, 32 polychaetes, 3 molluscs, 16 insects, and 7 others) on the 39 beaches (Fig. 2A–B; Tables S2–S3). The most diverse assemblage corresponded to Carnota (39 species, H’ = 3.09) with 7 species contributing to > 73% of the beach similarity, and the most uneven beach, as far as diversity is concerned, was Salvaje (J’ = 0.627, H’ = 1.44) with only one species (isopod Eurydice pulchra)

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contributing to 80% of the similarity (Table 1; Table S4). As happens in other systems (Ellingsen et al., 2007), most of the individuals collected belonged to a few abundant species and most of the species were represented by a small number of individuals (Fig. 2B–C). Two of the most abundant species, the crustaceans Eurydice pulchra and Pontocrates are- narius, were present on all the beaches, with a combined total of 13382 (>44% of the total abundance across beaches) individuals (11250 and 2132, respectively; see Tables S3–S4) (Fig. 2A–B). The polychaete Sco- lelepis squamata and the amphipod Talitrus saltator were the second (4062 counts, 36 beaches) and third (3524 counts, 25 beaches) most abundant species, respectively (Fig. 2A–B, Table S3). Twelve typical beach macroinvertebrate species were the most widespread (i.e., present at ≥20 of the 39 sites; F species were restricted to only one or two beaches cited to one or two beaches only (37 unique and 11 duplicates;

Fig. 2C). The maximum abundance of all unique species was 80 individuals and the maximum abundance of all duplicates was 82 individuals (Fig. 2C–D, Table S3). This group of restricted-range species represented only 0.53% of all individuals. As a result, there was a strong positive relationship (p < 0.001; R²adj = 0.63) between the mean abundance at beaches where species were found and the number of beaches occupied (Fig. 2D).

The oceanographic-related geographic pattern (Fig. 1) affected significantly the beach community structure, including macrofauna abundance (p < 0.001; R²adj =0.25) and AFDM (p < 0.01; R²adj =0.36), with higher values corresponding to the most western beaches (Fig. S1;

Table 1). Similarly, the α-diversity metrics (i.e., SR, and H^′) increased linearly (p < 0.05) following the geographic gradient towards the western area (Fig. 3A–B). Our results showed that total β-diversity (βsor

=0.90) was mainly due to the replacement of species (βsim = 0.85; i.e., turnover) rather than to species loss (βnes = 0.05; i.e., nestedness) across beaches (Fig. S2). The range of evenness was similar across beaches (J’

=0.733–0.935), except for Salvaje beach (Table 1), and there was not a

strong significant relationship between community evenness and the geographic gradient (Fig. S3).

3.2. Variance partitioning of the macrofauna assemblage secondary production

Macrofauna secondary production (Ptotal) ranged between 0.46 (Laga, #37) and 12.61 (Carnota, #3) mg AFDM m⁻²d⁻¹(Table 1, Table S5), and those beaches with the highest P_totalwere located in the western area (Table 1, Fig. 3C). The DistLM model for the secondary production of the macrofauna assemblages (Ptaxon) explained 43.0% of the variance in the data (Table 2). Beach length and width, mean seawater, and beach face slope combined were retained as significant determinants of the Ptaxon (20.5% of the total variance). However, the variables explaining individually the largest significant part of the variation observed were exposure rate (ER = 11.6%) and the number of taxa (SR = 10.8%) (Table 2). The first 2 dbRDA axes captured most of the salient patterns in the fitted model with 67.1% of the fitted variability, and 28.8% of the total variation in the data cloud (Fig. 4A). The dbRDA displayed a clear geographic distribution of the sandy beaches linking the number of taxa and western beaches (Fig. 4A). Observation of taxon scores relative to beach sites provided insight into taxa contributing to beach differences in community structure and their relationships to environmental conditions and geographic distribution (Fig. 4B). Similarly, when we used the H^′-index instead of the number of taxa, the contribution of H^′was the largest among all the variables, i.e., 11.3% (Fig. S4; Table S6).

