Response of Moorea reef to marine heatwaves: spatiotemporal heterogeneity in geomorphology, water depth, and community dynamics

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Our study integrated satellite SST with high-resolution in situ temperature and coral cover data from six sites across three geomorphic reef types (fringing reef, backreef, forereef) in Moorea reef. We examined the spatiotemporal patterns of MHW and coral cover dynamics from 2005 to 2024 and identified key drivers of coral variation. Results reveal a west-north-east gradient in MHW intensity around Moorea, with thermal stress being most severe in shallow fringing reef and attenuating with depth. Coral cover decline and recovery exhibited strong spatiotemporal heterogeneity, with lower mortality and recovery rates in shallow fringing reef and backreef compared to the forereef (10–17 m). Over the past decade, fringing reef recovery rates remained below 10%, while forereef recovery was higher at 10 m than at 17 m. Notably, no clear depth-dependent recovery pattern was observed in the fringing reef. Generalized linear mixed models confirmed coral cover correlates with thermal stress and water depth, with variation across geomorphic zones. By elucidating the interactions among reef geomorphology, depth, thermal stress, and coral cover, our findings provide a scientific foundation for targeted coral conservation strategies. Marine heatwaves Geomorphic gradient Spatiotemporal heterogeneity Coral recovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coral reefs support over a quarter of the world's fish species and biodiversity, making them one of Earth's most vital ecosystems. In recent years, due to the interference of natural factors and human factors, coral reefs have been rapidly deteriorating. Among them, the abnormal rise of sea surface temperature (SST) is the most serious and threatening factor affecting coral reefs (Glynn 1993 ; Hughes et al. 2017a , 2018 ; Kleinhaus et al. 2020 ). Marine heatwaves (MHWs) are becoming more frequent, more intense, and longer (Hoegh-Guldberg et al. 2019 ; Laufkotter et al. 2020 ; Bove et al. 2022 ). For example, the recurring MHWs on the Great Barrier Reef from 2016 to 2022 (Hughes et al. 2021 ; Huang et al. 2024 ), the multiple 2015–2016 Pacific heatwaves (Sen Gupta et al. 2020 ), and the severe global MHWs of 2023–2024 (Hoegh-Guldberg et al. 2023 ). All of these events have led to frequent and widespread coral bleaching and mortality. Since 1998, four global coral bleaching events (1998, 2010, 2014–2017, 2023–2024) have affected 50–70% of the world's coral reefs, each more severe than the last. In 2023–2024, record-breaking DHW values were reported in many places (Hughes et al. 2017b ; Reimer et al. 2024 ). Global warming of 1.5°C is projected to increase the likelihood of MHWs by an average of 16 times (Frolicher et al. 2018 ), with 70–90% of coral reefs disappearing. If the temperature rises by 2°C, 99% of the world's coral reefs will face the risk of death and disappearance (Schleussner et al. 2015 ; Armstrong McKay et al. 2022 ; Dixon et al. 2022 ). Therefore, fully assessing the spatiotemporal variability of MHWs and their impacts on coral reefs is crucial for the protection and restoration of coral reef ecosystems. The intensity of MHWs that cause coral bleaching is spatially and temporally heterogeneous, and so too are the factors affecting coral recovery during and after these events. Consequently, coral community responses to thermal stress similarly demonstrate spatiotemporal differentiation (Asner et al. 2020 ; Srednick et al. 2023 ). On the horizontal scale, MHWs' intensity can vary from meters to kilometers. For instance, at the geomorphological scale, fringing reef and backreef typically exhibit high-frequency temperature fluctuations (Craig et al. 2001 ; Sheppard 2009 ; Edmunds et al. 2010 ). While the forereef may show more moderate yet persistent heat accumulation patterns, owing to its greater depth and offshore position. Roelfsema et al. ( 2021 ) conducted a site-to-subsite-level analysis of coral cover on Heron Island in the Great Barrier Reef to investigate these spatial variations on a fine scale. On the vertical scale, factors such as light penetration and water flow at different depths result in vertical differentiation of temperature and MHWs' intensity (Han et al. 2016 ; Miyama et al. 2021 ). On a temporal scale, MHWs' intensity can vary from hours to days, with SST fluctuations capable of reaching 5°C within 24 hours (Leichter et al. 2006 ). Habitats experiencing greater thermal variability sometimes enhance coral acclimatization and heat tolerance (Oliver and Palumbi 2011 ; Fox et al. 2021 ). Consequently, corals inhabiting different thermal environments exhibit great variations in heat tolerance even at small spatiotemporal scales (Brown et al. 2023 )。 