Habitat comparison: Realized niche breadth and overlap
The realized niche breadth obtained for scleractinians was considerably greater than for octocoral recruits, in both, geographic (0.71 for scleractinian recruits and 0.54 for octocoral recruits), and environmental space (0.31 for scleractinians and 0.19 for octocorals). Niche overlap measured with Schoener’s D and Warren’s Iwere 0.79 and 0.96 in geographic space, respectively, whereas in environmental space were 0.39 and 0.63. Rho indicated both taxa had similar positive responses to the environmental predictors, and asD and I, this metric was also higher in geographic space compared to in environmental space (0.81 vs 0.46). The identity test showed both, Dand I were significantly lower than expected by chance in geographic space, whereas only I was significant in environmental space. Thus, we rejected the hypotheses the taxa niches were identical in geographic space (usingD and I ) and in environmental space using I (Figure 5A; p ≤ 0.05). Similarly, the background test was significant using both metrics of niche overlap in geographic space, but not in environmental space. Finally, habitats suitable for scleractinian coral recruits were more abundant than for octocorals (Figure 6), and the median suitability value for the scleractinians was also higher than for octocoral recruits (W= 6.6159e+13, p < 0.01), indicating their relative abundances were independent of the amount of suitable habitat available for each taxon.
DISCUSSION
Understanding recruitment niche dynamics is crucial to improve our knowledge of processes assembling communities and to detect sensitivity to environmental change and shifts in community composition. In our study, we fitted and projected an SDM on digital 3D reproductions of the reef. This new methodology allowed us, for the first time to quantitatively characterize and map the realized spatial niche for recruits of sessile taxa on a coral reef ecosystem. Habitats suitable for octocoral and scleractinian recruits were very similar but not identical, indicating that each taxon has advantages in different subsets of the microhabitat space. The niches overlapped due to the wider niche breadth of scleractinian recruits. The result is consistent with the hypothesis that declining scleractinian coral cover is providing habitat suitable for octocoral recruitment. However, the amount of habitat available for recruits of each taxon did not explain their relative abundances in the reef, which emphasizes spatial niche dynamics solely are not responsible of these taxa recruit dynamics.
In general, microhabitats suitable for both taxa were very similar, and displayed high levels of overlap. Fine-scale (< 10 mm) roughness was the most important predictor to characterize the recruitment niche of both taxa, which is consistent with the numerous studies reporting higher settlement and post-settlement survival on settlement tiles with complex surfaces (Carleton & Sammarco 1987; Lasker & Kim 1996; Raimondi & Morse 2000; Harrington et al.2004; Nozawa 2008; Edmunds et al. 2014; Whalan et al.2015; Gallagher & Doropoulos 2017; Zelli et al. 2020). Microhabitats suitable for each taxon were not identical. Holes, crevices, and grooves < 10 mm size, were more suitable for octocorals whereas hummocks, ridges and bumps of similar dimensions were better suited for scleractinians. At larger scales, microhabitats suitable for octocorals were located on boulders, whereas, flat and exposed areas of the reef were better suited for scleractinians. Distribution patterns of octocoral recruits on reef surfaces have not been previously studied, but our predictions for scleractinian corals are consistent with the few observational studies reporting similar associations between juvenile corals and exposed habitats of the reef (Edmunds et al. 2004; Roth & Knowlton 2009; Trapon et al.2013).
The disparity between the distribution of scleractinian corals on exposed natural substratum observed in our study and reports of higher settlement and survival within refuges of settlement tiles (Whalanet al. 2015; Gallagher & Doropoulos 2017; Zelli et al.2020) can be attributed to settlement and post-settlement survival being assessed on settlement tiles in the absence of competitors (Brandlet al. 2014). Including potential competitors in our study revealed scleractinian recruits have a wider niche breadth compared to octocorals, spatially overlapping with the latter taxon and potentially competing for similar microhabitats on the reef. A wider niche breadth indicates scleractinians can recruit to a broader range of microhabitats compared to octocorals. We surveyed scleractinian recruits from the genera Madracis , Siderastrea, Agaricia and Porites , which are stress-tolerant and “weedy” species (Aronson et al.2004; Green et al. 2008; Darling et al. 2012; Crameret al. 2021). Whether the realized niche breadth of competitive and specialized scleractinians, such as Acropora spp. andOrbicella spp. is also broad needs to be quantified. In any case, among co-existing species, those with the narrower niche, such as the octocorals in this study, will be more likely to persist if they outperform those with the wider niche in the locations where they overlap (Adler et al. 2007). Low-profile and weedy scleractinian species show low to medium interspecific aggression (Lang 1973; Crameret al. 2021), and some octocorals can use sweeper tentacles against neighboring scleractinians, and even overgrow them (Sebens & Miles 1988; Alino et al. 1992). Alternatively, distribution patterns of octocoral recruits may reflect the outcome of non-transitive competition against other common taxa within concealed microhabitats.
