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Ichthyoplanktonic dataset
Spatial distribution of anchovy early-life stage was estimated from ichthyoplanktonic surveys in the GSA 16 from 2009 to 2012. During each survey, the same systematic sampling was carried out following a regular grid of stations (1/10° × 1/10° on the continental shelf and 1/5° × 1/5° further offshore). Mesozooplanktonic samples were collected in the study area using a bongo net (40-cm opening) towed obliquely from the surface to a 100-m depth, equipped with a 200-µm mesh size net. In each tow, the volume of filtered water was estimated using mechanical flowmeters (General Oceanics Inc., FL, USA). Over the period included in the analyses for anchovy eggs data, the average number of sampling stations was 154.
Samples were immediately fixed after collection and preserved in a 70% alcohol solution for further analysis in laboratory by stereomicroscopy. Anchovy larvae were identified in a land-based laboratory and otoliths were extracted for the age determination. Both sagittae were extracted using insect pins and fixed on a slide using a mounting medium for microscopy. Daily rings were counted at 1000× magnification (oil immersion) starting from the spawning mark31; each otolith was read blind (i.e., with no size or collection location information available) by a single reader43. When all otoliths were completed, the process was repeated. If the difference in age between the 2 readings was {rho }_{min}^{Chl}), where ({rho }_{min}^{Chl}=frac{1}{2}K) and (K) = 0.09 mg m−3, i.e., half of the saturation value for anchovy larvae feeding50.
This idea is justified by the relationship between the pelagic growth rate and the Chlorophyll concentration: only those individuals that travel within Chlorophyll-rich marine waters have a significant survival probability. In this way, we have a criterion to rule out all the final positions of the simulated backward trajectories, which have poor or null Chlorophyll content.
Bioenergetic modeling
DEB represents a reliable and powerful tool to mechanistically describe the whole life cycle of an organism individual performance and to make predictions of life-history traits27, 40,50,51,52. In particular, DEB is a complex mechanistic model that relies on several differential equations that are solved to obtain the final amount of energy allocated to vital functions, such as metabolic maintenance and to growth and reproduction53. Here we adapted this model to the European anchovy and we adopted the standard version of the DEB model, which considers one reserve, one structure compartment and isomorphic growth54.
The energy gathered through feeding processes is stored in a reserve pool, from which it is allocated according to the κ-rule: part of the energy (κ) sustains the somatic tissues and the growth of structures, while the rest (1 − κ) maintains the maturity level and maturation or gamete production in adults. Temperature controls the rates of all energetic flows and it follows the Arrhenius rules within the thermal-tolerance range26,54,55. The Type II functional response56 instead models the relationship between food density and ingestion rate54. DEB theory therefore allows, through the explicit modeling of energy and mass fluxes through organisms, to derivate individual performance in terms of the most important life-history traits of a species such as, for instance, the total reproductive output (TRO) and maximum length.
In the present study, we followed a well-tested spatially explicit contextualized approach already successfully adopted in several companion studies27,40,57. This approach consists in running DEB models in each spatial pixel of the study area using organismal body temperature (which is assumed to be similar to the Sea Surface Temperature, as extracted through satellite imagery for every single pixel) and environmental food availability. Food availability was expressed as density (wet mass mg m−3), which for anchovy primarily comprise zooplankton, and obtained as a spatially continuous dataset on the distribution of food throughout the study area40. Nevertheless, to run DEB model in each pixel with local temperature and food density is a long computational process. Thus, due to the large number of pixels of the study area, we moved the standard DEB model from the original Kooijman’s Matlab code (https://www.bio.vu.nl/thb/)—once adapted it using anchovy DEB parameters40—to an R code. In doing so, we were able to automatize and speed up the process since R coding improve the computational effort and allows running DEB models at larger spatial and temporal scales27,40,58. The coding re-arrangement was performed by the Ecology Lab of the University of Palermo, which is one among the DEB node of the DEB world net (https://www.debtheory.org/wiki/index.php?title=DEBnet).
Our simulations were restricted to the continental shelf, based on depth (from 0 to 200 m below sea level). A vector polygon grid feature class of 346 square cells (having a size of 0.11° × 0.11° [~ 150 km2]) covering the study area was used. Food availability is an important forcing of the model and, for anchovy, primarily comprises zooplankton52. Since locally collected data for zooplankton were spatially and temporally fragmented due to sampling effort, we followed the recent approach proposed by Strömberg et al.59 and applied by Mangano et al.28,40 to obtain a continuous (space and time wise) dataset on the distribution of food throughout the study area and across time (i.e. weekly Net Primary Productivity was transformed into wet mass of zooplankton). Due to the short life span of the anchovy (~ 4 years), we extracted daily sea surface temperatures (SST; 1 km resolution) from JPL MUR SST data (2010) (https://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST) over a time range of 4 years (2009–2012) for each cell (see the recent proof of concept by Mangano et al.40 for a detailed presentation of the model and its validation).
