An analysis of the macroalgal δ 13 C variability in the Gulf of California 1 2

Abstract. The C isotopic composition in macroalgae (δ13C) is highly variable, and its prediction is very complex relative to terrestrial plants. To contribute to the knowledge on the variations and determinants of δ13C-macroalgal, we analyzed a large stock of specimens varying in taxa and morphology and inhabiting shallow marine habitats from the Gulf of California (GC) featured by distinctive environmental conditions. A large δ13C variability (−34.61 ‰ to −2.19 ‰) was observed, mostly explained on the life form (taxonomy, morphology, and structural organization), and modulated by the interaction between habitat features and environmental conditions. The intertidal zone specimens had less negative δ13C values than in the subtidal zone. Except for pH, environmental conditions of the seawater do not contribute to the δ13C variability. Specimens of the same taxa showed δ13C similar patterns, to increase or decrease, with latitude (21º–30° N). δ13C-macroalgal provides information on the inorganic carbon source used for photosynthesis (CO2 diffusive entry vs HCO3− active uptake). Most species showed a δ13C belong into a range that indicates a mix of CO2 and HCO3− uptake; the HCO3− uptake by active transport is widespread among GC macroalgae. About 20–34 % of species showed the presence of carbon concentrating mechanism (CCM). Ochrophyta presented a high number of species with δ13C > −10 ‰, suggesting widespread HCO3− use by non-diffusive mechanisms. Few species belonging to Rhodophyta relied on CO2 diffusive entry (δ13C 


analytical control quality. The analytical uncertainties reported for the SIF lab were 0.2‰ for δ 13 C 206 (https://stableisotopefacility.ucdavis.edu/13cand15n.html). We also included triplicate aliquots of 207 several specimens of the same species and condition, collected from one patch or attached to the 208 same substrate, to assess the method error by sampling and processing procedural. The 209 methodological uncertainties were <0.4‰. 210

Analysis of δ 13 C-macroalgal variability 211
The variability of δ 13 C values in macroalgae was analyzed in function of the taxonomy (phylum, 212 genus, and species) and morpho-functional groups (e.g., thallus structure, growth form, branching 213 pattern, and taxonomic affinities; Balata et al. 2011; Ochoa-Izaguirre and Soto-Jiménez, 2015). 214 Sampled specimens belong to three phyla, 63 genera, and 167 species. The phyla were identified as 215 Rhodophyta (53%), Ochrophyta (22%) and Chlorophyta (25%). The most representative genus 216 (and their species) were Ulva (U. lactuca, U. lobata, U. flexuosa, and U. intestinalis), Codium (C. 217 amplivesiculatum and C. simulans), Chaetomorpha (C. antenina), Padina (P. durvillaei), Dictyota 218 and calculate the arithmetic mean, standard deviation, minimum and maximum. Because not all 244 macroalgal species were present in sufficient numbers at different collection habitats, several 245 macroalgal groups were not considered for statistical analysis. Regarding the life form, we compared 246 among morphofunctional groups, taxon collected in the same habitat (within-subjects factor) by 247 multivariate analysis of variance. When differences were noted, a Tukey-Kramer HSD (Honestly 248 Significant Difference) test was performed. Besides, variations of  13 C macroalgal in specimens of 249 the same morpho-functional and taxon collected in different habitats were also investigated with a 250 Kruskal-Wallis test. 251 In this study, the relationships between  13 C with each independent variable related to the inherent 252 macroalgae properties (morphology and taxon), biogeographical collection zone (GC coastline and 253 coastal sector), habitat features (substrate, hydrodynamic, protection, and emersion level) and 254 environmental conditions (temperature, pH, and salinity) were examined through simple and 255 multiple linear regression analyses. Excepting temperature, pH, and salinity, most of the independent 256 variables are categorical independent variables. However, these continue variables were also 257 categorized, such as previously was described. Analyses of simple linear regression were performed 258 to establish the relationships between  13 C-macroalgal with each environmental parameter analyzed 259 as possible driving factors (e.g., temperature, salinity, pH). Multiple linear regression analyses were 260 conducted to evaluate the combined effects of those independent variables (macroalgae properties, 261 biogeographical collection zone, habitat features, and environmental conditions) on the  13 C-262 macroalgal. In the multivariable regression model, the dependent variable,  13 C-macroalgal, is 263 described as a linear function of the independent variables Xi, as follows:  13 C-macroalgal = a + 264 b1(X1) + b2(X2) + …+ bn(Xn) (1). Where a is regression constant (it is the value of intercept and its 265 value is zero); b1, b2, and bn, are regression coefficients for each independent variable Xi. From each 266 one of the fitted regression models, we extracted the estimated regression coefficients for each of the 267 predictor variables (e.g., Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), 268

