Conservation at the edge: connectivity and opportunities from non-protected coral reefs close to a national park in the Colombian Caribbean

doi:10.21203/rs.3.rs-1654684/v1

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Conservation at the edge: connectivity and opportunities from non-protected coral reefs close to a national park in the Colombian Caribbean

https://doi.org/10.21203/rs.3.rs-1654684/v1

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published 08 Feb, 2023

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Confronting a sustained coral reef conservation crisis, we need new opportunities to rethink how to protect areas successfully and efficiently in the face of a changing world. We studied the benthic community, including foraminifera, fish community, and genetic connectivity (SSRs and SNPs) of main reef-building coral, Orbicella faveolata and Agaricia undata coral, along a Non-Protected Area (NPA) reef tract in Barú peninsula, including some isolated banks, near Cartagena and the National Natural Park Corales del Rosario y San Bernando (NNP CRySB), Colombia. The fringing reef track studied is homogeneous in benthic components, including algae, corals, sponges, and foraminifera between all the sites studied. Many reef sites sustain between 42.8% and 53% coral cover, which are among the highest recorded in this region, even higher than values in the nearby NNP. A total of 82 fish species were found, and the Foram Index-FI varies between 2 and 2.5, showing environmental conditions marginal for reef growth. The Barú NPA reef system can be considered spatial refugia under climate change and Anthropocene conditions, including resilient reefs at the mouth of Cartagena Bay (Magdalena River), a place of increased stressing factors. The admixture between NPA and NNP populations, the high coral cover in the NPA, the fish density and composition, the uniqueness of the diapiric banks, and the disturbance resistance are major arguments to protect this reef tract. We suggest designing a co-management scheme to ensure species connectivity, avoid further degradation, and involve different stakeholders.

Non-protected Areas (NPA)

National Natural Park (NNP)

Marine Protected Areas (MPA)

genetic connectivity

Orbicella faveolata

Agaricia undata

diapiric coral banks

The first marine protected areas-MPAs were chosen decades ago on an ad hoc basis by a combination of aesthetics and opportunism, e.g., untouched ecosystems, which usually included little scientific input (Knight and Cowling 2007; UNEP-WCMC 2008; Grorud-Colvert et al. 2014). In any case, fully protected areas, i.e., closed to fishing, achieve better results despite the method for their selection (Roberts 2000). However, many MPAs worldwide have been degraded so much that they no longer serve the purpose they were created; hence, we need to spatially re-design the MPA network in many countries.

Connectivity, leading to dispersal and recruitment, is a major paradigm to delineate MPAs since spillover is the most desirable ecosystem service from marine reserves (Brown et al. 2016; Bryan-Brown et al. 2017; White et al. 2019). In tropical coastal areas, the adaptive capacity of local communities, together with opportunities for co-management, greatly improve governance and effectiveness of MPAs (Camargo et al. 2009; Moreno-Sánchez and Maldonado 2010; López-Angarita et al. 2014; Kockel et al. 2019; Muhl et al. 2020). In any case, marine ecosystems face threats due to global warming and ocean acidification, where the traditional management tools do not seem to buffer deterioration (Hoegh-Guldberg and Bruno 2010; Jackson et al. 2014; Pendleton et al. 2016; Hoegh-Guldberg et al. 2017).

The steady decline of coral reefs, greatly exacerbated in the Caribbean, introduces urgent challenges to MPAs. Even the remotest reefs, hundreds of kilometers away from direct continental influence, have lost coral cover and bottom tridimensionality (Sánchez et al. 2005; Bruno and Valdivia 2016; Sánchez et al. 2019a). Such a level of deterioration can lead to reef bioerosion and further loss of ecological integrity, or even the functional nature of reefs as water motion regulators, which will intensify in synergy with water level rise (Perry et al. 2014, 2018). We are reaching a consensus on the causes leading to coral reef deterioration, which point to global issues related to warming temperatures producing coral bleaching and disease (Hughes et al. 2017a, b, 2018; Sully et al. 2019), together with local stressors such as nutrient input and overfishing (Carpenter et al. 2008; Camargo et al. 2009; Velásquez et al. 2011; Jackson et al. 2014; Pendleton et al. 2016; Thiault et al. 2019). MPAs should confront this situation, focusing on adaptative management and accounting for changing or deteriorating conditions (McCook et al. 2010; López-Angarita et al. 2014; Kroeker et al. 2019; Nickols et al. 2019).

The decline of ecosystems and wildlife can sometimes occur at the same pace in both protected and non-protected areas (NPAs), which is not surprising, given their inherent shortcomings, in many cases, during reserve design (Western et al. 2009). Although protected areas usually provide better ecosystem services, NPAs, usually more extensive, are worth managing or integrating for the same conservation purposes (Avigliano et al. 2019; Benez-Secanho and Dwivedi 2020). In marine ecosystems, MPAs are known to be largely ineffective for global conservation purposes (Mora et al. 2006), which prompted the need for a larger target (Aichi 11) under the protection of at least 10% (Gairin and Andréfouët 2020). This target, still far from being achieved, also stands for efficiently managed MPAs, which in coastal waters lack social engagement with little retribution to local communities (Camargo et al. 2009; López-Angarita et al. 2014; Rasheed 2020). Consequently, MPAs need to increase their coverage and social involvement to become an efficient management tool for conservation.

In the face of a sustained conservation crisis of coral reefs, new opportunities to find key conservation areas and resilient or resistant reef ecosystems to protect nowadays successfully and efficiently are greatly needed. Despite the steady deterioration of natural ecosystems, there are emerging natural conservation cases for reasons not entirely obvious. One such case is the Varadero reef located in one of the entries to Cartagena Bay (López-Victoria et al. 2015), one of the busiest and most contaminated ports in the Caribbean sea, which exhibits high coral cover and overall “good condition” by Caribbean standards (Pizarro et al. 2017). Varadero reef is located at the end of a 22 km fringing reef system along the Barú peninsula (Cendales et al. 2002), Cartagena, Colombia. Varadero and Barú reefs are affected directly by the Magdalena River, the longest in Colombia (≅1500 km), through the Dique channel with 144 mill tons/yr of sediment input to the Caribbean (Restrepo et al. 2006a).

Barú peninsula reef system is characterized by a high coral cover of lettuce coral (Undaria tenuifolia) on the fore-reef with large coral heads towards valley-like sand channels (Cáceres and Sánchez 2015a; Grajales and Sánchez 2016). Over half of the fringing reef system from the Barú peninsula is part of the National Natural Park Corales del Rosario and San Bernardo (NNP CRySB), representing the highest protection category in the Colombian protected areas system (Camargo et al. 2009). The track towards Varadero reef, is threatened by a dredging license for opening a new channel that will serve the Cartagena Port (Pizarro et al. 2017). Fortunately, a group of citizens and local ONGs have managed to stop the dredging activities, yet the threat persists.

What makes this reef so unique? Varadero reef is near Cartagena Bay, one of the pollution hotspots of the Caribbean. It thrives under constant freshwater run-off (Tosic et al. 2019), conditions totally opposed to coral growth and health (Restrepo et al. 2006b). Reefs growing under suboptimal conditions are not commonplace in the Caribbean (Pizarro et al. 2017), where Varadero is the end of a long-fringing reef system. Nevertheless, this area also includes an even more remarkable singularity: reef banks growing on mud diapirs. The continental platform in this area comprises an extensive uninterrupted tectonic arc that is part of the prism of the San Jacinto and Sinú belts, where the Pleistocene cover of the platform is disrupted by older unconsolidated shale forming large dome-like mud diapirs that are capped by coral formations (Vernette et al. 1992). These banks in the proximity of the Barú fringing reef provide mesophotic coral ecosystems with well-developed mixed coral zones and unique octocoral and black coral animal forests (Sánchez 1999a, 2017). A realistic ecological assessment of this NPA reef system should integrate the fringing reefs and the diapiric banks. Moreover, we assessed the reef system morphology in both fringing and reef banks. We quantified coral cover and abundance of other benthic organisms, including foraminifera assemblages and fish communities, together with genetic connectivity estimates using two hard corals, Orbicella faveolata and Agaricia undata.

Benthic components and Scleractinia community

For description of the study sites see Sánchez (1999), Cáceres & Sánchez (2015a) and Pizarro et al. (2017). In 2013, using SCUBA and Closed-Circuit Rebreather-CCR diving, 14 coral reef sites (Figure 1) were surveyed for main benthic components (coral, coralline algae, and fleshy/turf algae cover), through photo-quadrants. Three different belt transects were placed parallel to the coast at three depth ranges: 0-8 m, 9-15 m, and 16-22 m, to measure the cover percentage of benthic organisms in 10 coastal reefs at the shallow zone (0 – 30 m; V: Varadero, V2: Varadero, L: Lincon, N: Niko, LC: La Caida, CV: Ciénaga de los Vásquez, LQ: La Quebrada, PB; Playa Bonita, LM: Lado Montañíta, PG: Punta Gigante). 10 quadrants of 1x1 m were zigzagged, along each transect, sampling 30 m² per depth (90 m² per site). At 4 diapiric domes (I: Imelda, M: Montañita, OR: Octubre Rojo, TR: Trompadas; Figure 1), one transect was located at each depth range at shallow (0 – 30 m) and mesophotic zone (30 – 70 m); 16-22 m, 24-30 m, 34-40 m, 44-50 m, 54-60 m and 64-70 m. A variable number of quadrants of 0.5x0.5 m were zigzagged due to the air(gas)/time restrictions. The total sampled area was 870 m² at the shallowest zone and an area of 62.75 m²at the deepest zones. Photos were analyzed using CPCe software (Kohler and Gill 2006) for the identification and determination of the percentage cover of the broad benthic categories (Coral, Turf/Fleshy algae, Calcareous algae, Octocorals, Sponges, Sand/Hard substratum/Rubble, Cyanobacteria, Black coral) and Scleractinia corals at species level.