Macroalgae and fast-growing heterotrophic invertebrates are abundant in cryptic microhabitats (Jackson 1977; Rützler et al. 2014). In the Caribbean, low grazing pressure and high levels of eutrophication have been correlated to an increase in their abundances (Edmunds & Carpenter 2001; Carpenter & Edmunds 2006; Mumby et al. 2006; Idjadiet al. 2010; Davies et al. 2013). We did not include macroalgae in our analyses, but we found microhabitats in direct contact with other benthic invertebrates, such as sponges, were less suitable for scleractinian recruits than for octocorals. Caribbean octocorals can be effective competitors against sponges (Slattery & Lesser 2021), whereas sponges can interact with algae to negatively affect scleractinian corals (González-Rivero et al. 2011). It has been hypothesized the upright form in octocorallians could be allowing them to “escape in height” within the microhabitats where heterotrophic invertebrates are abundant (Meesters et al. 1996; Precodaet al. 2017). Although, intransitive or non-hierarchical competition could allow species to coexist even without niche differences (Laird & Schamp 2006). Mechanistic studies to test non-hierarchical competition during the recruitment of historically dominant taxa in the region are necessary to gain a better understanding of its role in shaping reef’s benthic communities.
Importantly, we found microhabitats in direct contact with adult scleractinians were unsuitable for octocoral recruits, and calcareous rock was highly suitable. These results provide evidence of competitive release, and that the decline of scleractinian coral cover is providing suitable space for octocorals to recruit. Additionally, microhabitats in direct contact with other invertebrates were suitable for octocoral recruitment, which indicates this taxon is suited to deal with the high abundances of macroalgae and heterotrophic invertebrates, characteristic of contemporary coral reefs in the Caribbean. However, both, the amount of suitable habitat available for recruits and the median suitability scores were higher for scleractinians than for octocorals, suggesting niche dynamics alone are not driving the higher recruit abundances of octocorals on these reefs. Previous studies have found larval supply as a major driver of octocoral recruit abundances (Martínez-Quintana & Lasker 2021). Our results provide further evidence pre-settlement processes might be driving the differences in the relative abundances between recruits of these taxa, as well as the different trends the populations are following on the Caribbean.
The physical 3-dimensional structure of habitats plays a critical role on species distributions, and influences the function and resilience of global ecosystems (Ishii et al. 2004; Taniguchi & Tokeshi 2004; Ferrari et al. 2016). Developments in remote sensing such as, the Global Ecosystem Dynamics Investigation (GEDI) Lidar (Coyle et al. 2019), and next-generation optical sensing technologies capable of imaging without distortion through ocean waves (NASA FluidCam, MiDAR and NeMO-Net; Chirayath & Li 2019) are collecting geospatial information of terrestrial and marine ecosystems in 3D at sub-cm resolution. These data, in combination with methods such as the meshSDM we developed, have the potential to transform landscape and seascape ecology, improving our ability to analyze species-habitat relationships, infer ecological processes and detect changes in habitat structure. On coral reefs, living coral cover has declined globally by half since the 1950s (Eddyet al. 2021), and the decline has been even more precipitous in the Caribbean (Gardner et al. 2003). While some studies projected the collapse of reef ecosystems (Pandolfi et al. 2005), other analyses hypothesize coral reefs will change rather than disappear completely (Hughes et al. 2003; Pandolfi et al. 2011). In this latter view, variability in physiological responses to temperature, acidification, and nutrients; and rates to adaptation to rapid warming will drive spatial heterogeneity on the degradation of coral reefs (Pandolfi et al . 2011). Populations maintenance requires successful recruitment. Understanding recruitment niches of reef species and the availability of suitable 3D microhabitats is equally important to project coral reef futures. The meshSDM approach we have used is not limited to recruitment studies alone. Ultimately, the method has implications for the long-term conservation of structurally complex ecosystems where species use 3D stratified habitats, such as forest canopies, roots systems, caves, mesas and canyons.