Taxonomy versus habitat features 329
Variability of δ 13 C values for the most representative genera was evaluated by multiple comparative 330 analyses in the habitat features' function, including the substrate, hydrodynamic, and emersion level. 331 Large δ 13 C variability observed between specimens of the same genus collected in the different 332 habits does not show any significant pattern, and non-significant differences were observed. An 333 exception was observed with the emersion level (showed in Fig. 5

Variation latitudinal of  13 C-macroalgal 368
The δ 13 C-macroalgal variation in the GC biogeography was evaluated by regression linear analysis 369 between δ 13 C values along the nine degrees latitude in both GC coastlines. A non-significant 370 latitudinal trend was observed for datasets, but for the three taxa's most representative genera, δ 13 C 371 values correlated with latitude ( Fig. 8a-f). In Chlorophyta, with the higher genera number, δ 13 C 372 values increased with latitude ( Fig. 8a)  Methods; however, our results description focused on the coefficients of determination (R 2 and 388 adjusted R 2 ). The coefficient R 2 describes the overall relationship between the independent 389 variables Xi with the dependent variable Y ( 13 C-macroalgal), and it is interpreted as the % of 390 contribution to the  13 C variability. While the adjusted R 2 statistics compensate for possible 391 confounding effects between variables. 392 Results of the analysis of the relationships between  13 C with each independent variable are 393 summarized in Table 4. Regarding the inherent macroalgae properties, Phyla explain only 394 7% variability, the morphofunctional properties 35%, and taxon by genus 46%, and by 395 species 57%. The biogeographical collection zone, in terms of coastline (continental vs. 396 peninsular) and coastal sectors (C1-C3 and P1-P3), explained a maximum 5% variability. 397 Related to the habitat features, only emersion level (6%) contributed to the  13 C variability. 398 The contribution of the seawater's environmental conditions was marginal for pH (4%) and 399 negligible for temperature and salinity. A marginal reduction in the percentage of 400 contribution was observed for Phyla (1%) and morphofunctional properties (1%), but 401 significant for genus (5%) and species (10%). 402 Multiple regression analyses were also performed to interpret the complex relationships 403 among δ 13 C-macroalgal, considering the life form (morphofunctional and taxon by genus) 404 and their responses to environmental parameters. Results for the fitted regression models 405 performed for morphofunctional groups (Table 5) and genus (Table 6) evidenced that the 406 effect of the coastal sector and pH ranges on the δ 13 C-macroalgal increased the contribution 407 by 9-10% each one. The emersion level increased by 5-6%, the contribution respect to 408 individual effect of morphofunctional group and genus, the temperature and pH in 1 and 3%, 409 respectively, while salinity decreased by 1-2%. Adding the effect of the biogeographical 410 collection zone, represented by the coastline sector, to those for morphofunctional group 411 (Table 5) and genus (Table 7), a notable increase of 11-12% was observed. 412 The full model considering the combined effect of the coastline sector + Habitats features for 413 Morphofunctional group or Genus (Table 7), showed R 2 of 0.60 and 0.71. In contrast, 414 Coastline sector + Environmental conditions + Morphofunctional group or Genus the R 2 415 increased to 0.62 and 0.72, respectively. The interactive explanations of environmental 416 factors increased the explanation percentage of δ 13 C variability; however, these contributions 417 were significantly lower than the explained by life forms, such as the morphofunctional 418 properties and taxa by genus and species. 419 The combined effect of environmental condition on the δ 13 C variability was tested for the best-420 represented morphological groups and genus. Results evidenced that 9 of 21 morphological groups 421 showed significant effects on the δ 13 C variability (Table 8)

