Propagation method and species drive survival patterns across reef zones in coral seeding on the Great Barrier Reef

Propagation method and species drive survival patterns across reef zones in coral seeding on the Great Barrier Reef | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Propagation method and species drive survival patterns across reef zones in coral seeding on the Great Barrier Reef Saskia Jurriaans, Carine Lefèvre, Sophie Ferguson, Carly J Randall This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8283449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Coral reef restoration increasingly relies on scalable methods, yet outcomes vary across species, propagation techniques, and habitats. Coral seeding, where coral propagules are settled on deployment units before outplanting, provides a flexible approach that accommodates both asexual (e.g., microfrags) and sexual (e.g., spat) propagation. Objectives: To improve predictability and efficiency of coral seeding, we tested how propagation method, species and habitat shape early survival after seeding. Methods: We conducted a multi-species coral seeding experiment at Davies Reef (central Great Barrier Reef), deploying microfrags and spat on tabs within seeding devices across ten sites spanning lagoon, back, flank, and front reef zones. Survival was monitored for ~ 10 months. Analyses included time-to-mortality, growth and generalized mixed models testing the effects of zone, flow, benthic composition and density dependence at the tab-level. Results: Microfrags outperformed spat in survival and reached ~ 10× larger mean size. Species effects zone-specific: spat survival declined at exposed (flank/front) sites, whereas microfragments remained comparatively robust. Reef zone improved model fit relative to flow alone, while site-level benthic composition did not predict survival. Microhabitat effects accounted for ~ 30% of variance, with higher survival on tabs dominated by crustose coralline algae (CCA) and lower on macroalgae-dominated surfaces. Positive density dependence was detected for Galaxea fascicularis and Montipora turtlensis , but not for Acropora loripes . Conclusion: Propagation method, species, and reef zone jointly shape coral survival, but centimetre-scale microhabitat factors are key. Microfragmentation provides more reliable early survival and growth, whereas spat contribute genetic diversity. Implications for practice: Reef-zone context should guide deployment. Exposed zones should be avoided for spat but are suitable for microfrags. Settlement substrates should minimise macroalgae and prioritise CCA. Species-specific seeding densities are recommended: higher densities benefit Galaxea fascicularis and Montipora turtlensis but not Acropora loripes . Given high within-site variability, deploying many devices at fewer well-chosen sites and incorporating fine-scale monitoring will improve outcome predictability. Combine propagation methods strategically, deploy microfrags for reliable early cover and spat to sustain genetic diversity and adaptive potential. Lastly, practical proxies such as reef zone and tab-level substrate checks are more reliable predictors of survival than coarse site-level benthic summaries. microfragmentation spat wave exposure density dependence spawning reef restoration post-settlement mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Coral reef restoration is rapidly expanding as a key strategy to conserve reef ecosystems under accelerating climate change (Knowlton et al. 2021 ). While it cannot substitute for emission reductions (Hughes et al. 2023 ), restoration is increasingly recognized as a necessary complement to global mitigation and local management actions (Suggett et al. 2023 ; Peixoto et al. 2024 ). Over the past decade, restoration techniques have diversified, with numerous studies exploring methods to enhance coral cover and ecological function on degraded reefs (McLeod et al. 2022 ; Miller et al. 2022 ; Vida et al. 2024 ). Two primary approaches underpin coral propagation for restoration: asexual and sexual reproduction (Boström-Einarsson et al. 2020 ). Asexual propagation (i.e., transplanting or seeding coral fragments or microfragments) can produce large colonies via rapid encrusting and fusion of arrays of small (∼1 cm²) microfragments; this has been demonstrated across Atlantic and Pacific species and is now widely used to accelerate cover of slow-growing massive corals (Forsman et al. 2015 ; Page et al. 2018 ; Knapp et al. 2022) and fast-growing branching species (Tortolero-Langarica et al. 2020 ; van Woesik et al. 2021 ). However, asexual propagation is labour-intensive and limited in scalability and genetic diversity (Baums 2008 ; Baums et al. 2019 ). Sexual propagation uses gametes from natural spawning to produce coral offspring (‘spat’), generating high numbers and novel genetic combinations that can enhance adaptive potential (van Oppen et al. 2015 ). Yet, spat often experience high post-settlement mortality, making survival outcomes variable and returns on effort relatively low (Guest et al. 2014 ; Smith et al. 2025 ). Furthermore, the temporal constraints associated with annual mass-spawning result in a limited window of opportunity for sexual propagation, while asexual propagation is more accessible throughout the year. Understanding the strengths and limitations of both propagation methods, and identifying the environmental and logistical contexts in which each is most effective, is essential for improving the efficiency and scalability of reef restoration efforts (Banaszak et al. 2023). Across both modes of propagation, coral seeding has emerged as a deployment strategy in which corals are attached to settlement substrates or devices that are then “seeded” onto reefs (Randall et al. 2020 ; Banaszak et al. 2023). Seeding devices can provide refuge from grazers, fouling and improve retention and survival (Randall et al. 2023 ; Whitman et al. 2024 ; Montalvo-Proano et al. 2025 ; Ramsby et al. 2025 ). Advances in three-dimensional structures such as cogs, tetrapods, and plug-based designs have improved survivorship and deployment efficiency (Chamberland et al. 2017 ; Randall et al. 2023 ; Mendoza Quiroz et al. 2025) while new diver-less systems show promise for large-scale, low-labour restoration (Ramsby et al. 2025 ). Equally important to how corals are propagated is where they are deployed to maximize survival and ecological impact (Quigley et al. 2022 ). Site selection typically relies on broad-scale environmental criteria, such as depth, exposure and turbidity (Humanes et al. 2025 ). Broad-scale gradients and reef zonation strongly shape early coral survival, with declines observed under higher turbidity, sedimentation, flow velocity, and depth (Penin et al. 2010 ; Gouezo et al. 2019 ; Edmunds 2023 ; Drake et al. 2025 ). However, coral seeding trials show that small-scale variation (on the order of tens of centimetres) within sites can have even stronger effects on spat survival than site-level factors like benthic community composition or flow regime (Page et al. 2024 ; Jurriaans et al. 2025 ). This presents a key challenge for restoration: while broad-scale site selection is necessary for scaling up efforts, survival outcomes may hinge on microhabitat conditions that are difficult to incorporate into large-scale planning. Understanding how fine-scale environmental variability interacts with reef zonation is therefore essential to improving survival predictions and developing scale-aware restoration strategies. To date, a majority of coral restoration efforts have focused on fast-growing coral species, mostly in the genus Acropora , due to their ecological importance and rapid growth (Boström-Einarsson et al. 2020 ). However, this narrow taxonomic focus limits our ability to restore the full complexity and functionality of reef ecosystems (Quigley et al. 2022 ; Madin et al. 2023 ). Broadening both the environmental and taxonomic scope of restoration is therefore critical for improving outcomes. In this study, we address three key objectives: (i) expand the breadth of taxa tested using a coral-seeding approach; (ii) assess environmental drivers of coral survival across reef zones and wave exposure regimes; and (iii) compare survival outcomes of asexually (microfragment) and sexually (spat) propagated corals across multiple species and habitats. We conducted a multi-species, multi-habitat field deployment at Davies Reef (central, mid-shelf Great Barrier Reef), examining species- and habitat-specific differences in survival between microfrags and spat. Our findings provide insights into optimizing coral propagation and placement strategies to enhance the success and scalability of reef restoration. Methods Asexual propagation: microfragmentation Four to six phenotypically distinct colonies of Acropora loripes , Goniastrea retiformis, Montipora turtlensis and Galaxea fascicularis were collected late October 2022 from Davies Reef (18°49’S, 147°38’E) on the central Great Barrier Reef (GBR; Fig. 1 ). Colonies were transported to the National Sea Simulator (SeaSim) at the Australian Institute of Marine Science (AIMS; Townsville, Queensland) and maintained in outdoor flow-through seawater tanks under ambient light and at temperatures representative of the long-term daily average at Davies Reef. under ambient light. Colonies were cut into 8 x 8 mm (64 mm 2 ) microfragments (hereafter ‘microfrags’) late November, using a seawater-cooled diamond band saw (C-40, Gryphon Corporation). Excess skeleton from the underside of each microfrag was trimmed to ensure the tissue sat flush with the mounting base. Microfrags from the same parent colony (putative genotype) were attached using cyanoacrylate gel (Gorilla® Super Glue Gel, USA) to concrete tiles that had been pre-conditioned indoors for six weeks in flow-through seawater. Colonies were not genotyped, so genotypic distinctness among colonies and potential chimerism within colonies cannot be excluded. Tiles compromised 400 individual Tables (14 x 14 mm each; Ramsby et al. 2025 ) made from a 9:2 mix of concrete and mortar (Dingo Cement Pty Ltd, Australia). Microfrags were held in indoor flow-through aquaria under the same environmental conditions as described above until device assembly. See Table S1 for assembly details. Sexual propagation: spawning, settlement and grow-out Eight to fourteen gravid colonies of A. loripes , M. turtlensis and G. fascicularis were collected from Davies Reef in early December 2022. Reproductive maturity was confirmed by the presence of pigmented oocytes visible in branch cross-sections prior to collection. Colonies were transported to SeaSim and held in outdoor tanks under long-term daily average temperatures at Davies Reef and natural light until spawning. Mycedium elephantotus colonies, collected from Davies Reef in 2017, were already housed at SeaSim and had spawned successfully in prior years. Following the full moon on December 8, 2022, colonies were monitored nightly for bundle setting. Once gamete bundles were visible, colonies were isolated in separate containers. Spawning occurred between 13–20 December (depending on species; Table S1). Gametes were collected, fertilised, and larvae reared following Pollock et al. ( 2017 ). Four to eight days post-fertilisation, larvae were competent to settle (Randall et al. 2024 ). Settlement substrates were identical to those used for microfrags. Acrylic settlement tanks (50 L) with flow-through 4 µm-filtered seawater and overflow mesh filters (112 or 212 µm, depending on larval size) received ~ 5,000 larvae and two pre-conditioned tiles. After 2–3 days of settlement, tiles with settled spat were transferred to indoor aquaria (50 L) supplied with flow-through filtered seawater at Davies Reef specific temperature, low light (Ramsby et al. 2024 ) and fed daily with enriched Artemia , rotifers, and microalgae. Symbiont uptake was facilitated by co-culturing spat with adult donor colonies. Spat remained in these tanks until device assembly (see Table S1 for timeline). Experimental design We used coral “star” seeding devices (Fig. 1 A) engineered by AIMS under the Reef Restoration and Adaptation Program (RRAP) and described in Ramsby et al. 2025 . Each device holds three coral tabs. We assembled 250 multi-species spat devices and 250 multi-species microfrag