Impact of environmental gradients on juvenile coral demography across the Great Barrier Reef and Torres Strait

Impact of environmental gradients on juvenile coral demography across the Great Barrier Reef and Torres Strait | 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 Impact of environmental gradients on juvenile coral demography across the Great Barrier Reef and Torres Strait Christopher Doropoulos, Mariana Alvarez-Noriega, Katharina Fabricius, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7060133/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Coral Reefs → Version 1 posted 9 You are reading this latest preprint version Abstract Demographic rates of juvenile corals are critical to reef recovery, yet their variation across environmental gradients remains understudied. Over three years, we assessed juvenile coral vital rates across the Great Barrier Reef (GBR) and Torres Strait (TS), spanning 14° latitude and diverse inshore to offshore environments. Environmental conditions varied, with annual temperatures ranging 24–28°C, high turbidity in the southern inshore GBR, and elevated chlorophyll a in the southern offshore GBR. Despite these differences, juvenile assemblages of five common coral groups ( Montipora , Acropora , Pocilloporidae, Merulinidae, and Porites ) were broadly similar. Recruitment patterns varied with Acropora highest in southern offshore and central reefs, and Porites in southern offshore and northern reefs. Annual mortality rates were consistent across locations but taxa-specific negative responses to turf height and sedimentation were observed. Net juvenile density increased by 3 m⁻² yr⁻¹, with higher gains for Acropora in southern offshore reefs (3.8 m⁻² yr⁻¹) and Porites in northern offshore reefs (3.5 m⁻² yr⁻¹). Individual survival improved with size, and temperature effects were taxon-dependent – negative for Acropora , neutral for Montipora and Pocilloporidae, and positive for Merulinidae and Porites . Temperature did not correlate with linear growth in any group, but water flow positively influenced growth in most taxa. Fast-growing Acropora and Pocilloporidae showed positive size-growth relationships, unlike slower-growing Merulinidae and Porites . These findings provide baseline demographic data across the GBR and TS, revealing both congruence and divergence with ecological theory. They offer essential input for predictive models, site-specific restoration planning, and evaluating interventions relative to natural background dynamics. growth recruitment recovery restoration resilience survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Environmental filtering across gradients plays a crucial role in shaping biodiversity, community assemblages and the vital rates of organisms within natural ecosystems (Hutchings 2021). Gradients in temperature, salinity, and nutrient availability create varying conditions that influence growth, survival, recruitment, and turnover rates of habitat-forming organisms in terrestrial and marine environments. In terrestrial ecosystems, temperature and moisture gradients significantly impact plant growth and survival (Breshears et al. 2005, Allen et al. 2010). Species adapted to warmer, drier conditions may exhibit higher growth rates under warming conditions, while those adapted to cooler, wetter environments could experience stress, leading to decreased survival and recruitment under climate change (Chapin et al. 2002). Similarly, in coastal marine systems, environmental gradients strongly influence the distribution and performance of foundational species like seagrasses, mangroves, and sessile invertebrates (Menge and Sutherland 1987, Krauss et al. 2008, Chefaoui et al. 2016). For example, intertidal oyster reefs exhibit high growth and recruitment in estuarine zones with moderate salinity and stable temperatures, while extreme shifts in salinity due to freshwater influx or temperature fluctuations can cause stress, reducing filtration capacity and leading to mass mortality events (Beck et al. 2011). Such habitat-building organisms play a crucial role in maintaining biodiversity, and their responses to environmental gradients can have cascading effects on coastal ecosystems. Different communities exhibit diverse responses to environmental gradients due to their unique traits and life histories (McGill et al. 2006). In a study of Californian woody plant species, Cornwell and Ackerly (2009) found that drought tolerance and leaf traits strongly shaped species distributions along moisture gradients. Species with conservative traits, such as thicker leaves and slower growth rates, were more prevalent in drier habitats, whereas species with more acquisitive traits such as vertical partitioning of light dominated wetter environments, demonstrating how functional traits can mediate ecological responses to environmental variability. In marine environments, corals exhibit a range of thermal optima (Álvarez-Noriega et al. 2023) and reproductive strategies (Baird et al. 2009), resulting in differing responses to acute (Baird and Marshall 2002) and chronic (Edmunds 2024) temperature changes. These variations highlight the importance of functional diversity within communities, as species with complementary traits can enhance resilience and adaptability in the face of environmental change (Díaz et al. 2007). Understanding the role of functional traits in environmental filtering is essential for predicting how coral assemblages may respond to ongoing climate change. Despite the importance of environmental gradients in driving vital rates of sessile organisms (e.g., Connolly et al. 2001, Montero‐Serra et al. 2018), juvenile coral assemblages remain understudied. For example, recent demographic data spanning six years for 11 coral species captured less than 1% of the sampled population in terms of juvenile coral (≤4 cm maximum diameter) growth and survival from a dataset of over 450 tagged colonies (Madin et al. 2023a). It is well known that coral recruitment, growth, and survival are critical to coral population growth and reef recovery (e.g., Caley et al. 1996, Connell et al. 1997, Gilmour et al. 2013, Doropoulos et al. 2015, Graham et al. 2015, Gouezo et al. 2019, Doropoulos et al. 2022a, Edmunds 2023), yet there is a significant gap in knowledge regarding how juvenile corals respond to varying environmental conditions (but see Bak and Engel 1979, Edmunds et al. 2004, Nozawa et al. 2021, Doropoulos et al. 2022b). Investigating environmental filtering and its effect on demographic rates in coral early life stages is crucial for effective coral reef conservation and management, because the prominence of allometric scaling means that the biology of juvenile corals cannot be linearly scaled from larger corals (Burgess et al. 2017, Edmunds 2023). Understanding the nuanced interactions between environmental gradients and coral vital rates can be utilised to parameterise life table response experiments for comparing the effectiveness of management interventions (Hughes et al. 2023). By focusing on how environmental drivers influence the vital rates of diverse juvenile coral assemblages, we can gain insights into the complex relationships that govern community dynamics and their future under changing environmental conditions. Here, we tested how the vital rates of juvenile corals vary across the Great Barrier Reef (GBR) and Torres Strait (TS). Data were collected from 2021-23, coinciding with (i) a period of relatively high coral cover observed since continuous monitoring began in 1985 (Emslie et al. 2024); and (ii) during a time where no major disturbances occurred in 2021-22 but a mild mass bleaching event occurred in the central GBR in 2022-23. A high resolution study shows the effects of the bleaching event were highly variable across 13 adult taxa in the central GBR in 2022 (Álvarez-Noriega et al. 2025), and there were no reductions in coral cover observed across any region (Australian Institute of Marine Sciences 2023). Thus, the event was relatively minor compared to previous, recent bleaching events on the GBR (Hughes et al. 2018). Utilising this period with only minor thermal stress and no other acute events (e.g. floods, cyclones, predator outbreaks) provides baseline values of vital rates. They may serve as comparison values for against those observed during interventions such as the deployment of corals with higher thermal tolerances, growth and survival of juvenile corals via selective breeding (e.g., Quigley and van Oppen 2022, Humanes et al. 2024) or producing corals on seeding devices for mass deployment (e.g., Randall et al. 2021, Waters et al. 2025). We initially tagged >1560 individual juvenile coral colonies (≤4 cm in maximum diameter) and tracked their size-specific growth and survival, as well as the recruitment of new individuals to establish background rates of turnover. Given the previously observed positive influence of temperature on juvenile coral growth (e.g., Nozawa et al. 2021, Doropoulos et al. 2022b), size on juvenile coral survival (e.g., Doropoulos et al. 2012, Doropoulos et al. 2016), and differences in life-history strategies across major coral taxa (e.g., Darling et al. 2012, Madin et al. 2023b), we hypothesized that: (i) juvenile coral growth rates would increase with increasing temperature, (ii) survival rates would increase with increasing size, and (iii) growth and survival rates would trade-off across major coral groups due to functional trait trade-offs, such that the fastest growers would have the highest mortality. In contrast, the influence of latitudinally driven temperature clines on the recruitment of corals into natural populations is largely unknown. Studies covering the latitudinal cline of the GBR utilising settlement tiles show conflicting trends (Hughes et al. 2002, Drake et al. 2025), and recent work shows that vital rates derived from settlement tiles represent population dynamics on the reef substrate poorly (Gouezo et al. 2025). Thus, we hypothesized that (iv) recruitment rates of new juvenile corals onto the reef would not differ across environmental gradients. Materials and Methods Study design To capture a large gradient in environmental drivers, particularly gradients in long-term annual mean temperature, this study spanned 14° of latitude from the southern to northern GBR and Torres Strait (Figure 1). The southernmost reef was Lady Musgrave (latitude 23.9°S), while the northernmost reef was Masig Island (Masig = 9.45°S). Across this latitudinal gradient, reefs were situated across the shelf from inshore to offshore, and sites within reefs were situated in a variety of wave exposures on nominal ‘fore-reefs’, ‘back-reefs’, and ‘lagoonal reefs’. In total, there were six ‘reef clusters’, with 2-3 reefs nested in each cluster, and 2-6 sites nested in each reef, with a total of 56 sites and 253-263 quadrats (Table S1). Annual sampling was conducted three times from January to May: sites were established in 2021, then resurveyed in 2022 and 2023. Benthic surveys At the beginning of the study, permanent quadrats (0.5 x 0.5 m) were placed around ≥1 juvenile coral, standardised to a 5 m depth profile, with an average of n = 4 fixed quadrats per site (range = 1-6 per site; Table S1). What defines a ‘juvenile coral’ is partly operational and partly biological. Biologically, immature colonies are juveniles, and the size at which corals reach reproductive maturity is largely size-based and differs across taxa (e.g., Álvarez‐Noriega et al. 2016). Thus, operational size-based definitions are often utilised, which is typically <40 mm or <50 mm diameter, depending on the study and region (Roth and Knowlton 2009). In this study, we utilise a maximum diameter of 40 mm to define the size limit of juvenile corals, particularly because some coral taxa that brood their progeny reach maturity at sizes as small as 40-50 mm (e.g., Rinkevich and Loya 1979, Kojis 1986). Each permanent quadrat was demarcated by a stainless-steel stake in up to three corners, with a numbered tag in one corner for orientation and quadrat identification. Once quadrats were established, all juvenile corals within each quadrat had their location mapped, size measured using callipers (maximum diameter to closest mm) and identified to the lowest taxon possible (typically genus). An image of the entire quadrat was taken, plus four detailed images of each quadrant (0.25 x 0.25 m) within each quadrat, and close-ups of individual juveniles to confirm taxonomic identity. Post-processing included transcribing the initial hand-drawn maps onto the images of each quadrat in preparation for the next census period. Quadrats were resurveyed every 12 months. Resurveys included in situ measuring all previously mapped corals for changes in size or mortality, any new colonies were added to the maps (i.e., recruitment), and associated images were taken. Data extractions then allowed for quantifying rates of individual juvenile coral colony growth, mortality, and recruitment. Data cleaning involved the removal of 417 colonies: those that were >40 mm at the beginning of the study, chimeras that formed during the study, small fragments created by fission of colonies, and any free-living scleractinian corals ( Fungia, Heliofungia ). Across the initial 1,560 individual juvenile colonies demarcated, 39 genera were present, dominated by Acropora (36%) and Porites (20%), with the remaining genera composing ≤5% each. Therefore, an initial total of 1,299 juvenile colonies from five distinct groups of corals were used for the study: Montipora (n = 67 at t 0 ) , Acropora (n = 568 at t 0 ) , Merulinidae (n = 236 at t 0 , included Dipsastraea, Favites and Goniastrea ), Pocilloporidae (n = 123 at t 0 , included Pocillopora, Seriatopora and Stylophora ), and Porites (n = 305 at t 0 ) . Overall rates of recruitment, mortality, and turnover were quantified within each plot. As with definitions of ‘juvenile corals’, definitions of recruitment are poorly defined and often operational (Edmunds 2023). Here, we define recruitment as the first detectable observation of a new juvenile coral colony (≤40 mm) between surveys per unit area (i.e., 2021 to 2022 or 2022 to 2023) (Keough and Downes 1982, Harrison and Wallace 1990, Caley et al. 1996), thus it combines both settlement and early post-settlement survival prior to census (Doropoulos et al. 2016). It is possible that some of the individual colonies classified as new ‘recruitment’ between time periods may have already been present in a plot but located in cryptic microhabitats (Doropoulos et al. 2022a). Mortality rates represent whole colony mortality of juveniles that were present at the time of a census but not present at the proceeding census period. Given the yearly time periods between censuses, sources of mortality could not be estimated. Turnover was then calculated by subtracting the number of individual colonies dying from the number of individual colonies recruiting within each plot. All recruitment, mortality, and turnover counts were standardized by quadrat area to obtain rates m⁻². These were then calculated as annual rates and summarized as mean values per taxa and reef cluster by pooling across all available