3.3. Macrofauna community secondary production and diversity- production relationships

Total secondary production (Ptotal) of the macrofauna community Fig. 2. Plots showing (A) counts for the main taxa, (B) the macrofauna range size, (C) the distribution of taxa range sizes, and (D) the mean (logx+1) macrofauna abundance (counts) versus range size across all the beaches. Range size is the number of beaches occupied by a species out of a total of 39 beaches. ‘Others’ refers to a combination of species with low total counts (<80 counts per taxa) and small range size (<9). Taxa information is in the Supplementary material (Table S3).

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was exponentially (p < 0.001) enhanced as α-diversity (i.e., SR and Hʹ) increased across the geographic extent of sandy beaches (Fig. 5A–B).

Two beaches (Carnota and Seiruga, respectively) showed the highest secondary production (Table 1; Fig. 3C). Carnota showed the highest biomass and diversity (32 species and H’ = 3.09) among all the beaches with a high representation of crustaceans (mainly isopods), polychaetes (Scolelepis squamata), and bivalves (Donax trunculus) (Table S3). Seiruga showed the highest abundance (mainly crustacean species such as the amphipod Talitrus saltator and the isopod Eurydice pulchra, but also oligochaete worms) (Table S3), the second-highest biomass among all beaches, and high diversity (18 species and H’ = 2.59) (Table 1, Fig. 5).

Our study showed that beach secondary production is higher than deep-sea estimates and similar to other sedimentary habitats such as tidal flats, but much lower when compared to the productivity of coastal systems with large biogenic structures (seagrass, kelps, and mussel/

oyster reefs) (Fig. 6: Table S7).

4. Discussion

4.1. Macrofauna diversity patterns across a geographic extent of exposed sandy beaches

Exposed sandy beaches, traditionally regarded as marine deserts in terms of faunal abundance and taxonomic diversity, are more diverse

ecosystems than previously anticipated, with a high level of endemic species (Harris et al., 2014; present study). Oceanographic variables such as temperature, salinity, and nutrients in the nearshore water column have been proved significant predictors that structure beach macrofauna communities (abundance and biomass) and species richness worldwide (Ortega-Cisneros et al., 2011; Rodil et al., 2012; Barboza and Defeo, 2015). We describe the complexity of beach macrofauna communities using a variety of α-diversity metrics (i.e., SR and H^′). We showed that α-diversity increased across the geographic extent of beaches following an oceanographic-related gradient of decreasing temperature and increasing water column nutrients, which highlights the importance of the oceanographic conditions for beach community diversity.

The assessment of local beach α-diversity was complemented with the spatial differentiation of their communities (i.e., β-diversity) by using the two typical patterns of β-diversity, namely ‘turnover’ and

‘nestedness’ (Baselga, 2010). Nestedness (βnes) occurs when sites with low diversity are subsets of sites with high diversity, reflecting species loss (or gain) as a result of significant local environmental and morphodynamic features (Baselga, 2010; Checon et al., 2018). In our study, there was a clear variation in environments and communities along the oceanographic-related spatial gradient. Hence, turnover (βsim) was the dominant component, suggesting the replacement of some species by others as a consequence of environmental, spatial, and historical Table 1

Mean (±standard error) macrofauna secondary production and α-diversity (species richness, and Shannon diversity indices) of the macrofauna communities across the 39 beaches studied.

Beach Code# Geographic coordinates Abundance (indm⁻²)^a AFDM (mg m⁻²)^a Production (mg C m⁻²d⁻¹) α-diversity