devices, with 1 tab each of A. loripes , M. turtlensis , and M. elephantotus . Because G. fascicularis is aggressive and has long sweeping tentacles, we also produced 50 single-species microfrag devices (with the same genotype per device) and 50 single-species spat devices. Devices were assembled several days before deployment (see Table S1 for timeline). Settlement tiles were cut into individual tabs using either a blunt slicer submerged in seawater or a seawater-cooled diamond saw. Tabs with spat were inspected under magnification to count colonies (median range: 3–8 colonies per tab) before insertion. Microfrag tabs were randomly distributed among devices to avoid genotype bias. Tabs were inserted into the ceramic “star” devices and secured with 3D-printed plastic caps and marine-grade adhesive. Each device received a unique ID tag. Devices were maintained upright on steel rods in indoor flow-through aquaria at reef-specific conditions until deployment. Devices were photographed using an Olympus TG-6 camera 24–48 hr prior to ship departure to establish baseline colony and polyp counts prior to deployment. Devices were deployed at ten sites across Davies Reef spanning a natural wave energy gradient (Fig. 1 B). Site selection followed the nominal wave-energy classification described in Jurriaans et al. ( 2025 ), developed from high-resolution spatial and benthic models (Callaghan et al. 2015 ; Radford et al. 2024). At each site, 25 paired multi-species spat and microfrag devices were deployed by SCUBA divers at ~ 6 m depth along 25 m transects. Device pairs were spaced ~ 1 m apart. At each transect end, five pairs of single-species ( G. fascicularis ) spat and microfrag devices were deployed. Devices were secured to the reef with zip-ties. Coral survival was monitored ~ 3-month intervals for ~ 10 months. Colony size was measured at the final census by recording the maximum length, perpendicular length, and height and calculated as the product of these dimensions (cm³). Biological and environmental data Flow conditions – In-situ flow was measured using Marotte HS drag-tilt current meters (James Cook University) mounted ~ 50 cm above the substrate, recording velocity at 1-minute intervals. Instruments were cleaned and redeployed at each census. Median flow velocity was used as the descriptor of near-bottom hydrodynamic conditions, as maximum and standard deviation values were strongly correlated with the median. Sites spanned a wave-energy gradient encompassing four reef zones: lagoon (D1b: 0.040 m s⁻¹), back reef (D1a, D2b, D3a: 0.039–0.070 m s⁻¹), flank reef (D2a, D3b, D5a: 0.067–0.104 m s⁻¹), and front reef (D4a, D4b, D5b: 0.051–0.113 m s⁻¹). Site-level benthic cover – At each site, 35 photographs (1–2 m²) were taken along the transect to quantify benthic cover. Images were annotated in ReefCloud (AIMS 2024) to estimate the percent cover of key benthic groups: crustose coralline algae (CCA), epilithic algal matrix (EAM), macroalgae, soft corals, Acropora , non- Acropora hard corals, sand, sponge, and other sessile invertebrates. Fifty grid points per image were classified: 12 were manually classified and the remaining were auto-classified using a model trained on human annotations. Model accuracy (F1 scores) ranged from 0.74 (sand) to 0.92 (EAM; Table S2). Tab-level benthic cover – To examine the relationship between tab-level benthic cover and spat survival, each tab was scored at the final census for its dominant benthic group (CCA, EAM, macroalgae, or other). For tabs where a coral spat survived and was itself dominant, the surrounding community was scored as the next most abundant benthic group, ensuring that dominance reflected the benthic assemblage rather than the surviving coral. Percent cover of CCA was estimated in 10% increments and calculated only over the space available on each tab excluding live coral. If a coral spat occupied part of the tab, that area was excluded from the estimate; thus, percent CCA cover was assessed relative to the remaining ‘unoccupied’ surface. Data analyses All analyses were conducted in R version 4.4.3 (R Core Team 2025 ) using ggplot2 (Wickham 2016 ) for figures. Survival (time-to-event) - Survival was summarised as time to tab-level mortality (first census at which all corals on a tab were dead), with censored observations where at least one coral remained alive. Kaplan–Meier curves were fitted by species and propagation method ( survminer; Kassambara et al. 2025 ), and compared with log-rank tests. Species-specific method effects were quantified with Cox proportional hazards models ( survival; Therneau 2024 ) including a species × method interaction. We report hazard ratios (HR ± 95% CI) and likelihood-ratio χ² tests to compare models with and without the interaction. Growth - Growth of surviving colonies at the final census was modelled with a Gamma Generalized Linear Mixed Effects Model (GLMM, log link; lme4 , Bates et al. 2015 ) including species, propagation method, reef zone, and their interaction as fixed effects and site as a random intercept. Post-hoc pairwise contrasts were computed from estimated marginal means (EMM; emmeans , Lenth 2025 ) with Tukey-adjusted comparisons. Flow conditions - Effects of hydrodynamics on survival were tested with binomial GLMMs (logit link) including propagation method, species, and reef zone (and interactions) as fixed effects, and device ID and site as random intercepts. Spatial predictors (wave energy level, in-situ flow velocity, or reef zone) were compared among alternative models (Table S3). Interactions were tested with likelihood-ratio χ² tests. Intraclass correlation coefficients (ICC) quantified the variance explained by random effects. Model residuals were assessed using DHARMa (Hartig 2024 ); no overdispersion or zero inflation was detected. EMMs (± 95% CI) of predicted survival probabilities by species, propagation method, and reef zone were computed using emmeans , as above. Post-hoc pairwise contrasts were extracted as odds ratios (OR) to interpret effect sizes. Genotype effects – To test whether genotype influenced microfrag survival, we fit a binomial generalized linear model (GLM) with species, reef zone, and genotype as fixed effects. To evaluate whether survival varied among genotypes overall, we also fit a GLMM with genotype and site included as random intercepts. Models were validated as described above. Benthic composition – Site-level benthic composition (proportional cover) was ordinated via a principal component analysis (PCA; vegan , Oksanen et al. 2022 ) to visualise among-site variation. To test whether benthic composition explained survival, binomial GLMMs were fitted with PC1, PC2, species, propagation method, and reef zone as fixed effects, and site and device as random intercepts. Likelihood-ratio tests and AIC were used to compare models including reef zone, site effects, and species × PCA interactions. Tab-level benthic cover - Spat survival at the final census was analysed with binomial GLMs (logit link) including species and reef zone as covariates, and the dominant benthic group (excluding surviving coral) as a categorical predictor (CCA as reference). Model support for the benthic group effect and for species × community interactions was evaluated using likelihood-ratio tests. Coral dominated tabs were excluded because coral cover reflects the surviving individual itself and is therefore not independent of the response. In a separate analysis, percentage CCA cover (expressed as the proportion of free substrate) was fitted as a continuous predictor alongside species and reef zone. Species × CCA cover interactions were also tested to evaluate species-specific responses to variation in CCA cover. Spat density – Spat density effects were tested with binomial GLMMs (logit link) with site as a random intercept. Colony density (colonies per tab) was included as a continuous covariate interacting with species. Odds ratios per additional colony were derived from species-specific slopes estimated with emtrends() in emmeans . Colony and polyp density were strongly correlated (Pearson r = 0.78, 95% CI 0.75–0.80), so only colony density was analysed. Results Survival over time, and growth After 294 days (~ 10 months), survival differed significantly between propagation methods (K-M log-rank, χ² = 561, df = 1, p < 0.001) and among species (χ² = 467, df = 4, p < 0.001; Fig. 2 A,C) with microfrags generally outperforming spat. Survival by propagation method was species-specific (method × species: χ² = 150.1, df = 2, p < 0.001): relative to microfrags, Galaxea fascicularis spat had ~ 9.3× higher mortality rate (Cox HR = 9.35, 95% CI 6.07–14.41, p < 0.001) and ~ 6.6× higher in Montipora turtlensis spat compared with frags (HR = 6.60, 4.56–9.56, p < 0.001). Acropora loripes survival was similar between methods (microfrag vs spat respectively 54% vs 57% at the final census; HR = 1.19, 0.93–1.52, p = 0.16). Mycedium. elephantotus (spat) died rapidly, with ~ 20% survival at three months and 0% by 10 months (Fig. 2 C). Colony size differed significantly by propagation method, species, and their interaction (Fig. 2 B; GLMM, interaction: χ² = 55.92, p < 0.001). On average, microfrags were ~ 10.7× larger than spat (β = 2.37 ± 0.11, z = 22.54, p < 0.001). When expressed relative to their initial size at deployment (microfrag ≈ 0.19 cm³; spat ≈ 0.03 cm³), microfrags increased in volume by roughly 12–13×, compared to ~ 3× for spat, indicating higher proportional as well as absolute growth. Among microfrags, G. fascicularis reached the largest mean size (3.67 ± 0.41 cm³, mean ± SE), followed by M. turtlensis (2.39 ± 0.22 cm³) and A. loripes (1.74 ± 0.18 cm³). G. retiformis microfrags were the smallest (0.90 ± 0.09 cm³). All pairwise species differences were significant ( p < 0.02). Among spat, A. loripes reached the largest mean size (0.25 ± 0.03 cm³), which was 3.6× larger than G. fascicularis (0.07 ± 0.02 cm³) and 2.7× larger than M. turtlensis (0.09 ± 0.02 cm³; all p < 0.001). Lastly, colonies on the front reef were ~ 44% smaller than those on the back (β = −0.570 ± 0.167, z = − 3.43, p = < 0.001), while flank and lagoon differences were not significant (p = 0.13 and 0.51, respectively). Flow conditions Survival varied little among sites, with random site effects explaining < 1% of variance in survival (ICC = 0.004; σ² = 0.013; Figure S1). In contrast, device-level variation accounted for ~ 30% (ICC = 0.30; σ² = 1.432), highlighting the importance of fine-scale heterogeneity within sites. Flow velocity was a poor predictor of survival (AIC = 5090.6), whereas grouping by reef zone substantially improved model fit (AIC = 5032.9; ΔAIC = 57.7) and adding flow as a covariate to the zone model did not improve fit (ΔAIC = 0.9; χ² = 1.12, p = 0.29). Propagation method strongly influenced survival patterns across reef zones (χ² = 14.8, df = 3, p < 0.01; Fig. 3 A). Microfragments consistently outperformed spat (Table S4), with 78% lower odds of spat survival overall (OR = 0.22, 95% CI: 0.13–0.35, p < 0.001). This effect was most pronounced at exposed sites: spat survival was 72% lower at the flank (OR = 0.28, 95% CI: 0.14–0.57, p < 0.001) and 80% lower at the front (OR = 0.20, 95% CI: 0.10–0.41, p < 0.001). No significant difference in survival between propagation methods was found in the lagoon (OR = 1.01, 95% CI: 0.39–2.63, p = 0.98). Species survival also differed significantly across reef zones (χ² = 71.8, df = 12, p < 0.001; Fig. 3 B). A. loripes had lower survival in the lagoon (EMMs, 0.43 ± 0.07 SE, 95% CI: 0.31–0.57; Table S5) compared to the more exposed zones, including the flank (0.75 ± 0.03 SE, 95% CI: 0.68–0.80) and front reef (0.77 ± 0.03 SE, 95% CI: 0.70–0.82). In contrast, G. fascicularis showed a trend toward higher survival in the lagoon (0.62 ± 0.12, 95% CI: 0.38–0.81) than at the front reef (0.36 ± 0.07, 95% CI: 0.23–0.51) although this difference was not statistically significant (Table S6). G. retiformis (microfrag only) and M. turtlensis showed the highest survival at the back reef (0.86 ± 0.03 SE, 95% CI = 0.79–0.91 and 0.65 ± 0.04 SE, 95% CI = 0.58–0.72, respectively), with survival odds 5.4 times higher for G. retiformis ( p < 0.001) and 1.9 times higher for M. turtlensis ( p = 0.023) compared to the front (Table S6). M. elephantotus (spat only) performed poorly across zones and suffered