quadrats. Biophysical benthic parameters Additional data on sedimentation, reef slope, and turf height were collected in situ at every site by a single surveyor (S. N.) in 2022. Two proxies were used for sedimentation: (i) sediment weight (average g m -2 ) and (ii) a visual rating of sediment deposition (0 to 3). Sediment weight was derived from the amount of sediment accumulated on five replicate 11.4 x 11.4 cm PVC tiles that were deployed horizontally at each site from 2021-2023. Upon collection, individual tiles were carefully placed into ziplock bags underwater, and the accumulated sediment was then resuspended and isolated by filtration through pre-weighed glass fibre filters (Whatman GF/F 47mm diameter, nominal pore size = 0.7 µm) and a vacuum manifold. The filters were rinsed with fresh water, dried at 60°C for 48 hours, weighed to the nearest 0.001 g, and sediment dry weight was calculated by subtracting the initial filter weight from the final filter weight. Sedimentation levels per m 2 were calculated by scaling up the average sediment weight from the five tiles per site. The visual rating of sediment deposition aimed to capture the site level sedimentation on the reef using an ordinal scale of 0-3, with: 0 = none, 1 = little, 2 = moderate, 3 = lots (cannot be resuspended by fanning). Reef slope was quantified as a visual estimate of the steepness of the reef slope in degrees (i.e., 0° = flat, 90° = vertical). Turf height was calculated as the average of triplicate turf height measurements (mm) taken at three to four points per site using callipers in regular intervals along a transect line. Environmental water parameters For each reef site and sampling period (2021-2022 and 2022-2023), key environmental water parameters were extracted from eReefs hydrodynamic (GBR1 v2.0) and biogeochemical (GBR4) models (Steven et al. 2019). eReefs is an information system with publicly available hydrodynamic and biogeochemical three-dimensional predictions for the GBR. Hydrodynamic predictions are available at a ~1 x 1 km spatial resolution, while biogeochemical predictions are available at a ~4 x 4 km spatial resolution. Daily data at 5 m depth were extracted for each sampling period and then averaged for each interval. The extracted 34 variables included temperature and salinity, as well as water quality metrics such as chlorophyll a concentration, dissolved organic and inorganic compounds, and aragonite saturation, among others (Table S2). Data analysis - overview Data analysis utilised a combination of multivariate and univariate statistical techniques to investigate spatial and temporal patterns in environmental conditions, juvenile coral assemblages, and vital rates. Multivariate analyses were conducted in PRIMER with PERMANOVA+ (Clarke and Gorley 2006, Anderson et al. 2008), using Euclidean and Bray-Curtis dissimilarity matrices to assess environmental and community structure, respectively. Principal coordinates ordination (PCO), hierarchical clustering, and PERMDISP tests were used to visualise and test group differences. In all PERMANOVA main effect and pair-wise tests, the p values generated by 999 permutations were used when the number of unique permutations were >20%, else the Monte Carlo asymptotic p value was used (Anderson et al. 2008). Univariate analyses were conducted in R (R Development Core Team 2024) and visualised using the ggplot2 package (Wickham 2009). Generalised linear mixed-effects models (GLMMs) were used to model ecological responses of continuous, count-based, and proportional data types, with model fitting conducted using the glmmTMB package (Brooks et al. 2017). Model selection was guided by Akaike information criterion (AIC) and R² values using the MuMIn package (Barton and Barton 2019), with residual diagnostics assessed using DHARMa (Hartig 2018). Analyses of variance (ANOVA; Type II sum of squares) were conducted using the car package (Fox et al. 2012), and significant effects explored using emmeans for post-hoc comparisons (Lenth 2021). Where applicable, multicollinearity was assessed using correlation matrices generated with corrplot (Wei et al. 2017). Conservative alpha levels (α ≤ 0.01) were applied in cases of non-homogeneous dispersion or overdispersed residuals for multivariate and univariate analyses (Underwood 1997). Data analysis - detailed An initial investigation assessed the spatial and temporal structure of environmental water quality parameters. Yearly averaged values for 34 environmental variables were normalised, and a Euclidean dissimilarity matrix was used to visualise patterns via principal coordinates ordination (PCO), grouped by reef cluster and time period. Vectors representing highly correlated variables (Pearson’s r > 0.9) were overlaid on the PCO, and hierarchical clustering was used to examine group structure. PERMANOVA tested for differences using a nested design: time period (2 levels) and reef cluster (6 levels) as fixed orthogonal factors, with reefs nested within clusters and sites nested within reefs. Three prominent environmental drivers identified from the PCO – temperature, vertical light attenuation, and chlorophyll a – were further analysed across latitudes using GLMMs. A series of additive and interactive models with linear and quadratic terms for latitude were fitted, and the best model selected using AIC and R². Model diagnostics confirmed assumptions of normality and dispersion. To understand whether there was any spatial structure of coral assemblages or relative abundances across the study design, abundances across five major coral groups and six reef clusters were converted to m⁻² for the initial time point only – i.e., 2021 – when the quadrats were installed. To investigate assemblage structure, juvenile coral community data were log(x+1) transformed and PERMANOVA tested for spatial differences using the same nested design as for the environmental variables. Relative abundance patterns across reef clusters were qualitatively investigated. Annual recruitment and mortality rates m -2 were analysed with GLMMs using a negative binomial distribution with zero-inflation following a model selection process. Initial model selection compared the AIC values and residuals using bootstrapping of models with Poisson and negative binomial distributions for recruitment and mortality rates, respectively, with and without zero-inflation terms. Fixed factors included coral taxa (5 levels) and reef cluster (6 levels), with time period included as a random effect to account for temporal autocorrelation, and sites nested within reefs and reef clusters to account for spatial autocorrelation. To examine how environmental gradients influenced recruitment and mortality, multiple regression models were fitted using six dominant, uncorrelated environmental variables – i.e., temperature, water clarity, current speed, visual sediment deposition, reef slope, turf height. Latitude and chlorophyll a were excluded due to high collinearity with temperature (>0.85). Reef cluster and year were included as random effects to include the spatial and temporal autocorrelation structure when predicting the effects of environmental drivers on recruitment and mortality. Two sites lacked environmental data so were excluded from the analysis. Size-based growth rates and survival probabilities of individual juvenile coral colonies were analysed using GLMMs. Data from the Keppel Islands and Montipora were excluded due to model convergence issues resulting from data replication deficiencies. Initial models included colony size at t 0 of each transition, coral taxa (4 levels), reef cluster (5 levels), and their interactions as fixed effects, with replicate reefs, sites, and time periods included as random terms to account for spatial and temporal autocorrelation. Subsequent models included environmental predictors, again with reef cluster and year included as random effects to include the spatial and temporal autocorrelation structure when predicting the effects of environmental drivers on growth and survival. Growth models used a Gaussian distribution; survival models used a binomial distribution. Size-based growth rates were calculated for individual juvenile coral colonies as linear extension per year (mm y -1 ), and growth data exceeding +100 (n = 6) or -100 (n = 3) mm y -1 was removed to satisfy a normal distribution of the data. Residuals indicated overdispersion and heterogeneity, therefore conservative α values were applied. Significant effects were further explored with pairwise comparisons. Results Environmental water quality parameters Ordination (PCO) of the annual mean environmental water quality parameters derived from eReefs for each site showed three major groupings at >10% similarity: (i) southern inshore, (ii) southern offshore, and (iii) central and northern inshore and offshore and far northern reefs (Figure 2a). Overall, the first three axes explained 83% of the variation (PCO1 = 46%, PCO2 = 21%, PCO3 = 16%). Temperature was highly correlated (>0.9) with the PCO1 axis; the PCO2 axis was highly correlated with p CO 2 and O 2 ; fine sediment, dust, and vertical attenuation (Kd 490) were highly correlated the southern inshore reefs, while chlorophyll a , pH, dissolved inorganic nitrogen, and nitrate were highly correlated with the southern offshore reefs. A highly significant time period by reef cluster interaction was observed ( p < 0.001). Pairwise comparisons between 2021–22 and 2022–23 revealed significant differences in environmental water quality parameters within the southern inshore, central offshore, and far northern clusters ( p ≤ 0.017). Within 2021–22 and 2022–23, pairwise comparisons among reef clusters revealed widespread and significant differences in environmental water quality parameters ( p ≤ 0.005 in most comparisons), particularly for southern inshore and southern offshore reefs, indicating strong spatial variation across the GBR and TS. Increasing mean annual temperature significantly correlated with decreasing latitude ( p < 0.001, R 2 = 0.96, Figure 2b), being cooler in the southern offshore (24.1-24.4 °C) and southern inshore (24.9-25.4 °C) reefs, and warmer in the central offshore and central inshore (26.2-27.5 °C), northern offshore (27.5-28.9 °C) and far northern reefs (27.8-28.4 °C). Chlorophyll a was correlated with latitude ( p = 0.04, R 2 = 0.34, Figure 2d), being highest in the southern offshore reefs (1.10-1.55 mg Chl m -3 ) and much lower elsewhere (<0.6 mg Chl m -3 ). Juvenile coral assemblage composition and abundances Juvenile coral assemblage structure was similar across all reef clusters at the beginning of the study in 2021 ( p = 0.161; Figure S2a). There were some groupings in the ordination, though these were not related to reef clusters, reefs nested within clusters, nor sites nested within reefs. The correlation vector shows that Acropora , Merulinidae, and Porites drove the spread of the data. Despite no differences in assemblage composition among the reef clusters, there was a shift in the relative abundances of juvenile corals from the major taxa across the reef clusters. At the beginning of the study, high relative abundances of Acropora (43-78% of community) were apparent in the southern and central reefs compared to northern offshore and far northern reefs (26-34%). Merulinidae and Porites relative abundances were slightly higher in northern offshore and far northern reefs on average (23-36%) compared to central and southern reefs (4-32%). Juvenile coral recruitment, mortality, and turnover Patterns of coral recruitment were distinct among reef clusters and taxa (interaction term, p < 0.001; Figure 3a). Acropora recruitment was highest overall (average 5.5 m -2 y -1 ), particularly in the southern offshore (8.3 m -2 y -1 ) and central offshore (7.4 m -2 y -1 ) reefs, followed by the central inshore reefs (5.3 m -2 y -1 ). All other reef clusters had significantly lower Acropora recruitment rates (<4.0 m -2 y -1 ). Porites recruitment averaged 3.6 ind. m -2 y -1 and was highest at southern offshore and northern offshore reefs (4.9-5.1 m -2 y -1 ), significantly higher at southern offshore reefs compared to the far northern reefs (2.5 m -2 y -1 ). Montipora, Merulinidae, and Pocilloporidae recruitment averaged 2.3-2.9 m -2 y -1 across reef clusters. Multiple regression analysis showed two interactions of annual recruitment rates varying across taxa x temperature ( p = 0.031) and taxa x sedimentation (visible; p = 0.028). Acropora recruitment demonstrated a clear negative relationship with both temperature and sedimentation, whereas the other taxa had slightly negative, neutral, or slightly positive relationships (Figure S3). Coral mortality rates were also distinct among reef clusters and taxa (interaction term, p = 0.004; Figure 3b). Similar to recruitment, Acropora mortality was highest overall (average 4.7 m -2 y -1 ), but the highest mortality rates were found in the central offshore reefs (6.9 m -2 y -1 ) and lowest in the northern offshore reefs (2.3 m -2 y -1 ), with significantly lower rates in the northern offshore reefs compared to all other reef clusters. Mortality rates of all other taxa ranged from 1.4 m -2 y -1 in Montipora to 2.5 m -2 y -1 in Porites, with no significant differences across clusters . Multiple regression analysis showed two interactions of annual mortality rates varying across taxa x turf height ( p = 0.023) and taxa x sedimentation (visible; p = 0.004). Acropora mortality had slightly positive and negative relationships with turf height and sedimentation, respectively, whereas the other taxa had slightly negative, neutral, or positive relationships (Figure S4). Net turnover rates (i.e., recruitment minus mortality) averaged 3.1 juvenile colonies m -2 y -1 when pooled across all taxa and reef clusters. When assessed separately by taxon and cluster, turnover averaged 0.7 to 1.1 m -2 y -1 , indicating a low but typically positive net addition of new individuals to the community annually for each reef cluster; no cluster had significant losses (Figure 3c). Acropora net turnover rates in the offshore southern reefs and Porites net turnover rates in the northern offshore reefs were notably high, averaging 3.8 and 3.5 m -2 y -1 , respectively. Although the difference across reef clusters was statistically significant ( p = 0.03), the effects were relatively weak, and differences across taxa ( p = 0.91) and their interaction ( p = 0.21) were not statistically significant due to the high variability observed. Juvenile coral growth rates Juvenile coral growth rates varied across taxa x initial size ( p = 0.002) and across taxa x reef cluster ( p = 0.004). Relationships between growth and size (Figure 4a) were positive and strongest in Pocilloporidae, where growth increased from 11 mm yr⁻¹ at 3 mm maximum diameter to 28 mm yr⁻¹ at 40 mm maximum diameter. Acropora and Montipora also showed positive relationships, with growth increasing from 13 and 9 mm yr⁻¹ at 2 and 5 mm maximum diameter, respectively, to 18 and 14 mm yr⁻¹ at 40 mm maximum diameter. Growth in Merulinidae was largely size-independent, ranging from 7 mm yr⁻¹ at 4 mm maximum diameter to 9 mm yr⁻¹ at 40 mm maximum diameter. Porites