We used four approaches to overview the results: 1) PCA analysis, 2) t-tests, 3) controls analysis and 4) connectance analysis, then considered more detailed 5) bivariate analyses of plant redox, water and nutrient variables that are then summarized in a 6) model of relative growth rates (RGR). Final sections of Results consider 7) cations, and 8) A Case Study: fingerprinting human disturbance at a mangrove forest site.
1. PCA analysis. Species were very distinct in PCA plots (Fig. 1A), and sites were moderately well separated (Fig. 1B) using a reduced suite of 13 variables.
2) t tests. Univariate analyses with t tests showed that all markers showed one or more significant differences between the 3 species, and that 80% of all possible differences were significant (Fig. 2A). Significant differences across individual sites were much rarer (about 7% of total possible). But when sites were aggregated into two groups, a higher salinity, more marine group vs. a lower salinity, more inland group, then group-level significant differences were common, constituting 45% of total possible significant differences (Fig. 2B). These two site groups reflected the hydrogeomorphic fringe, riverine and inland groupings, in that the fringe and inland stations were in the two opposite groups, with riverine stations intermediate and in both groups.
The three species means in Fig. 2A generally separated by a factor of 4 or less, from 50 to 200, while the pooled site means in Fig. 2B varied much more narrowly, by about 1.5x, from 80 to 120. These results indicated species are maintaining strong separation across sites that were fairly similar. Tables S5 and S6 give species averages and grouped site averages corresponding to normalized data shown in Fig. 2.
3) Controls analysis. We used SD and F measurements from ANOVA to estimate how patchiness, physiology and ancestry interact to control each metric. Figure 3A shows the original data with 3 sources as the apices of the triangle mixing their influences for each metric. Calculated contributions can be summarized in a ternary plot (Fig. 3B), with details for each metric given in Fig. 3C. The overall average finding (Fig. 3C) was that contributions of ancestry dominated (53%), patch contributions were subordinate (32%) and physiological contributions were least (14%).
The analysis of Fig. 4 seemed reasonable when individual variables were considered, as illustrated by four examples. 1) For variables such as leaf carbon that represent use of a common unlimited resource (CO2 in air in the case of leaf C), there was little patch-associated SD along the x-axis (Fig. 4B), but strong physiological specialization in F values along the y-axis. This leaf C specialization likely reflects leaf construction from different mixtures of lignitic and cellulosic carbon (Benner et al. 1987, Xing et al. 2021). Like C, N and f13C showed relatively little patch-associated variation, but in contrast to C, showed relatively little physiological differentiation. 2) Mn is a sediment variable that could logically be associated with both horizontal and vertical sediment patches; Mn had the highest SD along the x-axis (Fig. 3B) and was the most patch-associated variable. 3) P and S both plot in the same central portion of Fig. 3B not far from Mn. This indicates that P and S are responding in the same controls, uptake from sediment patches. 4) The last example is a new ratio identified in this study, f13C/P or the ratio of water to P. This ratio had the highest physiological control (Fig. 3B,C), and of all variables, best separated both species and sites (Tables S5,S6). This variable has not been previously identified to our knowledge, but could be expected from fundamental considerations that plants balance water and P during their growth. Overall, Fig. 3 showed that the mangrove species are mostly similar in their metric chemistry with shared or undifferentiated common ancestry, but they diverged especially in patch use and to a lesser degree in physiology.
4) Connectance analysis. To estimate which metrics were most centrally connected and influential we used average r2 values from correlation analysis. Main results were that 1) Average r2 values were about 3x higher for species than sites, 0.38 vs. 0.14 (Fig. 4). 2) For species, RGR, P and water (indicated by B in this analysis) were the strongest organizing variables at 1.4x average connectance; thus 1.4x the 0.38 average = 0.54 for r2 so that average abs(r) for P and water across species = 0.73. 3) For sites, N and P were strongest organizing variable at 1.7x and 1.8x, respectively; N and P were also the most important site variables in the SIMPER analysis of Fig. 1B. 4) Most variables fit into a “background” group, consistent with a broadly distributed and diffuse network of interactions. Overall, while N, P and water were acting to coordinate and control the chemical network and profiles, diffuse controls likely associated with patchiness were acting to counter these organizing effects (Fig. 4).
3) Bivariate analyses of plant redox, water and nutrient variables. There were several variables that helped orient a model for mangrove productivity, and it was instructive to consider those related to redox conditions in soils (Fig. 5), water (Fig. 6) and nutrients (Fig. 7).