Benthic components data was analyzed to establish the differences between sites and depths using the Kruskal-Wallis test. A multidimensional scaling (NMDS) was made using Bray-Curtis dissimilarity measures. Data of corals species was analyzed to establish the variations in the coral community between sites and depths through the Kruskal-Wallis and Mood's median tests. Pairwise comparisons using Dunn's test were applied to evaluate differences between pairs of sites and depths. We carried out an NMDS analysis to observe the grouping of the samples. A permutational multivariate analysis of variance (PERMANOVA) was made, using the Bray-Curtis method to determine differentiations in the composition and Pairwise PERMANOVAs. Additionally, we analyzed similarity percentages (SIMPER) to identify the main species of corals contributing more to differences.

Fish community

In 2013, the reef fish community was assessed by visual census using linear transects of 50 m at 14 sites, 10 coastal reefs (V, V2, VL, L, N, CV, LQ, PB, LM, and PG; Figure 1), and 4 diapiric domes (M, I, OR and TR; Figure 1). For the shallow depths (0-8 m, 9-15 m, 16-22 m, 24-30 m), in the 14 sites, the transects were extended in four directions (north, south, east, and west) from the center of the sample area at each depth range, for a plot area near 200 m². The data was registered at two meters from each side of the transect, where all fish individuals were recorded. The deepest transects (34-40 m, 44-50 m, 54-60 m, 64-70 m) were made in 3 sites (I, OR and TR) and were extended in one direction with 1 m wide and an area of 50 m² due to the geomorphology of the domes and dive time restrictions. To avoid oversampling by the two observers, only one of the observers counted the individual when crossing simultaneously in the range of vision. Data was standardized using density per m² for comparisons due to the differences in the total areas sampled between shallow and deepest zones (See Supplementary Table S2).

Fish community density differentiation was evaluated with the non-parametric Kruskal-Wallis’s test between sites and depths. Pairwise comparisons using Dunn's test and Wilcoxon’s test were used for both factors depth and site. An NMDS graphically represented samples to visualize the fish community grouping by the depth and site, and ANOSIM was applied to verify differences between the groups. PERMANOVA with Bonferroni corrections and square root transformation was made using the Bray-Curtis method to determine differences between the depth and sites. Pairwise PERMANOVAs were used to determine differentiation between groups. A SIMPER analysis was made to identify the fish species contributing most to the dissimilarity between depths and sites.

FORAM Index - Foraminifera assemblages

Foraminifera assemblages were surveyed at 9 sites (V, V2, L, N, LC, CV, LQ, PB, PG; Figure 1), taking 3 samples of 10 gr at each site of superficial undisturbed sandy sediment at depths between 5-12 m. Each of the 27 samples was stored with absolute ethanol, dried (>24 h, 60 ºC), and thoroughly mixed. 1-gram portion was washed with freshwater over a 63-µm mesh sieve to remove mid-size sediments and dried on filter paper. The dried sample was gently disaggregated, thoroughly mixed, and poured on a clean, smooth surface. A subsample was taken (approximately 0.1 g), weighed, and placed in a 90 mm-diameter petri dish to examine.

All foraminifera were removed and counted until a minimum of 150 individuals were found in each sample for each site, excluding heavily worn and reworked specimens (Hallock et al. 2003). The foraminifers were counted, identified by genus, and sorted into three functional groups: symbiont-bearing, opportunistic, and other small heterotrophic taxa (Hallock et al. 2003). The proportion (P) of specimens for each functional group was calculated by summing the specimens of each genus of that group (N) and dividing by the total number of specimens counted (T) to calculate the original Foram Index (FI) (Hallock et al. 2003). The FI ranges from 1 to 10, where FI < 2 indicates unfavorable conditions for coral growth, 2 < FI < 4 represents the limit for coral growth and is unsuitable for recovery, and FI > 4 allows coral growth and recovery (Hallock et al. 2003).

If the number of foraminiferal specimens is approximately

150–200, the subsample is sufficient. If fewer specimens were present in the first

portion, then a second portion is removed from the mound, weighed, and sorted. This

procedure is repeated until 150–200 specimens are obtained or until the entire gram of

sample is processed

Genetic diversity and population genetic structure

Orbicella faveolata

A total of 231 individuals of O. faveolata were sampled at 8 sites (V, V2, LC, M, LM, PG, CU, PE, and SD; Figure 1), including 6 sites in the NPA and two sites in the PNN CRySB (research permit DTC‐CR‐T‐036–03–09). Coral fragments were collected haphazardly, maintaining a distance of 5 m between samples to minimize sampling clones. Each sample consisted of a 1-2 cm fragment from the basal portion of the colony. Samples were preserved in DMSO and snapped frozen at -80 °C in liquid nitrogen. The DNA extraction was carried out following the modified CTAB protocol (Coffroth et al. 1992). RNAse A and Proteinase K were added during the extraction to eliminate contaminants.

Samples were genotyped using five microsatellite primers pairs designed for Orbicella annularis (Severance et al., 2004). After screening seven loci, five loci were retained in our study, excluding two microsatellites: MS2-17 because amplified less efficiently (Severance et al., 2006) and MaMS12, which had a high frequency of null alleles and heterozygotes deficit across all populations. The primers were combined in three multiplex PCR with a total volume of 15 ml containing 0.2 mM dNTPs, 1x buffer, 3 mM MgCl2, 10 mg/ml BSA, 0.04 µM of forward primer, 0.16 µM of reverse primer, and fluorescently labeled M13-tail primer, 3U Taq polymerase and 10 ng of DNA template. The PCR cycling parameters included an initial denaturation step for 2 min at 94 °C, followed by 35 cycles of denaturation for 30 s at 94 °C, annealing for 60 s at 56.3-58.8 °C, and 60 s at 72 °C. The final extension was done for 15 min at 72 °C. The PCR products were analyzed with a GeneScan™ 500 LIZ® Size Standard (Applied Biosystems) on an ABI 3500 DNA Analyzer. The resulting electropherograms were scored manually with GeneMapper version 4.5.

Six sets of clones were found in Varadero (V), La Caida (LC) and Punta Gigante (PG), one set in each location, and three in Varadero2. Therefore, 7 individuals were excluded from the analysis. Colonies that had missing data were also excluded (n=16). After adjusting the data set, 201 individuals were analyzed. Genetic diversity indices, the number of alleles per locus, effective number of alleles (Ne), expected (He), and observed (Ho) heterozygosity and fixation index (F) for each population and each microsatellite per population were calculated in the GenAlEx version 6.5 (Peakall and Smouse 2012). The probability of identity (P_ID) was also calculated in GenAlEx. To confirm the genetic identity of all collected samples of O. faveolata, we ran STRUCTURE 2.3.4 (Pritchard et al. 2000) with the samples collected in this study and additional 295 samples of O. annularis (Foster et al. 2012). Results separated the two species (K = 2); therefore, we kept samples with a >0.9 posterior probability of belonging to the O. faveolata cluster.

Deviations from Hardy–Weinberg equilibrium and linkage equilibrium were performed using ARLEQUIN v. 2.00 (Excoffier and Lischer 2010). Pairwise genetic differentiation (F_ST values) between eight populations was estimated in ARLEQUIN v. 2.00 (Excoffier and Lischer 2010). An analysis of molecular variance (AMOVA) was conducted with GenAlEx, testing population structure within and among populations. Distinct genetic clusters of the eight populations of O. faveolata were identified in STRUCTURE. 10 runs were performed for each K value (K = 1 to 8) with a burn-in period of 100,000 iterations and 50,000 MCMC after burning under the admixture model. The best-fit number of clusters was determined based on the DK method (Evanno et al. 2005) and ln Pr(X|K) summarized by Structure Harvester (Earl and vonHoldt 2012) and Clumpp (Jakobsson and Rosenberg 2007). A Discriminant Analysis of Principal Components (DAPC) was implemented in the R package adegenet (Jombart 2008).

Based on migration probability, estimation of gene flow was made with the divMigrate function of the diveRsity package (Sundqvist et al. 2016), with Nei's genetic distance Gst (Nei 1973), a measure of genetic distance with 1,000 bootstrap repetitions and an arbitrary filter threshold of 0.30. This analysis detects symmetric and asymmetric migration under the assumption of neutral selection. Gene flow patterns were visualized using network graphics produced with the R package qgraph (Epskamp et al. 2012) and represented in a map (ArcGIS Pro 2.8).

Agaricia undata

21 coral tissues of A. undata were collected pooling six sites and samples from shallow (0 – 30 m) and mesophotic (30 – 45 m) zones (7 vs. 14 respectively; Table 1) in 5 diapirs: Montañita (M), Imelda (I), Octubre Rojo (OR), Trompadas (TR), Isla Tesoro (IT) and one coastal reef: La caida (LC) (Research permit No. 1177/2014 - IBD 0359). All collected samples were ID-labelled (location and depth), and samples preservation and DNA extraction were carried out following the same method mentioned for O. faveolata.

NextRAD libraries were sequenced, converting genomic DNA into nextRAD genotyping (Next-tagmented-reductively-amplified-DNA) following the manufacturer's protocol (Baird et al., 2008; Etter et al., 2012; Bongaerts et al., 2017; Gonzalez-Zapata, Bongaerts, et al., 2018a). PyRAD v3.0.66 (Eaton 2014) was used for quality control, clustering, and SNP variant calling. After initial runs at different thresholds, individuals were analyzed using a clustering threshold of 90%. Clusters with coverage of ≥2 were reduced to a single representative sequence and used to compile a BLASTN database. Parsing and analyses were carried out using custom Python (v3.7) scripts based on Bongaerts et al. (2017). One representative sequence was extracted for each coral RAD locus and compared in BLASTN against the Symbiodinium RAD database (Bongaerts et al. 2017); these sequences were removed. Cleaning of microbial contamination was compared in BLASTN against the National Center for Biotechnology (NCBI) nonredundant nucleotide database. RAD loci matching to non-cnidarian taxa were removed. Clones were identified using a genetic distance/similarity (Hamming-based) to reduce them to a single representative. The remaining SNPs were evaluated for significant deviations from the Hardy-Weinberg equilibrium using the pegas v0.12 R package (Paradis 2010), and the SNPs with a <0.05 p-value were removed.

