Study area and benthic community description
The study was conducted on subtidal rocky outcrops (2–26 m depth) of the central and northern parts of the Israeli Mediterranean coastline (Fig. 1a). Most of the Israeli shelf is covered with loose sediment dominated by quartz sand in the nearshore (up to 50 m depth) where patches of rocky outcrops become more abundant toward the north. These rocky outcrops are composed of submerged aeolianite ridges (calcareous cemented sand dune called “Kurkar”) that run parallel to the coastline (Emery et al. 1960; Lipkin and Safriel 1971). The benthic species inhabiting subtidal hard substrates are documented in species checklists and inventories of native and invading species, but the community composition is poorly described (Fishelson 2000; Einav and Israel 2008; Israel and Einav 2017). The macroalgae community is mainly dominated by turf algae along with patches of canopy and erect macroalgae of both native and non-native species (Rilov et al. 2018). It should be noted that the term ‘turf’ suffers from inconsistent and vague definitions in the literature (Connell et al. 2014). Hereafter, we refer to ‘turf’ as a low-lying benthic algal mat shorter than two cm, which is mainly a mixture of filamentous algae (e.g., Polysiphonia spp.) and other heavily grazed algae.
Underwater visual surveys
To determine the contribution of invading bivalves to the local benthic community, and to study their distribution along the Mediterranean Israeli coastline, we conducted large-scale underwater surveys within the framework of the Israel Nature and Parks Authority’s (INPA) marine reserves monitor program “Marine Bioblitz”. The surveys were conducted at four sites: Achziv, Shikmona, Dor-Habonim, and Gdor (hereafter refer to as 'sites', Fig. 1a) during the spring and fall of 2019. To ensure good representation of the rocky outcrops in each site, sampling points (4–13 per site) were predetermined on a bathymetric map and then marked with surface buoys from a small skiff. SCUBA divers used the marks as a starting point and laid longshore line transects along the rock contour. Sampling depth was adapted to the distribution of rocky outcrops in each site and ranged between 2–26 m. Due to the scale of the survey, we trained several survey teams. Before the onset of the surveys, teams practiced and perform cross-calibrations trials between surveyors to validate the reproducibility of the method.
Line transects (n = 263 transects for bivalve density, of which 196 also recorded percent cover of bivalves and other invertebrates) were 10 m long and 2–6 transects were surveyed at each predetermined sampling point. A fit of the line to the local relief (to represent different rocky features such as overhangs, crevices, etc.) was achieved by using several small fisher weights attached to the lines with plastic clips. Lines were surveyed at 0.1 m intervals (100 points per line). At every point along the line, we documented the substrate type (rock, sediment covering the rock, or algae) or invertebrate present to allow percent cover estimation. If an invertebrate was observed, the maximum length was measured using a plastic caliper to allow subsequent estimation of density and size-frequency distributions (Zvuloni and Belmaker 2016). Bivalves were identified to species level (when possible), but other invertebrates (> 1 cm) were classified to a higher taxonomic level (phylum or class) to ensure consistency among samplers (see table S1 in Online Resource 1). In cases where the bivalve shell was closed, it was impossible to discriminate between Spondylus spinosus and Chama pacifica. Since all examined specimens (60) were tentatively identified as S. spinosus, we designated all surveyed ‘rocky oysters’ as S. spinosus. However, we note that this classification may also contain C. pacifica specimens. Efforts are now being made to further clarify the taxonomic affiliation of these ‘rocky oysters'.
Bivalve exclusion experiment
To assess the effects of invading bivalves on the local macroalgae and invertebrate communities an in-situ experiment with three treatments was deployed at the Gdor site (32°24'07.2"N; 34°51'30.3"E). Treatment plots consisted of 0.3 x 0.3 m quadrats with a 0.4 m width buffer zone surrounding each plot to eliminate the effects of bivalves near the main quadrat. The total treated area of each plot and buffer zone that surrounded it was 1.1 m2. The treatments included: (1) ‘bivalve removal’- where all bivalves within a plot boundary and a surrounding buffer zone were physically removed. This treatment removed both the biological and physical effects of bivalves. (2) ‘Bivalve poisoning’-where all bivalves within a plot and surrounding buffer zone were poisoned, retaining their empty shells in place, and thus removing only the biological effects of the bivalve activity. (3) ‘Control plot’ - marked identically to the treatment plots but received no treatment (Fig. 1b).