Latitude Longitude mean Se mean se mean se SR H^′

Rostro 1 42.9509 −9.2180 197.1 72.6 49.5 19.7 0.83 0.29 16 2.50

Area Longa 2 43.1389 −9.1714 248.1 149.4 275.9 160.6 3.12 1.84 19 2.51

Carnota 3 42.8186 −9.1084 424.2 100.7 1262.9 473.5 12.61 4.23 32 3.09

Louro 4 42.7437 −9.0908 148.5 42.7 216.9 89.3 2.84 1.22 22 2.74

Traba 5 43.1964 −9.0387 212.8 92.7 94.6 51.1 1.22 0.62 17 2.58

Corrubedo 6 42.5099 −9.0058 631.0 127.9 481.4 205.5 4.78 1.44 27 2.84

Lanzada 7 42.4231 −8.9251 548.8 388.7 278.5 130.7 3.36 1.49 21 2.57

Xu˜no 8 42.5698 −8.8913 98.2 18.5 135.8 117.3 1.11 0.75 20 2.64

Am´erica 9 42.0942 −8.8860 472.5 235.1 251.9 126.2 2.99 1.16 20 2.49

Seiruga 10 43.3167 −8.8588 1128.9 756.3 688.5 616.4 12.09 10.87 18 2.59

Rodas 11 42.2574 −8.8261 417.6 144.2 98.2 29.7 1.73 0.53 15 2.42

Baldaio 12 43.3197 −8.6998 252.4 60.1 143.1 41.0 1.93 0.54 23 2.80

Barra˜n´an 13 43.3503 −8.5343 135.9 58.4 106.7 39.3 1.01 0.32 18 2.54

Doni˜nos 14 43.4887 −8.3215 209.5 49.9 161.7 68.3 1.79 0.55 18 2.59

Frouxeira 15 43.6135 −8.1586 740.3 547.5 105.8 45.8 2.07 1.09 17 2.36

Bares 16 43.7669 −7.6769 128.5 30.5 319.6 91.1 3.13 0.84 22 2.66

San Rom´an 17 43.7175 −7.6188 380.6 159.7 154.6 55.6 2.04 0.48 19 2.49

Vivero 18 43.7119 −7.5583 153.5 38.5 239.9 117.1 2.06 0.75 24 2.82

Ll´as 19 43.5797 −7.2595 137.8 39.9 62.1 19.4 0.99 0.29 21 2.62

San Cosme 20 43.5584 −7.1770 142.9 30.7 160.6 67.7 2.01 0.73 23 2.77

Pe˜narronda 21 43.5580 −6.9835 124.2 23.7 105.6 22.6 1.43 0.23 15 2.39

Otur 22 43.5536 −6.5939 67.3 11.4 64.0 39.2 0.63 0.31 12 2.19

San Pedro 23 43.5769 −6.2176 338.0 49.1 221.2 80.6 2.92 0.60 17 2.34

Xag´o 24 43.6342 −5.9175 410.9 129.0 283.0 73.6 3.58 0.81 14 2.11

Xivares 25 43.5704 −5.7177 395.3 123.1 250.0 78.6 3.54 0.89 16 2.33

Espasa 26 43.4758 −5.2175 178.5 44.0 55.1 10.9 1.04 0.21 16 2.30

Vega 27 43.4798 −5.1385 182.8 40.5 124.5 70.0 1.93 0.96 18 2.40

Toranda 28 43.4711 −4.8541 160.2 23.8 284.1 163.9 2.85 1.34 14 2.29

Andrín 29 43.4777 −4.6994 362.3 54.7 112.9 21.2 1.82 0.33 11 2.09

Oyambre 30 43.3900 −4.3255 107.2 39.8 121.7 38.3 1.69 0.44 14 2.41

Liencres 31 43.4634 −3.9651 31.0 12.6 132.0 80.0 0.77 0.61 12 2.32

Langre 32 43.4758 −3.7344 59.3 13.0 44.5 16.0 0.88 0.27 16 2.41

Berria 33 43.4639 −3.4586 62.3 20.3 40.9 12.4 0.67 0.17 16 2.48

Laredo 34 43.4159 −3.4530 142.5 29.9 291.2 103.2 4.11 1.23 28 2.91

Salvaje 35 43.3906 −2.9938 328.7 138.6 72.0 23.3 1.68 0.59 10 1.44

Bakio 36 43.4578 −2.8069 75.3 48.0 30.2 14.6 0.58 0.27 9 1.81

Laga 37 43.4091 −2.6026 45.0 10.2 26.9 8.2 0.46 0.11 14 2.22

Zarautz 38 43.3157 −2.1681 155.8 39.7 19.1 3.6 0.51 0.10 10 1.78

Hendaya 39 43.3964 −1.7664 127.9 18.0 178.1 61.6 2.52 0.83 17 2.38

Beaches were ordered by decreasing geographic longitude and categorical code number were assigned accordingly.

aData compiled from Rodil et al., (2012).

