Oceanographic connectivity explains the intra-specific diversity of mangrove forests at global scales

The systematic literature search listed 540 potential studies, from which 43 reported genetic differentiation data for mangroves. These studies resulted in 59 datasets covering 20 species of the most common genera (Avicennia, Bruguiera, Sonneratia, and Rhizophora; SI Appendix, Table S1) and life strategies, i.e., viviparous and cryptoviviparous (9), 7 molecular markers, and 691 populations (SI Appendix, Table S1) spanning across the known distribution of mangrove forests (i.e., Indo-West Pacific and Atlantic-East Pacific; Fig. 1). On average, 11.71 ± 7.16 populations were sampled per study (range between 4 and 38 populations), with an average genetic differentiation of 0.31 ± 0.17 (range between 0.01 and 0.68, although estimates cannot be compared between studies, species or genetic marker). The average and maximum distance recorded between sampled populations per study were 1,361.51 ± 1,273.02 km and 12,149.20 km, respectively (SI Appendix, Table S1).

The biophysical model released a total of 21,891,914 particles from 11,989 source and sink sites over the 5-y period. These particles resulted in 6,106,904 connectivity events (average of 8.37 ± 29.68 per site) linking 561,247 pairs of sites. Longer propagule duration (PD) periods, i.e., up to 180 d, resulted in larger traveled distances. This pattern spanned an average distance of 55.12 ± 63.70 km with 10 d of propagule duration to 715.44 ± 765.59 km with 180 d (maximum of 10,582.96 km; Figs. 2 and 3). The number of connectivity events also increased with propagule duration, from 4,539,045 to 6,106,904 (Fig. 3). The probability of connectivity and dispersal distances showed a positively skewed relationship, regardless of the propagule duration (Fig. 2). Mapping betweenness centrality allowed identifying important hubs for stepping-stone connectivity. For reduced PD, connectivity hubs were mostly distributed along the coastlines of the western Atlantic and the Indo-West Pacific regions, while for increased PD, hubs were distributed in key islands and offshore regions that bridge distant populations (e.g., between continents) and along the Indo-West Pacific region (Fig. 3).

Isolation by distance models (IBD) were significant for 72.88% of the linear regression models (significant P values in 43 out of 59 models), with an average R-square of 0.33 ± 0.21 and a Person’s correlation of 0.55 ± 0.18 (Fig. 3 and SI Appendix, Table S1 and Supplement 2, (30)). Multigenerational stepping-stone oceanographic connectivity (OC) estimates considering centrality indices were significant for 94.91% of the linear regression models (significant P-values in 56 out of 59 models), with an average R-square of 0.44 ± 0.23 (up to 0.97) and a Person’s correlation of 0.65 ± 0.17 (Fig. 3 and SI Appendix, Table S1 and Supplement 2, (30)). The remaining non-significant models had marginal P values ranging between 0.06 and 0.08 (SI Appendix, Table S1). All individual comparisons between IBD and OC models at the study, species, and genetic marker levels are presented in Supplement 2 (30).

Comparing Akaike information criterion (AIC) values between approaches showed that 100% of the OC models (considering centrality) explained more variability in genetic differentiation data than the IBD. This resulted in an average gain in R-square of 0.18 ± 0.16 (up to 0.76) and in Person’s correlation of 0.21 ± 0.19 (up to 0.78; Fig. 4). The same general patterns were held when PD was varied to capture the uncertainty (SI Appendix, Table S3). The use of centrality indices enhanced the goodness of fit of 100% of the models, by an average R-square of 0.05 ± 0.07 (up to 0.42; SI Appendix, Table S1; Supplement 4, (30)).

The global distribution of intra-specific diversity of mangrove forests can be explained by multigenerational stepping-stone oceanographic connectivity, regardless of the species, region, or genetic marker. The modeling approach, accounting for the temporal and spatial complexity of oceanographic processes, as well as key ecological dispersal traits, improved the ability to explain genetic differentiation when compared to more simplistic, yet widely used isolation by distance models. The integration of centrality indices derived from graph theory showed the potential role of particular sites as hubs for connectivity (Fig. 3), which may bridge populations across large water masses, facilitating gene flow and therefore reducing genetic differentiation levels.

The biophysical modeling based on high-resolution ocean circulation data coupled with graph theory showed how taxon-specific traits may strongly impact the potential for population dispersal, as well as the overall levels of genetic differentiation (SI Appendix, Fig. S1). Mangrove species have contrasting dispersal periods (29) that can result in oceanographic connectivity events at scales differing by more than one order of magnitude (from an average of 55.12 km to 715.44 km between 10 and 180 d of propagule duration). The number of population connectivity events also increased with dispersal potential, a result that is somewhat expected in biophysical modeling simulation connectivity of advected passive particles (31). However, the relationship between increasing dispersal periods and potential connectivity did not show a linear relationship, but rather a positively skewed one, independently of the propagule duration considered, which can result in connectivity events of up to 10,582 km. This is in line with theoretical dispersal kernels (29), as well as empirical evidence suggesting that mangrove propagules do not tend to disperse far away from source locations (32, 10, 11), yet rare long-distance dispersal events can occur, as proposed across the Atlantic (33, 34), Indian (35), and Pacific Oceans (33, 35). These general traits of mangroves’ dispersal ecology may be key in driving intra-specific biodiversity levels. While limited dispersal potential explains the strong genetic structure found across species distribution ranges (36), the rare events at larger scales must be crucial during the first colonization of distant habitats (10).