exhibited a negative relationship, with growth decreasing from 11 mm yr⁻¹ at 4 mm maximum diameter to 6 mm yr⁻¹ at 40 mm maximum diameter. Overall growth rates independent of initial size for Acropora (Figure 4b) were lower in the central inshore (10 mm yr -1 ) than northern offshore reefs (21 mm yr -1 ), and southern offshore and far northern reefs (18 mm yr -1 ). Faster growth rates for Pocilloporidae were observed in the central inshore reefs (37 mm yr -1 ) compared to the southern offshore reefs (17 mm yr -1 ), while they did not vary for Merulindae (8 mm yr -1 ) or Porites (9 mm yr -1 ) across all reef clusters. Pocilloporidae (20 mm yr -1 ) and Acropora (16 mm yr -1 ) typically had growth rates >2-times faster than Merulinidae and Porites (8 mm yr -1 ) within each reef cluster, apart from the central inshore reefs where Pocilloporidae growth was faster than all other taxa. Of the six environmental variables tested (temperature, water clarity, current speed, visual sediment deposition, reef slope, turf height), only current speed had a significant association with the rates of juvenile coral growth ( p < 0.001; Figure 4c). A positive relationship was observed, ranging from 8.7 mm y -1 at 0.02 m s -1 in the central inshore reefs to 16.6 mm y -1 at 0.44 m s -1 in the far northern reefs. The effect of current speed on juvenile coral growth rates varied among taxa (Figure S5), being strongly positive for Acropora , slightly positive for Merulinidae and Porites , and negative for Pocilloporidae, although the interaction was marginally non-significant ( p = 0.052). Juvenile coral survival Juvenile coral survival probabilities showed a positive linear relationship with increasing size ( p < 0.001) for all taxa (Figure S6a). Mean annual survival increased from 41% at 2 mm, 49% at 10 mm, 60% at 20 mm, to 76% at 40 mm maximum diameter (Figure 5a). There was moderate variability in the relationship between size and survival across locations ( p = 0.054). While a positive relationship between size and annual survival was observed in four reef clusters, juvenile corals in the central inshore reefs exhibited size-independent survival, averaging 70% per year (Figure 5b). Individual juvenile coral survival also varied across taxa x reef cluster ( p < 0.001; Figure S6b), although no major trends were apparent. Acropora survival was lower in the far northern reefs (40% annual survival) compared to the southern offshore (70%), central inshore (70%), and northern offshore (64%) reefs; Merulindae survival was lower in the central offshore (62%) compared to the northern offshore (83%) reefs; and Porites survival was higher in the northern offshore (79%) compared to the southern offshore (47%) reefs. Seawater temperature was the only environmental variable that had a significant impact on proportional juvenile coral survival, and this varied among taxa ( p < 0.001). Given the extremely high correlation between temperature and latitude, the patterns described also reflect differences in proportional survival across latitudes. Acropora survival had a negative response to increasing mean annual temperature; Pocilloporidae and Montipora had temperature-independent responses in survival to mean annual temperature; and Merulindae and Porites had positive responses in survival to increasing mean annual temperature (Figure 5c). Discussion Understanding the drivers of change in coral reef ecosystems is essential for predicting their future under increasing environmental stress. Yet despite decades of research, many questions remain about how environmental variability shapes the demographic processes that are critical to reef recovery. While most studies assess the effects of acute drivers on reef dynamics due to their prominent and detectable impacts, recent work has established that chronic variation in temperature can also be a significant long-term driver of change in coral reef communities (Edmunds 2024). In this study, we assessed juvenile coral demographics across the Great Barrier Reef (GBR) and Torres Strait (TS) during a disturbance-free period (2021-2022) and a subsequent period that included a relatively minor bleaching event in the central GBR (2022–2023), providing critical insights into how environmental gradients influence juvenile coral demographics. We found recruitment of juvenile Acropora corals was higher at higher latitudes, with Acropora recruitment declining with increasing temperature and sedimentation, and recruitment of Porites corals was higher at higher and lower latitudes, with slight reductions with increasing temperature – patterns that were not observed in the other three coral groups. A recent study characterising recruitment using settlement tiles at the same study sites also found negative associations of recruit densities with sedimentation, but no latitudinal trends and a negative association with currents (Drake et al. 2025). Mortality in our present showed relatively minor latitudinal differences, although Acropora mortality was lower in northern offshore reefs. Sedimentation consistently had a negative effect on survival. Combining annual recruitment and survival data revealed net positive juvenile coral yields across most reef clusters, typically around 1 colony m⁻² yr⁻¹, with especially high gains for Acropora (3.8 ind. m⁻² yr⁻¹) in the offshore southern reefs and Porites (3.5 ind. m⁻² yr⁻¹) in the northern offshore reefs. Contrary to expectations from studies in other regions (Nozawa et al. 2021, Doropoulos et al. 2022b), we found no correlation between temperature and juvenile coral linear growth rates. However, growth showed positive size-based relationships in some coral taxa, and a consistent positive association with water flow across all groups. Survival also increased with size across all taxa (Doropoulos et al. 2012, Doropoulos et al. 2016), while the effect of temperature on survival varied – negative for Acropora , neutral for Montipora and Pocilloporidae, and positive for Merulinidae and Porites . Overall, this work underscores the value of baseline demographic data for understanding coral resilience and response mechanisms. It also provides key benchmarks on natural recruitment and survival that can inform and contextualize restoration goals – such as the target of deploying ~5 corals m⁻² at restoration sites (Gibbs et al. 2024), aligning with the net turnover rates identified here. While the generally positive turnover rates observed across most reef clusters are encouraging, it is important to consider potential biases that may influence these estimates. For instance, previously mapped juvenile colonies are often easier to relocate and assess for survival than newly settled recruits are to detect for the first time, particularly if they occur in cryptic microhabitats (Doropoulos et al. 2022a). This could lead to underestimation of mortality or overestimation of recruitment, respectively. Nonetheless, the overall patterns — including strong taxon-specific trends and consistent spatial variation — suggest that the turnover estimates reflect real demographic processes, even if the absolute values should be interpreted with caution. By identifying drivers of coral dynamics and highlighting positive size-based growth relationships, our findings provide a foundation for assessing future interventions such as selective breeding to enhance thermal tolerance (Quigley and van Oppen 2022, Humanes et al. 2024), and the implementation of size-informed outplanting strategies using common, widely distributed coral species (Ladd et al. 2018, McLeod et al. 2022). These strategies should ideally incorporate mixed-species and functional group configurations to optimize ecological function and restoration success across the GBR and broader Indo-Pacific. Contrary to expectation, one of the major findings of our study is that juvenile coral growth rates did not increase with temperature. Despite a strong correlation between temperature and latitude, with mean annual temperatures ranging from 24.0 to 28.5°C, the 4.5°C difference may have been too narrow to elicit measurable growth differences, particularly when compared to other studies with broader thermal gradients. For instance, Nozawa et al. (2021) reported a positive correlation between annual growth rates and average seawater temperature across 16° of latitude in the West and South Pacific, where temperatures varied from 20.5 to 29.7°C. Similarly, Doropoulos et al. (2022b) found a unimodal relationship between juvenile coral growth and temperature in the Exmouth Gulf and Ningaloo Reef, spanning a temperature range of 20.5 to 28.1°C. An additional consideration is the potential masking effect of taxonomic diversity within our morpho-taxa groupings. Our classifications combined multiple species into five major groups ( Acropora , Montipora , Pocilloporidae, Merulinidae, Porites ), which may obscure species-specific responses to temperature in diverse coral communities where species within genera represent varying ecological traits and where cryptic taxa are common (Bongaerts et al. 2021, Riginos et al. 2024, Ricardo et al. 2025). However, both Nozawa et al. (2021) and Doropoulos et al. (2022b) used similarly broad taxonomic groupings and still detected temperature-related growth patterns, suggesting that species diversity alone is unlikely to fully explain our findings. An alternative explanation is that juvenile corals are locally adapted to their regional thermal regimes, with adaptation potentially offsetting expected temperature-growth responses across latitudes. Dispersal distances are typically lower than the spatial scale of temperature gradients, enabling localized adaptation to override uniform physiological responses to warming (Mumby et al. 2011). This may also help explain observations such as Porites extension rates being highly location-specific and responsive to long-term climate trends rather than showing synchronous variation across latitude (Cantin et al. 2010, Razak et al. 2020). Finally, the data used in this study were based on annual averages for both demographic rates and environmental predictors. As a result, more subtle or short-term responses – such as temporarily reduced growth rates following brief thermal stress events – may not have been captured (Razak et al. 2020). Thus, it’s likely that multiple factors are contributing to the absence in detecting a positive temperature-growth correlation in our study. Differences in growth rates across major morpho-taxa found in juvenile corals across the GBR and TS in this study generally align with other studies of juvenile (Trapon et al. 2013, Doropoulos et al. 2015, Nozawa et al. 2021) and adult (Madin et al. 2016) corals from the GBR and other parts of the world. We found that branching Pocilloporidae and Acropora juvenile corals typically had higher growth rates (averaging 11-28 and 13-18 mm y -1 ) than those of submassive Merulinidae (7-9 mm y -1 ) and massive Porites (6-11 mm y -1 ) corals. Juvenile coral growth rates were strongly influenced by both size and environmental factors in our study, with significant variability across taxa and reef clusters. The positive size-based growth correlations observed in Pocilloporidae and Acropora largely align with patterns observed in earlier studies on the GBR (Trapon et al. 2013, Doropoulos et al. 2015) and Central Pacific (Kayal et al. 2018), highlighting the rapid size-based growth of these fast growing coral taxa, which drives rapid reef-scale recovery in coral cover (Gilmour et al. 2013, Doropoulos et al. 2015). The neutral and negative size-based growth relationships observed with Merulinidae and Porites corals, respectively, somewhat agree with patterns from the reef flat of the GBR (Trapon et al. 2013, Doropoulos et al. 2015). Whilst temperature had no effect on linear growth rates, current speed had a positive effect on juvenile coral colony growth, with faster currents promoting higher rates. Increasing flow rates from 0 to 25 cm s -1 have been shown to have positive effects on the growth rates of corals in experimental settings – by reducing water boundary layer thickness and increasing rates of coral metabolism (Martins et al. 2024), and increasing heterotrophic nutrient uptake, and possibly reducing algal competition (Schutter et al. 2010). Combined, despite known relationships between temperature and growth in adult corals on the GBR (Lough 2008), we found in our study that juvenile coral growth rates across the GBR and TS were not predictable across latitudes and temperatures, however do follow known patterns across common coral groups and most have positive relationships with increasing size and current speed. Juvenile coral survival rates increased with size, in agreement with ecological theory (Paine 1976, Connell 1985) and other studies from across the GBR (Trapon et al. 2013, Doropoulos et al. 2015), South East Asia (Doropoulos et al. 2016, Baria‐Rodriguez et al. 2019), Caribbean (Box and Mumby 2007, Lirman et al. 2014), and Indian Ocean (Doropoulos et al. 2022b). Thus, escaping early size-dependent bottlenecks in survival has long-term impacts for coral reef recovery, and data synthesized from ~26 studies suggests that cohorts of settlers surviving >1 year have a high chance of persisting (Edmunds 2023). However, the interaction between size and survival varied across reef clusters, with size-independent survival observed in the central inshore reefs, emphasizing that prominent patterns don’t necessarily always hold in every setting. In addition, temperature influenced juvenile coral survival, but this effect varied among taxa and reef clusters. For instance, Acropora showed higher survival in the cooler southern reefs, while Merulinidae and Porites exhibited positive survival associations with warmer conditions in the north. These patterns may reflect local adaptation or thermal tolerance differences, though caution is warranted in interpreting temperature as the sole driver. The spatial heterogeneity in environmental conditions – including differences in bleaching severity (particularly during 2022–2023; Álvarez-Noriega et al. 2025), predation pressure, and other habitat features – could also be contributing to the observed variation. For example, the spatial distribution of the bumphead parrotfish ( Bolbometopon muricatum ), which is known to damage corals through physical disturbance and is largely restricted to the central and northern GBR, may influence juvenile coral survival patterns. Its absence from southern reefs is likely driven by physiological constraints associated with cooler winter temperatures (Bellwood et al. 2003), potentially creating a natural predation gradient aligned with latitude. Moreover, while this study focused on juveniles, it remains an open question whether similar taxon-specific survival patterns are mirrored in adult coral populations. This underscores the complexity of attributing causality in broad-scale latitudinal patterns, as temperature covaries with numerous other environmental factors such as light quality, water flow, and benthic habitat complexity (Done 2011). Furthermore, temperature may serve as an ultimate driver that influences more proximate causes of coral mortality – such as increased susceptibility to predation or bleaching. Taken together, these findings highlight the importance of integrating both species-specific and location-specific processes when interpreting demographic trends and call for caution when attributing survival outcomes solely to temperature. Understanding the interplay between direct and indirect drivers will be critical for accurately predicting coral resilience and informing effective conservation strategies. The study’s findings have implications for reef management and restoration. Baseline demographic data, such as those presented here, have been effectively used in other regions to inform and optimize site-specific management actions (Gouezo et al. 2021) and guide restoration strategies globally. For example, the Coral Restoration Foundation’s work in the Caribbean employs site-specific data to improve coral fragment outplanting strategies and enhance survival rates (Lirman et al. 2014, Ladd et al. 2019). In Asia, studies have utilized hydrodynamic models to identify optimal sites for coral nurseries, demonstrating how data-driven approaches can be utilised to maximize restoration success (Omori 2019). Similarly, Doropoulos et al. (2022b) emphasized the integration of demographic data into large-scale restoration planning, reinforcing the value of location-specific ecological insights. These examples underscore the importance of context-specific strategies that align with local environmental conditions, species traits, and restoration goals. Such approaches may involve, for example, selecting coral taxa with higher survival under prevailing local conditions, or adjusting deployment timing and locations based on water flow or temperature profiles. By establishing baseline demographic rates of recruitment, growth, and survival across environmental gradients, this study enables direct comparisons to assess the effectiveness of future restoration interventions – such as larval enhancement, selective breeding for thermal tolerance, or targeted outplanting (Randall et al. 2020, Humanes et al. 2024) – in increasing juvenile coral abundance and persistence. The identification of trade-offs between growth and survival across coral taxa provides valuable guidance for prioritizing interventions based on specific ecological contexts. Moreover, the demonstrated influence of environmental gradients on juvenile coral dynamics highlights the necessity of regionally tailored restoration strategies. As the GBR faces increasing threats from climate change (Emslie et al. 2024, Bozec et al. 2025), this research contributes critical knowledge for designing adaptive and resilient restoration interventions that support long-term reef recovery. Declarations The authors declare no competing interests that interfered with the completion of this work. Data availability Data associated with this study will be freely available at the CSIRO data access portal (http://hdl.handle.net/102.100.100/489497?index=1) upon publication of the manuscript. Funding This work was supported by the EcoRRAP Subprogram (https://gbrrestoration.org/program/ecorrap/) that is part of the Reef Restoration and Adaptation Program (https://gbrrestoration.org/). The Reef Restoration and Adaptation Program is funded by the partnership between the Australian Government’s Reef Trust and the Great Barrier Reef Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements We acknowledge and thank the Traditional Owners of the Great Barrier Reef and Far northern for granting free, prior and informed consent (FPIC) to enter and conduct this research on Traditional Sea Country; and thank the Indigenous Partnerships Team at Australian Institute of Marine Science for their knowledge and time in facilitating FPIC with the relevant Traditional Owner groups. All work was conducted under GBRMPA permit number G21/44774.1. We thank the many EcoRRAP personnel who contributed to the field work logistics; Peran Bray, Anna Cresswell, Anthea Donovan and Grant Milton who contributed to some of the data collection; and Barbara Robson who helped with the extractions of eReefs data. Author contributions Conceptualisation: CD, PJM; Design: CD, KF , RF; Data collection: CD, MAN, KF, SN, MO, KS; Data analysis: CD, MAN; Writing – original draft: CD; Writing – review and editing: MAN, PJM, MO, SN, KF, RF. Funding acquisition: CD, KF, PJM. References Allen, C. 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Post-settlement growth and mortality rates of juvenile scleractinian corals in Moorea, French Polynesia versus Trunk Reef, Australia. Marine Ecology Progress Series 488 :157-170. Underwood, A. J. 1997. Experiments in ecology: their logistical design and interpretation using analysis of variance. Cambridge University Press, Cambridge. Waters, C., P. L. Harrison, M. Gouezo, A. Severati, and C. Doropoulos. 2025. Early-stage coral settlement and survivorship using wild larval assemblages on coral seeding devices for reef restoration. Restoration Ecology. Wei, T., V. Simko, M. Levy, Y. Xie, Y. Jin, and J. Zemla. 2017. Package ‘corrplot’. Statistician 56 :316-324. Wickham, H. 2009. ggplot2: elegant graphics for data analysis. Springer Science & Business Media. Additional Declarations No competing interests reported. Supplementary Files DoropoulosGBRjuvcoraldemographicsSI.docx Cite Share Download PDF Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Coral Reefs → Version 1 posted Editorial decision: Accepted 25 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 26 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 06 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7060133","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493252498,"identity":"5f11db60-c06b-431d-a985-a78e7bba3e33","order_by":0,"name":"Christopher Doropoulos","email":"data:image/png;base64,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","orcid":"","institution":"CSIRO Environment","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Doropoulos","suffix":""},{"id":493252499,"identity":"507ad348-4041-4b79-a6f6-5385917b8465","order_by":1,"name":"Mariana Alvarez-Noriega","email":"","orcid":"","institution":"Australian Institute of Marine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Alvarez-Noriega","suffix":""},{"id":493252500,"identity":"eba54e2d-89ea-4f0c-8d21-f4a0ca0b496a","order_by":2,"name":"Katharina Fabricius","email":"","orcid":"","institution":"Australian Institute of Marine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Fabricius","suffix":""},{"id":493252504,"identity":"c238a7e5-a9ab-4abb-abe8-10cd4cd1dad8","order_by":3,"name":"Renata Ferrari","email":"","orcid":"","institution":"Australian Institute of Marine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Renata","middleName":"","lastName":"Ferrari","suffix":""},{"id":493252506,"identity":"fc040f40-d5d9-4273-9ffc-faf2690614bd","order_by":4,"name":"Peter J Mumby","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"J","lastName":"Mumby","suffix":""},{"id":493252507,"identity":"b1081d99-8a51-40be-90b5-9a92231ada95","order_by":5,"name":"Sam HC Noonan","email":"","orcid":"","institution":"Australian Institute of Marine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sam","middleName":"HC","lastName":"Noonan","suffix":""},{"id":493252508,"identity":"13f565ca-530d-4d2a-b06b-a2a359b3093d","order_by":6,"name":"Melanie Orr","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Orr","suffix":""},{"id":493252509,"identity":"f06d05b0-24f5-4f4a-8e29-c63e14d71fa9","order_by":7,"name":"Kinam Salee","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Kinam","middleName":"","lastName":"Salee","suffix":""}],"badges":[],"createdAt":"2025-07-07 00:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7060133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7060133/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00338-025-02742-6","type":"published","date":"2025-09-15T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88073548,"identity":"bf0d4b89-355b-4979-bea3-e73d6ce1005d","added_by":"auto","created_at":"2025-08-01 06:30:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148899,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study reefs across the Great Barrier Reef and Torres Strait with the 2022 annual mean temperature gradient across the region. Letters in parentheses following each reef or reef group name indicate the six reef clusters, with 2-3 reefs nested in each cluster, 2-6 sites nested in each reef, and 1-6 (mean = 4) fixed quadrats nested within each site. SO = southern offshore; SI = southern inshore; CO = central offshore; CI = central inshore; NO = northern offshore; FN = far northern.\u003c/p\u003e","description":"","filename":"Fig1singlePanel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/cb3de2281d4de3bdc1cde107.jpg"},{"id":88073551,"identity":"19ccfac7-014f-4c7b-a677-8ac8cc55ba6d","added_by":"auto","created_at":"2025-08-01 06:30:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1086805,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Principal coordinate ordination (PCO) of the environmental variables for the study sites, with individual regressions of (b) temperature, (c) water clarity, and (c) chlorophyll a across latitudes. For all plots, each data point represents an annual mean of daily values for a site nested within a cluster. For the PCO (a), the circles surrounding the groupings represent a resemblance of \u0026gt;10%, the vector plot shows individual variables with Pearson correlations \u0026gt;0.9, filled symbols are for 2021-22 and hollow symbols are for 2022-23. Please note that PCO1 is plotted as the ‘y-axis’, and PCO2 as the ‘x-axis’. Model fits in b-d represent mean trends (solid blue lines) with standard error of the fitted values (grey shading); b and d were fit with second-order polynomial models, and c was fit with a linear model.\u003c/p\u003e","description":"","filename":"Fig2plotPCOScatter.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/8cc8aaf9c7f29adb4926b25c.jpg"},{"id":88073840,"identity":"77fa21d3-8563-484a-aa9a-abcabb5dbfe1","added_by":"auto","created_at":"2025-08-01 06:38:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2758344,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean (a) recruitment, (b) mortality, and (c) turnover rates of juvenile corals (≤40 mm maximum diameter) for each major taxa across the reef clusters. All rates are calculated per fixed quadrat. Error bars represent standard error.\u003c/p\u003e","description":"","filename":"Fig3panel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/0d9afb622778fff0cf6bbc54.jpg"},{"id":88073836,"identity":"bb33d324-ecb1-4343-b595-10c5392df91a","added_by":"auto","created_at":"2025-08-01 06:38:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4951585,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual growth rates (mm yr\u003csup\u003e-1\u003c/sup\u003e) of juvenile corals as a function of (a) initial size for each major taxon, and independent of initial size for (b) taxon across the reef clusters and (c) current speed partial effects plot across the reef clusters. Panels show the outcomes of the mixed effects models that tested the effects of (i) reef cluster, growth form, and initial size on growth and (ii) 6-8 environmental covariates on growth with the effect of reef cluster blocked as a random effect. Large circles represent the mean, error bars represent standard error, and small translucent circles represent individual data points. Linear model fits in a and c represent mean trends (solid blue lines) with standard error of the fitted values (grey shading). Note: Southern inshore and \u003cem\u003eMontipora \u003c/em\u003edata (dashed blue line) were not included in the statistical models due to low replication, but data are shown on the plots to indicate trends. \u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig43panel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/52fa9d38976db5c86318497c.jpg"},{"id":88073570,"identity":"4e75b66b-3202-4836-b978-dd07111f55e4","added_by":"auto","created_at":"2025-08-01 06:30:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4431578,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual survival probabilities of juvenile corals as a function of (a) initial coral colony size, (b) initial size across reef clusters, and (c) seawater temperature for each major taxon across the reef clusters. Panels show the outcomes of the mixed effects models that tested the effects of (i) reef cluster, growth form, and initial size on survival and (ii) 6-8 environmental covariates on survival with the effect of reef cluster blocked as a random effect. Model fits represent mean trends (solid blue lines) with standard error of the fitted values (grey shading), and small circles represent individual data points. Note: Southern inshore and \u003cem\u003eMontipora \u003c/em\u003edata (dashed blue line) were not included in the statistical models due to low replication, but data are shown on the plots to indicate trends\u003c/p\u003e","description":"","filename":"Fig5panel.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/170501f1fb0aabf2f4ae0c33.jpg"},{"id":91890049,"identity":"1967a101-8e94-47c2-b237-e1c53d4a7bef","added_by":"auto","created_at":"2025-09-22 16:03:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5371334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/45a016a2-9435-4300-83f5-d703acbebd94.pdf"},{"id":88073549,"identity":"e747f411-e665-483d-a755-8484069436ed","added_by":"auto","created_at":"2025-08-01 06:30:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1721206,"visible":true,"origin":"","legend":"","description":"","filename":"DoropoulosGBRjuvcoraldemographicsSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7060133/v1/724f3c8201cb98358de99478.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of environmental gradients on juvenile coral demography across the Great Barrier Reef and Torres Strait","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnvironmental filtering across gradients plays a crucial role in shaping biodiversity, community assemblages and the vital rates of organisms within natural ecosystems (Hutchings 2021). Gradients in temperature, salinity, and nutrient availability create varying conditions that influence growth, survival, recruitment, and turnover rates of habitat-forming organisms in terrestrial and marine environments. In terrestrial ecosystems, temperature and moisture gradients significantly impact plant growth and survival (Breshears et al. 2005, Allen et al. 2010). Species adapted to warmer, drier conditions may exhibit higher growth rates under warming conditions, while those adapted to cooler, wetter environments could experience stress, leading to decreased survival and recruitment under climate change (Chapin et al. 2002). Similarly, in coastal marine systems, environmental gradients strongly influence the distribution and performance of foundational species like seagrasses, mangroves, and sessile invertebrates (Menge and Sutherland 1987, Krauss et al. 2008, Chefaoui et al. 2016). For example, intertidal oyster reefs exhibit high growth and recruitment in estuarine zones with moderate salinity and stable temperatures, while extreme shifts in salinity due to freshwater influx or temperature fluctuations can cause stress, reducing filtration capacity and leading to mass mortality events (Beck et al. 2011). Such habitat-building organisms play a crucial role in maintaining biodiversity, and their responses to environmental gradients can have cascading effects on coastal ecosystems.