Using alignment-based clustering, it was recovered 27,012 nextRAD sequence loci (“RAD loci”); 200,321 SNPs. An 8% of the dataset was removed for contaminants from Symbiodinium. One of the 23 samples was removed because only 9.18% was genotyped (IM-M5), and three clones were removed. We identified two clonal sets with a 94.5-99% match, the first one with two samples (MO-S1 and MO-M4) and the second one with three samples (TR-S2, TR-S3, and TR-M1). 6,039 bi-allelic SNPs were retained after data filtering, and for the STRUCTURE analysis, 3,803 loci were used for the overall dataset and 3,294 for the neutral dataset. After the filtering, 17 coral samples were used from the 6 sites (M, I, OR, TR, IT, and LC) and grouped in shallow and mesophotic samples (6 vs. 11, respectively) to run the genetic analysis.

The R package hierfstat was used to calculate Observed (Ho) and to perform Goudet’s G-statistic Monte Carlo test (Goudet 2005) to test population structure (99 permutations). Also, genetic structure analysis was assessed through principal component analysis PCA and a DAPC with the adegenet R package. To determine the number of PCs to retain in the DAPC, we used the α-score (See Supplementary Figure S4a). Pairwise F_ST value was calculated between shallow and mesophotic populations, between sites, and within depths zone at each site to determine the differentiation among the 6 populations.

The genetic STRUCTURE analysis was run using an admixture model with burn-in 100,000, MCMC reps 50,000, and 10 runs for each K = 1-5. It was visualized via a plot of posterior probabilities for each individual and population with Distruct (Rosenberg 2003), and the Evanno method (Evanno et al. 2005) was tested to identify the single k value, which captures the uppermost level of structure (See Supplementary Figure S4b). All diversity and population differentiation analyses mentioned above were calculated for the all-inclusive and neutral loci. The relative migration levels (Nm) between 4 populations (MO, TR, IT, and I) were estimated to elucidate the recent migration patterns based on 5,516 neutral SNPs, using the divMigrate function, with 1,000 bootstrap repetitions and without any filter threshold. Gene flow patterns were represented in a map using ArcGIS Pro 2.8.

Raw Data availability

Project: https://figshare.com/projects/Conservation_at_the_edge_connectivity_and_opportunities_from_non-protected_coral_reefs_close_to_a_national_park_in_the_Colombian_Caribbean/123118

Orbicella faveolata genetic data

https://doi.org/10.6084/m9.figshare.16786243

Foraminifera

https://doi.org/10.6084/m9.figshare.16786126

Fish Data

https://doi.org/10.6084/m9.figshare.16786102

Benthic cover data

https://doi.org/10.6084/m9.figshare.16640341

Benthic cover

Fleshy algae were the group with the highest bottom cover at NPA sites, with a mean of 34.91%, followed by 24.50% from 47 hard coral species and calcareous algae with 14.66% (Figure 2). The coral species with the highest mean cover values were Agaricia agaricites (3.8%), Orbicella faveolata (3.5%), Agaricia tenuifolia (3.1%), Orbicella annularis (3.0%), Agaricia lamarcki (1.9%), Porites astreoides (1.9%) and Colpophyllia natans (1.7%) (See Supplementary Table S1). The other species represented between 0.7% and 0.003% of the coral cover, the least frequent Porites colonensis and Porites branneri (See Supplementary Figure S1a). Percent cover of the major benthic categories evaluated did not show significant differences between all the sites (Kruskal-Wallis H=6.94, p = 0.9) and all the depths ranges (Kruskal-Wallis H=3.49, p = 0.83), including the coastal reef-tract and the diapirs.

However, the mean coral cover values (Figure 3a) between NPA sites showed significant differences (Kruskal-Wallis H=93.69, p<0.001; Mood’s Median=92.46, p<0.001) and between depth ranges (Kruskal-Wallis H=105.79, p<0.001; Mood’s Median=104.17, p<0.001). Additionally, coral community structure (composition and cover) showed significant differences between sites (PERMANOVA, F=1.59, p=0.001) and depths (PERMANOVA, F =3.25, p=0). Nevertheless, pairwise comparisons using the Dunn test indicated that all the shallow ranges (0-8 m, 9-15 m, 16-22 m, 24-30 m) do not have significant differences between them (Bonferroni-corrected p > 0.05), which indicates homogeneity of the covers percentage in the reef tract located at the shallow coastal zone (See Supplementary Table S3). Shallow coral covers differ from the mesophotic ranges (34-40 m, 44-50 m, 54-60 m; p<0.001) located at the diapirs, exhibiting a lower coral cover. Differences between pairwise sites (Dunn test) were found between the reefs located in the diapiric domes (OR, M, I, and TR; p<0.001) and some coastal sites (CV, L, LC, PB, PG, and N; p<0.001, Supplementary Table S3). Thus, the diapiric domes are the sites and depths with coral cover explaining the differentiation displayed.

Pairwise PERMANOVA comparisons within the shallow depths (0-30 m) indicated significant differences, and also between shallow and mesophotic ranges (PERMANOVA, p<0.05), while within the deeper ranges below 30 m showed no differences between them (PERMANOVA, p> 0.05, see Supplementary Table S3). These results were confirmed through a non-metric multidimensional scaling (NMDS: Figure 4), indicating that coral species located in the shallow zone (0 – 30 m) exhibit differentiation between depth ranges, while the mesophotic zone (30 – 60 m) seems to be more homogeneous.

The differences between sites may be explained by the coral species A. tenuifolia, O. faveolata, P. astreoides, and A. agaricites, where the former two species have a cumulative contribution of around the 34 – 35% to the differences, and the last two of 10 – 13 % (SIMPER, see Supplementary Table S3). The differentiation found between the shallower depths against the mesophotic sites is mainly due to the presence-dominance of certain coral species, O. annularis at 0-8 m, A. tenuifolia at 9-15 m, A. agaricites at 16 – 22 m, and A. lamarcki at 24 – 30 m (SIMPER, see Supplementary Table S3). Additionally, it was evident that shallow reefs exhibit important reef-building species such as O. annularis, O. faveolata, Montastraea cavernosa, Siderastrea siderea, Pseudodiploria strigosa, P. astreoides, Porites porites. In contrast, fast-growing species dominate deeper reefs, such as Agaricia spp., Helioseris cucullata, and Madracis spp.

Fish communities

We found 82 fish species in all the sites and depths sampled (Figure 5b), with the highest richness between 9 and 15 m with 60 species and the lowest at 64 and 70 m with only 6 species (Figure 5b). The highest mean density values for the fish species were Clepticus parrae 71.52 ind/100 m², Chromis cyanea 27.24 ind/100 m², Scarus iseri 24.32 ind/100 m², Chromis multilineata 21.05 ind/100 m², Thalassoma bifasciatum 11.09 ind/100 m², and Stegastes partitus 9.38 ind/100 m². The least frequent groups were Caranx bartholomaei, Diodon hystrix, Gymnothorax moringa, Halichoeres poeyi, Ripticus saponaceus, Sphyraena barracuda, Urobates jamaicensis with a mean density of 0.0036 ind/100 m² for each species and Haemulon chrysargyreum, Hypoplectrus guttavarius, Sargocentron coruscum with 0.0073 ind/100 m² (See Supplementary Figure S1b).

The mean fish density values between sites (Figure 5a) showed significant differences (Kruskal-Wallis H=69.26, p<0.001), as well as between depth ranges (Kruskal-Wallis H=67.28, p<0.001). Moreover, the fish community composition exhibited significant differences between sites (PERMANOVA, F=1.92, p=0.001) and depths (PERMANOVA, F =2.66, p=0.001). Pairwise comparisons to determine which sites were different showed that CV, N, LM, OR, and TR are the sites with differences related to other sites (Wilcoxon signed-ranks, p<0.05; Dunn’s test, p<0.05, See Supplementary Table S4). The comparisons by depth range indicated fish density differences between 0-8 m with all the ranges from 16-70 m, 9-15 m with 24-30 m and 64–70 m, and 16-22 m with 64 – 70 m (Wilcoxon signed-ranks, p<0.05; Dunn’s test, p<0.05, See Supplementary Table S4). Therefore, the fish density is heterogenous along the reef tract due to the differentiation found in Ciénaga de Los Vásquez, Nico, Octubre Rojo, and Trompadas bank, the last one the most highly differentiated from the rest of the sites. Pairwise PERMANOVA comparisons by sites showed differences between Octubre Rojo and several coastal reef sites; V, V2, CV, L, and N. Additionally, Fish community comparisons between pairwise depth range indicated differences between shallow ranges (0 – 8 m and 9 – 15 m) with the rest of the depths (PERMANOVA, p<0.05, see Supplementary Table S4). Furthermore, these results agree with the non-metric multidimensional scaling (NMDS: Figure 6), indicating that fish species located within the shallow zone (0 – 30 m) exhibit differentiation between depth ranges (Figure 5), while from 30 to 60 m at the mesophotic zone, it seems to be homogeneous.