complete mortality by the final census. Finally, we detected little evidence for genotype effects among microfrags (random-effect variance = 0.061; strongest fixed‐effect signal p = 0.07) showing that genotype had little influence on survival outcomes. Benthic composition Benthic composition varied among reef zones, with lagoon and back sites dominated by sand and epilithic algal matrix (EAM), and flank and front sites characterised by higher hard coral cover (Fig. 4 A). The first two PCA axes explained 69% of variation in benthic composition among sites, representing a gradient from sand/EAM- to coral-dominated habitats (Figure S2). Including PC1 and PC2 in survival models did not improve fit (ΔAIC = 0.5; χ² = 6.5, df = 3, p = 0.09), indicating that site-level benthic composition did not predict coral survival. At a finer spatial scale (tab level), the dominant benthic group on the tab at the final census was significantly related to spat survival (GLM; χ2 = 13.8, df = 3, p = 0.003). On CCA-dominated tabs, mean survival was 32% (95% CI: 23–42%) and varied among species ( A. loripes = 0.68 [0.58-.076], G . fascicularis = 0.14 [0.08–0.25], M. turtlensis = 0.23 [0.15–0.32]). Survival was lower on tabs dominated by macroalgae (OR = 0.46, p = 0.003) and by other taxa (OR = 0.40, p = 0.002), but survival on EAM dominated tabs did not differ from CCA-dominated tabs (OR = 0.77, p = 0.43). A species × community interaction did not improve model fit (χ2 test, p > 0.05). Survival was positively related to percent CCA cover on tabs (GLM; β = 0.60 ± 0.29 SE, p = 0.038), and this relationship was consistent across species (interaction χ² = 1.51, df = 2, p = 0.47) but with higher overall survival probabilities for A. loripes compared to G. fascicularis and M. turtlensis (χ² = 91.8, df = 2, p < 0.001; Fig. 4 C). A reef zone × CCA cover interaction did not improve model fit (χ² = 1.20, df = 3, p = 0.75). Spat density Initial deployment densities varied among species (Fig. 5 A). M. turtlensis and G. fascicularis had the lowest medians (3 and 4 colonies per tab, respectively), whereas A. loripes and M. elephantotus each had 8 colonies per tab. Density-dependent survival was species-specific (Fig. 5 B): survival increased with colony density for G. fascicularis (OR per colony = 1.23, 95% CI 1.02–1.49, p = 0.031) and M. turtlensis (OR = 1.26, 1.11–1.43, p < 0.001), but not for A. loripes (OR = 1.00, 0.92–1.09, p = 0.98). Discussion Survival varied strongly among propagation methods, species, and reef zones. Overall, microfragments outperformed sexually produced spat across taxa and habitats, although survival and growth were species-specific and influenced by interactions between life-history traits and habitat context. Reef zone, rather than flow velocity or benthic composition, best explained variation in survival, underscoring the importance of broad habitat context combined with fine-scale heterogeneity in coral performance post-deployment. Our findings also illustrate a fundamental trade-off in coral restoration: sexual propagation enhances genetic diversity and long-term adaptive potential but suffers high early mortality, whereas asexual propagation yields higher short-term survival but is clonal and more labour-intensive to scale. Restoration programs must therefore balance the scalability of sexual reproduction with the short-term reliability of asexual propagation, deploying both in species-and zone-specific ways to optimise outcomes. Additionally, our results highlight a guiding principle for restoration design: broad reef-scale context structures the playing field, but centimetre-scale conditions decide many of the early winners. Microfragments showed markedly higher survival and were ~ 10× larger than spat after ten months, especially at sites in more exposed zones (flank, front). Our findings align with prior coral-seeding trials reporting higher survival and growth of microfragments relative to spat (Chamberland et al. 2017 ; Page et al. 2018 ; Whitman et al. 2024 ), likely due to their larger initial size, thicker tissue and greater energy reserves, which together confer a “size-escape” advantage against grazers, sedimentation, and overgrowth (Doropoulos et al. 2016 ; Whitman et al. 2024 ). Sexually produced spat face well-documented bottlenecks (Edmunds 2023 ) driven by grazing, sediment smothering, and turf-algal competition (Trapon et al. 2013 ; Doropoulos et al. 2016 ; Gallagher and Doropoulos 2017). The microfrags in our study effectively bypassed this vulnerable stage, maintaining high survival across sites. Furthermore, we detected no genotype effects on microfrag survival, indicating that during the first year, environmental context and species traits tend to dominate survival patterns. Genotypic differences may become apparent later through variations in growth, recovery, or stress tolerance (Bairos-Novak et al. 2021). Species effects were strong and ecologically consistent. Acropora loripes performed well across propagation methods and reef zones (no difference in survival between methods; largest mean spat size), marking it as a versatile species for restoration via seeding. Montipora turtlensis and Galaxea fascicularis achieved high survival and growth as microfrags but not as spat, suggesting that both species benefit from the size and robustness conferred by microfragmentation, consistent with evidence that micro-colony fusion and tissue expansion enhance early size escape and self-attachment (Forsman et al. 2015 ; Page et al. 2018 ). This pattern reinforces the need to tailor propagation strategies to species-specific life-history traits and ecological niches. Goniastrea retiformis (deployed as microfrags only) performed best on the back reef, consistent with its occurrence in moderate-flow environments balancing sedimentation and light availability (Veron 2000 ; Baird et al. 2003 ). In contrast, Mycedium elephantotus spat suffered complete mortality, likely reflecting a mismatch between our attachment method (fixing devices onto open substrates) and the more protected environments this species typically occupies (Veron 2000 ; Madin et al. 2016 ), such as shaded crevices and low-light refuges. Free deployment of devices, as described in Ramsby et al. ( 2025 ), which allows devices to naturally lodge into the reef matrix and crevices may be better suited for such cryptic taxa. While Acropora loripes performed consistently well across propagation method and reef zones, over-reliance on Acropora risks narrowing functional roles (Madin et al. 2023 ). In line with calls to diversify “restoration portfolios” (Madin et al. 2023 ), incorporating massive and submassive growth forms such as Goniastrea or Galaxea can enhance structural complexity, ecological function, and resilience (McClanahan et al. 2012; Darling et al. 2012 ). Restoration science should also expand beyond spawning taxa to include brooding species, which release competent larvae throughout the year and thereby offer greater flexibility and continuity for restoration efforts. Despite a strong wave-energy gradient across deployment sites, median flow velocity was a poor predictor of survival, whereas reef zone substantially improved model fit and explained more variation in survival. This suggests that unmeasured environmental factors that co-vary with reef zone (e.g., sediment resuspension, turbidity, irradiance and grazer assemblages) play a major role in shaping post-settlement trajectories (Penin et al. 2010 ; Gouezo et al. 2019 ; Drake et al. 2025 ). The effects of propagation method were zone-dependent: spat survival declined sharply at flank and front, while microfrags were comparatively robust, consistent with the idea that the larger size and established tissue of microfrags provide resistance to hydrodynamic stress and sediment abrasion (Forsman et al. 2015 ; Knapp et al. 2022). Species by zone patterns also aligned with ecological niche expectations. For example, A. loripes performed better in exposed zones, while G. fascicularis survived better in sheltered zones, highlighting the potential to develop taxon- and zone-specific deployment strategies. Device-level variation explained ~ 30% of survival variance, far exceeding site-level effects (< 1%). These results mirrored earlier findings for Acropora spat at Davied Reef (Jurriaans et al. 2025 ) and were consistent with microfrag studies showing that local site conditions strongly influence early growth and fusion (Page et al. 2018 ). These results highlight a scale mismatch: early post-settlement processes operate at millimetre-to-centimetre scales, shaped by factors such as surface roughness, boundary-layer flow, sediment retention, turf growth form and micro-refugia from grazers (Doropoulos et al. 2016 ; Edmunds 2023 ), which broad site-level descriptors (e.g., site-level benthic composition) cannot adequately capture. Although benthic composition varied among zones, it failed to predict survival at the site-level, likely because categories such as “epilithic algal matrix” or “sand” mask functional variation within each area (e.g., sediment-laden short turf versus long filamentous turf; (Connell et al. 2014; Tebbett et al. 2020 ). Grazer abundance and identity, not quantified here, also modulate these interactions (Whitman et al. 2024 ; 2025 ), although we note that the devices exclude a certain size class of grazers through its engineered protective features. At the tab scale, spat survival was lower on macroalgal-dominated or “other” substrates (sponges, ascidians, tunicates) relative to crustose coralline algae (CCA) and showed a positive linear association with CCA cover. Although this pattern is correlative and does not necessarily imply a facilitative mechanism, it aligns with evidence that CCA can promote settlement and early survival through its physical structure and microbial cues (Vermeij 2005 ). At the same time, extremely high CCA cover may also coincide with competitive interactions and overgrowth (Jorissen et al. 2020 ), but our data did not detect a non-linear or peak response. Spat density also mattered: initial colony density positively influenced spat survival for G. fascicularis and M. turtlensis but not for A. loripes . Positive density-dependent effects possibly result from aggregated spat forming colonies that enhance early size-escape through processes like fusion or chimerism in some taxa (Raymundo and Maypa 2004). By contrast, branching Acropora may rely more on rapid vertical growth (Doropoulos et al. 2012 ), making their early survival less dependent on initial density, although we note that A. loripes density did not start out as low as the other taxa, potentially masking positive density-dependent effects. Regardless, this means that seeding density can be adjusted to improve outcomes, but must be tailored to each species’ life-history strategy. Additional work is therefore needed to quantify optimal spat densities amongst taxa and to test these patterns across varying levels of genetic relatedness in larval cohorts, as relatedness is also known to influence the likelihood of fusion (Puill-Stephan et al. 2012 ). Taken together, our findings emphasize that broad reef-scale zonation sets the context, but fine-scale microhabitat structure (and its interaction with species and initial density) ultimately drives early survival. Restoration designs that integrate both reef-scale zonation and fine-scale microhabitat variability are therefore likely to yield more predictable and robust outcomes (Humanes et al. 2025 ). Our conclusions are constrained by the number of sites per reef zone and the relative short deployment duration compared to coral growth and maturation. Although reef zone explained a large proportion of variance in survival, low replication (particularly the single lagoon site) reduced statistical power to detect subtler benthic or genotype effects. Benthic data were also averaged at the site level which may overlook the fine-scale microhabitat variation most relevant to early survival. While we additionally quantified tab-level benthic composition, these data were only obtained at the final census, meaning that the community composition may not fully represent conditions experienced by spat that died earlier in the deployment. Nonetheless, these tab-level differences likely capture some of the small-scale environmental heterogeneity driving local variation in survival and warrant further investigation. The complete mortality of M. elephantotus may also reflect cohort-specific factors, such as poor larval quality or suboptimal crosses, rather than habitat unsuitability alone. Repeating deployments across spawning cohorts and using genotyped parental stock would help distinguish cohort effects from species-level ecological constraints. A scale-aware strategy emerges. Use reef-zone context to place devices in broadly favourable environments; within those zones, engineer for micro-success: choose device designs that provide refuge from early hazards (fish-exclusion, fouling-release; Whitman et al. 2024 ; Montalvo-Proano et al. 2025 ), curate substrates with CCA cover and minimal macroalgae, and adjust seeding density by species. Propagation type and deployment scale must also be strategically balanced: microfragments yield higher short-term survival, while spat provide genetic diversity. A combined approach (i.e. deploying both) within restoration programs may balance near-term restoration success with long-term adaptive capacity. Deployed together, and matched to species and reef zone, these choices can increase predictability and return on effort for seeded reef restoration. Declarations Author contributions: SJ, CL, CJR conceived and designed the study; SJ, CL, SF, CJR coordinated and conducted the field experiments; SJ performed the analyses and wrote the manuscript; all authors contributed to manuscript revisions and approved the final version. Acknowledgements We acknowledge the Bindal Peoples as the Traditional Custodians of the land and sea Country where this research took place and pay our respects to elders past and present. 