\u003c/p\u003e\n\u003cp\u003eDifferent communities exhibit diverse responses to environmental gradients due to their unique traits and life histories (McGill et al. 2006). In a study of Californian woody plant species, Cornwell and Ackerly (2009) found that drought tolerance and leaf traits strongly shaped species distributions along moisture gradients. Species with conservative traits, such as thicker leaves and slower growth rates, were more prevalent in drier habitats, whereas species with more acquisitive traits such as vertical partitioning of light dominated wetter environments, demonstrating how functional traits can mediate ecological responses to environmental variability. In marine environments, corals exhibit a range of thermal optima (\u0026Aacute;lvarez-Noriega et al. 2023) and reproductive strategies (Baird et al. 2009), resulting in differing responses to acute (Baird and Marshall 2002) and chronic (Edmunds 2024) temperature changes. These variations highlight the importance of functional diversity within communities, as species with complementary traits can enhance resilience and adaptability in the face of environmental change (D\u0026iacute;az et al. 2007). Understanding the role of functional traits in environmental filtering is essential for predicting how coral assemblages may respond to ongoing climate change.\u003c/p\u003e\n\u003cp\u003eDespite the importance of environmental gradients in driving vital rates of sessile organisms (e.g., Connolly et al. 2001, Montero‐Serra et al. 2018), juvenile coral assemblages remain understudied. For example, recent demographic data spanning six years for 11 coral species captured less than 1% of the sampled population in terms of juvenile coral (\u0026le;4 cm maximum diameter) growth and survival from a dataset of over 450 tagged colonies (Madin et al. 2023a). It is well known that coral recruitment, growth, and survival are critical to coral population growth and reef recovery (e.g., Caley et al. 1996, Connell et al. 1997, Gilmour et al. 2013, Doropoulos et al. 2015, Graham et al. 2015, Gouezo et al. 2019, Doropoulos et al. 2022a, Edmunds 2023), yet there is a significant gap in knowledge regarding how juvenile corals respond to varying environmental conditions (but see Bak and Engel 1979, Edmunds et al. 2004, Nozawa et al. 2021, Doropoulos et al. 2022b). Investigating environmental filtering and its effect on demographic rates in coral early life stages is crucial for effective coral reef conservation and management, because the prominence of allometric scaling means that the biology of juvenile corals cannot be linearly scaled from larger corals (Burgess et al. 2017, Edmunds 2023). Understanding the nuanced interactions between environmental gradients and coral vital rates can be utilised to parameterise life table response experiments for comparing the effectiveness of management interventions (Hughes et al. 2023). By focusing on how environmental drivers influence the vital rates of diverse juvenile coral assemblages, we can gain insights into the complex relationships that govern community dynamics and their future under changing environmental conditions.\u003c/p\u003e\n\u003cp\u003eHere, we tested how the vital rates of juvenile corals vary across the Great Barrier Reef (GBR) and Torres Strait (TS). Data were collected from 2021-23, coinciding with (i) a period of relatively high coral cover observed since continuous monitoring began in 1985 (Emslie et al. 2024); and (ii) during a time where no major disturbances occurred in 2021-22 but a mild mass bleaching event occurred in the central GBR in 2022-23. A high resolution study shows the effects of the bleaching event were highly variable across 13 adult taxa in the central GBR in 2022 (\u0026Aacute;lvarez-Noriega et al. 2025), and there were no reductions in coral cover observed across any region (Australian Institute of Marine Sciences 2023). Thus, the event was relatively minor compared to previous, recent bleaching events on the GBR (Hughes et al. 2018). Utilising this period with only minor thermal stress and no other acute events (e.g. floods, cyclones, predator outbreaks) provides baseline values of vital rates. They may serve as comparison values for against those observed during interventions such as the deployment of corals with higher thermal tolerances, growth and survival of juvenile corals via selective breeding (e.g., Quigley and van Oppen 2022, Humanes et al. 2024) or producing corals on seeding devices for mass deployment (e.g., Randall et al. 2021, Waters et al. 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe initially tagged \u0026gt;1560 individual juvenile coral colonies (\u0026le;4 cm in maximum diameter) and tracked their size-specific growth and survival, as well as the recruitment of new individuals to establish background rates of turnover. Given the previously observed positive influence of temperature on juvenile coral growth (e.g., Nozawa et al. 2021, Doropoulos et al. 2022b), size on juvenile coral survival (e.g., Doropoulos et al. 2012, Doropoulos et al. 2016), and differences in life-history strategies across major coral taxa (e.g., Darling et al. 2012, Madin et al. 2023b), we hypothesized that: (i) juvenile coral growth rates would increase with increasing temperature, (ii) survival rates would increase with increasing size, and (iii) growth and survival rates would trade-off across major coral groups due to functional trait trade-offs, such that the fastest growers would have the highest mortality. In contrast, the influence of latitudinally driven temperature clines on the recruitment of corals into natural populations is largely unknown. Studies covering the latitudinal cline of the GBR utilising settlement tiles show conflicting trends (Hughes et al. 2002, Drake et al. 2025), and recent work shows that vital rates derived from settlement tiles represent population dynamics on the reef substrate poorly (Gouezo et al. 2025). Thus, we hypothesized that (iv) recruitment rates of new juvenile corals onto the reef would not differ across environmental gradients.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo capture a large gradient in environmental drivers, particularly gradients in long-term annual mean temperature, this study spanned 14\u0026deg; of latitude from the southern to northern GBR and Torres Strait (Figure 1). The southernmost reef was Lady Musgrave (latitude 23.9\u0026deg;S), while the northernmost reef was Masig Island (Masig = 9.45\u0026deg;S). Across this latitudinal gradient, reefs were situated across the shelf from inshore to offshore, and sites within reefs were situated in a variety of wave exposures on nominal \u0026lsquo;fore-reefs\u0026rsquo;, \u0026lsquo;back-reefs\u0026rsquo;, and \u0026lsquo;lagoonal reefs\u0026rsquo;. In total, there were six \u0026lsquo;reef clusters\u0026rsquo;, with 2-3 reefs nested in each cluster, and 2-6 sites nested in each reef, with a total of 56 sites and 253-263 quadrats (Table S1). Annual sampling was conducted three times from January to May: sites were established in 2021, then resurveyed in 2022 and 2023.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBenthic surveys\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the beginning of the study, permanent quadrats (0.5 x 0.5 m) were placed around \u0026ge;1 juvenile coral, standardised to a 5 m depth profile, with an average of n = 4 fixed quadrats per site (range = 1-6 per site; Table S1). What defines a \u0026lsquo;juvenile coral\u0026rsquo; is partly operational and partly biological. Biologically, immature colonies are juveniles, and the size at which corals reach reproductive maturity is largely size-based and differs across taxa (e.g., \u0026Aacute;lvarez‐Noriega et al. 2016). Thus, operational size-based definitions are often utilised, which is typically \u0026lt;40 mm or \u0026lt;50 mm diameter, depending on the study and region (Roth and Knowlton 2009). In this study, we utilise a maximum diameter of 40 mm to define the size limit of juvenile corals, particularly because some coral taxa that brood their progeny reach maturity at sizes as small as 40-50 mm (e.g., Rinkevich and Loya 1979, Kojis 1986).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach permanent quadrat was demarcated by a stainless-steel stake in up to three corners, with a numbered tag in one corner for orientation and quadrat identification. Once quadrats were established, all juvenile corals within each quadrat had their location mapped, size measured using callipers (maximum diameter to closest mm) and identified to the lowest taxon possible (typically genus). An image of the entire quadrat was taken, plus four detailed images of each quadrant (0.25 x 0.25 m) within each quadrat, and close-ups of individual juveniles to confirm taxonomic identity. Post-processing included transcribing the initial hand-drawn maps onto the images of each quadrat in preparation for the next census period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuadrats were resurveyed every 12 months. Resurveys included \u003cem\u003ein situ\u003c/em\u003e measuring all previously mapped corals for changes in size or mortality, any new colonies were added to the maps (i.e., recruitment), and associated images were taken. Data extractions then allowed for quantifying rates of individual juvenile coral colony growth, mortality, and recruitment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData cleaning involved the removal of 417 colonies: those that were \u0026gt;40 mm at the beginning of the study, chimeras that formed during the study, small fragments created by fission of colonies, and any free-living scleractinian corals (\u003cem\u003eFungia, Heliofungia\u003c/em\u003e). Across the initial 1,560 individual juvenile colonies demarcated, 39 genera were present, dominated by \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003e(36%) and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003e(20%), with the remaining genera composing \u0026le;5% each. Therefore, an initial total of 1,299 juvenile colonies from five distinct groups of corals were used for the study: \u003cem\u003eMontipora\u0026nbsp;\u003c/em\u003e(n = 67 at t\u003csub\u003e0\u003c/sub\u003e)\u003cem\u003e, Acropora\u0026nbsp;\u003c/em\u003e(n = 568 at t\u003csub\u003e0\u003c/sub\u003e)\u003cem\u003e,\u0026nbsp;\u003c/em\u003eMerulinidae (n = 236 at t\u003csub\u003e0\u003c/sub\u003e, included \u003cem\u003eDipsastraea, Favites\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Goniastrea\u003c/em\u003e), Pocilloporidae (n = 123 at t\u003csub\u003e0\u003c/sub\u003e, included \u003cem\u003ePocillopora, Seriatopora\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Stylophora\u003c/em\u003e), and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003e(n = 305 at t\u003csub\u003e0\u003c/sub\u003e)\u003cem\u003e.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall rates of recruitment, mortality, and turnover were quantified within each plot. As with definitions of \u0026lsquo;juvenile corals\u0026rsquo;, definitions of recruitment are poorly defined and often operational (Edmunds 2023). Here, we define recruitment as the first detectable observation of a new juvenile coral colony (\u0026le;40 mm) between surveys per unit area (i.e., 2021 to 2022 or 2022 to 2023) (Keough and Downes 1982, Harrison and Wallace 1990, Caley et al. 1996), thus it combines both settlement and early post-settlement survival prior to census (Doropoulos et al. 2016). It is possible that some of the individual colonies classified as new \u0026lsquo;recruitment\u0026rsquo; between time periods may have already been present in a plot but located in cryptic microhabitats (Doropoulos et al. 2022a). Mortality rates represent whole colony mortality of juveniles that were present at the time of a census but not present at the proceeding census period. Given the yearly time periods between censuses, sources of mortality could not be estimated. Turnover was then calculated by subtracting the number of individual colonies dying from the number of individual colonies recruiting within each plot. All recruitment, mortality, and turnover counts were standardized by quadrat area to obtain rates m⁻\u0026sup2;. These were then calculated as annual rates and summarized as mean values per taxa and reef cluster by pooling across all available quadrats.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiophysical benthic parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditional data on sedimentation, reef slope, and turf height were collected \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eat every site by a single surveyor (S. N.) in 2022. Two proxies were used for sedimentation: (i) sediment weight (average g m\u003csup\u003e-2\u003c/sup\u003e) and (ii) a visual rating of sediment deposition (0 to 3). Sediment weight was derived from the amount of sediment accumulated on five replicate 11.4 x 11.4 cm PVC tiles that were deployed horizontally at each site from 2021-2023. Upon collection, individual tiles were carefully placed into ziplock bags underwater, and the accumulated sediment was then resuspended and isolated by filtration through pre-weighed glass fibre filters (Whatman GF/F 47mm diameter, nominal pore size = 0.7 \u0026micro;m) and a vacuum manifold. The filters were rinsed with fresh water, dried at 60\u0026deg;C for 48 hours, weighed to the nearest 0.001 g, and sediment dry weight was calculated by subtracting the initial filter weight from the final filter weight. Sedimentation levels per m\u003csup\u003e2\u003c/sup\u003e were calculated by scaling up the average sediment weight from the five tiles per site. The visual rating of sediment deposition aimed to capture the site level sedimentation on the reef using an ordinal scale of 0-3, with: 0 = none, 1 = little, 2 = moderate, 3 = lots (cannot be resuspended by fanning). Reef slope was quantified as a visual estimate of the steepness of the reef slope in degrees (i.e., 0\u0026deg; = flat, 90\u0026deg; = vertical). Turf height was calculated as the average of triplicate turf height measurements (mm) taken at three to four points per site using callipers in regular intervals along a transect line. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEnvironmental water parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor each reef site and sampling period (2021-2022 and 2022-2023), key environmental water parameters were extracted from eReefs hydrodynamic (GBR1 v2.0) and biogeochemical (GBR4) models (Steven et al. 2019). eReefs is an information system with publicly available hydrodynamic and biogeochemical three-dimensional predictions for the GBR. Hydrodynamic predictions are available at a ~1 x 1 km spatial resolution, while biogeochemical predictions are available at a ~4 x 4 km spatial resolution. Daily data at 5 m depth were extracted for each sampling period and then averaged for each interval. The extracted 34 variables included temperature and salinity, as well as water quality metrics such as chlorophyll a concentration, dissolved organic and inorganic compounds, and aragonite saturation, among others (Table S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis - overview\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData analysis utilised a combination of multivariate and univariate statistical techniques to investigate spatial and temporal patterns in environmental conditions, juvenile coral assemblages, and vital rates. Multivariate analyses were conducted in PRIMER with PERMANOVA+ (Clarke and Gorley 2006, Anderson et al. 2008), using Euclidean and Bray-Curtis dissimilarity matrices to assess environmental and community structure, respectively. Principal coordinates ordination (PCO), hierarchical clustering, and PERMDISP tests were used to visualise and test group differences. In all PERMANOVA main effect and pair-wise tests, the \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues generated by 999 permutations were used when the number of unique permutations were \u0026gt;20%, else the Monte Carlo asymptotic \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue was used (Anderson et al. 2008). Univariate analyses were conducted in R (R Development Core Team 2024) and visualised using the ggplot2 package (Wickham 2009). Generalised linear mixed-effects models (GLMMs) were used to model ecological responses of continuous, count-based, and proportional data types, with model fitting conducted using the glmmTMB package (Brooks et al. 2017). Model selection was guided by Akaike information criterion (AIC) and R\u0026sup2; values using the MuMIn package (Barton and Barton 2019), with residual diagnostics assessed using DHARMa (Hartig 2018). Analyses of variance (ANOVA; Type II sum of squares) were conducted using the car package (Fox et al. 2012), and significant effects explored using emmeans for post-hoc comparisons (Lenth 2021). Where applicable, multicollinearity was assessed using correlation matrices generated with corrplot (Wei et al. 2017). Conservative alpha levels (\u0026alpha; \u0026le; 0.01) were applied in cases of non-homogeneous dispersion or overdispersed residuals for multivariate and univariate analyses (Underwood 1997).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis - detailed\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn initial investigation assessed the spatial and temporal structure of environmental water quality parameters. Yearly averaged values for 34 environmental variables were normalised, and a Euclidean dissimilarity matrix was used to visualise patterns via principal coordinates ordination (PCO), grouped by reef cluster and time period. Vectors representing highly correlated variables (Pearson\u0026rsquo;s r \u0026gt; 0.9) were overlaid on the PCO, and hierarchical clustering was used to examine group structure. PERMANOVA tested for differences using a nested design: time period (2 levels) and reef cluster (6 levels) as fixed orthogonal factors, with reefs nested within clusters and sites nested within reefs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree prominent environmental drivers identified from the PCO \u0026ndash; temperature, vertical light attenuation, and chlorophyll \u003cem\u003ea\u003c/em\u003e \u0026ndash; were further analysed across latitudes using GLMMs. A series of additive and interactive models with linear and quadratic terms for latitude were fitted, and the best model selected using AIC and R\u0026sup2;. Model diagnostics confirmed assumptions of normality and dispersion.\u003c/p\u003e\n\u003cp\u003eTo understand whether there was any spatial structure of coral assemblages or relative abundances across the study design, abundances across five major coral groups and six reef clusters were converted to m⁻\u0026sup2; for the initial time point only \u0026ndash; i.e., 2021 \u0026ndash; when the quadrats were installed. To investigate assemblage structure, juvenile coral community data were log(x+1) transformed and PERMANOVA tested for spatial differences using the same nested design as for the environmental variables. Relative abundance patterns across reef clusters were qualitatively investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnnual recruitment and mortality rates m\u003csup\u003e-2\u003c/sup\u003e were analysed with GLMMs using a negative binomial distribution with zero-inflation following a model selection process. Initial model selection compared the AIC values and residuals using bootstrapping of models with Poisson and negative binomial distributions for recruitment and mortality rates, respectively, with and without zero-inflation terms. Fixed factors included coral taxa (5 levels) and reef cluster (6 levels), with time period included as a random effect to account for temporal autocorrelation, and sites nested within reefs and reef clusters to account for spatial autocorrelation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine how environmental gradients influenced recruitment and mortality, multiple regression models were fitted using six dominant, uncorrelated environmental variables \u0026ndash; i.e., temperature, water clarity, current speed, visual sediment deposition, reef slope, turf height. Latitude and chlorophyll \u003cem\u003ea\u003c/em\u003e were excluded due to high collinearity with temperature (\u0026gt;0.85). Reef cluster and year were included as random effects to include the spatial and temporal autocorrelation structure when predicting the effects of environmental drivers on recruitment and mortality. Two sites lacked environmental data so were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003eSize-based growth rates and survival probabilities of individual juvenile coral colonies were analysed using GLMMs. Data from the Keppel Islands and \u003cem\u003eMontipora\u003c/em\u003e were excluded due to model convergence issues resulting from data replication deficiencies. Initial models included colony size at t\u003csub\u003e0\u003c/sub\u003e of each transition, coral taxa (4 levels), reef cluster (5 levels), and their interactions as fixed effects, with replicate reefs, sites, and time periods included as random terms to account for spatial and temporal autocorrelation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequent models included environmental predictors, again with reef cluster and year included as random effects to include the spatial and temporal autocorrelation structure when predicting the effects of environmental drivers on growth and survival. Growth models used a Gaussian distribution; survival models used a binomial distribution. Size-based growth rates were calculated for individual juvenile coral colonies as linear extension per year (mm y\u003csup\u003e-1\u003c/sup\u003e), and growth data exceeding +100 (n = 6) or -100 (n = 3) mm y\u003csup\u003e-1\u003c/sup\u003e was removed to satisfy a normal distribution of the data. Residuals indicated overdispersion and heterogeneity, therefore conservative \u0026alpha; values were applied. Significant effects were further explored with pairwise comparisons.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eEnvironmental water quality parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOrdination (PCO) of the annual mean environmental water quality parameters derived from eReefs for each site showed three major groupings at \u0026gt;10% similarity: (i) southern inshore, (ii) southern offshore, and (iii) central and northern inshore and offshore and far northern reefs (Figure 2a). Overall, the first three axes explained 83% of the variation (PCO1 = 46%, PCO2 = 21%, PCO3 = 16%). Temperature was highly correlated (\u0026gt;0.9) with the PCO1 axis; the PCO2 axis was highly correlated with \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e; fine sediment, dust, and vertical attenuation (Kd 490) were highly correlated the southern inshore reefs, while chlorophyll \u003cem\u003ea\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003epH, dissolved inorganic nitrogen, and nitrate were highly correlated with the southern offshore reefs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA highly significant time period by reef cluster interaction was observed (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Pairwise comparisons between 2021\u0026ndash;22 and 2022\u0026ndash;23 revealed significant differences in environmental water quality parameters within the southern inshore, central offshore, and far northern clusters (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.017). Within 2021\u0026ndash;22 and 2022\u0026ndash;23, pairwise comparisons among reef clusters revealed widespread and significant differences in environmental water quality parameters (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.005 in most comparisons), particularly for southern inshore and southern offshore reefs, indicating strong spatial variation across the GBR and TS.\u003c/p\u003e\n\u003cp\u003eIncreasing mean annual temperature significantly correlated with decreasing latitude (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, R\u003csup\u003e2\u003c/sup\u003e = 0.96, Figure 2b), being cooler in the southern offshore (24.1-24.4 \u0026deg;C) and southern inshore (24.9-25.4 \u0026deg;C) reefs, and warmer in the central offshore and central inshore (26.2-27.5 \u0026deg;C), northern offshore (27.5-28.9 \u0026deg;C) and far northern reefs (27.8-28.4 \u0026deg;C). Chlorophyll \u003cem\u003ea\u0026nbsp;\u003c/em\u003ewas correlated with latitude (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.04, R\u003csup\u003e2\u003c/sup\u003e = 0.34, Figure 2d), being highest in the southern offshore reefs (1.10-1.55 mg Chl m\u003csup\u003e-3\u003c/sup\u003e) and much lower elsewhere (\u0026lt;0.6 mg Chl m\u003csup\u003e-3\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJuvenile coral assemblage composition and abundances\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJuvenile coral assemblage structure was similar across all reef clusters at the beginning of the study in 2021 (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.161; Figure S2a). There were some groupings in the ordination, though these were not related to reef clusters, reefs nested within clusters, nor sites nested within reefs. The correlation vector shows that \u003cem\u003eAcropora\u003c/em\u003e, Merulinidae, and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003edrove the spread of the data. Despite no differences in assemblage composition among the reef clusters, there was a shift in the relative abundances of juvenile corals from the major taxa across the reef clusters. At the beginning of the study, high relative abundances of \u003cem\u003eAcropora\u003c/em\u003e (43-78% of community) were apparent in the southern and central reefs compared to northern offshore and far northern reefs (26-34%). Merulinidae and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003erelative abundances were slightly higher in northern offshore and far northern reefs on average (23-36%) compared to central and southern reefs (4-32%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJuvenile coral recruitment, mortality, and turnover\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatterns of coral recruitment were distinct among reef clusters and taxa (interaction term, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; Figure 3a). \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003erecruitment was highest overall (average 5.5 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e), particularly in the southern offshore (8.3 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e) and central offshore (7.4 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e) reefs, followed by the central inshore reefs (5.3 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e). All other reef clusters had significantly lower \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003erecruitment rates (\u0026lt;4.0 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e). \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003erecruitment averaged 3.6 ind. m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e and was highest at southern offshore and northern offshore reefs (4.9-5.1 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e), significantly higher at southern offshore reefs compared to the far northern reefs (2.5 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e). \u003cem\u003eMontipora,\u0026nbsp;\u003c/em\u003eMerulinidae, and Pocilloporidae recruitment averaged 2.3-2.9 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e across reef clusters. Multiple regression analysis showed two interactions of annual recruitment rates varying across taxa x temperature (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.031) and taxa x sedimentation (visible; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.028). \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003erecruitment demonstrated a clear negative relationship with both temperature and sedimentation, whereas the other taxa had slightly negative, neutral, or slightly positive relationships (Figure S3).\u003c/p\u003e\n\u003cp\u003eCoral mortality rates were also distinct among reef clusters and taxa (interaction term, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004; Figure 3b). Similar to recruitment, \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003emortality was highest overall (average 4.7 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e), but the highest mortality rates were found in the central offshore reefs (6.9 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e) and lowest in the northern offshore reefs (2.3 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e), with significantly lower rates in the northern offshore reefs compared to all other reef clusters. Mortality rates of all other taxa ranged from 1.4 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e in \u003cem\u003eMontipora\u0026nbsp;\u003c/em\u003eto 2.5 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e in \u003cem\u003ePorites,\u003c/em\u003e with no significant differences across clusters\u003cem\u003e.\u0026nbsp;\u003c/em\u003eMultiple regression analysis showed two interactions of annual mortality rates varying across taxa x turf height (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.023) and taxa x sedimentation (visible; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004). \u003cem\u003eAcropora\u003c/em\u003e mortality had slightly positive and negative relationships with turf height and sedimentation, respectively, whereas the other taxa had slightly negative, neutral, or positive relationships (Figure S4).