The differences found between OR with V, V2, L, and N may be explained by the presence and density of two fish species, C. cyanea, with a cumulative contribution of around the 22 to 27%, and C. multilineata with 21 to 24% cumulative contribution for all the pairwise comparisons (SIMPER, See Supplementary Table S4). Differentiation between OR and CV could be explained by the species S. iseri and C. parrae, with a cumulative contribution of around 22% and 18%, respectively. The differences in the depth range of 0 – 8 m with the other depths were explained by the cumulative contribution of the species C. parrae, S. iseri, T. bifasciatum, and C. cyanea. The presence of C. parrae and C. cyanea may explain the differences from the 9-15 m with the rest of the depth ranges (0 – 40 m, SIMPER analysis; Supplementary Table S4).

FORAM Index - Foraminifera assemblages.

We examined 27 sediment samples in 9 sites, counted 1483 individuals, and identified 43 genera: 7 symbiont-bearing, 6 opportunistic, and 30 “other small heterotrophic” (Figure 7a). The most abundant genera for each functional group were Amphistegina, Bolivina, and Discorbis, respectively (see supplementary Table S5). 92% of all individuals from the foraminifera assemblage belong to the small heterotrophic genera group, followed by 5% opportunistic and 3% symbiont-bearing. All sites sampled (CV, L, LC, N, PB, PG, Q, V, and V2) had an average Foram Index-FI between 2 and 2.5 (Figure 7b), and additionally, a negligible variation among sites was observed (Kruskal-Wallis chi-squared = 8, df = 8, p = 0.43). Therefore, these results show that the nutrient input in the Barú zone evaluated is homogeneous and conditions are marginal to unsuitable for recovery of coral communities after a mortality event but not unsuitable for reef growth in normal environmental conditions.

Genetic diversity and population genetic structure

Orbicella faveolata

The number of alleles (Na) ranges from 6 to 10.4, and the number of effective alleles (Ne) ranges from 3.272 to 5.647. Observed heterozygosity (Ho) was between 0.540 and 0.669, while expected heterozygosity (He) ranged between 0.576 and 0.789. V2, M, P, and SD showed positive F-values, indicating heterozygote deficits, ranging between 0.072 to 0.151. The other sites (V, LC, PG, and CU) showed negative F-values, ranging between -0.007 and -0.090 (Table 1). A significant heterozygote deficit was observed in five populations for one locus STR4 (V2, LC, PG, CU, SD), one population for STR8 (LC), and three populations for STR11 (M, CU, P) (See Supplementary Table S6).

Significant genetic structuring was found among all localities, with a small but significant overall F_ST-value of 0.037 (p = 0.001); F-statistics from the AMOVA analysis showed that the largest percentage of variation corresponds to within-individual variation (90%) followed by variation among individuals (6%), and finally, variation among locations (4%) (Table 2). Montañita was significantly different from other localities with moderate genetic differentiation (Table 3). The Bayesian clustering from STRUCTURE was not conclusive (Figure 8a). Delta K and Ln Pr(X|K) resulted in different best K values. Evanno method (Delta K) resulted in K=2 and Ln Pr(X|K) in K = 3 (Supplementary Figure S3). DAPC analysis revealed a pattern consistent with K=2 where Montañita is different from the other populations (Figure 8b).

The directional relative migration using divMigrat (Figure 8c) shows that CU and PG are probably sinks from almost all the sites evaluated. PG with 100% of relative immigration from V2, 82% from V, 89% from LC, 58% from PE, and 30% from SD. At the same time, CU has immigration from 35 to 52% from V, V2, LC, and PE. It seems that V, V2, and SD are larvae sources contributing to migrants to all the sites. M exhibited a meager relative migration rate, under 10%, which supports the result observed in the STRUCTURE. This analysis showed that Varadero Reef has been historically connected with populations inside the NNP and demonstrated that Punta Gigante could be a sink, highlighting the importance of this reef tract for genetic connectivity.

Agaricia undata

No genetic structuring across populations was identified for A. undata (G-statistic Monte Carlo test for all SNPs: Gobs = 9613.4, P = 0.85). The lack of genetic structure was further confirmed by assessing pairwise genetic distances between individuals at bathymetric comparison with an F_ST of 0.0055 (F_ST over all loci = -0.009 ± 0.13), also geographically resulting in all the possible pairwise comparisons between sites and depths with negative values, interpreted as 0 (F_ST = 0, P < 0.05; Table 4), and the lack of clustering identified by Principal component analysis (PCA; Figure 9a) and the Discriminant Analysis of Principal Components (DAPC; Figure 9b).

In the admixture analysis with STRUCTURE (Figure 9c), the Evanno method (Delta K) identified the uppermost level of structure of 2 K (this method did not evaluate K=1) and Ln Pr(X|K) in K = 1 (Supplementary Fig. S5), where two samples (TR_S₁and MO_M₂) showed a differentiation from the rest of the samples, as the PCA results. However, no introgression was found from one community to another, confirmed with the PCA, DAPC, and F_ST values; hence it may be interpreted as one population. Genetic clustering for the “neutral” data set, as inferred by STRUCTURE, was very similar to the overall data set. Genetic diversity was higher in mesophotic populations (0.143) than in shallow (0.132).

The directional relative migration for A. undata using divMigrate (Figure 9d) shows that a genetic sink could be possible for the IT population (Next to the NNP-CRySB), with 100% of relative immigration from I, 80% from M, and 62% from TR. In turn, the relative migration from IT to the coastal zone was 89% to I and 62% to M. The diapiric domes TR, I, and M have a relative migration turnover rate between 30% and 67% (Table 6). This analysis showed the high connectivity between the A. undata populations at the diapirs near Barú and the NNP populations. Vertical migration exhibited a turnover between the shallow and mesophotic zones, resulting in 100% of relative migration from the shallow to the mesophotic zone and 73% from the mesophotic to the shallow.

The NPA reef-tract system from Barú peninsula, Cartagena (Colombia), located outside the MPA—near the PNN Corales del Rosario and San Bernando—but without any practical or formal regulation and control, comprises a unique opportunity for coral reef conservation and stakeholder management approach. Our results showed at least four biological attributes that sustain a well-connected and diverse reef community beyond traditional coral reef environments involving mesophotic coral ecosystems of great singularity.

First, the coral community is homogeneous within the sites and shallow zones along the coastal reefs, yet pairwise differences were found between shallow and mesophotic zones and between the diapiric domes with some coastal zones. Thus, the diapiric domes are sites of exceptional singularity with different coral communities towards mesophotic depths. Additionally, the coastal reef and diapiric banks are homogeneous in the broad benthic components, including algae, corals, sponges, octocorals, and foraminifera assemblages between sites and depths.

Second, fish communities were heterogeneous along the reef tract, exhibiting differences in Ciénaga de Los Vásquez, Nico (near the coastal lagoon), Trompadas, and Octubre Rojo (farthest diapirs), and differences between depths were found within the shallow zone (0-30 m). However, this differentiation was not displayed within the mesophotic zone (30 – 70 m). Moreover, higher values of richness and density were found at both sites near the coastal lagoon (CV and N), which could indicate that the Ciénaga de Los Vásquez Lagoon may function as nursery grounds/or shelter during different stages life cycle. The fish community could migrate (functional connectivity) to complete their life cycles by using mangroves, coral reefs, seagrass meadows, and various soft bottoms around the study NNP sites.

Third, the population genetics from O. faveolata and A. undata supports the dispersal and potential rescue effect between populations, extending well into the nearby NNP CRySB. Regarding source-sink dynamics for O. faveolata, Varadero and Santo Domingo (inside NNP) are displayed as possible sources of larvae, and the Punta Gigante sector as a sink. However, O. faveolata exhibits a remarkable genetic structure and a low migration rate in Montañita. Additionally, a turnover in the gene flow was found between mesophotic and shallow zone for A. undata.

Lastly, the existence of the Varadero reef, which is the end tip of the reef system right at the mouth of Cartagena Bay under increased stressing factors and anthropogenic perturbations (pollution, ship traffic, fresh water, micro pathogens, eutrophication, sediments, and turbidity), provides information of resistant corals to high sedimentation and turbidity and possibly serves as a source of material for reseeding other reefs zones.

Despite the worldwide decline of coral reefs, this fringing reef remains resilient with high coral cover. In the Colombian Caribbean, in 2004 coral cover fluctuates between 5.4% and 67.2% (Rodríguez-Ramírez et al. 2010), and some sites are showing a steady decline in the last decade (Sánchez & Pizarro, 2005; Sánchez et al., 2019). The NPA reefs in the Barú area had a mean coral cover of 24.70±16.91%, with many reefs sites sustaining between 42.8% and 53%, which are among the highest recorded in this region, even higher than the coral cover values in the nearby MPA (35.0% in 2001, 23.2% in 2009 and 23.88% in 2012; Camargo et al., 2009; López-Angarita et al., 2014). These high coral covers correspond to the shallower portions of the reef tract, which appear to be autotrophic, as indicated by relatively high Qm values (Pizarro et al. 2017).

Conditions of the NPA reef tract are typical of the Anthropocene, which besides local stressors like overfishing and pollution, includes global climate change (Hughes et al., 2017), according to the Foram Index-FI range, the values observed at the NPA reef tract, between 2 and 4, correspond to reefs with water conditions from marginal to marginal unsuitable for recovery of coral communities after a mortality event (Hallock et al. 2003). The results were high in smaller foraminifera tests, indicating eutrophication and pollution. This situation along Colombia's Caribbean coast was already noted with the FI Index, where the water quality was related to the vicinity of freshwater inputs rather than management or protection status (Velásquez et al. 2011). This reef tract has been under the strong continental siliciclastic influence, probably before opening artificial freshwater canals, from the Magdalena River to Barbacoas and Cartagena bays. This river contributes approximate 9% of the total sediment load discharged from the eastern basins of South America (Restrepo and Kjerfve 2000), which has a tremendous environmental impact on the adjacent coastal ecosystems and may influence the light penetration, exacerbating the decline of corals at the other end of Barú peninsula and the Rosario and San Bernardo archipelago (Restrepo et al. 2006b).