TRO values are validated by using in situ data, collected during ad-hoc oceanographic surveys40. This approach assumes stationarity in biological parameters (i.e. DEB parameter values estimated for populations in one location/time are valid for populations elsewhere). We adopted the DEB parameters designed for the Mediterranean anchovy by Pethybridge et al.50, the degree of uncertainty of our simulations was low (see Fig. 2 in Mangano et al.40) and sufficiently robust to allow reliable predictions of anchovy life-history traits. We are aware that phenotypic plasticity and/or local adaptation have the potential to increase the degree of uncertainty of modeling outcomes and we suggest the use of DEB parameters values that, to the extent possible, realistically match those of local populations rather than global (species specific) parameters. More

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Genotypic heterogeneity in single Rivularia-like colonies
Rivularia-like colonies have a global distribution, occurring in marine or freshwater habitats, where they are usually attached to a rocky substrate; however, many studies have reported that Rivularia spp. are associated with unpolluted environments14. In addition, the relationships between some morphological or physiological features and the environment make these species excellent environmental indicators of changes in running water quality, mainly related to eutrophication processes14,30. Therefore, they have been included in biomonitoring programs21,31,32. On the other hand, because Rivularia colonies sometimes persist for very long periods, avoiding grazing, the toxicity of these colonies is being investigated33. It is undoubted that in all of these approaches, where genera and species must be strictly identified from environmental samples, accurate cyanobacterial characterization is essential.
Traditional identification of cyanobacteria involves assigning a colony to a morphospecies, and conventionally, a bacterial colony is defined as a visible mass of clonal microorganisms, all of which originated from a single cell. However, the results from the present study show that the majority of the analyzed colonies consist of different clones growing together. Among the 28 Rivularia-like colonies, the phylotype corresponding to Rivularia sp. was present in 19 colonies, with abundances ranging from 59.4 to 99.8% depending on the studied colony. Nevertheless, it should also be noted that in most of the colonies, this phylotype dominated, whereby in 14 colonies, it presented an abundance of ≥ 90% (and within 7 of these colonies, the abundance was close to 99%). However, in three colonies, the abundance ranged from 72 to 85%, and in two of them, the abundance decreased to approximately 60%. The other highly abundant phylotypes found in these colonies, which reached abundances up to approximately 21%, corresponded to Calothrix sp. and Oculatella sp., the latter a genus morphologically similar to Leptolyngbya but separated from it because of genetic differences34. These results indicated great variability in the abundance of the phylotype corresponding to Rivularia depending on the analyzed colony, as well as variation in the other phylotypes and their abundances found in these colonies.
One of the surprising findings was that among the twenty-eight analyzed Rivularia-like colonies, seven corresponded to the new, recently described genus Cyanomargarita, which as the authors described, is virtually indistinguishable from Rivularia in field samples15. In these colonies, genotypic heterogeneity was also found, in which the abundance of the phylotype corresponding to Cyanomargarita varied from 57,28% in a colony with clear lamination resembling R. haematites (see Fig. 3b) to 99.2% in a soft colony resembling R. biasolettiana. Interestingly, in these colonies, Phormidium sp. was the dominant nonheterocystous cyanobacterium instead of Oculatella from Rivularia colonies, but the phylotype corresponding to Calothrix was also found.
Furthermore, phylotypes corresponding to Cyanomargarita and Rivularia were never found together in the same colony, although both types of colonies coexisted in the same rivers (e.g., Gordale Beck and Endrinales). Allelopathic effects could explain these results, as previously suggested for other cyanobacteria35. In fact, García-Espín et al.33 showed that extracts obtained from Rivularia colonies affected the photosynthetic activity of several diatoms and a red alga. Further experiments with extracts from both colonies would confirm this possible effect.
Another very surprising finding was that two Rivularia-like colonies did not present any phylotypes corresponding to Rivularia or Cyanomargarita (or contained them at an abundance ≤ 0.7%). In one of these colonies (colony BAT4), five different phylotypes were found at similar abundances (approximately 15–20%), of which three corresponded to different Calothix spp. and the others corresponded to other Nostocaceae and Leptolyngbyaceae. In the other colony (BAT13), the dominant phylotype corresponded to the new genus Macrochaete16. This genus has been described only from cultures, so to the best of our knowledge, this is the first report in which a natural population is morphologically and genetically characterized. Nevertheless, it is noteworthy that the morphological characteristics of filaments and trichomes in this environmental sample were different from those reported in the description of this new genus, in which the phenotypic features resembled those of Calothrix. However, these features corresponded only to isolated strains, which are known to exhibit morphological variability and differences from natural populations7,12,13.