ACKNOWLEDGEMENTS
We thank the staff of the University of the Virgin Islands, Virgin Islands Environmental Resource Station, for their logistical support in the field. Special thanks to Jacqueline Krawiecki, for providing help and support during the field work and data collection. Thanks to Peter J. Edmunds (California State University Northridge) for the logistics in the field, and to Mary Alice Coffroth for providing insightful and helpful comments throughout the project. In addition, special thanks to Stuart A. Sandin, Clinton Edwards and Nicole Pedersen (Scripps Institution of Oceanography) for teaching Ángela Martínez-Quintana Structure from Motion photogrammetry during the earliest stages of the project.
REFERENCES
Adler, P.B., HilleRisLambers, J. & Levine, J.M. (2007). A niche for neutrality. Ecology Letters , 10, 95–104.
Alino, P., Sammarco, P. & Coll, J.C. (1992). Competitive strategies in soft corals (Coelenterata, Octocorallia). IV. Environmentally induced reversals in competitive superiority. Marine Ecology -Pprogress Series, - MAR ECOL-PROGR SER , 81, 129–145.
Anderson, R.P. & Gonzalez, I. (2011). Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling , 222, 2796–2811.
Aronson, R.B., Macintyre, I.G., Wapnick, C.M. & O’Neill, M.W. (2004). Phase shifts, alternative states, and the unprecedented convergence of two reef systems. Ecology , 85, 1876–1891.
Barbosa, R.V., Davies, A.J. & Sumida, P.Y.G. (2020). Habitat suitability and environmental niche comparison of cold-water coral species along the Brazilian continental margin. Deep Sea Research Part I: Oceanographic Research Papers , 155, 103147.
Bayley, D.T.I. & Mogg, A.O.M. (2020). A protocol for the large-scale analysis of reefs using Structure from Motion photogrammetry.Methods in Ecology and Evolution , 11, 1410–1420.
Bohl, C.L., Kass, J.M. & Anderson, R.P. (2019). A new null model approach to quantify performance and significance for ecological niche models of species distributions. Journal of Biogeography , 46, 1101–1111.
Brakel, W.H. (1979). Small-scale spatial variation in light available to coral reef benthos: quantum irradiance measurements from a Jamaican rReef. Bulletin of Marine Science , 29, 406–413.
Brandl, S.J., Hoey, A.S. & Bellwood, D.R. (2014). Micro-topography mediates interactions between corals, algae, and herbivorous fishes on coral reefs. Coral Reefs , 33, 421–430.
Burns, J., Delparte, D., Kapono, L., Belt, M., Gates, R. & Takabayashi, M. (2016). Assessing the impact of acute disturbances on the structure and composition of a coral community using innovative 3D reconstruction techniques. Methods in Oceanography , 15, 49–59..
Burns, J.H.R., Delparte, D., Gates, R.D. & Takabayashi, M. (2015). Uutilizing underwater three-dimensional modeling to enhance ecological and biological studies of coral reefs. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,  40(5), 61Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W5, 61–66.
Burns, J.H.R., Fukunaga, A., Pascoe, K.H., Runyan, A., Craig, B.K., Talbot, J., et al. (2019). 3D habitat complexity of coral reefs in the Northwestern Hawaiian Islands is driven by coral assemblage structure. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,  42, 61-67.
Carleton, J.H. & Sammarco, P.W. (1987). Effects of substratum irregularity on success of coral settlement: quantification by comparative geomorphological techniques, 14.. Bulletin of Marine Science , 40(1), 85-98.
Carpenter, R.C. & Edmunds, P.J. (2006). Local and regional scale recovery of Diadema promotes recruitment of scleractinian corals.Ecology Letters , 9, 271–280.
Chirayath, V., & Li, A. (2019). Next-Generation optical sensing technologies for exploring ocean worlds—NASA FluidCam, MiDAR, and NeMO-Net. Frontiers in Marine Science , 6, 521.
Combs, I.R., Studivan, M.S., Eckert, R.J. & Voss, J.D. (2021). Quantifying impacts of stony coral tissue loss disease on corals in Southeast Florida through surveys and 3D photogrammetry. PLoS ONE , 16, e0252593.
Connell, J.H. (1978). Diversity in Tropical Rain Forests and Coral Reefs. Science , 199, 1302–1310.
Couch, C.S., Oliver, T.A., Suka, R., Lamirand, M., Asbury, M., Amir, C.,et al. (2021). Comparing coral colony surveys from in-water observations and structure-from-motion imagery shows low methodological bias. Frontiers in Marine Science . 622
Coyle, D. B., Stysley, P. R., Chirag, F. L., Frese, E., & Poulios, D. (2019). The global ecosystem dynamics investigation (GEDI) Lidar laser transmitter. In Infrared Remote Sensing and Instrumentation XXVII  (Vol. 11128, pp. 112-125). SPIE.