The complete admixture between NPA and NNP populations of the corals in Barú peninsula, O. faveolata, and A. undata, is paramount to protecting this reef tract. Likewise, Alegría‐Ortega et al. (2021), in a study with O. faveolata in the MPA, with samples from Varadero (VR), the NNP CRySB, found no genetic structure and relatively high migration from VR to CR and SB. Although A. undata is probably a brooding species, no local-scale genetic differentiation between shallow and mesophotic zone was observed, which was not expected for this reproductive strategy as has been reported for other species (e.g., Favia fragum, Seriatopora hystrix, Stylophora pistillata; Ayre and Hughes 2000; Underwood et al. 2006; Goodbody-Gringley et al. 2010; Warner et al. 2016). However, planulae features and reproduction traits in agariciid corals may indicate the potential dispersal process at considerable distances due to the capacity to swim and to stay in the water column for several days before the settlement and metamorphosis in species like A. agaricites and A. humilis (Van Moorsel 1983; Raimondi and Morse 2000; Petersen et al. 2007).

Additionally, the pattern where geographically distant populations are genetically more differentiated than populations closer geographically appears not to apply to corals species (Gorospe and Karl 2013). This coral population genetic paradox could be explained by local oceanographic conditions, such as the larvae and gamete exchange during spawning events could be facilitated by sea surface currents. In the Colombian Caribbean, the Panama–Colombia Countercurrent (PCC) is the dominant superficial water current (south-west to north-east direction) throughout the year and mainly during the rainy season (August–November; Gordon 1967; Pujos et al. 1986), when most of the spawning events of these species occur. However, during spawning events, larval and gamete migration may be affected during the dry season (June–September) due to winds frequently changing direction (north to the south direction; Pineda et al. 2006).

Ecological and genetic connectivity are essential criteria for increasing the effectiveness of MPA networks (Balbar and Metaxas 2019). However, it is not often included in studies to find resilient ecosystems (Botsford et al. 2009; Beger et al. 2010; Mumby et al. 2011; Kininmonth et al. 2011) and to determine which areas should be protected. The exclusion of the NPA Barú reef tract as part of the NNP CRySB, which includes most of Barú’s fringing reef, was a major MPA design failure that apparently did not affect the conservation of this NPA reef. The NNP CRySB was established in 1977, where some of the reefs dominated by Acropora spp. were the central objective for conservation (Sánchez et al., 2004). In 2005, Colombia declared the Corales del Rosario, San Bernando e Isla Fuerte MPA, where the Barú reef tract was included without monitoring, evaluation instruments, strategies, and management programs.

The Barú NPA reef tract and diapiric banks can be considered spatial refugia under global climate change and Anthropocene conditions (Hoegh-Guldberg et al. 2008; Bongaerts et al. 2010; Smith et al. 2017; Bates et al. 2019). The overlapping of hard coral species and fish species between different sites and between mesophotic and the shallow zones (See Supplementary Figure S2), and the genetic connectivity of the two coral species observed in this study, are conditions that could make this area be considered as a refuge (Bongaerts et al. 2010).

Whereas the algae-dominated reefs prevent Caribbean coral reefs from recovering, the presence of fast-growing corals such as A. tenuifolia and P. astreoides ameliorate the conditions for more vulnerable slow-growing corals such as O. faveolata. Mainly, A. tenuifolia can endure highly sedimented reefs (Cáceres and Sánchez 2015a), conferring this reef an advantage for competing with fleshy algae. The monopolization of algae in coral reefs affects corals twofold by shedding labile carbon altering the reef microbiota, promoting coral diseases (Cárdenas et al. 2018), and limiting phosphorous availability needed for coral symbionts (Lapointe et al. 2019). Corals on this reef tract thrive under these stressful conditions is a remarkable conservation opportunity because reef rehabilitation measures could be more efficient.

The uniqueness of the associated reefs along the Barú NPAs sums up its conservation value. The independent diapiric banks of the Barú reef system, such as Montañita, Imelda, Octubre Rojo, and Trompadas, may comprise isolated reefs with different communities and population structures as seen with O. faveolata genetics for Montañita bank. However, it also may facilitate the connectivity between the coastal zone and the NNP, as was observed with A. undata. In addition, these diapirs include dense octocoral, black coral and sponge communities (Granados et al. 2008; Escobar et al. 2012; Velásquez and Sánchez 2015; Grajales and Sánchez 2016), together with mesophotic coral ecosystems (MCEs) containing unique coral symbionts (Gonzalez-Zapata et al., 2018a; Gonzalez-Zapata et al., 2018b). This distinct coral reef comprises a living laboratory for studying several aspects of marine biodiversity and MCEs. Likewise, the paradoxical Varadero reef located at the mouth of Cartagena Bay, as suggested before (Pizarro et al. 2017), is a living proof of coral reef resilience maintaining the same conditions as less stressed portions of the reef: the result of a robust and connected reef system that should be protected in its integrity.

Conservation status of the NPA reef tract and future recommendations

The Barú area is within a national conservation figure declared in 2005, at the time called the Rosario and San Bernardo Archipelago MPA (AMP-ARS; Resolution number 0679 of May 31, 2005), which was driven by local communities demanding the need to conserve marine ecosystems through the articulation of various institutions. However, it was not until 2018 that this area became legitimate for the local communities through a widespread consultation process introducing a Management Plan and the Sustainable Development Model (Ministry of Environment and Sustainable Development-MADS of Colombia), documents that to date have not been legally adopted. The AMP-CRSBeIF is not a protected figure registered in the Single National Registry of Protected Areas from Colombia or the World Database of Protected Areas (IUCN).

In conclusion, the coral reefs of Barú do not currently have a strongly constituted conservation figure that rules or suggests concrete conservation actions in the area. Additionally, the reefs of Barú outside the limits of the National Park were not included in the Official Atlas of Coral Areas of Colombia (Díaz et al. 2000), a significant setback because the laws protecting coral reefs in Colombia, whether or not under protection, only act on the areas laid out in the Atlas. Despite the above, various environmental planning documents for this region (Invemar and Cardique, 2014), and PNN CRySB and PNN Corales de Profundidad management plans highlight the need to study the composition and structure of these reefs. These documents acknowledge connectivity can be essential for conserving other reefs in the region or improving the marine protected areas network. Furthermore, these reefs, depending on the live coral cover, are considered to be in "acceptable" (31%) or "good" (52%) conditions (Gomez and Yap 1988; Gomez et al. 1994; Tun et al. 2005). Therefore, this study provides the necessary scientific basis for the corresponding environmental authorities (Cardique and MADS) to propose strategies that allow these reefs to be established as protection zones, avoiding further alteration or degradation and maintaining their connectivity and ecosystem function. Albeit co-management schemes (e.g., Integrated Managed Districts: "Distrito de Manejo Integrado") should be preferred over more restrictive options, particularly given the multiplicity of stakeholders in this pivotal place Cartagena and Barú areas. This co-management should be part of a broader, institutionalized management scheme to be successful and receive external support from NGOs or other nested governance institutions, as demonstrated in some studies in marine zones (Gurney et al. 2015; Voorberg and Van der Veer 2020). Likewise, restoring zones gives place to the recovery of degraded sectors and establishing areas of sustainable use that allow local communities to make use of fishery resources through participatory conservation.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding: This work was supported by an agreement between Argos-Ecoral-Pontificia Universidad Javeriana. Additional information from Pontificia Universidad Bolivariana was used to complete multibeam data. This project was possible with the funds provided by National Geographic Society from early career grant, Colombia Biodiversa grant from Fundación Ángel Escobar, the support from Universidad de Cartagena and PNN Corales de Profundidad. Additional funding was possible thanks to the University of Los Andes (Vicerrectoría de Investigaciones and Facultad de Ciencias) and MinCiencias (COLCIENCIAS, Project code 120465944147).

Conflicts of interest/Competing interests: The authors declare that they have no conflict of interest.

Availability of data and material: all the raw data is published as supplementary material.

Authors' contributions: LR, JAS, AA, LFD and EA-Ch conceived the idea. JAS and LR wrote the paper with inputs from FL-GZ, MG-C, MM, EA-Ch, AA, JA and LFD. All authors collected data, LR, FL-GZ, AA, MG-C, EA-Ch and MM did the analyses. All authors revised and approved the final document.

Acknowledgments

We greatly acknowledge the generous support from (Ecoral) and Federico Botero to accomplish this research. Grupo Argos kindly allowed us to use their premises (Finca Polonia), provided boat logistics to explore and study the non-protected marine areas along the Barú peninsula, and coordinated the assembly of scientists with multiple stakeholders of the study area. Alejandro Henao, Milena Marrugo from PNN Corales de Profundidad, Adrian Devia from CEINER and Javier Torres from Makarela Dive Center for their field support. We express great appreciation to several BIOMMAR members for lab and field assistance. We acknowledge the participation of local communities from the Barú peninsula.