Comparing the inferred patterns of oceanographic connectivity against empirical genetic data per species, study, and genetic marker corroborated our main hypothesis. Connectivity estimates explained genetic differentiation in nearly all tests performed (95%), and, compared to isolation by distance models, the approach improved the explanatory power by an average R-square of 0.18 ± 0.16 (up to 0.76) and Person’s correlation of 0.21 ± 0.19 (up to 0.77). This result builds upon existing studies performed with a limited number of species and regions (21, 37). Such an improvement in modeling genetic differentiation likely resulted from considering the combined effect of realistic oceanographic features (17, 38), the directionality of ocean currents (16), and the potential for stepping-stone connectivity (17). On the one hand, fronts, eddies, and bifurcations generate oceanographic barriers that can isolate neighboring populations (39) and increase their genetic differentiation (16, 17), as verified for Rhizophora species considered in our models in regions of Southeast Asia (14), Eastern Atlantic (22), Western Indian Ocean (39). On the other hand, the directionality of ocean currents can further play a role in the relative degree of gene flow, by promoting either unidirectional or bidirectional connectivity between pairs of populations (16, 40). Multigenerational stepping-stone connectivity is also key while performing comparisons against genetic data, because differentiation levels reflect both colonization and gene flow events acting across large temporal scales, and direct oceanographic transport between sampled populations is very unlikely. Finally, our analyses also considered the add-on effect of centrality indices, which improved all models explaining genetic data (average R-square of 0.06 ± 0.07, and maximum of 0.42). Regions of high centrality provided information about particular sites functioning as stepping-stones between populations and with a higher degree of reachability (41, 42), which may promote gene flow and reduce genetic differentiation levels [i.e., hubs for genetic connectivity (43)]. Conversely, regions of lower centrality are linked to dispersal barriers and, therefore, genetic discontinuities (16). Mapping centrality allowed verifying that the Indo-West Pacific concentrates most hubs for stepping-stone connectivity, matching mangroves’ hotspot of interspecific diversity (3), and that islands and offshore regions are key for long-distance dispersal events, bridging populations of species with longer dispersal periods.

Despite the general agreement found between oceanographic connectivity and population genetic differentiation, our models were not significant in 3 out of the 59 tests (yet P-values were marginally significant), and even in those that were significant, not all variability was explained (~43% variability explained, on average), most likely due to the effect of additional drivers not related to present-day oceanographic processes. For instance, the direction and intensity of ocean currents could have changed in the past (44), shaping distinct gene flow patterns and barriers to dispersal (e.g., the Central American Isthmus) that could have left a strong imprint in the distribution of intra-specific diversity (45). Alongside population connectivity, genetic diversity can also be structured by past demographic processes. The sea-level fluctuations during glacial-interglacial cycles of the Quaternary might have repeatedly shifted mangroves’ distribution, also influencing genetic diversity (2). More recently, ongoing climate changes (24, 46) and human-induced disturbance (47, 48) may have also impacted genetic levels owing to population size reductions in the face of unfavorable niche conditions or by founder effects at the leading edge of new colonizations (49). Alternative means of dispersal, such as wind and insect-driven pollination (21, 50), and human-induced reforestation (51), can also shape the distribution of genetic diversity in ways (i.e., distance and direction) that may contrast with those from the prevalent oceanographic currents. Finally, the available genetic data compiled by systematic search of the literature did not cover all mangrove species (20 out of ~80 species), and the genetic markers used could have also biased the estimates of population differentiation. For instance, some markers like mitochondrial DNA have reduced resolution and can be prone to selecting specific genotypes (52), potentially confounding the actual degree of population connectivity.

Overall, our biophysical modeling shows that oceanographic connectivity explains the population differentiation of mangrove forests at global scales. This is of high relevance not only for biogeography and evolution (15, 29, 31) but also for climate change ecology since ocean currents can modify the link between climate change and biogeographical shifts (53, 54). The future persistence and expansion of mangrove populations, as well as their gene pools, may become a process primarily dependent on the regional patterns of ocean currents, which may facilitate or hinder distributional shifts depending on their agreement with temperature, precipitation (53, 55, 56) and salinity changes (57). Global conservation strategies can also benefit from our findings. The highlighted importance of stepping-stone processes and central populations acting as hubs for gene flow can pave the way for future research pinpointing priority conservation areas for mangroves under the post-2020 global biodiversity framework [e.g., high-betweenness hot spots; (58, 59), a new framework that targets the conservation of genetic diversity of wild populations, as well as the establishment of well-connected networks of protected areas (27)].

This study received Portuguese national funds from FCT - Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, PTDC/BIA-CBI/6515/2020, SFRH/BSAB/150485/2019, SFRH/BD/144878/ 2019, the transitional norm DL57/2016/ CP1361/CT0035, and the Individual Call to Scientific Employment Stimulus 2022.00861.CEECIND. M.B.A. also acknowledges funding from BNP-PARIBAS Foundation, through the CORESCAM project (“Coastal Biodiversity Resilience to Increasing Extreme Events in Central America”).