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differences among sites (Baselga, 2010). It has been previously shown that the lack of nestedness in sandy beaches is a result of a species filtering process along an environmental gradient due to differences in the tolerance of species to that gradient (Checon et al., 2018). This suggests beach species sorting by an interplay of environmental and

spatial features and processes related to dispersal modes, rather than by local environmental features exclusively (Checon et al., 2018; Rodil et al., 2018). The distinction between the two components of β-diversity has been proved relevant to habitat-management planning because nestedness suggests that conservation should target the richest sites, whereas turnover would prioritize the conservation of multiple sites (Baselga, 2010). In our study, the dominant turnover component suggests that conservation efforts should be devoted to a large number of different beaches, even when some individual beach sites are not particularly diverse.

4.2. Variance partitioning of secondary production across sandy beaches Given that beach macrofauna communities are closely related to beach morphodynamic characteristics (Rodil and Lastra, 2004; Defeo and McLachlan, 2005; Barboza et al., 2012), secondary production was expected to be limited by typical beach abiotic variables such as wave exposure, slope or beach size (e.g., Brazeiro, 2001; Rodil and Lastra, 2004; Defeo and McLachlan, 2005; present study). Our study also showed that secondary production was related to a geographic gradient of food availability linked to specific oceanographic conditions (i.e., decreasing sea surface temperature) already known to be capable of largely structuring beach macrofauna communities in terms of abundance and biomass (Lastra et al., 2006; Rodil et al., 2012). Importantly, although variance partitioning results implied that a set of beach physical and oceanographic factors affected secondary production (beach size, slope, exposure rate, or sea surface temperature), secondary production was strongly related to macrofauna diversity. Hence, the number of species (and Shannon-Hʹ) alone explained more of the secondary production variability than the majority of the individual abiotic (and similar to exposure rate) or oceanographic variables.

Among all beaches, Seiruga and Carnota showed the highest secondary production. The highest macrofauna abundance and biomass in Seiruga were linked to the presence of sandhopper amphipods (mainly Talitrus saltator). Active sandhoppers can gather in high numbers in the upper-shore levels, typically associated with algal wrack, becoming an essential prey for shorebirds (Dugan et al., 2003; Schlacher et al., 2017), and thus an important component of energy flow and organic matter cycling in sandy beaches. Carnota showed the highest diversity among all the beaches with a high occurrence of different species of poly- chaetes, bivalves, and crustaceans. The wedge clam Donax trunculus was the main responsible for the high macrofauna biomass and secondary production (Tables S3–S5). Wedge clams are an important constituent of the low intertidal benthic fauna inhabiting sandy beaches, and an important food source for predators such as crabs, shorebirds, and fish Fig. 3. Plots showing the significant relationships of (A) the log(x+1)-trans-

formed number of species (SR), (B) Shannon-H^′, and (C) ln(x+1)-transformed secondary production (mg C m⁻²d⁻¹) and the geographic extent of the 39 beaches (see Fig. 1 for beach names and locations). Carnota (#3) and Seiruga (#10) were the beaches with the highest estimates of secondary production (Table 1).

Table 2

Results of variation partitioning analysis (DistLM) quantifying the sequential effects (step-wise selection, 4999 permutations) of the specific contribution of macrofauna diversity (i.e., species richness), oceanographic (mean sea surface temperature anomaly), and beach environmental predictors (i.e., exposure rate, beach length and width, log-transformed slope) on macrofauna secondary production (i.e., taxon-specific assemblages; Ptaxon) across the 39 beaches studied.