Alegría‐Ortega A, Sanín‐Pérez MJ, Quan‐Young LI, Londoño‐Mesa MH (2021) Genetic structure of Orbicella faveolata population reveals high connectivity among a marine protected area and Varadero Reef in the Colombian Caribbean. 14

Avigliano E, Rosso JJ, Lijtmaer D, et al (2019) Biodiversity and threats in non-protected areas: A multidisciplinary and multi-taxa approach focused on the Atlantic Forest. Heliyon 5:e02292. https://doi.org/10.1016/j.heliyon.2019.e02292

Ayre DJ, Hughes TP (2000) Genotypic diversity and gene flow in brooding and spawning corals along the Great Barrier Reef, Australia. Evolution 54:1590–1605. https://doi.org/10.1111/j.0014-3820.2000.tb00704.x

Baird NA, Etter PD, Atwood TS, et al (2008) Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers. PLoS ONE 3:e3376. https://doi.org/10.1371/journal.pone.0003376

Balbar AC, Metaxas A (2019) The current application of ecological connectivity in the design of marine protected areas. Glob Ecol Conserv 17:e00569. https://doi.org/10.1016/j.gecco.2019.e00569

Bates AE, Cooke RSC, Duncan MI, et al (2019) Climate resilience in marine protected areas and the ‘Protection Paradox.’ Biol Conserv 236:305–314. https://doi.org/10.1016/j.biocon.2019.05.005

Beger M, Grantham HS, Pressey RL, et al (2010) Conservation planning for connectivity across marine, freshwater, and terrestrial realms. Biol Conserv 143:565–575. https://doi.org/10.1016/j.biocon.2009.11.006

Benez-Secanho FJ, Dwivedi P (2020) Analyzing the provision of ecosystem services by conservation easements and other protected and non-protected areas in the Upper Chattahoochee Watershed. Sci Total Environ 717:137218. https://doi.org/10.1016/j.scitotenv.2020.137218

Bongaerts P, Ridgway T, Sampayo EM, Hoegh-Guldberg O (2010) Assessing the ‘deep reef refugia’ hypothesis: focus on Caribbean reefs. Coral Reefs 29:309–327. https://doi.org/10.1007/s00338-009-0581-x

Bongaerts P, Riginos C, Brunner R, et al (2017) Deep reefs are not universal refuges: Reseeding potential varies among coral species. Sci Adv 3:e1602373. https://doi.org/10.1126/sciadv.1602373

Botsford LW, White JW, Coffroth M-A, et al (2009) Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs. Coral Reefs 28:327–337. https://doi.org/10.1007/s00338-009-0466-z

Brown CJ, Harborne AR, Paris CB, Mumby PJ (2016) Uniting paradigms of connectivity in marine ecology. Ecology 97:2447–2457. https://doi.org/10.1002/ecy.1463

Bruno JF, Valdivia A (2016) Coral reef degradation is not correlated with local human population density. Sci Rep 6:29778. https://doi.org/10.1038/srep29778

Bryan-Brown DN, Brown CJ, Hughes JM, Connolly RM (2017) Patterns and trends in marine population connectivity research. Mar Ecol Prog Ser 585:243–256

Cáceres SM, Sánchez JA (2015a) Growth strategies of an abundant reef-building coral in the southern Caribbean (Undaria tenuifolia). Rev Acad Colomb Cienc Exactas Físicas Nat 39:348. https://doi.org/10.18257/raccefyn.195

Cáceres SM, Sánchez JA (2015b) Growth strategies of an abundant reef-building coral in the southern Caribbean (Undaria tenuifolia). Rev Acad Colomb Cienc Exactas Físicas Nat 39:348–357

Camargo C, Maldonado JH, Alvarado E, et al (2009) Community involvement in management for maintaining coral reef resilience and biodiversity in southern Caribbean marine protected areas. Biodivers Conserv 18:935–956

Cárdenas A, Neave MJ, Haroon MF, et al (2018) Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. ISME J 12:59–76. https://doi.org/10.1038/ismej.2017.142

Carpenter KE, Abrar M, Aeby G, et al (2008) One-Third of Reef-Building Corals Face Elevated Extinction Risk from Climate Change and Local Impacts. Sci New Ser 321:560–563

Cendales MH, Zea S, Díaz JM (2002) Geomorfología y unidades ecológicas del complejo de arrecifes de las Islas del Rosario e Isla Barú (Mar Caribe, Colombia). Rev Acad Colomb Cienc 26:497–510

Coffroth MA, Lasker HR, Diamond ME, et al (1992) DNA fingerprints of a gorgonian coral: a method for detecting clonal structure in a vegetative species. Mar Biol 114:317–325

Díaz JM, Barrios LM, Cendales MH, et al (2000) Áreas coralinas de Colombia. INVEMAR Ser Publicaciones Espec 5:176

Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361. https://doi.org/10.1007/s12686-011-9548-7

Epskamp S, Cramer AOJ, Waldorp LJ, et al (2012) qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw 48:. https://doi.org/10.18637/jss.v048.i04

Escobar D, Zea S, Sánchez JA (2012) Phylogenetic relationships among the Caribbean members of the Cliona viridis complex (Porifera, Demospongiae, Hadromerida) using nuclear and mitochondrial DNA sequences. Mol Phylogenet Evol 64:271–284. https://doi.org/10.1016/j.ympev.2012.03.021

Etter PD, Bassham S, Hohenlohe PA, et al (2012) SNP Discovery and Genotyping for Evolutionary Genetics Using RAD Sequencing. In: Orgogozo V, Rockman MV (eds) Molecular Methods for Evolutionary Genetics. Humana Press, Totowa, NJ, pp 157–178

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x

Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x

Foster NL, Paris CB, Kool JT, et al (2012) Connectivity of Caribbean coral populations: Complementary insights from empirical and modelled gene flow. Mol Ecol 21:1143–1157. https://doi.org/10.1111/j.1365-294X.2012.05455.x

Gairin E, Andréfouët S (2020) Role of habitat definition on Aichi Target 11: Examples from New Caledonian coral reefs. Mar Policy 116:103951. https://doi.org/10.1016/j.marpol.2020.103951

Gomez ED, Aliño PM, Yap HT, Licuanan WY (1994) A review of the status of Philippine reefs. Mar Pollut Bull 29:62–68. https://doi.org/10.1016/0025-326X(94)90427-8

Gomez ED, Yap HT (1988) Monitoring Reef Condition. P: 187-195 dalam RA Kenchington dan BET Hudson. Coral Reef Manag Handb UNESCO Reg Off Sci Technol South East Asia Jkt ID

Gonzalez-Zapata FL, Bongaerts P, Ramírez-Portilla C, et al (2018a) Holobiont Diversity in a Reef-Building Coral over Its Entire Depth Range in the Mesophotic Zone. Front Mar Sci 5:29. https://doi.org/10.3389/fmars.2018.00029

Gonzalez-Zapata FL, Bongaerts P, Ramírez-Portilla C, et al (2018b) Holobiont Diversity in a Reef-Building Coral over Its Entire Depth Range in the Mesophotic Zone. Front Mar Sci 5:29. https://doi.org/10.3389/fmars.2018.00029

Gonzalez-Zapata FL, Gómez-Osorio S, Sánchez JA (2018c) Conspicuous endolithic algal associations in a mesophotic reef-building coral. Coral Reefs 37:705–709

Goodbody-Gringley G, Vollmer SV, Woollacott RM, Giribet G (2010) Limited gene flow in the brooding coral Favia fragum (Esper, 1797). Mar Biol 157:2591–2602. https://doi.org/10.1007/s00227-010-1521-6

Gordon AL (1967) Circulation of the Caribbean Sea. J Geophys Res 1896-1977 72:6207–6223. https://doi.org/10.1029/JZ072i024p06207

Gorospe KD, Karl SA (2013) Genetic relatedness does not retain spatial pattern across multiple spatial scales: dispersal and colonization in the coral, Pocillopora damicornis. Mol Ecol 22:3721–3736. https://doi.org/10.1111/mec.12335

Goudet J (2005) hierfstat, a package for r to compute and test hierarchical F-statistics. Mol Ecol Notes 5:184–186. https://doi.org/10.1111/j.1471-8286.2004.00828.x

Grajales A, Sánchez JA (2016) Holobiont assemblages of dominant coral species (Symbiodinium types and coral species) shape Caribbean reef community structure. Rev Acad Colomb Cienc Exactas Físicas Nat 40:300–311. https://doi.org/10.18257/raccefyn.294

Granados C, Camargo C, Zea S, Sánchez JA (2008) Phylogenetic relationships among zooxanthellae (Symbiodinium) associated to excavating sponges (Cliona spp.) reveal an unexpected lineage in the Caribbean. Mol Phylogenet Evol 49:554–560

Grorud-Colvert K, Claudet J, Tissot BN, et al (2014) Marine Protected Area Networks: Assessing Whether the Whole Is Greater than the Sum of Its Parts. PLoS ONE 9:e102298. https://doi.org/10.1371/journal.pone.0102298

Gurney GG, Pressey RL, Cinner JE, et al (2015) Integrated conservation and development: evaluating a community-based marine protected area project for equality of socioeconomic impacts. Philos Trans R Soc B Biol Sci 370:20140277. https://doi.org/10.1098/rstb.2014.0277

Hallock P, Lidz BH, Cockey-Burkhard EM, Donnelly KB (2003) Foraminifera as bioindicators in coral reef assessment and monitoring: the FORAM index. In: Coastal Monitoring through Partnerships. Springer, pp 221–238

Hoegh-Guldberg O, Bruno JF (2010) The Impact of Climate Change on the World’s Marine Ecosystems. Science 328:1523–1528. https://doi.org/10.1126/science.1189930

Hoegh-Guldberg O, Hughes L, McIntyre S, et al (2008) Assisted Colonization and Rapid Climate Change. Science 321:345–346. https://doi.org/10.1126/science.1157897

Hoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S (2017) Coral Reef Ecosystems under Climate Change and Ocean Acidification. Front Mar Sci 4:. https://doi.org/10.3389/fmars.2017.00158

Hughes TP, Anderson KD, Connolly SR, et al (2018) Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359:80. https://doi.org/10.1126/science.aan8048

Hughes TP, Barnes ML, Bellwood DR, et al (2017a) Coral reefs in the Anthropocene. Nature 546:82

Hughes TP, Kerry JT, Álvarez-Noriega M, et al (2017b) Global warming and recurrent mass bleaching of corals. Nature 543:373–377