Cramer, K.L., Donovan, M.K., Jackson, J.B.C., Greenstein, B.J., Korpanty, C.A., Cook, G.M., et al. (2021). The transformation of Caribbean coral communities since humans. Ecology and Evolution , 11, 10098–10118.
Darling, E. S., Graham, N. A., Januchowski-Hartley, F. A., Nash, K. L., Pratchett, M. S., & Wilson, S. K. (2017). Relationships between structural complexity, coral traits, and reef fish assemblages. Coral Reefs , 36(2), 561-575.
Darling, E.S., Alvarez-Filip, L., Oliver, T.A., McClanahan, T.R. & Côté, I.M. (2012). Evaluating life-history strategies of reef corals from species traits. Ecology Letters , 15, 1378–1386.
Davies, A.J., Wisshak, M., Orr, J.C. & Murray Roberts, J. (2008). Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep Sea Research Part I: Oceanographic Research Papers , 55, 1048–1062.
Davies, S.W., Matz, M.V. & Vize, P.D. (2013). Ecological complexity of coral recruitment processes: effects of invertebrate herbivores on coral recruitment and growth depends upon substratum properties and coral species. PLOS ONE , 8, e72830.
Dayton, P.K. (1971). Ccompetition, disturbance, and community organization: the provision and subsequent utilization of space in a rocky intertidal community. - Dayton - 1971 - Ecological Monographs. 41(4), 351-389. - Wiley Online Library . Available at: https://esajournals-onlinelibrary-wiley-com.gate.lib.buffalo.edu/doi/abs/10.2307/1948498. Last accessed 3 July 2020.
Eddy, T. D., Lam, V. W., Reygondeau, G., Cisneros-Montemayor, A. M., Greer, K., Palomares, M. L. D., et al. (2021). Global decline in capacity of coral reefs to provide ecosystem services. One Earth , 4(9), 1278-1285.
Edmunds, P., Bruno, J. & Carlon, D. (2004). Effects of depth and microhabitat on growth and survivorship of juvenile corals in the Florida Keys. Marine Ecology Progress Series , 278, 115-124.Mar. Ecol. Prog. Ser. , 278, 115–124.
Edmunds, P.J. & Carpenter, R.C. (2001). Recovery of Diadema antillarum reduces macroalgal cover and increases abundance of juvenile corals on a Caribbean reef. Proceedings of the National Academy of Sciences , 98, 5067–5071.
Edmunds, P.J., Nozawa, Y. & Villanueva, R.D. (2014). Refuges modulate coral recruitment in the Caribbean and the Pacific. Journal of Experimental Marine Biology and Ecology , 454, 78–84.
Ehbrecht, M., Seidel, D., Annighöfer, P., Kreft, H., Köhler, M., Zemp, D. C., et al. (2021). Global patterns and climatic controls of forest structural complexity. Nature communications , 12(1), 1-12.
Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography , 29, 129–151.
Elith, J. & Leathwick, J.R. (2009). Sspecies distribution models: ecological explanation and prediction across space and time.Annual Review of Ecology, Evolution and Systematics , 40(1), 677-697Annu. Rev. Ecol. Evol. Syst. , 40, 677–697.
Ferrari, R., Bryson, M., Bridge, T., Hustache, J., Williams, S. B., Byrne, M., & Figueira, W. (2016). Quantifying the response of structural complexity and community composition to environmental change in marine communities. Global change biology , 22(5), 1965-1975.
Ferrari, R., Figueira, W.F., Pratchett, M.S., Boube, T., Adam, A., Kobelkowsky-Vidrio, T., et al. (2017). 3D photogrammetry quantifies growth and external erosion of individual coral colonies and skeletons.Scientific reports , 7(1), 1-9. Sci Rep , 7, 16737.
Fukunaga, A., Burns, J.H.R., Craig, B.K. & Kosaki, R.K. (2019). Integrating three-dimensional benthic habitat characterization techniques into ecological monitoring of coral reefs. Journal of Marine Science and Engineering , 7(2), 27.
Fukunaga, A., Kosaki, R.K., Pascoe, K.H. & Burns, J.H.R. (2020). Fish assemblage structure in the northwestern hawaiian islands is associated with the architectural complexity of coral-reef habitats.Diversity , 12(11), 430.
Gallagher, C. & Doropoulos, C. (2017). Spatial refugia mediate juvenile coral survival during coral–predator interactions. Coral Reefs , 36, 51–61.