\u003c/p\u003e\n\u003cp\u003eNet turnover rates (i.e., recruitment minus mortality) averaged 3.1 juvenile colonies m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e when pooled across all taxa and reef clusters. When assessed separately by taxon and cluster, turnover averaged 0.7 to 1.1 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e, indicating a low but typically positive net addition of new individuals to the community annually for each reef cluster; no cluster had significant losses (Figure 3c). \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003enet\u003cem\u003e\u0026nbsp;\u003c/em\u003eturnover rates in the offshore southern reefs and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003enet turnover rates in the northern offshore reefs were notably high, averaging 3.8 and 3.5 m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e, respectively. Although the difference across reef clusters was statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.03), the effects were relatively weak, and differences across taxa (\u003cem\u003ep\u003c/em\u003e = 0.91) and their interaction (\u003cem\u003ep\u003c/em\u003e = 0.21) were not statistically significant due to the high variability observed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJuvenile coral growth rates\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJuvenile coral growth rates varied across taxa x initial size (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.002) and across taxa x reef cluster (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004). Relationships between growth and size (Figure 4a) were positive and strongest in Pocilloporidae, where growth increased from 11 mm yr⁻\u0026sup1; at 3 mm maximum diameter to 28 mm yr⁻\u0026sup1; at 40 mm maximum diameter. \u003cem\u003eAcropora\u003c/em\u003e and \u003cem\u003eMontipora\u003c/em\u003e also showed positive relationships, with growth increasing from 13 and 9 mm yr⁻\u0026sup1; at 2 and 5 mm maximum diameter, respectively, to 18 and 14 mm yr⁻\u0026sup1; at 40 mm maximum diameter. Growth in Merulinidae was largely size-independent, ranging from 7 mm yr⁻\u0026sup1; at 4 mm maximum diameter to 9 mm yr⁻\u0026sup1; at 40 mm maximum diameter. \u003cem\u003ePorites\u003c/em\u003e exhibited a negative relationship, with growth decreasing from 11 mm yr⁻\u0026sup1; at 4 mm maximum diameter to 6 mm yr⁻\u0026sup1; at 40 mm maximum diameter.\u003c/p\u003e\n\u003cp\u003eOverall growth rates independent of initial size for \u003cem\u003eAcropora\u003c/em\u003e (Figure 4b) were lower in the central inshore (10 mm yr\u003csup\u003e-1\u003c/sup\u003e) than northern offshore reefs (21 mm yr\u003csup\u003e-1\u003c/sup\u003e), and southern offshore and far northern reefs (18 mm yr\u003csup\u003e-1\u003c/sup\u003e). Faster growth rates for Pocilloporidae were observed in the central inshore reefs (37 mm yr\u003csup\u003e-1\u003c/sup\u003e) compared to the southern offshore reefs (17 mm yr\u003csup\u003e-1\u003c/sup\u003e), while they did not vary for Merulindae (8 mm yr\u003csup\u003e-1\u003c/sup\u003e) or \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003e(9 mm yr\u003csup\u003e-1\u003c/sup\u003e) across all reef clusters. Pocilloporidae (20 mm yr\u003csup\u003e-1\u003c/sup\u003e) and \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003e(16 mm yr\u003csup\u003e-1\u003c/sup\u003e) typically had growth rates \u0026gt;2-times faster than Merulinidae and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003e(8 mm yr\u003csup\u003e-1\u003c/sup\u003e) within each reef cluster, apart from the central inshore reefs where Pocilloporidae growth was faster than all other taxa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the six environmental variables tested (temperature, water clarity, current speed, visual sediment deposition, reef slope, turf height), only current speed had a significant association with the rates of juvenile coral growth (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; Figure 4c). A positive relationship was observed, ranging from 8.7 mm y\u003csup\u003e-1\u003c/sup\u003e at 0.02 m s\u003csup\u003e-1\u003c/sup\u003e in the central inshore reefs to 16.6 mm y\u003csup\u003e-1\u003c/sup\u003e at 0.44 m s\u003csup\u003e-1\u003c/sup\u003e in the far northern reefs. The effect of current speed on juvenile coral growth rates varied among taxa (Figure S5), being strongly positive for \u003cem\u003eAcropora\u003c/em\u003e, slightly positive for Merulinidae and \u003cem\u003ePorites\u003c/em\u003e, and negative for Pocilloporidae, although the interaction was marginally non-significant (\u003cem\u003ep\u003c/em\u003e = 0.052).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJuvenile coral survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJuvenile coral survival probabilities showed a positive linear relationship with increasing size (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) for all taxa (Figure S6a). Mean annual survival increased from 41% at 2 mm, 49% at 10 mm, 60% at 20 mm, to 76% at 40 mm maximum diameter (Figure 5a). There was moderate variability in the relationship between size and survival across locations (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.054). While a positive relationship between size and annual survival was observed in four reef clusters, juvenile corals in the central inshore reefs exhibited size-independent survival, averaging 70% per year (Figure 5b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividual juvenile coral survival also varied across taxa x reef cluster (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; Figure S6b), although no major trends were apparent. \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003esurvival was lower in the far northern reefs (40% annual survival) compared to the southern offshore (70%), central inshore (70%), and northern offshore (64%) reefs; Merulindae survival was lower in the central offshore (62%) compared to the northern offshore (83%) reefs; and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003esurvival was higher in the northern offshore (79%) compared to the southern offshore (47%) reefs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeawater temperature was the only environmental variable that had a significant impact on proportional juvenile coral survival, and this varied among taxa (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Given the extremely high correlation between temperature and latitude, the patterns described also reflect differences in proportional survival across latitudes.\u003cem\u003e\u0026nbsp;Acropora\u0026nbsp;\u003c/em\u003esurvival had a negative response to increasing mean annual temperature; Pocilloporidae and \u003cem\u003eMontipora\u0026nbsp;\u003c/em\u003ehad temperature-independent responses in survival to mean annual temperature; and Merulindae and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003ehad positive responses in survival to increasing mean annual temperature (Figure 5c).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the drivers of change in coral reef ecosystems is essential for predicting their future under increasing environmental stress. Yet despite decades of research, many questions remain about how environmental variability shapes the demographic processes that are critical to reef recovery. While most studies assess the effects of acute drivers on reef dynamics due to their prominent and detectable impacts, recent work has established that chronic variation in temperature can also be a significant long-term driver of change in coral reef communities (Edmunds 2024). In this study, we assessed juvenile coral demographics across the Great Barrier Reef (GBR) and Torres Strait (TS) during a disturbance-free period (2021-2022) and a subsequent period that included a relatively minor bleaching event in the central GBR (2022\u0026ndash;2023), providing critical insights into how environmental gradients influence juvenile coral demographics. We found recruitment of juvenile \u003cem\u003eAcropora\u003c/em\u003e corals was higher at higher latitudes, with \u003cem\u003eAcropora\u003c/em\u003e recruitment declining with increasing temperature and sedimentation, and recruitment of \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003ecorals was higher at higher and lower latitudes, with slight reductions with increasing temperature \u0026ndash; patterns that were not observed in the other three coral groups. A recent study characterising recruitment using settlement tiles at the same study sites also found negative associations of recruit densities with sedimentation, but no latitudinal trends and a negative association with currents (Drake et al. 2025). Mortality in our present showed relatively minor latitudinal differences, although \u003cem\u003eAcropora\u003c/em\u003e mortality was lower in northern offshore reefs. Sedimentation consistently had a negative effect on survival. Combining annual recruitment and survival data revealed net positive juvenile coral yields across most reef clusters, typically around 1 colony m⁻\u0026sup2; yr⁻\u0026sup1;, with especially high gains for \u003cem\u003eAcropora\u003c/em\u003e (3.8 ind. m⁻\u0026sup2; yr⁻\u0026sup1;) in the offshore southern reefs and \u003cem\u003ePorites\u003c/em\u003e (3.5 ind. m⁻\u0026sup2; yr⁻\u0026sup1;) in the northern offshore reefs. Contrary to expectations from studies in other regions (Nozawa et al. 2021, Doropoulos et al. 2022b), we found no correlation between temperature and juvenile coral linear growth rates. However, growth showed positive size-based relationships in some coral taxa, and a consistent positive association with water flow across all groups. Survival also increased with size across all taxa (Doropoulos et al. 2012, Doropoulos et al. 2016), while the effect of temperature on survival varied \u0026ndash; negative for \u003cem\u003eAcropora\u003c/em\u003e, neutral for \u003cem\u003eMontipora\u003c/em\u003e and Pocilloporidae, and positive for Merulinidae and \u003cem\u003ePorites\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, this work underscores the value of baseline demographic data for understanding coral resilience and response mechanisms. It also provides key benchmarks on natural recruitment and survival that can inform and contextualize restoration goals \u0026ndash; such as the target of deploying ~5 corals m⁻\u0026sup2; at restoration sites (Gibbs et al. 2024), aligning with the net turnover rates identified here. While the generally positive turnover rates observed across most reef clusters are encouraging, it is important to consider potential biases that may influence these estimates. For instance, previously mapped juvenile colonies are often easier to relocate and assess for survival than newly settled recruits are to detect for the first time, particularly if they occur in cryptic microhabitats (Doropoulos et al. 2022a). This could lead to underestimation of mortality or overestimation of recruitment, respectively. Nonetheless, the overall patterns \u0026mdash; including strong taxon-specific trends and consistent spatial variation \u0026mdash; suggest that the turnover estimates reflect real demographic processes, even if the absolute values should be interpreted with caution. By identifying drivers of coral dynamics and highlighting positive size-based growth relationships, our findings provide a foundation for assessing future interventions such as selective breeding to enhance thermal tolerance (Quigley and van Oppen 2022, Humanes et al. 2024), and the implementation of size-informed outplanting strategies using common, widely distributed coral species (Ladd et al. 2018, McLeod et al. 2022). These strategies should ideally incorporate mixed-species and functional group configurations to optimize ecological function and restoration success across the GBR and broader Indo-Pacific.\u003c/p\u003e\n\u003cp\u003eContrary to expectation, one of the major findings of our study is that juvenile coral growth rates did not increase with temperature. Despite a strong correlation between temperature and latitude, with mean annual temperatures ranging from 24.0 to 28.5\u0026deg;C, the 4.5\u0026deg;C difference may have been too narrow to elicit measurable growth differences, particularly when compared to other studies with broader thermal gradients. For instance, Nozawa et al. (2021) reported a positive correlation between annual growth rates and average seawater temperature across 16\u0026deg; of latitude in the West and South Pacific, where temperatures varied from 20.5 to 29.7\u0026deg;C. Similarly, Doropoulos et al. (2022b) found a unimodal relationship between juvenile coral growth and temperature in the Exmouth Gulf and Ningaloo Reef, spanning a temperature range of 20.5 to 28.1\u0026deg;C. An additional consideration is the potential masking effect of taxonomic diversity within our morpho-taxa groupings. Our classifications combined multiple species into five major groups (\u003cem\u003eAcropora\u003c/em\u003e, \u003cem\u003eMontipora\u003c/em\u003e, Pocilloporidae, Merulinidae, \u003cem\u003ePorites\u003c/em\u003e), which may obscure species-specific responses to temperature in diverse coral communities where species within genera represent varying ecological traits and where cryptic taxa are common (Bongaerts et al. 2021, Riginos et al. 2024, Ricardo et al. 2025). However, both Nozawa et al. (2021) and Doropoulos et al. (2022b) used similarly broad taxonomic groupings and still detected temperature-related growth patterns, suggesting that species diversity alone is unlikely to fully explain our findings. An alternative explanation is that juvenile corals are locally adapted to their regional thermal regimes, with adaptation potentially offsetting expected temperature-growth responses across latitudes. Dispersal distances are typically lower than the spatial scale of temperature gradients, enabling localized adaptation to override uniform physiological responses to warming (Mumby et al. 2011). This may also help explain observations such as \u003cem\u003ePorites\u003c/em\u003e extension rates being highly location-specific and responsive to long-term climate trends rather than showing synchronous variation across latitude (Cantin et al. 2010, Razak et al. 2020). Finally, the data used in this study were based on annual averages for both demographic rates and environmental predictors. As a result, more subtle or short-term responses \u0026ndash; such as temporarily reduced growth rates following brief thermal stress events \u0026ndash; may not have been captured (Razak et al. 2020). Thus, it\u0026rsquo;s likely that multiple factors are contributing to the absence in detecting a positive temperature-growth correlation in our study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferences in growth rates across major morpho-taxa found in juvenile corals across the GBR and TS in this study generally align with other studies of juvenile (Trapon et al. 2013, Doropoulos et al. 2015, Nozawa et al. 2021) and adult (Madin et al. 2016) corals from the GBR and other parts of the world. We found that branching Pocilloporidae and \u003cem\u003eAcropora\u0026nbsp;\u003c/em\u003ejuvenile corals typically had higher growth rates (averaging 11-28 and 13-18 mm y\u003csup\u003e-1\u003c/sup\u003e) than those of submassive Merulinidae (7-9 mm y\u003csup\u003e-1\u003c/sup\u003e) and massive \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003e(6-11 mm