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Int J Environ Res Public Health 17(18):6574 Trapon ML, Pratchett MS, Hoey AS, Baird AH (2013) Influence of Fish Grazing and Sedimentation on the Early Post-Settlement Survival of the Tabular Coral Acropora Cytherea. Coral Reefs 32:1051–1059 Veron JEN (2000) Corals of the World . no. C/593.6 V4. Australian Institute of Marine Science, Townsville Vida R, Talitha, Tries B, Razak, Andrew OM, Mogg et al (2024) Impacts of Reef Star Coral Restoration on Multiple Metrics of Habitat Complexity. Restor Ecol 32(8):e14263 Whitman TN, Saskia Jurriaans C, Lefevre et al (2025) ‘Seeded Acropora Digitifera Corals Survive Best on Wave-exposed Reefs with Grazing from Small Fishes’. Restoration Ecology , e70016 Whitman T, Nicole MO, Hoogenboom AP, Negri, Randall CJ (2024) Coral-Seeding Devices with Fish-Exclusion Features Reduce Mortality on the Great Barrier Reef. Sci Rep 14(1):13332 Wickham H (2016) Ggplot2: Elegant Graphics for Data Analysis. Springer-, New York. https://ggplot2.tidyverse.org van Woesik R, Raymond B, Banister E, Bartels et al (2021) Differential Survival of Nursery-reared Acropora Cervicornis Outplants along the Florida Reef Tract. Restor Ecol 29(1):e13302 Additional Declarations The authors declare no competing interests. Supplementary Files 2024YR2SupplementsRestorationEcology.docx JurriaansetalPropagationMethodCoralSeedingSupplementary2025 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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05:24:57","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139010,"visible":true,"origin":"","legend":"","description":"","filename":"rs82834490structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/ae07b017db4102729b137cf1.xml"},{"id":97648102,"identity":"7199748d-35d0-4450-93ee-dd5990c8a7e0","added_by":"auto","created_at":"2025-12-08 05:24:57","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149151,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/189d272b0b78d9a05016ef01.html"},{"id":97648089,"identity":"5542bdb4-c307-474c-825a-e4bbf202c90d","added_by":"auto","created_at":"2025-12-08 05:24:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":641191,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design and field deployment of seeded coral devices at Davies Reef. (\u003cstrong\u003eA\u003c/strong\u003e) Schematic overview of the experimental design comparing two coral propagation methods across five species. Two device types were deployed for each method: multispecies (n = 500 in total) and single-species (n = 100 in total). Each 25 m transect contained 25 paired multispecies devices with 5 paired single-species devices in 5 × 5 m plots. (\u003cstrong\u003eB\u003c/strong\u003e) Map of Davies Reef showing 10 deployment sites across four reef zones. Inset indicates reef location relative to the Great Barrier Reef along the Queensland coastline. (\u003cstrong\u003eC\u003c/strong\u003e) Field deployed sexually produced spat (\u003cem\u003eAcropora loripes\u003c/em\u003e) ~3 months old. (\u003cstrong\u003eD\u003c/strong\u003e) Field deployed asexually produced microfragment (\u003cem\u003eMontipora turtlensis\u003c/em\u003e) ~3 months old.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/b90770c084a4ef2adf72e279.jpeg"},{"id":97648105,"identity":"2797e7b5-c5be-4148-9cd8-680f8b51ad01","added_by":"auto","created_at":"2025-12-08 05:24:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":557127,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival and growth of coral colonies by species and propagation method. (\u003cstrong\u003eA\u003c/strong\u003e) Final survival (%) of spat (blue) and microfrag (orange) colonies across five coral species (\u003cem\u003eAcropora loripes\u003c/em\u003e, \u003cem\u003eGalaxea fascicularis\u003c/em\u003e, \u003cem\u003eGoniastrea retiformis\u003c/em\u003e, \u003cem\u003eMycedium elephantotus\u003c/em\u003e, and \u003cem\u003eMontipora turtlensis\u003c/em\u003e). NA indicates no data for \u003cem\u003eG. retiformis\u003c/em\u003espat and \u003cem\u003eM. elephantotus\u003c/em\u003e microfrag. (\u003cstrong\u003eB\u003c/strong\u003e) Estimated mean colony size (cm³ ± 95% CI) at final census for spat (top) and microfrags (bottom). Faded points show raw observations; note the y-axis varies between spat and microfrag to highlight variation in estimated means among species within propagation method. Letters show significant pairwise differences among species within each propagation method (Tukey-adjusted, p \u0026lt; 0.05). \u003cem\u003eM. elephantotus\u003c/em\u003e was excluded due to no survivors. (\u003cstrong\u003eC\u003c/strong\u003e) Kaplan–Meier survival probability over time (months) with 95% CI.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/87255f4950a96f518ba6bad3.jpeg"},{"id":97648096,"identity":"9cc3bf23-6dc4-499f-be12-dffdbc6ce2f5","added_by":"auto","created_at":"2025-12-08 05:24:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117143,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted survival probabilities (± 95% confidence interval) from GLMMs across reef zones. (\u003cstrong\u003eA\u003c/strong\u003e) Propagation method by reef zone. Asterisks show significant differences between method within each zone; letters show among-zone differences within each method (Tukey-adjusted emmeans contrasts, p \u0026lt; 0.05). (\u003cstrong\u003eB\u003c/strong\u003e) Species by reef zone. Letters show among-species differences within each zone (Tukey-adjusted emmeans, p \u0026lt; 0.05). For zone differences within species and full pairwise results, see Table S6.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/7783d573ffecfe6e7a1ee050.jpeg"},{"id":97674705,"identity":"6fa56734-a81a-4b82-a5b4-0ec4eac3512d","added_by":"auto","created_at":"2025-12-08 09:43:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204703,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between benthic composition and coral survival. (\u003cstrong\u003eA\u003c/strong\u003e) Site-level benthic community composition (mean percent cover) across reef zones. (\u003cstrong\u003eB\u003c/strong\u003e) Odds ratios (±95% CI) for spat survival by dominant benthic group on tab, expressed relative to crustose coralline algae (CCA); values below 1 indicate lower survival relative to CCA. (\u003cstrong\u003eC\u003c/strong\u003e) Species-specific relationships between predicted survival probability (±95% CI) and percent CCA cover on tabs (excluding live coral) averaged across reef zones. Grey points indicate observed survival outcomes (0 = dead, 1 = alive) for individual tabs.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/92102c7bfd44bc6318d0f293.jpeg"},{"id":97674055,"identity":"7bd11ccd-ef3d-458d-80ec-5d3863aee7ea","added_by":"auto","created_at":"2025-12-08 09:42:18","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149933,"visible":true,"origin":"","legend":"\u003cp\u003eInitial colony density at the time of deployment and density-dependent survival. (\u003cstrong\u003eA\u003c/strong\u003e) Number of colonies at deployment by species. \u003cem\u003eM. elephantotus\u003c/em\u003e shown for context only but excluded from models due to 100% mortality. (\u003cstrong\u003eB\u003c/strong\u003e) Predicted survival probability (±95% CI) as a function of the number of colonies at the time of deployment. Points represent individual counts.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/02656699b7e36eb569be2597.jpeg"},{"id":97678907,"identity":"ccd27946-9edf-4142-b07a-def4ad6e3e85","added_by":"auto","created_at":"2025-12-08 09:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2194214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/64745e3b-3664-41a8-a17b-a7ed01548b63.pdf"},{"id":97648093,"identity":"8ce89905-334c-47e9-8328-85eabff24efd","added_by":"auto","created_at":"2025-12-08 05:24:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":353925,"visible":true,"origin":"","legend":"\u003cp\u003eJurriaansetalPropagationMethodCoralSeedingSupplementary2025\u003c/p\u003e","description":"","filename":"2024YR2SupplementsRestorationEcology.docx","url":"https://assets-eu.researchsquare.com/files/rs-8283449/v1/4b84907a012c3642969707e9.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePropagation method and species drive survival patterns across reef zones in coral seeding on the Great Barrier Reef\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoral reef restoration is rapidly expanding as a key strategy to conserve reef ecosystems under accelerating climate change (Knowlton et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While it cannot substitute for emission reductions (Hughes et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), restoration is increasingly recognized as a necessary complement to global mitigation and local management actions (Suggett et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peixoto et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Over the past decade, restoration techniques have diversified, with numerous studies exploring methods to enhance coral cover and ecological function on degraded reefs (McLeod et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miller et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vida et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo primary approaches underpin coral propagation for restoration: asexual and sexual reproduction (Boström-Einarsson et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Asexual propagation (i.e., transplanting or seeding coral fragments or microfragments) can produce large colonies via rapid encrusting and fusion of arrays of small (∼1 cm²) microfragments; this has been demonstrated across Atlantic and Pacific species and is now widely used to accelerate cover of slow-growing massive corals (Forsman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Page et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Knapp et al. 2022) and fast-growing branching species (Tortolero-Langarica et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van Woesik et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, asexual propagation is labour-intensive and limited in scalability and genetic diversity (Baums \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Baums et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sexual propagation uses gametes from natural spawning to produce coral offspring (‘spat’), generating high numbers and novel genetic combinations that can enhance adaptive potential (van Oppen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Yet, spat often experience high post-settlement mortality, making survival outcomes variable and returns on effort relatively low (Guest et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the temporal constraints associated with annual mass-spawning result in a limited window of opportunity for sexual propagation, while asexual propagation is more accessible throughout the year. Understanding the strengths and limitations of both propagation methods, and identifying the environmental and logistical contexts in which each is most effective, is essential for improving the efficiency and scalability of reef restoration efforts (Banaszak et al. 2023).