Gámez, S., & Harris, N. C. (2022). Conceptualizing the 3D niche and vertical space use. Trends in Ecology & Evolution .
Gardner, T.A., Côté, I.M., Gill, J.A., Grant, A. & Watkinson, A.R. (2003). Long-term region-wide declines in caribbeanCaribbean corals.Science , 301, 958–960.
Gastón, A. & García-Viñas, J.I. (2011). Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models. Ecological Modelling , 222(13), 2037–2041.
González-Rivero, M., Yakob, L. & Mumby, P.J. (2011). The role of sponge competition on coral reef alternative steady states. Ecological Modelling , 222, 1847–1853.
Graham, M.H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology , 84, 2809–2815.
Graham, N. A., & Nash, K. L. (2013). The importance of structural complexity in coral reef ecosystems. Coral reefs , 32(2), 315-326.
Grant, A.-G. & Kalisz, S. (2020). Do selfing species have greater niche breadth? Support from ecological niche modeling. Evolution , 74, 73–88.
Green, D., Edmunds, P. & Carpenter, R. (2008). Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Marine Ecology Progress Series , 359, 1-10. Mar. Ecol. Prog. Ser. , 359, 1–10.
Greenwell, B., M. & Boehmke, B., C. (2020). Variable importance plots—an introduction to the vip pPackage. The R Journal , 12, 343.
Harrington, L., Fabricius, K., De’ath, G. & Negri, A. (2004). Recognition and selection of settlement substrata determine post-settlement survival in corals. Ecology , 85, 3428–3437.
Hastie, T., Friedman, J. & Tibshirani, R. (2001). Model Assessment and Selection. In: The Elements of Statistical Learning: Data Mining, Inference, and Prediction , Springer Series in Statistics (eds. Hastie, T., Friedman, J. & Tibshirani, R.). Springer, New York, NY, pp. 193–224.
Hata, T., Madin, J.S., Cumbo, V.R., Denny, M., Figueiredo, J., Harii, S., et al. (2017). Coral larvae are poor swimmers and require fine-scale reef structure to settle. Scientific Reports , 7, 2249.
Hughes, T. P., Baird, A. H., Bellwood, D. R., Card, M., Connolly, S. R., Folke, C., et al. (2003). Climate change, human impacts, and the resilience of coral reefs. Science , 301(5635), 929-933.
Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., et al. (2003). Climate change, human impacts, and the resilience of coral reefs. Science , 301, 929–933.
Idjadi, J., Haring, R. & Precht, W. (2010). Recovery of the sea urchinDiadema antillarum promotes scleractinian coral growth and survivorship on shallow Jamaican reefs. Marine Ecology Progress Series, 403, 91–100.
Ishii, H. T., Tanabe, S. I., & Hiura, T. (2004). Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. Forest Science , 50(3), 342-355.
Jackson, J.B.C. (1977). Competition on marine hard substrata: the adaptive significance of solitary and colonial strategies. The American Naturalist , 111, 743–767.
Jones, C.G., Lawton, J.H. & Shachak, M. (1994). Organisms as ecosystem engineers. Oikos , 69, 373–386.
Keough, M.J. & Downes, B.J. (1982). Recruitment of marine invertebrates: the role of active larval choices and early mortality.Oecologia , 54, 348–352.
Kinzie, A. (1973). Tthe zonation of Wwest indianIndian gorgonians.Bulletin of Marine Science , 23(1), 93-155 63.
Kissling, W.D., Seijmonsbergen, A., Foppen, R. & Bouten, W. (2017). eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. RIO , 3, e14939.
Kovalenko, K. E., Thomaz, S. M., & Warfe, D. M. (2012). Habitat complexity: approaches and future directions. Hydrobiologia , 685(1), 1-17.
Laird, R.A. & Schamp, B.S. (2006). Competitive Intransitivity Promotes Species Coexistence. , The American Naturalist , 168(2), 182-193. 12.
Lang, J. (1973). Coral reef project—papers in memory of Dr. Thomas F. Goreau. 11. Interspecific aggression by scleractinian corals. 2. Why the race is not only to the swift. Bulletin of Marine Science , 23(2), 260-279.Interspecific aggression by scleractinian corals. 2. why the race is not only to the swift. Bulletin of Marine Science , 20.
Lasker, H.R. & Kim, K. (1996). Larval development and settlement behavior of the gorgonian coral Plexaura kuna (Lasker, Kim and Coffroth). Journal of Experimental Marine Biology and Ecology , 207, 161–175.