For redox variables (Fig. 5), several indicators consistently indicated that SOAL had relatively oxidized rhizosphere conditions, RHAP was opposite with much more reduced conditions and BRGY was intermediate. For example, uptake of sulfide-derived sulfur was indicated by increased leaf S contents with lower d34S values (Fig. 5A). As estimated by two-source mixing of sulfide-derived sulfur and sulfate-derived sulfur, RHAP had highest 69 ± 2% sulfide-derived S in leaves, SOAL lowest at 47 ± 4% and BRGY intermediate at 62 ± 3%.
Cu and Zn that are normally trapped as sulfides in deeper sediments can become more available in oxidized conditions, and were highest in SOAL (Fig. 5B,C). Mn becomes mobile under reducing conditions, and RHAP had highest Mn concentrations (Fig. 5D) consistent with accessing Mn from reducing sediments. Overall leaves sampled from the three species indicated a very consistent pattern of root interactions with redox conditions in soils.
There were two isotopic indicators of plant water use, d13C and dD (Fig. 6A). Both variables showed highest (least negative) values in SOAL and lowest (most negative) values in BRGY, with RHAP intermediate. While these isotopic indicators have been narrowly linked to stomatal opening and water use efficiency, their co-variation is also consistent with a broader interpretation of increased water supply to leaves. It was striking that the order of these water variables was SOAL-RHAP-BRGY (S-R-B) in Fig. 6 rather than the SOAL- BRGY-RHAP (S-B-R) order for redox variables in Fig. 5, so that plant water strategies were not tightly linked to redox conditions. Variations in d15N were relatively small and less than 2‰, but followed the trends shown in Fig. 6A for C and H isotopes. Measurements of B which is present in seawater as the minor anion borate show parallel patterns to the isotopic indicators, with more B associated with lower d13C (Fig. 6C); for this reason, B was included as a water indicator.
Plant species also differed greatly in how they used NPK nutrients. Graphs of K, N and water vs. P showed unique aspects of mangrove resource use strategies (Fig. 7). For K, SOAL and RHAP took up about 8x and 4x more K than P on a mole-for-mole basis, while BRGY took up about equal quantities (Fig. 7A). Use of more K than P meant that K/P ratios showed an upward trend in plots of K/P vs. P (Fig. 7B). Overall, K uptake was very correlated with P uptake across the species.
Increased N and water uptake accompanied P uptake, resulting in positive slopes in plots of N vs. P (Fig. 6C) and water (indicated by d13C) vs. P (Fig. 7E). However, uptake of N and water was not as fast as for P, so that in ratio plots of N/P and water/P, ratios declined as P increased (Figs. 7D, F). The N/PMOLAR data for BRGY and RHAP were in the 30–35 range, about 2x the Redfield ratio of 16, while N/P ratios for SOAL approximated this ratio at higher P. The shape of especially the SOAL N/P response is consistent with increased growth rates occurring at higher P in tropical trees (Cernusak et al. 2010), while growth rates for BRGY and RHAP are slower and in the range of N and P colimitation for tropical trees (Townsend et al. 2007). The slopes and curves of the water relations (Fig. 7E,F) are overall similar to those of the N relations (Fig. 7C, D), so that water and N were following trajectories set by P. This is generally expected in co-limitation situations, and growth rate modeling was oriented around resource co-limitation with a central focus on P effects.
4) Model of relative growth rates (RGR). To better quantify RGR effects of Fig. 7D, we developed an empirically fitted RGR model based on N, P and water. The models always included a dominance of P as the main forcing variable increasing RGR while N and water acted to dampen the P-alone response (Fig. 8). These dampening effects indicate that water and N can strongly co-limit in the RGR responses. Water increased relatively little as P increased, accounting for the strong co-limiting effect of water. In contrast, N increased nearly as fast as P and had much less dampening effect when included in the model. The effects of water co-limitation were 4x stronger than those of N co-limitation. Inclusion of N was not actually required in the models but water was required to achieve the fit criterion SOAL = 1.6x BRGY.
Empirical fitting showed a range of successful models with the form RGR = [(c*fN + 1*fP + (0.62–0.19c)* f13C)/[(c + 1 + (0.62–0.19c)] where the constant c could vary between 0 and 1 and f13C records water contributions. All models of this form achieved the fit criterion SOAL = 1.6x BRGY, and differed only somewhat in RGR estimates for RHAP that were slightly higher than that for BRGY, in the 1.03x to 1.15x range. Because model output was similar across the range of models, we chose the middle-of-the-range model (model c in Fig. 8) with all three variables for our subsequent use in this study, RGR = [(0.5*fN + 1*fP + 0.5*fWATER)/2.