Jackson EJ, Donovan M, Cramer K, Lam V (2014) Status and trends of Caribbean coral reefs: 1970-2012. 306

Jakobsson M, Rosenberg NA (2007) CLUMPP : a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. 23:1801–1806. https://doi.org/10.1093/bioinformatics/btm233

Jombart T (2008) Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405. https://doi.org/10.1093/bioinformatics/btn129

Kininmonth S, Beger M, Bode M, et al (2011) Dispersal connectivity and reserve selection for marine conservation. Ecol Model 222:1272–1282. https://doi.org/10.1016/j.ecolmodel.2011.01.012

Knight AT, Cowling RM (2007) Embracing Opportunism in the Selection of Priority Conservation Areas. Conserv Biol 21:1124–1126. https://doi.org/10.1111/j.1523-1739.2007.00690.x

Kockel A, Ban NC, Costa M, Dearden P (2019) Evaluating approaches for scaling‐up community‐based marine‐protected areas into socially equitable and ecologically representative networks. Conserv Biol 34:137–147. https://doi.org/10.1111/cobi.13368

Kohler KE, Gill SM (2006) Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Comput Geosci 32:1259–1269. https://doi.org/10.1016/j.cageo.2005.11.009

Kroeker KJ, Carr MH, Raimondi PT, et al (2019) Assessing the Potential Role of Marine Protected Areas and Fisheries Management Approaches for Resilience Management in a Changing Ocean. Oceanography 32:117–125

Lapointe BE, Brewton RA, Herren LW, et al (2019) Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3-decade study. Mar Biol 166:108. https://doi.org/10.1007/s00227-019-3538-9

López-Angarita J, Moreno-Sánchez R, Maldonado JH, Sánchez JA (2014) Evaluating Linked Social-Ecological Systems in Marine Protected Areas. Conserv Lett 7:241–252. https://doi.org/10.1111/conl.12063

López-Victoria M, Rodríguez-Moreno M, Zapata FA (2015) A paradoxical reef from Varadero, Cartagena Bay, Colombia. Coral Reefs 34:231–231. https://doi.org/10.1007/s00338-014-1246-y

McCook LJ, Ayling T, Cappo M, et al (2010) Adaptive management of the Great Barrier Reef: A globally significant demonstration of the benefits of networks of marine reserves. Proc Natl Acad Sci 107:18278–18285. https://doi.org/10.1073/pnas.0909335107

Mora C, Andréfouët S, Costello J, et al (2006) Coral reefs and the global network of marine protected areas. Science 312:1750–1751

Moreno-Sánchez RDP, Maldonado JH (2010) Evaluating the role of co-management in improving governance of marine protected areas: An experimental approach in the Colombian Caribbean. Ecol Econ 69:2557–2567. https://doi.org/10.1016/j.ecolecon.2010.07.032

Muhl E-K, Esteves Dias AC, Armitage D (2020) Experiences With Governance in Three Marine Conservation Zoning Initiatives: Parameters for Assessment and Pathways Forward. Front Mar Sci 7:629. https://doi.org/10.3389/fmars.2020.00629

Mumby PJ, Elliott IA, Eakin CM, et al (2011) Reserve design for uncertain responses of coral reefs to climate change: Reserve design for climate change. Ecol Lett 14:132–140. https://doi.org/10.1111/j.1461-0248.2010.01562.x

Nei M (1973) Analysis of Gene Diversity in Subdivided Populations. 3

Nickols KJ, White JW, Malone D, et al (2019) Setting ecological expectations for adaptive management of marine protected areas. J Appl Ecol 56:2376–2385. https://doi.org/10.1111/1365-2664.13463

Paradis E (2010) pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26:419–420. https://doi.org/10.1093/bioinformatics/btp696

Peakall R, Smouse P (2012) GenAlEx 6 . 5 : Genetic analysis in Excel . Population genetic soft- ware for teaching and research – an update . Bioinformatics 1:6–8

Pendleton L, Comte A, Langdon C, et al (2016) Coral Reefs and People in a High-CO2 World: Where Can Science Make a Difference to People? PLOS ONE 11:e0164699. https://doi.org/10.1371/journal.pone.0164699

Perry CT, Alvarez-Filip L, Graham NAJ, et al (2018) Loss of coral reef growth capacity to track future increases in sea level. Nature 558:396–400. https://doi.org/10.1038/s41586-018-0194-z

Perry CT, Murphy GN, Kench PS, et al (2014) Changing dynamics of Caribbean reef carbonate budgets: emergence of reef bioeroders as critical controls on present and future reef growth potential. Proc R Soc B Biol Sci 281:20142018. https://doi.org/10.1098/rspb.2014.2018

Petersen D, Laterveer M, Visser G (2007) Sexual recruitment of the coralsFavia fragum andAgaricia humilis in a 30-m3 exhibit aquarium: species-specific limitations and implications on reproductive ecology. Zoo Biol 26:75–91. https://doi.org/10.1002/zoo.20120

Pineda I, Martínez LA, Bedoya DM, et al (2006) Plan de manejo del Parque Nacional Natural Corales del Rosario y San Bernardo. UAESPNN Territ Costa Caribe

Pizarro V, Rodríguez SC, López-Victoria M, et al (2017) Unraveling the structure and composition of Varadero Reef, an improbable and imperiled coral reef in the Colombian Caribbean. PeerJ 5:e4119. https://doi.org/10.7717/peerj.4119

Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. https://doi.org/10.1111/j.1471-8286.2007.01758.x

Pujos M, Pagliardini J-L, Steer R, et al (1986) Influencia de la contra-corriente norte colombiana para la circulación de las aguas en la plataforma continental: su acción sobre la dispersión de los efluentes en suspensión del rio Magdalena. Bol Científico CIOH 3–16

Raimondi PT, Morse ANC (2000) The consequences of complex larval behaviour in a coral. 81:19

Rasheed AR (2020) Marine protected areas and human well-being – A systematic review and recommendations. Ecosyst Serv 41:101048. https://doi.org/10.1016/j.ecoser.2019.101048

Restrepo J, Zapata P, Diaz J, et al (2006a) Fluvial fluxes into the Caribbean Sea and their impact on coastal ecosystems: The Magdalena River, Colombia. Glob Planet Change 50:33–49. https://doi.org/10.1016/j.gloplacha.2005.09.002

Restrepo JD, Kjerfve B (2000) Magdalena river: interannual variability (1975–1995) and revised water discharge and sediment load estimates. J Hydrol 235:137–149. https://doi.org/10.1016/S0022-1694(00)00269-9

Restrepo JD, Zapata P, Díaz JM, et al (2006b) Fluvial fluxes into the Caribbean Sea and their impact on coastal ecosystems: The Magdalena River, Colombia. Glob Planet Change 50:33–49

Roberts CM (2000) Selecting marine reserve locations: optimality versus opportunism. Bull Mar Sci 66:12

Rodríguez-Ramírez A, Reyes-Nivia MC, Zea S, et al (2010) Recent dynamics and condition of coral reefs in the Colombian Caribbean. Rev Biol Trop 58:107–131

Rosenberg NA (2003) distruct: a program for the graphical display of population structure: PROGRAM NOTE. Mol Ecol Notes 4:137–138. https://doi.org/10.1046/j.1471-8286.2003.00566.x

Sánchez JA (1999a) Black coral-octocoral distribution patterns in Imelda bank, a deep-water reef, Colombia, Caribbean sea. Bull Mar Sci 65:215–225

Sánchez JA (2017) Diversity and evolution of octocoral animal forests at both sides of tropical America. In: Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots. pp 111–143

Sánchez JA (1999b) Black coral-octocoral distribution patterns in Imelda bank, a deep-water reef, Colombia, Caribbean sea. Bull Mar Sci 215–225

Sánchez JA, Gil MF, Chasqui LH, Alvarado EM (2004) Grazing dynamics on a Caribbean reef-building coral. Coral Reefs 23:578–583

Sánchez JA, Gómez-Corrales M, Gutierrez-Cala L, et al (2019a) Steady Decline of Corals and Other Benthic Organisms in the SeaFlower Biosphere Reserve (Southwestern Caribbean). Front Mar Sci 6:. https://doi.org/10.3389/fmars.2019.00073

Sánchez JA, Gómez-Corrales M, Gutierrez-Cala LM, et al (2019b) Steady decline of corals and other benthic organisms in the SeaFlower Biosphere Reserve (Southwestern Caribbean). Front Mar Sci 6:73

Sánchez JA, Pizarro V, Acosta AR, et al (2005) Evaluating coral reef benthic communities in remote atolls (Quitasueno, Serrana, and Roncador Banks) to recommend marine-protected areas for the Seaflower Biosphere Reserve. Atoll Res Bull 531:1–66

Sánchez, Pizarro V (2005) Evaluating coral reef benthic communities in remote Caribbean atolls(Quitasueno, Serrana, and Roncador Banks) to recommend Marine-Protected Areas for the seaflower biosphere reserve. Atoll Res Bull 531:1–65

Severance EG, Karl ÆSA, Karl SA (2006) Contrasting population genetic structures of sympatric , mass-spawning Caribbean corals. Mar Biol 150:57–68. https://doi.org/10.1007/s00227-006-0332-2

Severance EG, Szmant AM, Karl SA (2004) Microsatellite loci isolated from the Caribbean coral, Montastraea annularis. Mol Ecol Notes 4:74–76. https://doi.org/10.1111/j.1471-8286.2004.00604.x

Smith TB, Maté JL, Gyory J (2017) Thermal Refuges and Refugia for Stony Corals in the Eastern Tropical Pacific. In: Glynn PW, Manzello DP, Enochs IC (eds) Coral Reefs of the Eastern Tropical Pacific: Persistence and Loss in a Dynamic Environment. Springer Netherlands, Dordrecht, pp 501–515