Lasker, H.R., Martínez-Quintana, Á., Bramanti, L. & Edmunds, P.J. (2020). Resilience of octocoral forests to catastrophic storms.Scientific Reports , 10(1), 1-810, 4286.
Lepczyk, C. A., Wedding, L. M., Asner, G. P., Pittman, S. J., Goulden, T., Linderman, M. A., et al. (2021). Advancing landscape and seascape ecology from a 2D to a 3D science. BioScience , 71(6), 596-608.
Levins, R. (1968). Evolution in Changing Environments: Some Theoretical Explorations. (MPB-2) . Evolution in Changing Environments . Princeton University Press.
Lubchenco, J. (1983). Littornia and fucus: effects of herbivores, substratum heterogeneity, and plant escapes during succession.Ecology , 64, 1116–1123.
MacArthur, R.H. & MacArthur, J.W. (1961). On bird species diversity.Ecology , 42, 594–598.
Martínez-Quintana, Á. & Lasker, H.R. (2021). Early life-history dynamics of caribbeanCaribbean octocorals: the critical role of larval supply and partial mortality. Frontiers in Marine Science , 8, 1279.
Meesters, E.H., Wesseling, I. & Bak, R.P.M. (1996). Partial mortality in three species of reef-building corals and the relation with colony morphology. Bulletin of Marine Science , 58(3), 838-852BULLETIN OF MARINE SCIENCE , 58, 15.
Melo-Merino, S.M., Reyes-Bonilla, H. & Lira-Noriega, A. (2020). Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence.Ecological Modelling , 415, 108837.
Merow, C., Smith, M.J. & Silander, J.A. (2013). A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography , 36, 1058–1069.
Morales, N.S., Fernández, I.C. & Baca-González, V. (2017). MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ , 5, e3093..
Mumby, P.J., Hedley, J.D., Zychaluk, K., Harborne, A.R. & Blackwell, P.G. (2006). Revisiting the catastrophic die-off of the urchinDiadema antillarum on Caribbean coral reefs: Fresh insights on resilience from a simulation model. Ecological Modelling , 196, 131–148.
Muscarella, R., Soley-Guardia, M., Boria, R., Kass, J., Uriarte, M. & Anderson, R. (2014). ENMeval: an Rr package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution , 5(11), 1198-1205. Available at: https://cran.r-project.org/web/packages/ENMeval/citation.html. Last accessed 9 November 2020.
Norström, A., Nyström, M., Lokrantz, J. & Folke, C. (2009). Alternative states on coral reefs: beyond coral–macroalgal phase shifts.Marine ecology progress series , 376, 295-306Mar. Ecol. Prog. Ser. , 376, 295–306.
Nozawa, Y. (2008). Micro-crevice structure enhances coral spat survivorship. Journal of Experimental Marine Biology and Ecology , 367, 127–130.
Olinger, L.K., Chaves-Fonnegra, A., Enochs, I.C. & Brandt, M.E. (2021). Three competitors in three dimensions: photogrammetry reveals rapid overgrowth of coral during multispecies competition with sponges and algae. Marine Ecology Progress Series , 657, 109–121.
Palma, M., Rivas Casado, M., Pantaleo, U. & Cerrano, C. (2017). High resolution orthomosaics of africanAfrican coral reefs: a tool for wide-scale benthic monitoring. Remote Sensing , 9(7), 705.
Pandolfi, J. M., Connolly, S. R., Marshall, D. J., & Cohen, A. L. (2011). Projecting coral reef futures under global warming and ocean acidification. Science , 333(6041), 418-422.
Pandolfi, J. M., Jackson, J. B., Baron, N., Bradbury, R. H., Guzman, H. M., Hughes, T. P., … & Sala, E. (2005). Are US coral reefs on the slippery slope to slime?. Science , 307(5716), 1725-1726.
Pedersen, N.E., Edwards, C.B., Eynaud, Y., Gleason, A.C.R., Smith, J.E. & Sandin, S.A. (2019). The influence of habitat and adults on the spatial distribution of juvenile corals. Ecography , 42, 1703–1713.
Phillips, S.J. & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation, 15.
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling , 190, 231–259.
Porter, J.W. (1976). Autotrophy, Heterotrophy, and resource partitioning in caribbeanCaribbean reef-building corals. The American Naturalist , 110, 731–742.
Precoda, K., Allen, A.P., Grant, L. & Madin, J.S. (2017). Using traits to assess non-transitivity of interactions among coral species.The American Naturalist , 190, 420–429.