Total R²in bold. The position of the beach sites and main taxa according to predictor variables was plotted as dbRDA (Fig. 4). Exposure rate (ER) is a 20- point rating system (i.e., wave action, surf zone width, % very fine sand, me- dian particle diameter, and slope, depth of reduced layers, stable burrows) to estimate the wave exposure rate on a beach (see McLachlan and Brown, 2006).

Variable R² Pseudo-F P Prop.

ER 0.116 4.86 <0.001 11.6

SR 0.224 5.01 <0.001 10.8

Length 0.298 3.71 <0.001 7.4

Width 0.367 3.66 <0.001 6.8

LOG(Slope) 0.397 1.67 0.046 3.1

sstmean 0.430 1.80 0.028 3.2

43.0

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(Peterson et al., 2006). The presence of macrofauna, especially molluscs and polychaetes, in the surf and shallow subtidal zones of the beach is typically higher than in the intertidal or upper beach parts (Corte et al., 2022), highlighting the importance of the submerged zone for the beach macrofauna diversity. Since our study was limited to the beach intertidal area, we suggest that the secondary production of the sandy beaches will be likely enhanced when including the whole across-shore beach gradient.

4.3. Secondary production and diversity-production relationships across sandy beaches

Sandy beaches are coastal habitats with very low productivity, typically characterized as physically-controlled coastal environments with no large biomass of permanent primary producers associated with the sandy shore (McLachlan and Brown, 2006). Consequently, there is a lack of studies on the productivity of beach macrofauna communities, and the few existing studies on secondary production focus on large invertebrate species (Petracco et al., 2003, 2019; Veloso et al., 2003).

However, sandy beaches can be hotspots of biodiversity with large biomass of macroinvertebrates that contribute to regulating trophic connectivity and coastal functioning (Ince et al., 2007; Spiller et al., 2010; Mellbrand et al., 2011). The beach macrofauna community me- diates the energy flow from allochthonous primary producers (mainly phytoplankton and macroalgae cast ashore as wrack) and carrion to higher trophic levels (fish, shorebirds, and other large vertebrates), which functionally links marine with terrestrial ecosystems (Dugan et al., 2003; Spiller et al., 2010; Schlacher et al., 2017). Hence, beach secondary production can trigger a trophic cascade with multiple effects on coastal food webs and terrestrial ecosystems.

Our study confirms the low secondary production of sandy beaches when compared with highly productive coastal vegetated habitats such as seagrass meadows, kelp forests, or coastal habitats with large biogenic communities such as oyster or mussel reefs (Fig. 6). However, sandy beaches are far from being marine deserts, and estimates of beach

secondary production are higher than deep-sea estimates and similar to other sedimentary intertidal habitats, especially when considering beaches with large-sized crustaceans (e.g., Petracco et al., 2003).

Despite the generally low estimates of secondary production in exposed beaches compared to other coastal habitats, importantly our study shows the relevance of beach macrofauna diversity for secondary production. We showed that secondary production was exponentially and positively related to macrofauna community diversity across sandy beaches, suggesting that the higher the diversity of primary consumers, the higher the rates of beach secondary production. Having demon- strated that macrofauna diversity enhances secondary production across a complex natural setting, our field study supports the theoretical pre- diction of a positive influence of macrofauna diversity on community biomass production (Duffy et al., 2017). Recently, a positive exponential relationship between macrofauna diversity and secondary production has been reported for a natural field setting across seagrass meadows (Rodil et al., 2022). We suggest that this type of relationship is a strong ecological pattern across different coastal ecosystems.