To increase the statistical power and minimize the confounding effect of plot location and orientation, the experiment was organized in triplets (removal, poison, and control). A total of 16 triplets (48 plots) were positioned at depths of 7–12 m. The distances between the plots within each triplet was ~ 1.5 m. Within each triplet, the plots were deployed in the same orientation, minimizing differences in the effect of light, currents, wave activity, etc. The removal and poisoning treatments were applied simultaneously (maximum of two days apart) in each triplet. The number of bivalves removed or poisoned, as well as their taxonomic affiliation and status (dead or alive) was documented for each plot. Bivalves were physically removed using a hammer and chisel. Poisoning of bivalves was done using 8% formaldehyde injections. For Spondylus spinosus, a four mm small hole was drilled (NEMO hammer drill, NEMO® power tools) in the shell as close as possible to the pallial line, and five mL of formaldehyde was injected into the organism tissue after which the hole was immediately sealed with a small amount of epoxy putty (Aquamend®) to prevent leakage into the environment. A small amount of the epoxy putty was also applied to the anterior of the shell to keep both valves attached after the bivalve death. For the smaller and less armored Pinctada radiata and Malleus regula, two mL of formaldehyde was injected after piercing the shell with a stainless-steel needle. A small amount of epoxy polymer was placed on their byssus threads to prevent detaching after the death. Brachidontes pharaonis was rarely (< 5) observed at the experimental plots and specimens were removed from both removal and poison plots.
Post-treatment monitoring
Algae growth rate
To monitor the effect of the presence of invading bivalves on the total macroalgae growth rate, settlement plates were placed at each plot (total of 48 plots, n = 16 triplets) for 3–5 weeks. To account for seasonal differences in the algal growth rates, this experiment was repeated four times: Spring 2020, Fall 2020, and Spring 2021 (in Spring 2020 two experiments with 8 triplets each were deployed and combined for the analysis). Plates were made of 0.12 x 0.12 m plastic squares attached to the top of an four kg diving weight. Plastic netting was positioned over the plate to prevent grazing by large herbivores (see photo S3 in Online Resource 1). On retrieval, each plate was collected into a zip-lock bag and transferred in a dark cool box to the lab where it was drained and stored at -20 ℃ until analysis.
To quantify the total algae growth rate on each plate, chlorophyll-a was used as a proxy for the photosynthetic biomass. In the lab, the bulk of the algae biomass was scraped into a glass jar using a putty knife, the remaining material was removed using high-pressure jets of NaCl solution (Waterpik, Magic-Jet®, 40 gr L− 1 NaCl) and collected onto a glass fiber filter, GF/D 47 mm diameter (Whatman®). The scrapings and filter were extracted together with the Hot DMSO (Dimethyl Sulfoxide) method as described in Suari et al. (2019), with some modifications. Briefly, the GF/D filter and algae scraping were transferred to 40 mL EPA vials (cat no:9-105, Thermo Fisher Scientific®), eight mL DMSO was added to each vial, and the vials were tightly closed and incubated in the dark at 60℃ for 20 minutes. After cooling to room temperature, 16 mL of 90% buffer acetone was added to each vial, the vial was vigorously mixed using a vortex mixer and stored overnight in the dark at 4℃. Chlorophyll-a fluorescence was measured using a calibrated fluorometer (either TD-700 or Trilogy, Turner designs®) equipped with a non-acidification Chlorophyll-a kit (Welschmeyer 1994).
Macroalga community (photographic survey)
To measure post-treatment shifts in the macroalgae community composition and account for seasonality, five photo-quadrat surveys were conducted during 2020 and 2021. Surveys were conducted only when sea conditions were permissive, and visibility allowed high-quality photography (July 2020, August 2020, November 2020, December 2020, and May 2021). In each survey, a 0.3 x 0.3 m quadrat was placed on the central plot and a set of photos was taken using an underwater camera (tough TG-5, Olympus®) with two adjustable lights (Sea Dragon 2500, SeaLife®). Unidentified algae were collected for identification by Dr. Alvaro Israel. Photo orientation and edges were adjusted using Faststone image viewer (Faststone soft®) and uploaded to a designated ‘source’ (https://coralnet.ucsd.edu/; source name: Algal composition Michmoret) in the CoralNet platform (Beijbom et al. 2015). A set of 100 points was randomly imposed on each photo and each point was categorized as substrate (rock and loose sediment), sessile invertebrates (phylum or class), or algae. Algae were categorized to the lowest taxonomic rank possible (mainly genus, see table S4 in Online Resource 1). Points where the photo was out of focus or too dark for identification and points that fell on the quadrat frame were excluded (median of 11 points per quadrat). Percent cover was calculated as the number of data points for each category divided by the total number of data points after excluding the unidentified points.
Invertebrate surveys
To measure post-treatment shifts in the invertebrate community composition, three surveys were performed 3, 7, and 12 months after the treatments (July 2020, November 2020, and April 2021). All invertebrates larger than 0.5 cm in the main quadrat of each plot were counted and identified to the lowest taxonomic level possible (see summary table S5 in Online Resource 1). In addition to the detailed count-based survey, the percent cover of both algae and invertebrates was concurrently documented. For this, the point intercept method (PIM) was applied on a 0.3 x 0.3 m quadrat with a 3 cm grid (49 points). Algae were classified as one of three categories: crustose coralline algae (CCA), algae shorter than 2 cm, and algae longer than 2 cm. Invertebrates (> 1 cm) were classified to the class or phylum level (see table S6 in Online Resource 1).