Environment factors and δ 13 C values 551
We expected differences in δ 13 C values between eco-regions (e.g., north vs. south, peninsular vs. 552 continental), but non-geographical patterns were observed; neither differences associated with the 553 temperature for the same species o genus was observed. A slightly low δ 13 C signal in communities 554 from C2 eco-region was observed, influenced by the Sonora desert. 555 Based on pH, differences in δ 13 C were found only for a few genera (e.g., Amphiroa, Colpomenia, 556 Stepien (2015), the result of meta-analyzes between pH values and δ 13 C was positive only for 564 Rhodophyta (R 2 =0.41, p<0.001) and Ochrophyte (R 2 =0.19, p<0.001), but not for Chlorophyta 565 (R 2 =0.002, p<0.10). About 86% of the Stepien metadata met the theoretical CCM assignation based 566 on both parameters, exceptions for species with δ 13 C<-30‰ that has been capable of raising pH>9. 567 Our linear regression analyzes for latitudes showed a weak but significant correlation for the dataset 568 classified by morphofunctional groups and genus, negative in the cases of Rhodophyta and 569 Ochrophyta groups (R 2 =0.2 and 0.5, p<0.001), and a positive for Chlorophyta. The negative 570 correlation between latitude and δ 13 C-algal was described by Stepien (2015), concluding that δ 13 C 571 signal increased by 0.09‰ for each latitude degree from the Equator. Hofmann and Heesch (2018) 572 recently show a strong decrease in latitudinal effect (R 2 = 0.43 δ 13 Ctotal and 0.13, for δ 13 Corganic-tissue, 573 p=0.001) for rhodolite of the northern hemisphere and macroalgae from coral reefs in Australia. In 574 both cases, the latitude range is higher than we tested (30º to 80º and from 10º to 45º, respectively). 575 These differences on a big scale tend to be associated with a temperature effect (Stepien, 2015) and 576 their effect on CO2 solubility in S.W. (Zeebe & Wolf-Gladrow, 2007). Even so, our multivariate 577 linear regression analyses showed that the environmental factors were significant (p=0.001), 578 explaining up to 50% of the  13 C variability. 579

Morphofunctional groups and δ 13 C 580
The variability recorded on morphofunctional groups was high, mostly influenced by the genus. The by Amphiroa and Jania spp, respectively, also O-hollow with spherical composed Colpomenia spp. 583 Based on the literature, Stepien (2015) made an analysis about morphofunctional groups and δ 13 C 584 by following the group proposed by Littler & Littler (1980) and modified by Balata et al., (2012) 585 and they agreed that morphofunctional groups that are composed calcifying species (e.g., crust 586 calcifiers) have highest δ 13 C signal. Our regression models showed that morphofunctional groups 587 have a R 2 adjusted =0.34, and increase to genus (R 2 adjusted =0.41,) and to species (R 2 adjusted 588 =0.46). This result is consistent with reported by Lovelock et al., (2020), which found that 66% of 589 δ 13 C variability was explained by taxonomy. Although morphofunctional groups could explain less 590 than genus or species, it is a great tool to increase the possibility of analyzes on a big spatial scale, 591 especially when the species distribution could be limited. 592

Conclusions 593
Our work confirms that taxonomy is the main cause of δ 13 C variability among seaweed communities 594 analyzed and explained until 46%. Most species showed a δ 13 C belong into a range that indicates a 595 mix of CO2 and HCO3uptake. About 20-34% species depending on cutoff limits for CCM presence 596 showed at least one specimen with δ 13 C>-10‰, suggesting that potentially could have highly 597 efficient CCM. On the other extreme, some Rhodophyta species relied exclusively on diffusive CO2 598 entry, as inferred from their δ 13 C values (i.e. δ 13 C lower than -30‰; Schizymenia pacifica, 599