Sully S, Burkepile DE, Donovan MK, et al (2019) A global analysis of coral bleaching over the past two decades. Nat Commun 10:1264. https://doi.org/10.1038/s41467-019-09238-2

Sundqvist L, Keenan K, Zackrisson M, et al (2016) Directional genetic differentiation and relative migration. Ecol Evol 6:3461–3475. https://doi.org/10.1002/ece3.2096

Thiault L, Mora C, Cinner JE, et al (2019) Escaping the perfect storm of simultaneous climate change impacts on agriculture and marine fisheries. Sci Adv 5:eaaw9976. https://doi.org/10.1126/sciadv.aaw9976

Tosic M, Restrepo JD, Lonin S, et al (2019) Water and sediment quality in Cartagena Bay, Colombia: Seasonal variability and potential impacts of pollution. Estuar Coast Shelf Sci 216:187–203. https://doi.org/10.1016/j.ecss.2017.08.013

Tun K, Chou LM, Cabanban A, et al (2005) Status of coral reefs, coral reef monitoring and management in Southeast Asia 2004

Underwood JN, Smith LD, Van Oppen MJH, Gilmour JP (2006) Multiple scales of genetic connectivity in a brooding coral on isolated reefs following catastrophic bleaching: GENETIC CONNECTIVITY IN A BROODING CORAL. Mol Ecol 16:771–784. https://doi.org/10.1111/j.1365-294X.2006.03187.x

UNEP-WCMC (2008) National and regional networks of marine protected areas a review of progress. UNEP World Conservation Monitoring Centre, Cambridge

Van Moorsel G (1983) Reproductive strategies in two closely related stony corals (Agaricia, Scleractinia). Mar Ecol Prog Ser 13:273–283. https://doi.org/10.3354/meps013273

Velásquez J, López-Angarita J, Sánchez JA (2011) Evaluation of the FORAM index in a case of conservation. Biodivers Conserv 20:3591–3603. https://doi.org/10.1007/s10531-011-0152-7

Velásquez J, Sánchez JA (2015) Octocoral Species Assembly and Coexistence in Caribbean Coral Reefs. PloS One 10:e0129609

Vernette G, Mauffret A, Bobier C, et al (1992) Mud diapirism, fan sedimentation and strike-slip faulting, Caribbean Colombian margin. Tectonophysics 202:335–349

Voorberg W, Van der Veer R (2020) Co-Management as a Successful Strategy for Marine Conservation. J Mar Sci Eng 8:491. https://doi.org/10.3390/jmse8070491

Warner PA, Willis BL, van Oppen MJH (2016) Sperm dispersal distances estimated by parentage analysis in a brooding scleractinian coral. Mol Ecol 25:1398–1415. https://doi.org/10.1111/mec.13553

Western D, Russell S, Cuthill I (2009) The Status of Wildlife in Protected Areas Compared to Non-Protected Areas of Kenya. PLoS ONE 4:. https://doi.org/10.1371/journal.pone.0006140

White JW, Carr M, Caselle J, et al (2019) Connectivity, Dispersal, and Recruitment: Connecting Benthic Communities and the Coastal Ocean. Oceanography 32:50–59. https://doi.org/10.5670/oceanog.2019.310

Table 1. Collected samples of Agaricia undata at each site and depth. LC: La Caida, OR: Octubre Rojo, TR: Trompadas, M: Montañita, IT: Isla Tesoro and I: Imelda. 7 individuals collected at shallow zone and 14 at mesophotic zone. *Samples remove from the analysis after bioinformatic filtering.

Sample	Depth (m)	Place ID	Zone
LC-S1	17	LC	Shallow
OR-S1	30	OR
TR-S1	29	TR
TR-S2	29	TR
TR-S3*	29	TR
MO-S1	17	M
IT-S1	30	IT
Total samples	7
MO-M1	35	M	Mesophotic
MO-M2	35	M
MO-M3	35	M
MO-M4*	35	M
IM-M1	45	I
IM-M2	45	I
IM-M3	45	I
IM-M4	45	I
IM-M5*	45	I
IT-M1	35	IT
IT-M2	38	IT
IT-M3	37	IT
IT-M4	35	IT
TR-M1*	36	TR
Total samples	14

Table 2. Summary statistics of 8 sampling sites based on six microsatellites from Orbicella faveolata, including the number of individuals sampled (N), the number of alleles per locus (Na), the effective number of alleles (Ne) expected (He), and observed (Ho), heterozygosities and Fixation index (F) for each population. Standard deviation (SD). V: Varadero, V2: Varadero2, LC: La Caída, M: Montañita, PG: Punta Gigante, CU: as Cuevas, P: Pendales and SD: Santo Domingo.

Location	n	Na (SD)	Ne (SD)	Ho (SD)	He (SD)	F (SD)
V	26	6.2 (1.715)	3.272 (1.036)	0.646 (0.260)	0.604 (0.214)	-0.090(0.079)
V2	26	6 (1.581)	3.604 (1.215)	0.569 (0.302)	0.631 (0.204)	0.072(0.165)
LC	26	6 (2.121)	3.653 (1.455)	0.561 (0.310)	0.576 (0.293)	-0.025(0.138)
M	23	10.4 (2.522)	5.647 (1.532)	0.643 (0.220)	0.789 (0.116)	0.151(0.131)
PG	26	7.8 (1.934)	4.481 (1.771)	0.669 (0.235)	0.684 (0.170)	-0.007(0.125)
CU	27	6.4(1.435)	3.607 (1.105)	0.652 (0.234)	0.635 (0.213)	-0.047(0.087)
P	27	6.4 (1.470)	3.972 (1.569)	0.555 (0.241)	0.629 (0.224)	0.121(0.087)
SD	20	6(1.871)	3.528 (1.202)	0.540 (0.288)	0.603 (0.256)	0.115(0.102)

Table 3. AMOVA table of temporal and spatial genetic variation of Orbicella faveolata from locations. Statistically significant values are in bold.

Source	% of variation	F-statistics	Value	P
Among Locations	4	F_st	0.037	0.001
Among Individual	6	F_is	0.059	0.002
Within Individual	90	F_it	0.093	0.001

Table 4. Genetic differentiation among Orbicella faveolata populations based on 5 microsatellite loci (Pairwise F_ST). Statistically significant values are in bold.

	V	V2		LC		M	PG	CU	P	SD
V	0
V2	0.013		0
LC	-0.003		0.008	0
M	0.114		0.099	0.126	0
PG	0.004		-0.002	0.005	0.071		0
CU	0.011		0.014	0.006	0.106		0.010	0
P	0.000		0.013	0.004	0.108		0.009	0.005	0
SD	0.022		0.018	0.029	0.137		0.026	0.049	0.017	0

Table 5. Genome-wide pairwise differentiation (F_ST) of populations of Agaricia undata. Negative values are taken as cero (lack of genetic differentiation). MOS: Montañita shallow, MOM: Montañita mesophotic, ITS: Isla Tesoro shallow, ITM: Isla Tesoro mesophotic, LCS: La caida shallow, TRS: Trompadas shallow, ORS: Octubre Rojo shallow and IMM: Imelda mesophotic. Statistically significant values are in bold.

Category	Comparison	Pairwise F_ST	P-value
Between Depths	MOS vs MOM	0.121	0.270
Between Depths	ITS vs ITM	0.006	0.996
Within Shallow	LCS vs TRS	-0.002	0.466
	ORS vs TRS	0.046	0.060
	TRS vs MOS	0.001	0.463
	TRS vs ITS	-0.010	0.623
Within Deep	MOM vs IMM	0.025	0.013
	MOM vs ITM	0.009	0.220
	IMM vs ITM	0.008	0.140
Between Depths and sites	LCS vs MOM	-0.005	0.550
	LCS vs IMM	-0.008	0.710
	LCS vs ITM	-0.041	1.000
	ORS vs MOM	0.001	0.480
	ORS vs IMM	0.034	0.013
	ORS vs ITM	-0.011	0.803
	TRS vs MOM	-0.043	0.990
	TRS vs IMM	0.033	0.003
	TRS vs ITM	-0.001	0.586
	MOS vs IMM	-0.012	0.773
	MOS vs ITM	-0.033	0.983
	IMM vs ITS	0.006	0.996

Table 6. Paired comparisons matrix for migration analysis for Orbicella faveolate and Agaricia undata among sample sites obtained using the Nei’s Gst. O. faveolata estimations were based on 5 microsatellites among 8 sample sites (V: Varadero, V2: Varadero2, LC: La Caída, M: Montañita, PG: Punta Gigante, CU: as Cuevas, P: Péndales, SD: Santo Domingo.). A. undata estimations were based on 5,516 neutral SNPs among 4 sample sites (TR: Trompadas, MO: Montañita, IM: Imelda and IT: Isla Tesoro).

Source	Sink
	Orbicella faveolata
	V	V2	LC	MO	PG	CU	PE	SD
V		0.407	0.788	0.140	0.824	0.478	0.932	0.279
V2	0.363		0.433	0.078	1.000	0.455	0.364	0.307
LC	0.644	0.422		0.105	0.891	0.521	0.634	0.212
MO	0.029	0.029	0.021		0.044	0.024	0.029	0.020
PG	0.362	0.401	0.353	0.137		0.359	0.409	0.236
CU	0.233	0.325	0.336	0.094	0.359		0.364	0.160
PE	0.525	0.467	0.460	0.142	0.580	0.500		0.296
SD	0.332	0.305	0.294	0.077	0.308	0.211	0.362
	Agaricia undata
	TR		MO		IM		IT
TR			0.462		0.544		0.615
MO	0.300				0.672		0.804
IM	0.296		0.560				1.000
IT	0.315		0.617		0.891

No competing interests reported.

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Conservation at the edge: connectivity and opportunities from non-protected coral reefs close to a national park in the Colombian Caribbean

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