Data analysis
Statistical analysis was carried out within the R programming language (version 4.1.1) in the Rstudio environment (version 1.4.1717, (R Core Team 2020)). Data are presented as the mean ± 95% confidence interval for the mean, unless otherwise stated. The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Distribution patterns of the invading bivalves
Percent cover was calculated for each transect as the number of data points listed for a category divided by the total number of points identified for that transect (normally 100). Abundances and size-frequency distributions were deduced using the method of Zvuloni & Belmaker (2016) which transforms point-intercept measurements to unbiased count-based indices. Based on the size of each individual and the configuration of the sampling unit (the number of points and distance between the points) an ‘effectively sampled area’ (ESA, m2) is calculated according to Eq. 1 of Zvuloni & Belmaker (2016):
Eq. 1.\({ESA}_{i}=\bigcup _{k=1}^{n}{\pi r}_{{i }_{\left(k\right)}}^{2}\)
Where ESAi is the effectively sampled area for organism i with a radius ri, k is the sampling point index along the line, n is the total number of sampling points, and U is the union of the areas of all n circles with radius ri along the transect line. ESAi is much larger for large individuals and smaller for small individuals, thereby correcting the inherent bias of the overrepresentation of larger organisms in standard point-sampling techniques. The density of an individual i encountered during a point intercept survey (PIM) can be calculated as 1/ESAi. For example, a single 5 cm bivalve spotted on a 10 m line with 0.1 m intervals will have an ESA of 0.196 m2 and hence a calculated density of 5.11 individuals m− 2. The total density of a certain taxon was calculated for each transect as the sum of the calculated densities of all individuals from that taxon in the transect. For example, in a transect in which we encountered three bivalves with a diameter of 2, 5, and 8 cm, their respective ESAs would be 0.031, 0.196 and, 0.5 m2, and hence, their calculated densities (1/ESAi) would be 32, 5.1, and 2.0 individuals m− 2, respectively, producing a total estimated density of 39.1 individuals m2. The code (R script) for corrected density calculations is provided in the Online Resource 2. Once the unbiased densities of each taxon were calculated for each transect, standard diversity indices could be calculated. Size-frequency distributions were not calculated for Brachidontes pharaonis because the majority of individuals were smaller than the minimum size threshold (1 cm) and for Malleus regula that were spotted only sporadically during the survey. Examples of the sampling form and data table used for the surveys can be found in (Diga 2022).
Effects of the presence of invading bivalves on the macroalgae community
Algae growth rate
Differences in macroalgae growth rates (µg Chl-a m− 2 day− 1) between treatments and months were tested using Friedman non-parametric repeated measure ANOVA on log-transformed data, since the macroalgae growth rate did not meet the assumptions of normality and sphericity.
Macroalga community (photographic survey)
Dissimilarities in the macroalgae community composition between treatments were calculated using log-transformed Bray-Curtis dissimilarity and visualized with nMDS ordination. Significance of the dissimilarities between treatments was tested with ANOSIM test (Clarke et al. 2014). Before analysis, rarely observed taxa (less than 10 counts in all surveys combined) were removed. To analyze the contribution of different taxa to the difference between treatments and the similarity within treatments, a SIMPER procedure was used (Clarke et al. 2014). This procedure identifies the taxa that are likely to be the major contributors to any difference between treatments.
Taxa which accounted for at least 5% of the total cover in at least one of the months and the total algae cover were examined more closely using a Linear Mixed Effect (LME) models with the lme4 package (Bates et al. 2014) and tested for significance with lmerTest package in R. The response was logit-transformed percent cover, and the predictor was treatment. To account for values of zero and one in the data the smallest cover measurable (1%) was added to all values smaller than one or subtracted from values equal to one prior to logit transformation. Plot was used as a random effect (to account for the repeated measure nature of the design):
Eq. 2.\(P.cover \tilde Treatment + \left(1\right|Plot)\)
For taxa that were present in all months (turf and total algae), the month was added as an additional random effect in Eq. 2. Jania spp. was present only in three months, so month was added as a categorical fixed effect in Eq. 2.
Effects of the presence of invading bivalves on the invertebrate community
Invertebrates 𝛂-diversity indices (percent cover, richness, effective number of species, and the total number of individuals) were calculated for each plot and survey. Each parameter was fitted with General Linear Models (GLMs) using the `glmer` function in the lme4 package in R. The predictors were treatment and month while plot was used as a random effect in all models (to account for the repeated measure nature of the design):
Eq. 3.\(Index \tilde Treatment+Month+ \left(1\right|Plot)\)
A Poisson link function was used for count-based parameters (e.g., richness and number of individuals). For diversity and percent cover (logit transformed) the distribution family was Gaussian.
Dissimilarities in the invertebrate community composition between treatments and months were calculated using log-transformed Bray-Curtis and visualized with nMDS ordinations. The significance of treatments and months was tested with ANOSIM.
Disturbed areas (i.e., the treatment plots) tend to have greater variability than that of undisturbed areas (i.e., the control plots). A multivariate dispersion index (MVDISP, Warwick and Clarke 1993) was calculated (on log-transformed Bray-Curtis matrix) to compare the variability of the invertebrate community composition between the treatment in every survey. The average dispersion was calculated for each treatment (group) and tested for significance between treatments and among months with GLM as described in Eq. 3 with a Gaussian distribution.