\u003c/p\u003e\u003cp\u003eAcross both modes of propagation, coral seeding has emerged as a deployment strategy in which corals are attached to settlement substrates or devices that are then “seeded” onto reefs (Randall et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Banaszak et al. 2023). Seeding devices can provide refuge from grazers, fouling and improve retention and survival (Randall et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Whitman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Montalvo-Proano et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ramsby et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Advances in three-dimensional structures such as cogs, tetrapods, and plug-based designs have improved survivorship and deployment efficiency (Chamberland et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Randall et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mendoza Quiroz et al. 2025) while new diver-less systems show promise for large-scale, low-labour restoration (Ramsby et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEqually important to how corals are propagated is where they are deployed to maximize survival and ecological impact (Quigley et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Site selection typically relies on broad-scale environmental criteria, such as depth, exposure and turbidity (Humanes et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Broad-scale gradients and reef zonation strongly shape early coral survival, with declines observed under higher turbidity, sedimentation, flow velocity, and depth (Penin et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gouezo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Edmunds \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Drake et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, coral seeding trials show that small-scale variation (on the order of tens of centimetres) within sites can have even stronger effects on spat survival than site-level factors like benthic community composition or flow regime (Page et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jurriaans et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This presents a key challenge for restoration: while broad-scale site selection is necessary for scaling up efforts, survival outcomes may hinge on microhabitat conditions that are difficult to incorporate into large-scale planning. Understanding how fine-scale environmental variability interacts with reef zonation is therefore essential to improving survival predictions and developing scale-aware restoration strategies.\u003c/p\u003e\u003cp\u003eTo date, a majority of coral restoration efforts have focused on fast-growing coral species, mostly in the genus \u003cem\u003eAcropora\u003c/em\u003e, due to their ecological importance and rapid growth (Boström-Einarsson et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, this narrow taxonomic focus limits our ability to restore the full complexity and functionality of reef ecosystems (Quigley et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Madin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Broadening both the environmental and taxonomic scope of restoration is therefore critical for improving outcomes.\u003c/p\u003e\u003cp\u003eIn this study, we address three key objectives: (i) expand the breadth of taxa tested using a coral-seeding approach; (ii) assess environmental drivers of coral survival across reef zones and wave exposure regimes; and (iii) compare survival outcomes of asexually (microfragment) and sexually (spat) propagated corals across multiple species and habitats. We conducted a multi-species, multi-habitat field deployment at Davies Reef (central, mid-shelf Great Barrier Reef), examining species- and habitat-specific differences in survival between microfrags and spat. Our findings provide insights into optimizing coral propagation and placement strategies to enhance the success and scalability of reef restoration.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAsexual propagation: microfragmentation\u003c/p\u003e\u003cp\u003eFour to six phenotypically distinct colonies of \u003cem\u003eAcropora loripes\u003c/em\u003e, \u003cem\u003eGoniastrea retiformis, Montipora turtlensis\u003c/em\u003e and \u003cem\u003eGalaxea fascicularis\u003c/em\u003e were collected late October 2022 from Davies Reef (18°49’S, 147°38’E) on the central Great Barrier Reef (GBR; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Colonies were transported to the National Sea Simulator (SeaSim) at the Australian Institute of Marine Science (AIMS; Townsville, Queensland) and maintained in outdoor flow-through seawater tanks under ambient light and at temperatures representative of the long-term daily average at Davies Reef. under ambient light. Colonies were cut into 8 x 8 mm (64 mm\u003csup\u003e2\u003c/sup\u003e) microfragments (hereafter ‘microfrags’) late November, using a seawater-cooled diamond band saw (C-40, Gryphon Corporation). Excess skeleton from the underside of each microfrag was trimmed to ensure the tissue sat flush with the mounting base. Microfrags from the same parent colony (putative genotype) were attached using cyanoacrylate gel (Gorilla® Super Glue Gel, USA) to concrete tiles that had been pre-conditioned indoors for six weeks in flow-through seawater. Colonies were not genotyped, so genotypic distinctness among colonies and potential chimerism within colonies cannot be excluded. Tiles compromised 400 individual Tables\u0026nbsp;(14 x 14 mm each; Ramsby et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) made from a 9:2 mix of concrete and mortar (Dingo Cement Pty Ltd, Australia). Microfrags were held in indoor flow-through aquaria under the same environmental conditions as described above until device assembly. See Table S1 for assembly details.\u003c/p\u003e\u003cp\u003eSexual propagation: spawning, settlement and grow-out\u003c/p\u003e\u003cp\u003eEight to fourteen gravid colonies of \u003cem\u003eA. loripes\u003c/em\u003e, \u003cem\u003eM. turtlensis\u003c/em\u003e and \u003cem\u003eG. fascicularis\u003c/em\u003e were collected from Davies Reef in early December 2022. Reproductive maturity was confirmed by the presence of pigmented oocytes visible in branch cross-sections prior to collection. Colonies were transported to SeaSim and held in outdoor tanks under long-term daily average temperatures at Davies Reef and natural light until spawning. \u003cem\u003eMycedium elephantotus\u003c/em\u003e colonies, collected from Davies Reef in 2017, were already housed at SeaSim and had spawned successfully in prior years.\u003c/p\u003e\u003cp\u003eFollowing the full moon on December 8, 2022, colonies were monitored nightly for bundle setting. Once gamete bundles were visible, colonies were isolated in separate containers. Spawning occurred between 13–20 December (depending on species; Table S1). Gametes were collected, fertilised, and larvae reared following Pollock et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Four to eight days post-fertilisation, larvae were competent to settle (Randall et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Settlement substrates were identical to those used for microfrags. Acrylic settlement tanks (50 L) with flow-through 4 µm-filtered seawater and overflow mesh filters (112 or 212 µm, depending on larval size) received ~ 5,000 larvae and two pre-conditioned tiles. After 2–3 days of settlement, tiles with settled spat were transferred to indoor aquaria (50 L) supplied with flow-through filtered seawater at Davies Reef specific temperature, low light (Ramsby et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and fed daily with enriched \u003cem\u003eArtemia\u003c/em\u003e, rotifers, and microalgae. Symbiont uptake was facilitated by co-culturing spat with adult donor colonies. Spat remained in these tanks until device assembly (see Table S1 for timeline).\u003c/p\u003e\u003cp\u003eExperimental design\u003c/p\u003e\u003cp\u003eWe used coral “star” seeding devices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) engineered by AIMS under the Reef Restoration and Adaptation Program (RRAP) and described in Ramsby et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e. Each device holds three coral tabs. We assembled 250 multi-species spat devices and 250 multi-species microfrag devices, with 1 tab each of \u003cem\u003eA. loripes\u003c/em\u003e, \u003cem\u003eM. turtlensis\u003c/em\u003e, and \u003cem\u003eM. elephantotus\u003c/em\u003e. Because \u003cem\u003eG. fascicularis\u003c/em\u003e is aggressive and has long sweeping tentacles, we also produced 50 single-species microfrag devices (with the same genotype per device) and 50 single-species spat devices.\u003c/p\u003e\u003cp\u003eDevices were assembled several days before deployment (see Table S1 for timeline). Settlement tiles were cut into individual tabs using either a blunt slicer submerged in seawater or a seawater-cooled diamond saw. Tabs with spat were inspected under magnification to count colonies (median range: 3–8 colonies per tab) before insertion. Microfrag tabs were randomly distributed among devices to avoid genotype bias. Tabs were inserted into the ceramic “star” devices and secured with 3D-printed plastic caps and marine-grade adhesive. Each device received a unique ID tag. Devices were maintained upright on steel rods in indoor flow-through aquaria at reef-specific conditions until deployment. Devices were photographed using an Olympus TG-6 camera 24–48 hr prior to ship departure to establish baseline colony and polyp counts prior to deployment.\u003c/p\u003e\u003cp\u003eDevices were deployed at ten sites across Davies Reef spanning a natural wave energy gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Site selection followed the nominal wave-energy classification described in Jurriaans et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), developed from high-resolution spatial and benthic models (Callaghan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Radford et al. 2024). At each site, 25 paired multi-species spat and microfrag devices were deployed by SCUBA divers at ~ 6 m depth along 25 m transects. Device pairs were spaced ~ 1 m apart. At each transect end, five pairs of single-species (\u003cem\u003eG. fascicularis\u003c/em\u003e) spat and microfrag devices were deployed. Devices were secured to the reef with zip-ties. Coral survival was monitored ~ 3-month intervals for ~ 10 months. Colony size was measured at the final census by recording the maximum length, perpendicular length, and height and calculated as the product of these dimensions (cm³).\u003c/p\u003e\u003cp\u003eBiological and environmental data\u003c/p\u003e\u003cp\u003e\u003cem\u003eFlow conditions –\u003c/em\u003e In-situ flow was measured using Marotte HS drag-tilt current meters (James Cook University) mounted ~ 50 cm above the substrate, recording velocity at 1-minute intervals. Instruments were cleaned and redeployed at each census. Median flow velocity was used as the descriptor of near-bottom hydrodynamic conditions, as maximum and standard deviation values were strongly correlated with the median. Sites spanned a wave-energy gradient encompassing four reef zones: lagoon (D1b: 0.040 m s⁻¹), back reef (D1a, D2b, D3a: 0.039–0.070 m s⁻¹), flank reef (D2a, D3b, D5a: 0.067–0.104 m s⁻¹), and front reef (D4a, D4b, D5b: 0.051–0.113 m s⁻¹).