y\u003csup\u003e-1\u003c/sup\u003e) corals. Juvenile coral growth rates were strongly influenced by both size and environmental factors in our study, with significant variability across taxa and reef clusters. The positive size-based growth correlations observed in Pocilloporidae and \u003cem\u003eAcropora\u003c/em\u003e largely align with patterns observed in earlier studies on the GBR (Trapon et al. 2013, Doropoulos et al. 2015) and Central Pacific (Kayal et al. 2018), highlighting the rapid size-based growth of these fast growing coral taxa, which drives rapid reef-scale recovery in coral cover (Gilmour et al. 2013, Doropoulos et al. 2015). The neutral and negative size-based growth relationships observed with Merulinidae and \u003cem\u003ePorites\u0026nbsp;\u003c/em\u003ecorals, respectively, somewhat agree with patterns from the reef flat of the GBR (Trapon et al. 2013, Doropoulos et al. 2015). Whilst temperature had no effect on linear growth rates, current speed had a positive effect on juvenile coral colony growth, with faster currents promoting higher rates. Increasing flow rates from 0 to 25 cm s\u003csup\u003e-1\u003c/sup\u003e have been shown to have positive effects on the growth rates of corals in experimental settings \u0026ndash; by reducing water boundary layer thickness and increasing rates of coral metabolism (Martins et al. 2024), and increasing heterotrophic nutrient uptake, and possibly reducing algal competition (Schutter et al. 2010). Combined, despite known relationships between temperature and growth in adult corals on the GBR (Lough 2008), we found in our study that juvenile coral growth rates across the GBR and TS were not predictable across latitudes and temperatures, however do follow known patterns across common coral groups and most have positive relationships with increasing size and current speed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJuvenile coral survival rates increased with size, in agreement with ecological theory (Paine 1976, Connell 1985) and other studies from across the GBR (Trapon et al. 2013, Doropoulos et al. 2015), South East Asia (Doropoulos et al. 2016, Baria‐Rodriguez et al. 2019), Caribbean (Box and Mumby 2007, Lirman et al. 2014), and Indian Ocean (Doropoulos et al. 2022b). Thus, escaping early size-dependent bottlenecks in survival has long-term impacts for coral reef recovery, and data synthesized from ~26 studies suggests that cohorts of settlers surviving \u0026gt;1 year have a high chance of persisting (Edmunds 2023). However, the interaction between size and survival varied across reef clusters, with size-independent survival observed in the central inshore reefs, emphasizing that prominent patterns don\u0026rsquo;t necessarily always hold in every setting. In addition, temperature influenced juvenile coral survival, but this effect varied among taxa and reef clusters. For instance, \u003cem\u003eAcropora\u003c/em\u003e showed higher survival in the cooler southern reefs, while Merulinidae and \u003cem\u003ePorites\u003c/em\u003e exhibited positive survival associations with warmer conditions in the north. These patterns may reflect local adaptation or thermal tolerance differences, though caution is warranted in interpreting temperature as the sole driver. The spatial heterogeneity in environmental conditions \u0026ndash; including differences in bleaching severity (particularly during 2022\u0026ndash;2023; \u0026Aacute;lvarez-Noriega et al. 2025), predation pressure, and other habitat features \u0026ndash; could also be contributing to the observed variation. For example, the spatial distribution of the bumphead parrotfish (\u003cem\u003eBolbometopon muricatum\u003c/em\u003e), which is known to damage corals through physical disturbance and is largely restricted to the central and northern GBR, may influence juvenile coral survival patterns. Its absence from southern reefs is likely driven by physiological constraints associated with cooler winter temperatures (Bellwood et al. 2003), potentially creating a natural predation gradient aligned with latitude. Moreover, while this study focused on juveniles, it remains an open question whether similar taxon-specific survival patterns are mirrored in adult coral populations. This underscores the complexity of attributing causality in broad-scale latitudinal patterns, as temperature covaries with numerous other environmental factors such as light quality, water flow, and benthic habitat complexity (Done 2011). Furthermore, temperature may serve as an ultimate driver that influences more proximate causes of coral mortality \u0026ndash; such as increased susceptibility to predation or bleaching. Taken together, these findings highlight the importance of integrating both species-specific and location-specific processes when interpreting demographic trends and call for caution when attributing survival outcomes solely to temperature. Understanding the interplay between direct and indirect drivers will be critical for accurately predicting coral resilience and informing effective conservation strategies.\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s findings have implications for reef management and restoration. Baseline demographic data, such as those presented here, have been effectively used in other regions to inform and optimize site-specific management actions (Gouezo et al. 2021) and guide restoration strategies globally. For example, the Coral Restoration Foundation\u0026rsquo;s work in the Caribbean employs site-specific data to improve coral fragment outplanting strategies and enhance survival rates (Lirman et al. 2014, Ladd et al. 2019). In Asia, studies have utilized hydrodynamic models to identify optimal sites for coral nurseries, demonstrating how data-driven approaches can be utilised to maximize restoration success (Omori 2019). Similarly, Doropoulos et al. (2022b) emphasized the integration of demographic data into large-scale restoration planning, reinforcing the value of location-specific ecological insights. These examples underscore the importance of context-specific strategies that align with local environmental conditions, species traits, and restoration goals. Such approaches may involve, for example, selecting coral taxa with higher survival under prevailing local conditions, or adjusting deployment timing and locations based on water flow or temperature profiles. By establishing baseline demographic rates of recruitment, growth, and survival across environmental gradients, this study enables direct comparisons to assess the effectiveness of future restoration interventions \u0026ndash; such as larval enhancement, selective breeding for thermal tolerance, or targeted outplanting \u0026nbsp;(Randall et al. 2020, Humanes et al. 2024) \u0026ndash;\u0026nbsp;in increasing juvenile coral abundance and persistence. The identification of trade-offs between growth and survival across coral taxa provides valuable guidance for prioritizing interventions based on specific ecological contexts. Moreover, the demonstrated influence of environmental gradients on juvenile coral dynamics highlights the necessity of regionally tailored restoration strategies. As the GBR faces increasing threats from climate change (Emslie et al. 2024, Bozec et al. 2025), this research contributes critical knowledge for designing adaptive and resilient restoration interventions that support long-term reef recovery.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests that interfered with the completion of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData associated with this study will be freely available at the CSIRO data access portal (http://hdl.handle.net/102.100.100/489497?index=1) upon publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the EcoRRAP Subprogram (https://gbrrestoration.org/program/ecorrap/) that is part of the Reef Restoration and Adaptation Program (https://gbrrestoration.org/). The Reef Restoration and Adaptation Program is funded by the partnership between the Australian Government\u0026rsquo;s Reef Trust and the Great Barrier Reef Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge and thank the Traditional Owners of the Great Barrier Reef and Far northern for granting free, prior and informed consent (FPIC) to enter and conduct this research on Traditional Sea Country; and thank the Indigenous Partnerships Team at Australian Institute of Marine Science for their knowledge and time in facilitating FPIC with the relevant Traditional Owner groups. All work was conducted under GBRMPA permit number G21/44774.1. We thank the many EcoRRAP personnel who contributed to the field work logistics; Peran Bray, Anna Cresswell, Anthea Donovan and Grant Milton who contributed to some of the data collection; and Barbara Robson who helped with the extractions of eReefs data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation: CD, PJM; Design: CD, KF\u003cem\u003e,\u0026nbsp;\u003c/em\u003eRF; Data collection: CD, MAN, KF, SN, MO, KS; Data analysis: CD, MAN; Writing \u0026ndash; original draft: CD; Writing \u0026ndash; review and editing: MAN, PJM, MO, SN, KF, RF. Funding acquisition: CD, KF, PJM.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, and E. T. Hogg. 2010. 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Verreth, and R. H. Wijffels. 2010. The effect of different flow regimes on the growth and metabolic rates of the scleractinian coral Galaxea fascicularis. Coral Reefs \u003cstrong\u003e29\u003c/strong\u003e:737-748.\u003c/li\u003e\n\u003cli\u003eSteven, A. D., M. E. Baird, R. Brinkman, N. J. Car, S. J. Cox, M. Herzfeld, J. Hodge, E. Jones, E. King, and N. Margvelashvili. 2019. eReefs: An operational information system for managing the Great Barrier Reef. Journal of Operational Oceanography \u003cstrong\u003e12\u003c/strong\u003e:S12-S28.\u003c/li\u003e\n\u003cli\u003eTrapon, M. L., M. S. Pratchett, M. Adjeroud, A. S. Hoey, and A. H. Baird. 2013. Post-settlement growth and mortality rates of juvenile scleractinian corals in Moorea, French Polynesia versus Trunk Reef, Australia. Marine Ecology Progress Series \u003cstrong\u003e488\u003c/strong\u003e:157-170.\u003c/li\u003e\n\u003cli\u003eUnderwood, A. J. 1997. Experiments in ecology: their logistical design and interpretation using analysis of variance. Cambridge University Press, Cambridge.\u003c/li\u003e\n\u003cli\u003eWaters, C., P. L. Harrison, M. Gouezo, A. Severati, and C. Doropoulos. 2025. Early-stage coral settlement and survivorship using wild larval assemblages on coral seeding devices for reef restoration. Restoration Ecology.\u003c/li\u003e\n\u003cli\u003eWei, T., V. Simko, M. Levy, Y. Xie, Y. Jin, and J. Zemla. 2017. Package \u0026lsquo;corrplot\u0026rsquo;. Statistician \u003cstrong\u003e56\u003c/strong\u003e:316-324.\u003c/li\u003e\n\u003cli\u003eWickham, H. 2009. ggplot2: elegant graphics for data analysis. Springer Science \u0026amp; Business Media.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"growth, recruitment, recovery, restoration, resilience, survival","lastPublishedDoi":"10.21203/rs.3.rs-7060133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7060133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDemographic rates of juvenile corals are critical to reef recovery, yet their variation across environmental gradients remains understudied. Over three years, we assessed juvenile coral vital rates across the Great Barrier Reef (GBR) and Torres Strait (TS), spanning 14\u0026deg; latitude and diverse inshore to offshore environments. Environmental conditions varied, with annual temperatures ranging 24\u0026ndash;28\u0026deg;C, high turbidity in the southern inshore GBR, and elevated chlorophyll \u003cem\u003ea\u003c/em\u003e in the southern offshore GBR. Despite these differences, juvenile assemblages of five common coral groups (\u003cem\u003eMontipora\u003c/em\u003e, \u003cem\u003eAcropora\u003c/em\u003e, Pocilloporidae, Merulinidae, and \u003cem\u003ePorites\u003c/em\u003e) were broadly similar. Recruitment patterns varied with \u003cem\u003eAcropora\u003c/em\u003e highest in southern offshore and central reefs, and \u003cem\u003ePorites\u003c/em\u003e in southern offshore and northern reefs. Annual mortality rates were consistent across locations but taxa-specific negative responses to turf height and sedimentation were observed. Net juvenile density increased by 3 m⁻\u0026sup2; yr⁻\u0026sup1;, with higher gains for \u003cem\u003eAcropora\u003c/em\u003e in southern offshore reefs (3.8 m⁻\u0026sup2; yr⁻\u0026sup1;) and \u003cem\u003ePorites\u003c/em\u003e in northern offshore reefs (3.5 m⁻\u0026sup2; yr⁻\u0026sup1;). Individual survival improved with size, and temperature effects were taxon-dependent \u0026ndash; negative for \u003cem\u003eAcropora\u003c/em\u003e, neutral for \u003cem\u003eMontipora\u003c/em\u003e and Pocilloporidae, and positive for Merulinidae and \u003cem\u003ePorites\u003c/em\u003e. Temperature did not correlate with linear growth in any group, but water flow positively influenced growth in most taxa. Fast-growing \u003cem\u003eAcropora\u003c/em\u003e and Pocilloporidae showed positive size-growth relationships, unlike slower-growing Merulinidae and \u003cem\u003ePorites\u003c/em\u003e. These findings provide baseline demographic data across the GBR and TS, revealing both congruence and divergence with ecological theory. They offer essential input for predictive models, site-specific restoration planning, and evaluating interventions relative to natural background dynamics.\u003c/p\u003e","manuscriptTitle":"Impact of environmental gradients on juvenile coral demography across the Great Barrier Reef and Torres Strait","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 06:30:13","doi":"10.21203/rs.3.rs-7060133/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-08-25T14:12:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T01:47:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T13:04:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123354160481243942495659664388336759016","date":"2025-07-30T14:24:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265688623020132582099666517613751453454","date":"2025-07-29T05:09:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T13:40:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T00:33:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T13:58:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Coral Reefs","date":"2025-07-07T00:21:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf73fe0e-2808-4b2c-a2d9-bbc22768830b","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T16:02:59+00:00","versionOfRecord":{"articleIdentity":"rs-7060133","link":"https://doi.org/10.1007/s00338-025-02742-6","journal":{"identity":"coral-reefs","isVorOnly":false,"title":"Coral Reefs"},"publishedOn":"2025-09-15 15:57:24","publishedOnDateReadable":"September 15th, 2025"},"versionCreatedAt":"2025-08-01 06:30:13","video":"","vorDoi":"10.1007/s00338-025-02742-6","vorDoiUrl":"https://doi.org/10.1007/s00338-025-02742-6","workflowStages":[]},"version":"v1","identity":"rs-7060133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7060133","identity":"rs-7060133","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}