R. biasolettiana vs R. haematites
However, what was very interesting and deserves to be highlighted is that when we tried to differentiate the two typical Rivularia colonies found in calcareous streams, R. biasolettiana and R. haematites, we did not find genetic differences, at least at the studied level, the 16S rRNA gene.
16S rRNA is the most widely used marker gene36,37, which fits the criteria of ubiquity, regions of strong conservation, and regions of hypervariability38,39. This gene is supported by reference databases containing over a million full-length 16S rRNA sequences, therefore spanning a broad phylogenetic spectrum40. The 16S rRNA gene has served as the general framework and as the benchmark for the taxonomy of prokaryotes41. Advances in high-throughput sequencing technologies have enabled almost comprehensive descriptions of bacterial diversity through 16S rRNA gene amplicons, which have been used in surveys of microbial communities to characterize the composition of microorganisms present in environments worldwide42,43,44,45. Although some issues have been raised, such as identification of metabolic or other functional capabilities of microorganisms when studies focus only on this gene, recent studies have shown that the phylogenetic information contained in 16S marker gene sequences is sufficiently well correlated with genomic content to yield accurate predictions when related reference genomes are available46,47,48,49. Therefore, the 16S rRNA gene continues to be the mainstay of sequence-based bacterial analysis, vastly expanding our understanding of the microbial world50.
In particular, in cyanobacteria, as in other prokaryotes, the 16S rDNA gene is currently the most commonly used marker for molecular and phylogenetic studies51,52. The information obtained from 16S rDNA gene phylogenetic reconstructions, together with morphological, ultrastructural, and ecological data, led Komárek et al.53 to propose the current accepted classification of cyanobacteria. There have also been specific studies by this group concerning the problems associated with single-gene phylogenies, in which robust phylogenomic trees of cyanobacteria derived from multiple conserved proteins have also shown congruence between the multilocus and 16S rRNA gene phylogenies, which once again demonstrates the considerable strength of the 16S rRNA gene for phylogenetic inference and evaluation of prokaryote diversity54,55,56,57.
In this study, in contrast to the genetic identity found in R. biasolettiana and R. haematites colonies, showing a dominance of OTU1, the remainder of the studied representatives of Rivulariaceae showed a wide range of variation in the 16S rDNA sequences and with OTU1. Sequence identity between OTU1 and the remaining OTUs belonging to this family was as low as approximately 90%, ranging from 90.73 to 93.41%, and when it was compared with other Rivulariaceae from the databases, in the different clusters of the phylogenetic tree, this value ranged from 87.12 to 93.90%. A large difference between the sequences of this gene was also found in other studies on Rivulariaceae15,16,17,29,58. In fact, several new genera are emerging on the basis of these differences15,16,17. Comparisons of phylogenies using other markers, such as the phycocyanin operon and the intervening intergenic spacer (cpcBA-IGS) with the 16S rRNA gene in previous studies in Rivulariaceae, have shown largely consistent results, with a high level of divergence between the components of this family11.
In addition, the results of the present study showed correlations between morphological characteristics and the analyzed genes in all the cyanobacterial colonies/tufts, except for those of R. biasolettiana and R. haematites. In these two cyanobacteria, only distinct macroscopic phenotypic features were observed due to zonation and different degrees of calcification since no significant differences were found in the size measurements or other microscopic characteristics.
Therefore, although the remainder of the genome has not been studied in these populations, the genetic identity of the studied marker, phenotypic features, together with environmental preferences point out that R. biasolettiana and R. haematites are ecotypes of the same species, as previously suggested59.
R. biasolettiana and R. haematites have very similar morphotypes, and traditional taxonomical classification and studies have distinguished them primarily by their degrees of calcification. R. biasolettiana-type colonies are described as more gelatinous and less calcified, and the crystals are disseminated; however, R. haematites colonies are very hard and exhibit extensive calcification in concentric zones, which leads to clear lamination24,25,60,61. Because of its heavy mineralization, R. haematites is a model for stromatolite-binding organisms25,26.
Microscopic observations from this study showed that some colonies presented typical R. haematites morphology with concentric bands of intense calcification (see Fig. 2a,b), and others were soft and less calcified, such as R. biasolettiana, although all of them presented the same dominant phylotype. Many others with this dominant phylotype have also shown ambiguous morphology with no clear lamination, although some dark/light zones could be observed (see, e.g., Fig. 4b,d, f). Even in Cyanomargarita colonies, whose genotype was clearly separated from that of Rivularia, concentric zones and extensive calcification could be observed (see, e.g., Fig. 3b,d). These results suggested that these phenotypic features are not diagnostic characteristics for further identification.