\u003c/p\u003e\u003cp\u003e\u003cem\u003eSite-level benthic cover –\u003c/em\u003e At each site, 35 photographs (1–2 m²) were taken along the transect to quantify benthic cover. Images were annotated in ReefCloud (AIMS 2024) to estimate the percent cover of key benthic groups: crustose coralline algae (CCA), epilithic algal matrix (EAM), macroalgae, soft corals, \u003cem\u003eAcropora\u003c/em\u003e, non-\u003cem\u003eAcropora\u003c/em\u003e hard corals, sand, sponge, and other sessile invertebrates. Fifty grid points per image were classified: 12 were manually classified and the remaining were auto-classified using a model trained on human annotations. Model accuracy (F1 scores) ranged from 0.74 (sand) to 0.92 (EAM; Table S2).\u003c/p\u003e\u003cp\u003e\u003cem\u003eTab-level benthic cover –\u003c/em\u003e To examine the relationship between tab-level benthic cover and spat survival, each tab was scored at the final census for its dominant benthic group (CCA, EAM, macroalgae, or other). For tabs where a coral spat survived and was itself dominant, the surrounding community was scored as the next most abundant benthic group, ensuring that dominance reflected the benthic assemblage rather than the surviving coral. Percent cover of CCA was estimated in 10% increments and calculated only over the space available on each tab excluding live coral. If a coral spat occupied part of the tab, that area was excluded from the estimate; thus, percent CCA cover was assessed relative to the remaining ‘unoccupied’ surface.\u003c/p\u003e\u003cp\u003eData analyses\u003c/p\u003e\u003cp\u003eAll analyses were conducted in R version 4.4.3 (R Core Team \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) using ggplot2 (Wickham \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for figures.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSurvival (time-to-event)\u003c/em\u003e - Survival was summarised as time to tab-level mortality (first census at which all corals on a tab were dead), with censored observations where at least one coral remained alive. Kaplan–Meier curves were fitted by species and propagation method (\u003cem\u003esurvminer;\u003c/em\u003e Kassambara et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and compared with log-rank tests. Species-specific method effects were quantified with Cox proportional hazards models (\u003cem\u003esurvival;\u003c/em\u003e Therneau \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) including a species × method interaction. We report hazard ratios (HR ± 95% CI) and likelihood-ratio χ² tests to compare models with and without the interaction.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e - Growth of surviving colonies at the final census was modelled with a Gamma Generalized Linear Mixed Effects Model (GLMM, log link; \u003cem\u003elme4\u003c/em\u003e, Bates et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) including species, propagation method, reef zone, and their interaction as fixed effects and site as a random intercept. Post-hoc pairwise contrasts were computed from estimated marginal means (EMM; \u003cem\u003eemmeans\u003c/em\u003e, Lenth \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) with Tukey-adjusted comparisons.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFlow conditions\u003c/em\u003e - Effects of hydrodynamics on survival were tested with binomial GLMMs (logit link) including propagation method, species, and reef zone (and interactions) as fixed effects, and device ID and site as random intercepts. Spatial predictors (wave energy level, in-situ flow velocity, or reef zone) were compared among alternative models (Table S3). Interactions were tested with likelihood-ratio χ² tests. Intraclass correlation coefficients (ICC) quantified the variance explained by random effects. Model residuals were assessed using \u003cem\u003eDHARMa\u003c/em\u003e (Hartig \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); no overdispersion or zero inflation was detected. EMMs (± 95% CI) of predicted survival probabilities by species, propagation method, and reef zone were computed using \u003cem\u003eemmeans\u003c/em\u003e, as above. Post-hoc pairwise contrasts were extracted as odds ratios (OR) to interpret effect sizes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenotype effects –\u003c/em\u003e To test whether genotype influenced microfrag survival, we fit a binomial generalized linear model (GLM) with species, reef zone, and genotype as fixed effects. To evaluate whether survival varied among genotypes overall, we also fit a GLMM with genotype and site included as random intercepts. Models were validated as described above.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBenthic composition\u003c/em\u003e – Site-level benthic composition (proportional cover) was ordinated via a principal component analysis (PCA; \u003cem\u003evegan\u003c/em\u003e, Oksanen et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to visualise among-site variation. To test whether benthic composition explained survival, binomial GLMMs were fitted with PC1, PC2, species, propagation method, and reef zone as fixed effects, and site and device as random intercepts. Likelihood-ratio tests and AIC were used to compare models including reef zone, site effects, and species × PCA interactions.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTab-level benthic cover\u003c/em\u003e - Spat survival at the final census was analysed with binomial GLMs (logit link) including species and reef zone as covariates, and the dominant benthic group (excluding surviving coral) as a categorical predictor (CCA as reference). Model support for the benthic group effect and for species × community interactions was evaluated using likelihood-ratio tests. Coral dominated tabs were excluded because coral cover reflects the surviving individual itself and is therefore not independent of the response. In a separate analysis, percentage CCA cover (expressed as the proportion of free substrate) was fitted as a continuous predictor alongside species and reef zone. Species × CCA cover interactions were also tested to evaluate species-specific responses to variation in CCA cover.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSpat density –\u003c/em\u003e Spat density effects were tested with binomial GLMMs (logit link) with site as a random intercept. Colony density (colonies per tab) was included as a continuous covariate interacting with species. Odds ratios per additional colony were derived from species-specific slopes estimated with emtrends() in \u003cem\u003eemmeans\u003c/em\u003e. Colony and polyp density were strongly correlated (Pearson \u003cem\u003er\u003c/em\u003e = 0.78, 95% CI 0.75–0.80), so only colony density was analysed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSurvival over time, and growth\u003c/p\u003e\u003cp\u003eAfter 294 days (~\u0026thinsp;10 months), survival differed significantly between propagation methods (K-M log-rank, χ\u0026sup2; = 561, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and among species (χ\u0026sup2; = 467, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,C) with microfrags generally outperforming spat. Survival by propagation method was species-specific (method \u0026times; species: χ\u0026sup2; = 150.1, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): relative to microfrags, \u003cem\u003eGalaxea fascicularis\u003c/em\u003e spat had\u0026thinsp;~\u0026thinsp;9.3\u0026times; higher mortality rate (Cox HR\u0026thinsp;=\u0026thinsp;9.35, 95% CI 6.07\u0026ndash;14.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ~\u0026thinsp;6.6\u0026times; higher in \u003cem\u003eMontipora turtlensis\u003c/em\u003e spat compared with frags (HR\u0026thinsp;=\u0026thinsp;6.60, 4.56\u0026ndash;9.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eAcropora loripes\u003c/em\u003e survival was similar between methods (microfrag vs spat respectively 54% vs 57% at the final census; HR\u0026thinsp;=\u0026thinsp;1.19, 0.93\u0026ndash;1.52, p\u0026thinsp;=\u0026thinsp;0.16). \u003cem\u003eMycedium. elephantotus\u003c/em\u003e (spat) died rapidly, with ~\u0026thinsp;20% survival at three months and 0% by 10 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eColony size differed significantly by propagation method, species, and their interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; GLMM, interaction: χ\u0026sup2; = 55.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On average, microfrags were ~\u0026thinsp;10.7\u0026times; larger than spat (β\u0026thinsp;=\u0026thinsp;2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11, z\u0026thinsp;=\u0026thinsp;22.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When expressed relative to their initial size at deployment (microfrag\u0026thinsp;\u0026asymp;\u0026thinsp;0.19 cm\u0026sup3;; spat\u0026thinsp;\u0026asymp;\u0026thinsp;0.03 cm\u0026sup3;), microfrags increased in volume by roughly 12\u0026ndash;13\u0026times;, compared to ~\u0026thinsp;3\u0026times; for spat, indicating higher proportional as well as absolute growth. Among microfrags, \u003cem\u003eG. fascicularis\u003c/em\u003e reached the largest mean size (3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 cm\u0026sup3;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE), followed by \u003cem\u003eM. turtlensis\u003c/em\u003e (2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 cm\u0026sup3;) and \u003cem\u003eA. loripes\u003c/em\u003e (1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 cm\u0026sup3;). \u003cem\u003eG. retiformis\u003c/em\u003e microfrags were the smallest (0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 cm\u0026sup3;). All pairwise species differences were significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.02). Among spat, \u003cem\u003eA. loripes\u003c/em\u003e reached the largest mean size (0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 cm\u0026sup3;), which was 3.6\u0026times; larger than \u003cem\u003eG. fascicularis\u003c/em\u003e (0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 cm\u0026sup3;) and 2.7\u0026times; larger than \u003cem\u003eM. turtlensis\u003c/em\u003e (0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 cm\u0026sup3;; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lastly, colonies on the front reef were ~\u0026thinsp;44% smaller than those on the back (β = \u0026minus;0.570\u0026thinsp;\u0026plusmn;\u0026thinsp;0.167, z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.43, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while flank and lagoon differences were not significant (p\u0026thinsp;=\u0026thinsp;0.13 and 0.51, respectively).\u003c/p\u003e\u003cp\u003eFlow conditions\u003c/p\u003e\u003cp\u003eSurvival varied little among sites, with random site effects explaining\u0026thinsp;\u0026lt;\u0026thinsp;1% of variance in survival (ICC\u0026thinsp;=\u0026thinsp;0.004; σ\u0026sup2; = 0.013; Figure S1). In contrast, device-level variation accounted for ~\u0026thinsp;30% (ICC\u0026thinsp;=\u0026thinsp;0.30; σ\u0026sup2; = 1.432), highlighting the importance of fine-scale heterogeneity within sites.\u003c/p\u003e\u003cp\u003eFlow velocity was a poor predictor of survival (AIC\u0026thinsp;=\u0026thinsp;5090.6), whereas grouping by reef zone substantially improved model fit (AIC\u0026thinsp;=\u0026thinsp;5032.9; ΔAIC\u0026thinsp;=\u0026thinsp;57.7) and adding flow as a covariate to the zone model did not improve fit (ΔAIC\u0026thinsp;=\u0026thinsp;0.9; χ\u0026sup2; = 1.12, p\u0026thinsp;=\u0026thinsp;0.29).\u003c/p\u003e\u003cp\u003ePropagation method strongly influenced survival patterns across reef zones (χ\u0026sup2; = 14.8, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Microfragments consistently outperformed spat (Table S4), with 78% lower odds of spat survival overall (OR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.13\u0026ndash;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This effect was most pronounced at exposed sites: spat survival was 72% lower at the flank (OR\u0026thinsp;=\u0026thinsp;0.28, 95% CI: 0.14\u0026ndash;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 80% lower at the front (OR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.10\u0026ndash;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference in survival between propagation methods was found in the lagoon (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 0.39\u0026ndash;2.63, p\u0026thinsp;=\u0026thinsp;0.98).