Raimondi, P.T. & Morse, A.N.C. (2000). Tthe consequences of complex larval behavior in a coral. , Ecology , 81(11), 3193-3211. 81, 19.
Reidenbach, M.A., Koseff, J.R. & Koehl, M.A.R. (2009). Hydrodynamic forces on larvae affect their settlement on coral reefs in turbulent, wave-driven flow. Limnology and oceanography , 54(1), 318-330.Limnol. Oceanogr. , 54, 318–330.
Reidenbach, M.A., Stocking, J.B., Szczyrba, L. & Wendelken, C. (2021). Hydrodynamic interactions with coral topography and its impact on larval settlement. Coral Reefs , 40, 505–519.
Roff, G., Joseph, J. & Mumby, P.J. (2020). Multi-decadal changes in structural complexity following mass coral mortality on a Caribbean reef., Biogeosciences , 17(23), 5909-5918 10.
Roth, M.S. & Knowlton, N. (2009). Distribution, abundance, and microhabitat characterization of small juvenile corals at Palmyra Atoll.Marine Ecology Progress Series , 376, 133–142.
Rützler, K., Piantoni, C., Soest, R.W.M.V. & Díaz, M.C. (2014). Diversity of sponges (Porifera) from cryptic habitats on the Belize barrier reef near Carrie Bow Zootaxa , 3805(1), 1–129.
Ruzicka, R., Colella, M., Porter, J., Morrison, J., Kidney, J., Brinkhuis, V., et al. (2013). Temporal changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño.Marine Ecology Progress Series , 489, 125-141.
Schoener, T.W. (1968). The Anolis lLizards of Bimini: resource partitioning in a complex fauna. Ecology , 49, 704–726.
Sebens, K.P. & Miles, J.S. (1988). Sweeper tentacles in a gorgonian octocoral: morphological modifications for interference competition.The Biological Bulletin , 175, 378–387.
Shcheglovitova, M. & Anderson, R.P. (2013). Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling , 269, 9–17.
Slattery, M. & Lesser, M.P. (2021). Gorgonians are foundation species on sponge-dominated mesophotic coral reefs in the Caribbean.Frontiers in Marine Science , 8, 654268304.
Spearman, C. (1904). Tthe proof and measurement of association between two things. American Journal of Psychology , 15,72-101, 30.
Taniguchi, H., & Tokeshi, M. (2004). Effects of habitat complexity on benthic assemblages in a variable environment. Freshwater biology , 49(9), 1164-1178.
Trapon, M.L., Pratchett, M.S. & Hoey, A.S. (2013). Spatial variation in abundance, size and orientation of juvenile corals related to the biomass of parrotfishes on the Great Barrier Reef, Australia. PLoS ONE , 8, e57788.
Tsounis, G., Edmunds, P.J., Bramanti, L., Gambrel, B. & Lasker, H.R. (2018). Variability of size structure and species composition in Caribbean octocoral communities under contrasting environmental conditions. Marine Biology , 165(2), 1-14.Mar Biol , 165, 29.
Virtanen, E.A., Norkko, A., Nyström Sandman, A. & Viitasalo, M. (2019). Identifying areas prone to coastal hypoxia – the role of topography.Biogeosciences , 16, 3183–3195.
Warren, D.L. & Seifert, S.N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications , 21, 335–342.
Warren, D.L., Beaumont, L.J., Dinnage, R. & Baumgartner, J.B. (2019). New methods for measuring ENM breadth and overlap in environmental space. Ecography , 42, 444–446.
Warren, D.L., Glor, R.E. & Turelli, M. (2008). Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution , 62, 2868–2883.
Warren, D.L., Glor, R.E. & Turelli, M. (2010). ENMTools: a toolbox for comparative studies of environmental niche models. Ecography , 33, 607–611.
Warren, D.L., Matzke, N.J., Cardillo, M., Baumgartner, J.B., Beaumont, L.J., Turelli, M., et al. (2021). ENMTools 1.0: an R package for comparative ecological biogeography. Ecography , 44, 504–511.
Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J. & Reynolds, J.M. (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology , 179, 300–314.
Whalan, S., Abdul Wahab, M.A., Sprungala, S., Poole, A.J. & de Nys, R. (2015). Larval settlement: the role of surface topography for sessile coral reef invertebrates. PLoS ONE , 10, e0117675.
Williams, S.M., Chollett, I., Roff, G., Cortés, J., Dryden, C.S. & Mumby, P.J. (2015). Hierarchical spatial patterns in Caribbean reef benthic assemblages. Journal of Biogeography ,  42(7), 1327-1335. J. Biogeogr. , 42, 1327–1335.
Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H. & Guisan, A. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions , 14, 763–773.
Yesson, C., Taylor, M.L., Tittensor, D.P., Davies, A.J., Guinotte, J., Baco, A., et al. (2012). Global habitat suitability of cold-water octocorals: Global distribution of deep-sea octocorals. Journal of Biogeography , 39, 1278–1292.
Young, G.C., Dey, S., Rogers, A.D. & Exton, D. (2017). Cost and time-effective method for multi-scale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PLoS ONE , 12, e0175341.
Young, T.P., Petersen, D.A. & Clary, J.J. (2005). The ecology of restoration: historical links, emerging issues and unexplored realms: Ecology of restoration. Ecology Letters , 8, 662–673.
Zelli, E., Quéré, G., Lago, N., Di Franco, G., Costantini, F., Rossi, S., et al. (2020). Settlement dynamics and recruitment responses of Mediterranean gorgonians larvae to different crustose coralline algae species. Journal of Experimental Marine Biology and Ecology , 530–531, 151427.
FIGURE CAPTIONS
Figure 1: Digital reproduction of a 0.25 m2 area at Grootpan Bay in the US Virgin Islands comparing the profile of the reefscape generated with 3D data (A and C) versus 2.5D (B and D). The two panels on the left (A and B) the top view of the reef in 3D, and the digital elevation model (DEM) of the same quadrat in 2.5D, respectively. Panels C and D represent crossed sections of that area highlighted in white on A and B. The reef profile on panel C is more complex than the one calculated with a DEM in 2.5D (panel D). Note that the vertical and overhanging areas in panel C are detected in the 3D model (C) and are available to study the distribution of species and microhabitats, while areas overlooked in the 2.5D representation and are not available (D)..
Figure 2: A) Map illustrating the location of the study sites. B) A panoramic photograph of Grootpan Bay showing the benthic community of shallow fringing reefs in the area. C) A 3D model of a small area of the reef generated with SfM with stylized representations of where recruits can be located. Pictures of recruits of different sizes and genera are highlighted in brown for scleractinian corals and in blue for octocorals.
Figure 3: Stacked histograms showing the relative importance of the environmental predictors used to characterize suitable habitat for recruits (A and C), and marginal response curves (B and D) showing the predicted probability of suitable habitat (logistic, blue line), as a function of each environmental predictor, while all other variables are set to their mean. The responses curves in panels B and D are ordered from the most important predictor to the least. Panels A and B refer to octocoral recruits, and panels C and D to scleractinians. Grey dashed lines show the distribution of values of each environmental predictor on the reef (background data) whereas orange dashed lines depict the distribution of those variables within an area of 1 cm wide around the recruits (presence data). Roughness and TEI are expressed in meters, whereas Slope is expressed in degrees.
Figure 4: A and B) Images showing top and front views, respectively, of a 50 x 50 cm area reconstructed with SfM in Europa Bay and overlaid with natural color (RGB). From C to F, top and front views of the SDM projected onto the polygonal mesh derived from SfM. Colors represent the estimated probability of suitable habitat for octocoral (C and D) and scleractinian recruits (E and F). Suitability scores range from unsuitable (value = 0, in black) to highly suitable (value = 1, in yellow).
Figure 5: Results of the identity (A) and background tests (B) in geographic and environmental spaces. Dashed lines indicate Schoener’s D , Warren’s I and Spearman’s rank correlation values (rho) calculated from the empirical models generated with the recruit data. The histograms represent the distribution of D and I calculated from 100 null models. In geographic space, both metrics were significantly lower than expected by chance. Whereas only Warren’s I and rhowere significant in environmental space. These results indicate microhabitats suitable for each taxon were very similar, but not identical (Figure 5A; p ≤ 0.05).
Figure 6: Violin plots showing the distribution of suitability scores for scleractinian corals and octocorals at our sites. The violin plots show the kernel density trace of smoothed histograms to describe the distribution pattern of the data and are overlaid with boxplots. Microhabitats suitable for scleractinian recruits were more abundant and had higher suitability scores than those calculated for octocorals (Mann-Whitney U test W= 6.6159e+13, p < 0.01).
RICH MEDIA CAPTION
Video 1: Recording showing the rotation of the SDM projected onto the polygonal mesh derived from a 50 x 50 cm area reconstructed with SfM in Europa Bay (i.e., figure 4C and 4D). Colors represent the estimated probability of suitable habitat for octocoral recruits. Suitability scores range from unsuitable (value = 0, in black) to highly suitable (value = 1, in yellow).