In a two-year study, Obenlüneschloss and Schneider61 found that not all analyzed R. haematites colonies showed distinct concentric calcification layers. In the stromatolites of both types of Rivularia, the same lamination was observed, and the differences in calcification appeared later60. Pentecost and Franke26 compared populations of R. biasolettiana and R. haematites and argued that although both could be distinguished by their form of calcification and their trichome diameter, some populations of R. biasolettiana were more intensely calcified than others, suggesting that a continuity of forms may exist, even within the same stream, and therefore, a continuum of colony forms probably occurs between these taxa.
Differences in the calcification pattern have been attributed to seasonality and cyanobacterial activity, in particular to photosynthesis24,26,62. The calcification in R. haematites occurred in concentric bands, which varied in thickness and the density of crystals. Since characteristic zonation is formed by filaments of different successive generations, the thickness will vary depending on the growth rate, while crystal density will depend on the rate of calcification. Calcification is the result of photosynthesis (with a maximum of 14%) and evaporation during the warmer seasons, while it is entirely abiogenic during winter as a result of CO2 evasion63. Therefore, dense calcified bands similar to those formed in winter have been described that are caused by a reduction in trichome growth and EPS production, allowing the development of abiotic surface precipitate, and less calcified layers are formed during spring and summer, when calcification is associated with photosynthesis in zones of growth with cell division24,26. Thus, differences in climatic conditions and/or biological activity seem to lead to differences in the degrees of zonation and calcification.
The growth of Rivularia colonies is seasonal and strongly correlated with water temperature24,26. The colony growth rates were 12–14 µm/day in summer and 2 µm/day in winter24. The occurrence of R. biasolettiana was more closely related to high temperatures than that of R. haematites21. Moreover, colonies of R. haematites were generally collected under temperatures below 15 °C in mountain running waters64, and R. haematites stromatolites have been described as preferentially developed in wet periods, particularly in autumn and winter60. Our own field observations during the sampling for this and previous studies were that the gelatinous and weakly calcified R. biasolettiana type was more abundant in warmer locations, and in contrast, R. haematites was dominant in cold locations (data not shown).
One possible explanation for the results found in this study could be related to these differences in the degree of zonation and calcification in relation to climate, which could include microclimatic conditions. In warmer sites or climatic conditions, when growth is rapid, the number of filaments will increase, moving towards the surface in a weakly dense and unaligned arrangement, on which calcite crystals spread, providing a lighter and less calcified structure. Thus, increased growth of Rivularia colonies can lead to the R. biasolettiana type. Under colder conditions, such as in winter, or microclimatic conditions, when growth slows down for other reasons, such as low light, filaments become more densely packed, allowing the development of extensive precipitates and leading to a dark band. When these conditions change, e.g., in the spring and summer, increases in temperatures and/or light will result in increasing and faster growth, leading to a less calcified new layer, and successive seasonal and/or microenvironmental changes will result in the typical lamination of R. haematites. Therefore, warmer places with high temperatures and/or light will allow the occurrence of the R. biasolettiana type, while in colder sites and/or sites with alternating environmental conditions, the R. haematites type will develop. Shaded colonies and colonies that lie in the supratidal spraywater zone often contain small, irregular and more densely packed crystals61.
Cyanobacteria are known to modify EPS production, pigments, and morphology under environmental stimuli6. The production of EPS also varies depending on the cyanobacteria, whereby Rivularia has shown a well-developed exopolymer layer65, which is of great importance for this epilithic cyanobacteria, as it acts as an adhesive that allows cells to stick to the stones in the running waters, and it holds the filaments together, minimizing cell damage during intermittent drying exposure to the air and evaporation in the warmer seasons66. The C/N ratio is an important parameter for the variation in EPS production since high amounts of fixed C compared to N levels drive EPS synthesis to store excess C67,68. Therefore, Rivularia colonies that are exposed, in spring and summer, to high light intensities and temperatures will increase their photosynthetic rates and therefore the amount of EPS, as shown by the R. biasolettiana morphotype. In addition, most of the analyzed populations were dark in color, probably in relation to the accumulation of the yellow–brown scytonemin pigment in the sheaths or EPS, as previously observed in shallow and clear oligotrophic ecosystems, where water clarity allows UV radiation to penetrate well, protecting the cells from the damaging effects of this radiation69,70.
In conclusion, environmental factors can lead to differences in macroscopic phenotypic features, such as those found in the Rivularia colonies studied here. However, further sampling under different climatic conditions and/or microenvironmental conditions or of Rivularia cultures grown under distinct temperature and/or illumination conditions, as well as analysis of other genes, are needed to confirm this hypothesis. More