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpecies survival also differed significantly across reef zones (χ\u0026sup2; = 71.8, df\u0026thinsp;=\u0026thinsp;12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). \u003cem\u003eA. loripes\u003c/em\u003e had lower survival in the lagoon (EMMs, 0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 SE, 95% CI: 0.31\u0026ndash;0.57; Table S5) compared to the more exposed zones, including the flank (0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 SE, 95% CI: 0.68\u0026ndash;0.80) and front reef (0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 SE, 95% CI: 0.70\u0026ndash;0.82). In contrast, \u003cem\u003eG. fascicularis\u003c/em\u003e showed a trend toward higher survival in the lagoon (0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, 95% CI: 0.38\u0026ndash;0.81) than at the front reef (0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, 95% CI: 0.23\u0026ndash;0.51) although this difference was not statistically significant (Table S6). \u003cem\u003eG. retiformis\u003c/em\u003e (microfrag only) and \u003cem\u003eM. turtlensis\u003c/em\u003e showed the highest survival at the back reef (0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 SE, 95% CI\u0026thinsp;=\u0026thinsp;0.79\u0026ndash;0.91 and 0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 SE, 95% CI\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.72, respectively), with survival odds 5.4 times higher for \u003cem\u003eG. retiformis\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.9 times higher for \u003cem\u003eM. turtlensis\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) compared to the front (Table S6). \u003cem\u003eM. elephantotus\u003c/em\u003e (spat only) performed poorly across zones and suffered complete mortality by the final census. Finally, we detected little evidence for genotype effects among microfrags (random-effect variance\u0026thinsp;=\u0026thinsp;0.061; strongest fixed‐effect signal p\u0026thinsp;=\u0026thinsp;0.07) showing that genotype had little influence on survival outcomes.\u003c/p\u003e\u003cp\u003eBenthic composition\u003c/p\u003e\u003cp\u003eBenthic composition varied among reef zones, with lagoon and back sites dominated by sand and epilithic algal matrix (EAM), and flank and front sites characterised by higher hard coral cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The first two PCA axes explained 69% of variation in benthic composition among sites, representing a gradient from sand/EAM- to coral-dominated habitats (Figure S2). Including PC1 and PC2 in survival models did not improve fit (ΔAIC\u0026thinsp;=\u0026thinsp;0.5; χ\u0026sup2; = 6.5, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.09), indicating that site-level benthic composition did not predict coral survival.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt a finer spatial scale (tab level), the dominant benthic group on the tab at the final census was significantly related to spat survival (GLM; χ2\u0026thinsp;=\u0026thinsp;13.8, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.003). On CCA-dominated tabs, mean survival was 32% (95% CI: 23\u0026ndash;42%) and varied among species (\u003cem\u003eA. loripes\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68 [0.58-.076], \u003cem\u003eG\u003c/em\u003e. \u003cem\u003efascicularis\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14 [0.08\u0026ndash;0.25], \u003cem\u003eM. turtlensis\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23 [0.15\u0026ndash;0.32]). Survival was lower on tabs dominated by macroalgae (OR\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and by other taxa (OR\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), but survival on EAM dominated tabs did not differ from CCA-dominated tabs (OR\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43). A species \u0026times; community interaction did not improve model fit (χ2 test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eSurvival was positively related to percent CCA cover on tabs (GLM; β\u0026thinsp;=\u0026thinsp;0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 SE, p\u0026thinsp;=\u0026thinsp;0.038), and this relationship was consistent across species (interaction χ\u0026sup2; = 1.51, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.47) but with higher overall survival probabilities for \u003cem\u003eA. loripes\u003c/em\u003e compared to \u003cem\u003eG. fascicularis\u003c/em\u003e and \u003cem\u003eM. turtlensis\u003c/em\u003e (χ\u0026sup2; = 91.8, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). A reef zone \u0026times; CCA cover interaction did not improve model fit (χ\u0026sup2; = 1.20, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e\u003cp\u003eSpat density\u003c/p\u003e\u003cp\u003eInitial deployment densities varied among species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). \u003cem\u003eM. turtlensis\u003c/em\u003e and \u003cem\u003eG. fascicularis\u003c/em\u003e had the lowest medians (3 and 4 colonies per tab, respectively), whereas \u003cem\u003eA. loripes\u003c/em\u003e and \u003cem\u003eM. elephantotus\u003c/em\u003e each had 8 colonies per tab. Density-dependent survival was species-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB): survival increased with colony density for \u003cem\u003eG. fascicularis\u003c/em\u003e (OR per colony\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.02\u0026ndash;1.49, p\u0026thinsp;=\u0026thinsp;0.031) and \u003cem\u003eM. turtlensis\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.26, 1.11\u0026ndash;1.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not for \u003cem\u003eA. loripes\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.00, 0.92\u0026ndash;1.09, p\u0026thinsp;=\u0026thinsp;0.98).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSurvival varied strongly among propagation methods, species, and reef zones. Overall, microfragments outperformed sexually produced spat across taxa and habitats, although survival and growth were species-specific and influenced by interactions between life-history traits and habitat context. Reef zone, rather than flow velocity or benthic composition, best explained variation in survival, underscoring the importance of broad habitat context combined with fine-scale heterogeneity in coral performance post-deployment. Our findings also illustrate a fundamental trade-off in coral restoration: sexual propagation enhances genetic diversity and long-term adaptive potential but suffers high early mortality, whereas asexual propagation yields higher short-term survival but is clonal and more labour-intensive to scale. Restoration programs must therefore balance the scalability of sexual reproduction with the short-term reliability of asexual propagation, deploying both in species-and zone-specific ways to optimise outcomes. Additionally, our results highlight a guiding principle for restoration design: broad reef-scale context structures the playing field, but centimetre-scale conditions decide many of the early winners.\u003c/p\u003e\u003cp\u003eMicrofragments showed markedly higher survival and were ~\u0026thinsp;10\u0026times; larger than spat after ten months, especially at sites in more exposed zones (flank, front). Our findings align with prior coral-seeding trials reporting higher survival and growth of microfragments relative to spat (Chamberland et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Page et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Whitman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), likely due to their larger initial size, thicker tissue and greater energy reserves, which together confer a \u0026ldquo;size-escape\u0026rdquo; advantage against grazers, sedimentation, and overgrowth (Doropoulos et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Whitman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sexually produced spat face well-documented bottlenecks (Edmunds \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) driven by grazing, sediment smothering, and turf-algal competition (Trapon et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Doropoulos et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gallagher and Doropoulos 2017). The microfrags in our study effectively bypassed this vulnerable stage, maintaining high survival across sites. Furthermore, we detected no genotype effects on microfrag survival, indicating that during the first year, environmental context and species traits tend to dominate survival patterns. Genotypic differences may become apparent later through variations in growth, recovery, or stress tolerance (Bairos-Novak et al. 2021).\u003c/p\u003e\u003cp\u003eSpecies effects were strong and ecologically consistent. \u003cem\u003eAcropora loripes\u003c/em\u003e performed well across propagation methods and reef zones (no difference in survival between methods; largest mean spat size), marking it as a versatile species for restoration via seeding. \u003cem\u003eMontipora turtlensis\u003c/em\u003e and \u003cem\u003eGalaxea fascicularis\u003c/em\u003e achieved high survival and growth as microfrags but not as spat, suggesting that both species benefit from the size and robustness conferred by microfragmentation, consistent with evidence that micro-colony fusion and tissue expansion enhance early size escape and self-attachment (Forsman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Page et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This pattern reinforces the need to tailor propagation strategies to species-specific life-history traits and ecological niches. \u003cem\u003eGoniastrea retiformis\u003c/em\u003e (deployed as microfrags only) performed best on the back reef, consistent with its occurrence in moderate-flow environments balancing sedimentation and light availability (Veron \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Baird et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In contrast, \u003cem\u003eMycedium elephantotus\u003c/em\u003e spat suffered complete mortality, likely reflecting a mismatch between our attachment method (fixing devices onto open substrates) and the more protected environments this species typically occupies (Veron \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Madin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), such as shaded crevices and low-light refuges. Free deployment of devices, as described in Ramsby et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which allows devices to naturally lodge into the reef matrix and crevices may be better suited for such cryptic taxa.\u003c/p\u003e\u003cp\u003eWhile \u003cem\u003eAcropora loripes\u003c/em\u003e performed consistently well across propagation method and reef zones, over-reliance on \u003cem\u003eAcropora\u003c/em\u003e risks narrowing functional roles (Madin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In line with calls to diversify \u0026ldquo;restoration portfolios\u0026rdquo; (Madin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), incorporating massive and submassive growth forms such as \u003cem\u003eGoniastrea\u003c/em\u003e or \u003cem\u003eGalaxea\u003c/em\u003e can enhance structural complexity, ecological function, and resilience (McClanahan et al. 2012; Darling et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Restoration science should also expand beyond spawning taxa to include brooding species, which release competent larvae throughout the year and thereby offer greater flexibility and continuity for restoration efforts.\u003c/p\u003e\u003cp\u003eDespite a strong wave-energy gradient across deployment sites, median flow velocity was a poor predictor of survival, whereas reef zone substantially improved model fit and explained more variation in survival. This suggests that unmeasured environmental factors that co-vary with reef zone (e.g., sediment resuspension, turbidity, irradiance and grazer assemblages) play a major role in shaping post-settlement trajectories (Penin et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gouezo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Drake et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The effects of propagation method were zone-dependent: spat survival declined sharply at flank and front, while microfrags were comparatively robust, consistent with the idea that the larger size and established tissue of microfrags provide resistance to hydrodynamic stress and sediment abrasion (Forsman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Knapp et al. 2022). Species by zone patterns also aligned with ecological niche expectations. For example, \u003cem\u003eA. loripes\u003c/em\u003e performed better in exposed zones, while \u003cem\u003eG. fascicularis\u003c/em\u003e survived better in sheltered zones, highlighting the potential to develop taxon- and zone-specific deployment strategies.