The ecological theory proposes that diversity in ecological communities enhances ecosystem functioning by two mechanisms (Loreau and Hector, 2001): (1) more diverse consumer assemblages can contain species with complementary functional traits, releasing each population from the competition and allowing more efficient resource use among species (i.e., complementarity effect), or (2) more diverse assemblages may outperform less diverse assemblages of the same density or biomass of individuals because more diverse assemblages are more likely to include high-performing species (i.e., more efficient or highly productive), resulting in higher ecosystem functioning (i.e., selection effect). A positive selection effect has been previously suggested as the mechanism by which diversity enhances secondary production in seagrass meadows (Rodil et al., 2022). However, our study did not show a significant increase in macrofauna total abundance with increasing diversity (i.e., a positive selection effect) (Fig. S5). On the other hand, complementarity reflects mechanisms such as positive interactions or niche separation (Loreau and Hector, 2001; Gamfeldt et al., 2005). The latter can be Fig. 4. The distance-based redundancy analysis ordination based on macrofauna secondary production with overlaid (A) beach environmental and oceanographic predictors (Table 2), (B), and most abundant taxa (obtained from Fig. 2; see Table S3 for the full names) as vectors to species richness (SR) across beach sites (see Fig. 1). Different colours denote the geographic gradient from western (purple: 1–14) to eastern (orange: 29–39) beaches, and in between (green: 15–28). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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feasibly extrapolated to beach macrofauna communities from the northern coast of Spain. The typical beach community zonation of this geographic region divides the beach profile into the lower intertidal (represented by bivalves and polychaetes), the mid-intertidal below the drift line (cirolanid isopods), and the upper-shore (amphipod sandhoppers and insects) macrofauna levels (Rodil et al., 2006). This classical three-zone beach macrofauna pattern (Jaramillo et al., 1993) also shows complementary functional traits among species (e.g., bivalves are filter feeders whereas amphipods and isopods are scavengers) (Rodil et al., 2012).

5. Conclusions

Macrofauna benthic communities are key organisms for the functioning of coastal environments, especially on sandy shores where beach invertebrates functionally link marine and terrestrial ecosystems.

Although controlled experiments can provide mechanistic insight into diversity-productivity relationships, results are rarely directly appli- cable to complex natural ecosystems. Our study across a large geographic extent of exposed sandy beaches revealed that both macrofauna diversity and secondary production increased following an oceanographic gradient related to food provision. Our study also provided clear evidence that, under natural conditions, macrofauna community diversity is a major driver of beach secondary production,

enhancing energy transfer across trophic levels and ecosystems. We would like to highlight the importance of beach benthic diversity in the functioning of coastal systems under the current global scenario of biodiversity crisis (UNEP, 2021) and with some worrisome reports predicting the disappearance of one-third of the world’s sandy beaches by 2050 (Vousdoukas et al., 2020). The consequences of a receding beach shoreline and the associated decline in macrofauna community diversity might cause exponential reductions in beach secondary production with escalating adverse consequences to coastal food webs and the functioning of these fundamental sea-land interface areas.

CRediT authorship contribution statement

Iv´an F. Rodil: Writing – original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Mariano Lastra: Writing – review & editing, Methodology, Funding acquisition, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Fig. 5. Relationships between macrofauna community secondary production (mg C m⁻²d⁻¹) and (A) species richness and (B) Shannon diversity index (H^′).

Fig. 6. Macrofauna community secondary production average values (mini- mum-maximum) of various seafloor habitats worldwide for datasets that compute on mg C m⁻²d⁻¹. (Deep sea: Degen et al., 2015; exposed sandy beaches: this study; sandy beach with large invertebrates: Petracco et al., 2003;

sand and mud intertidal, muddy and sandy seagrass: Wong, 2018; mixed macrophyte, Fucus-bed, Z. marina seagrass and blue mussel reef: Rodil et al., 2020; Laminaria kelp: Nordehaug and Christie, 2011; Z. marina + Halodule wrightii seagrass mix, salt marsh and oyster reef: Wong et al., 2011). The inner panel is a close-up of those habitats with lower secondary production estimates.

See Table S7 for detailed information on the sites. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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Acknowledgements

This work has been possible thanks to the help and work of all the people that have been part of the benthos team (University of Vigo) over the years. We would like to thank two anonymous reviewers for their positive and constructive comments that improved this manuscript. This study was funded by the Autonomous Government of Galicia, Xunta de Galicia (Projects XUGA30105A98 and XUGA30103A95), and the Min- istry of Science and Technology (REN2002-03119/MAR). IFR is sup- ported by a Ram´on y Cajal Fellowship, MICIU (RYC-2019-026821-I).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.ecss.2022.108083.

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