Using this model (Fig. 8, model c), species RGR averaged 100, 110, and 162 for respectively BRGY, RHAP and SOAL, and these differences were significant (p < 0.05, Tukey’s HSD t test) for BRGY or RHAP vs. SOAL, but not for BRGY vs. RHAP. For sites, averages that were normalized to Utwe Fringe RGR = 100 ranged up to 153 at Finkol Inland. For sites, only the two lowest RGR sites (Utwe Fringe and Finkol Riverine A) were significantly different vs. the highest RGR site (Finkol Inland). However, when sites were aggregated into groups of Fig. 2B, the two aggregated groups did differ significantly (p < 0.05, Tukey’s HSD t test), with the lower salinity, more inland group having 1.25x RGR of the more marine sites.
We used the RGR model to test if the low-nutrient specialist BRGY would have higher RGR than other species at lower nutrient sites. Figure 9 shows that all species decreased in RGR at lower nutrient sites, but SOAL always had highest RGR and BRGY was tied with RHAP at lower nutrient sites.
5) Cations. Mangrove cation concentrations for K, Ca and Mg (Fig. 10A) were broadly similar to those of upland terrestrial trees (Lira-Martins et al. 2019), but had > 10x higher Na contents from seawater that is particularly rich in Na (Fig. 10B). The overall sums of cations by moles and charge were similar at 1500–2500 mmol kg dry wt across species and sites, and when expressed on a leaf water basis, showed that cations are concentrated in leaves by 0.8-2.5x vs. seawater. Although cation concentrations were high, rivalling those of N and carbon and much greater than those of trace elements like Fe, Mn, Cu and Zn (Table S5), most cations are thought to accumulate in vacuoles so that leaf water is still relatively low in salts (Medina et al. 2015).
Details of cation distributions differed markedly between the mangrove species, partly due to strong association of K with P (Fig. 7A,B). Particularly SOAL took up large amounts of K in association with P, and seemed to show a corresponding decrease in other cations such as Ca and Mg (Fig. 10B). Specializations for P thus seemed to strongly affect cation distributions. It was also remarkable that even though a doubling in cation concentrations was expected across sites (as salinity increased from about 16 at Finkol Inland to 31 at Utwe Fringe; Table S4, Fig. S1), mangrove total cation concentrations increased only 13% in BRGY, showed no change in RHAP, and actually declined in SOAL. Thus salinity had a surprisingly weak effect on cations, and other factors such as P and water desalination strategies (Reef and Lovelock 2015) are probably much more dominant controlling factors for cations. Lastly, because cations were all present in high concentrations in mangrove leaves, there was no question of resource scarcity or partitioning, but the many detailed ways cation concentrations varied between species (Fig. 10) was instead strong evidence for resource optimization for these salts.
7) A Case Study: fingerprinting human disturbance at a mangrove forest site. One site of particular interest was Finkol Inland that was disturbed by human tree harvesting. Decomposition of remaining stumps and belowground roots affected rhizosphere dynamics and changed plant leaf chemistry. PCA analysis showed this site was quite distinct from other sites, though still closest to the other reference inland site, Utwe Inland (Fig. 1B). We used more detailed multi-metric fingerprints (Fig. 11) to examine reasons for the PCA results.
Mn values were very low in all three species at this site, along with moderate or anomalously high S (Fig. 11; also see Fig. 5D, Finkol Inland points identified with *). Mn can be mobilized in very reducing conditions be lost via tidal export, so that low leaf Mn would be consistent with very reducing conditions in sediments. High leaf S would also indicate these strongly reducing conditions. Concentrations of other trace elements Cu, Zn and Fe were moderate or lowest for all species at this site (Fig. 11), consistent with more sulfidic conditions and precipitation of these trace metals. Leaf N and P concentrations were moderately high or highest at Finkol Inland, as was RGR (Fig. 11). Cation distributions at Finkol Inland were also generally high for SOAL, the species with the most accelerated growth rate (Fig. 9 asterisks at right, Fig. 11). Isotopic fractionation 34e was however the same 44.8‰ at both the reference Utwe Inland site and the Finkol Inland site, with smaller fractionations of 29.3 to 39.5‰ at the other sites. The similarity in 34e at the two inland sites is consistent with relatively slow sulfate reduction rates (Jorgensen 2021), perhaps controlled by poorer carbon quality in these more mature forests.
Overall, mangroves at Finkol Inland accumulated moderate or highest levels of P and N along with moderate or highest levels of S and lower levels of Mn, Cu, Zn and Fe. Thus, although human impacts associated with forest thinning at this site could be detected in altered rhizosphere dynamics involving P, N, S and trace element concentrations, the overall impact was elevated RGR for remaining mangrove trees. This RGR response is a very common outcome of forest thinning practices, detected here via chemical fingerprinting.