\u003c/p\u003e\u003cp\u003eDevice-level variation explained\u0026thinsp;~\u0026thinsp;30% of survival variance, far exceeding site-level effects (\u0026lt;\u0026thinsp;1%). These results mirrored earlier findings for \u003cem\u003eAcropora\u003c/em\u003e spat at Davied Reef (Jurriaans et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and were consistent with microfrag studies showing that local site conditions strongly influence early growth and fusion (Page et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results highlight a scale mismatch: early post-settlement processes operate at millimetre-to-centimetre scales, shaped by factors such as surface roughness, boundary-layer flow, sediment retention, turf growth form and micro-refugia from grazers (Doropoulos et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Edmunds \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which broad site-level descriptors (e.g., site-level benthic composition) cannot adequately capture. Although benthic composition varied among zones, it failed to predict survival at the site-level, likely because categories such as \u0026ldquo;epilithic algal matrix\u0026rdquo; or \u0026ldquo;sand\u0026rdquo; mask functional variation within each area (e.g., sediment-laden short turf versus long filamentous turf; (Connell et al. 2014; Tebbett et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Grazer abundance and identity, not quantified here, also modulate these interactions (Whitman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), although we note that the devices exclude a certain size class of grazers through its engineered protective features. At the tab scale, spat survival was lower on macroalgal-dominated or \u0026ldquo;other\u0026rdquo; substrates (sponges, ascidians, tunicates) relative to crustose coralline algae (CCA) and showed a positive linear association with CCA cover. Although this pattern is correlative and does not necessarily imply a facilitative mechanism, it aligns with evidence that CCA can promote settlement and early survival through its physical structure and microbial cues (Vermeij \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). At the same time, extremely high CCA cover may also coincide with competitive interactions and overgrowth (Jorissen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but our data did not detect a non-linear or peak response.\u003c/p\u003e\u003cp\u003eSpat density also mattered: initial colony density positively influenced spat survival for \u003cem\u003eG. fascicularis\u003c/em\u003e and \u003cem\u003eM. turtlensis\u003c/em\u003e but not for \u003cem\u003eA. loripes\u003c/em\u003e. Positive density-dependent effects possibly result from aggregated spat forming colonies that enhance early size-escape through processes like fusion or chimerism in some taxa (Raymundo and Maypa 2004). By contrast, branching \u003cem\u003eAcropora\u003c/em\u003e may rely more on rapid vertical growth (Doropoulos et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), making their early survival less dependent on initial density, although we note that \u003cem\u003eA. loripes\u003c/em\u003e density did not start out as low as the other taxa, potentially masking positive density-dependent effects. Regardless, this means that seeding density can be adjusted to improve outcomes, but must be tailored to each species\u0026rsquo; life-history strategy. Additional work is therefore needed to quantify optimal spat densities amongst taxa and to test these patterns across varying levels of genetic relatedness in larval cohorts, as relatedness is also known to influence the likelihood of fusion (Puill-Stephan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Taken together, our findings emphasize that broad reef-scale zonation sets the context, but fine-scale microhabitat structure (and its interaction with species and initial density) ultimately drives early survival. Restoration designs that integrate both reef-scale zonation and fine-scale microhabitat variability are therefore likely to yield more predictable and robust outcomes (Humanes et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur conclusions are constrained by the number of sites per reef zone and the relative short deployment duration compared to coral growth and maturation. Although reef zone explained a large proportion of variance in survival, low replication (particularly the single lagoon site) reduced statistical power to detect subtler benthic or genotype effects. Benthic data were also averaged at the site level which may overlook the fine-scale microhabitat variation most relevant to early survival. While we additionally quantified tab-level benthic composition, these data were only obtained at the final census, meaning that the community composition may not fully represent conditions experienced by spat that died earlier in the deployment. Nonetheless, these tab-level differences likely capture some of the small-scale environmental heterogeneity driving local variation in survival and warrant further investigation. The complete mortality of \u003cem\u003eM. elephantotus\u003c/em\u003e may also reflect cohort-specific factors, such as poor larval quality or suboptimal crosses, rather than habitat unsuitability alone. Repeating deployments across spawning cohorts and using genotyped parental stock would help distinguish cohort effects from species-level ecological constraints.\u003c/p\u003e\u003cp\u003eA scale-aware strategy emerges. Use reef-zone context to place devices in broadly favourable environments; within those zones, engineer for micro-success: choose device designs that provide refuge from early hazards (fish-exclusion, fouling-release; Whitman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Montalvo-Proano et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), curate substrates with CCA cover and minimal macroalgae, and adjust seeding density by species. Propagation type and deployment scale must also be strategically balanced: microfragments yield higher short-term survival, while spat provide genetic diversity. A combined approach (i.e. deploying both) within restoration programs may balance near-term restoration success with long-term adaptive capacity. Deployed together, and matched to species and reef zone, these choices can increase predictability and return on effort for seeded reef restoration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions:\u003c/h2\u003e\u003cp\u003eSJ, CL, CJR conceived and designed the study; SJ, CL, SF, CJR coordinated and conducted the field experiments; SJ performed the analyses and wrote the manuscript; all authors contributed to manuscript revisions and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe acknowledge the Bindal Peoples as the Traditional Custodians of the land and sea Country where this research took place and pay our respects to elders past and present. We thank them for providing Free Prior Informed Consent to undertake this work. Coral collections, deployments, and monitoring were conducted under Reef Authority permit G21/45348.1. Funding was provided by the Reef Restoration and Adaptation Program (RRAP), a partnership between the Australian Government\u0026rsquo;s Reef Trust and the Great Barrier Reef Foundation. We thank the vessel crews, SeaSim, AIMS colleagues, students, and volunteers for their contributions during spawning and in the field.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAIMS, Australian Institute of Marine Science (2024) \u0026lsquo;ReefCloud\u0026rsquo;. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25845/g5gk-ty57\u003c/span\u003e\u003cspan address=\"10.25845/g5gk-ty57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaird AH, Babcock RC, Mundy CP (2003) Habitat Selection by Larvae Influences the Depth Distribution of Six Common Coral Species. 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Restor Ecol 29(1):e13302\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Australian Institute of Marine Science","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"microfragmentation, spat, wave exposure, density dependence, spawning, reef restoration, post-settlement mortality","lastPublishedDoi":"10.21203/rs.3.rs-8283449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8283449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction: Coral reef restoration increasingly relies on scalable methods, yet outcomes vary across species, propagation techniques, and habitats. Coral seeding, where coral propagules are settled on deployment units before outplanting, provides a flexible approach that accommodates both asexual (e.g., microfrags) and sexual (e.g., spat) propagation.\u003c/p\u003e\n\u003cp\u003eObjectives: To improve predictability and efficiency of coral seeding, we tested how propagation method, species and habitat shape early survival after seeding.\u003c/p\u003e\n\u003cp\u003eMethods: We conducted a multi-species coral seeding experiment at Davies Reef (central Great Barrier Reef), deploying microfrags and spat on tabs within seeding devices across ten sites spanning lagoon, back, flank, and front reef zones. Survival was monitored for ~ 10 months. Analyses included time-to-mortality, growth and generalized mixed models testing the effects of zone, flow, benthic composition and density dependence at the tab-level.\u003c/p\u003e\n\u003cp\u003eResults: Microfrags outperformed spat in survival and reached ~ 10× larger mean size. Species effects zone-specific: spat survival declined at exposed (flank/front) sites, whereas microfragments remained comparatively robust. Reef zone improved model fit relative to flow alone, while site-level benthic composition did not predict survival. Microhabitat effects accounted for ~ 30% of variance, with higher survival on tabs dominated by crustose coralline algae (CCA) and lower on macroalgae-dominated surfaces. Positive density dependence was detected for \u003cem\u003eGalaxea fascicularis\u003c/em\u003e and \u003cem\u003eMontipora turtlensis\u003c/em\u003e, but not for \u003cem\u003eAcropora loripes\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eConclusion: Propagation method, species, and reef zone jointly shape coral survival, but centimetre-scale microhabitat factors are key. Microfragmentation provides more reliable early survival and growth, whereas spat contribute genetic diversity.\u003c/p\u003e\n\u003cp\u003eImplications for practice:\u003c/p\u003e\n\u003cp\u003eReef-zone context should guide deployment. Exposed zones should be avoided for spat but are suitable for microfrags. Settlement substrates should minimise macroalgae and prioritise CCA. Species-specific seeding densities are recommended: higher densities benefit \u003cem\u003eGalaxea fascicularis\u003c/em\u003e and \u003cem\u003eMontipora turtlensis\u003c/em\u003e but not \u003cem\u003eAcropora loripes\u003c/em\u003e. Given high within-site variability, deploying many devices at fewer well-chosen sites and incorporating fine-scale monitoring will improve outcome predictability. Combine propagation methods strategically, deploy microfrags for reliable early cover and spat to sustain genetic diversity and adaptive potential. Lastly, practical proxies such as reef zone and tab-level substrate checks are more reliable predictors of survival than coarse site-level benthic summaries.\u003c/p\u003e","manuscriptTitle":"Propagation method and species drive survival patterns across reef zones in coral seeding on the Great Barrier Reef","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 05:24:52","doi":"10.21203/rs.3.rs-8283449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f608a0f-e8a7-43ca-b345-0c054165ed03","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T05:24:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 05:24:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8283449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8283449","identity":"rs-8283449","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}