Connected habitats with taxonomically similar coral communities exhibiting asynchronous population dynamics can reduce the likelihood of local coral extinction (Srednick et al. 2023 ). For instance, forereef habitats demonstrate higher cover or faster recovery rates compared to lagoon habitats (Pratchett et al. 2011 ; Holbrook et al. 2018 ). Coral bleaching and cover vary across habitats, influenced by factors such as historical events, geographic zones, contemporary thermal conditions, and species composition (Donovan et al. 2021 ; Gilmour et al. 2022 ; Shlesinger and Van Woesik 2023 ). Coral resilience and recovery potential are closely linked to the frequency, duration, and interactive effects of successive disturbances (Pratchett et al. 2011 ). Coral recovery cycles typically span 7–10 years (Diaz-Pulido et al. 2009 ; Emslie et al. 2020 ). However, as marine heatwaves become more frequent, the recovery window narrows, leaving corals unable to withstand subsequent events and leading to collapse. One study indicates that 57% of corals experience at least one time Bleaching Alert Level 1 or higher within five years of recovery(Mulà et al. 2025 ). Nevertheless, temporal heterogeneity can provide a buffer period for species facing intense disturbances, enhancing adaptability through selection and acclimatization and promoting community succession(Stein et al. 2014 ). Temporal variation in community composition has emerged as a key pattern of change in biodiversity research (Dornelas et al. 2023 ). Currently, most studies rely on satellite SST data to analyze heatwaves in coral reef regions. However, their relatively low spatial resolution fails to capture fine-scale thermal heterogeneity inside the reef and within distinct microhabitats, while also lacking detailed observations of MHWs within the vertical water column (Schlegel et al. 2019 ; Diaz et al. 2023 ). In contrast, in situ temperature data collected across depths offer higher spatiotemporal resolution, enabling detection of temperature variation at hourly and meter scales. Nevertheless, such point-source measurements tend to underestimate MHWs, and obtaining long-term, three-dimensional in situ temperature datasets remains a challenge (Genevier et al. 2019 ; Garrabou et al. 2022 ). Meanwhile, studies show that high-resolution satellite-derived products can capture the spatial distribution patterns of temperature at the atoll and backreef, exhibiting minimal deviation from in situ measurements (Van Wynsberge et al. 2017 , 2020 , 2024 ). Few studies have focused on integrating satellite SST with in situ temperature data across depths to enable a more comprehensive analysis of the three-dimensional characteristics of MHWs in coral reef regions. Moorea Island in French Polynesia is an ideal location for our research, offering long-term in situ temperature data and ecological data across its three key geomorphic zones (forereef, backreef, fringing reef) and a depth gradient of 1–40 meters. Significant spatiotemporal variation in coral cover on Moorea has been attributed to a multitude of factors, including coral recruitment differences (Edmunds 2017 ; Holbrook et al. 2018 ), internal wave mechanisms (Williams et al. 2018 ; Wyatt et al. 2023 ), herbivore assemblage variations (Han et al. 2016 ), hydrodynamic connectivity patterns (Lama et al. 2024 ), colony size (Speare et al. 2022 ; Winslow et al. 2024 ), and the enigmatic Pocillopora species (Burgess et al. 2021 ) et al. Nevertheless, most of these studies have focused on a single shore aspect (north shore) or a single geomorphic zone (forereef), lacking a comprehensive and island-wide perspective across different geomorphic zones and shore aspects. Furthermore, the trajectory of coral recovery following the severe 2019 heatwave remains largely unexplored. This study provides a comprehensive assessment of MHWs around Moorea Island. First, we use coarse-resolution satellite data (CoralReefWatch SST) to identify all MHW events from 2005 to 2024, characterizing their island-wide attributes and spatial patterns. Second, high-resolution in situ temperature data were utilized to compare thermal stress across three geomorphic zones. Meanwhile, we further dissect thermal heterogeneity across habitats, depths, and sites during the representative 2019 heatwave. Finally, statistical analyses and generalized linear mixed models (GLMMs) were applied to quantify the effects of geomorphic type, depth, thermal stress, and coral taxa on coral cover. Our work aims to uncover the three-dimensional spatiotemporal dynamics of MHWs and coral responses at a fine scale and contribute to global coral conservation and restoration efforts. Materials and Methods Study site Moorea Island (17°30´S, 149°50´W) is a volcanic island with a circumference of approximately 60 kilometers, including multiple geomorphic types such as fringing reef, lagoon, reef flat, backreef, and forereef. The fringing reef is defined as a coral reef adjacent to the coast and within about 50m of the land. The Moorea Coral Reef Long-Term Ecological Research (MCR-LTER) project has established two research sites each along the northern (LTER1 and LTER2), eastern (LTER3 and LTER4), and western (LTER5 and LTER6) coasts of the island, totaling six sites. These sites collect long-term in situ SST data and conduct annual ecological surveys to update data in real time (Fig. 1 ). In-site temperature data We obtained in situ seawater temperature time series data for three geomorphic zones from 2005 to 2025: (1) Forereef: 10m, 20m, 30m, and 40m (LTER1 to LTER6 sites); (2) Backreef: 1m (LTER1), 2m (LTER2 to LTER6); (3) Fringing reef: 1m (LTER1 to LTER6), 3m (LTER5), 4m (LTER2/LTER6), 6m (LTER1/LTER4), 7m (LTER3). Before 2021, SST sampling intervals were 20 minutes. After July 2021, SB39 and SB56 instruments sampled every 2 minutes, while Hobo instruments sampled every 8 minutes. These data were mainly used for the analysis of heatwave characteristics and temperature variations across geomorphology, depth, and site. Data obtained from: http://mcr.lternet.edu/data . Remote sensing temperature data Remote sensing temperature data were obtained from the 5km CoralTemp product provided by the Coral Reef Observatory Program at the National Oceanic and Atmospheric Administration (NOAA) ( https://coralreefwatch.noaa.gov ). We obtained daily satellite-derived sea surface temperature (SST) data for the 1°×1° grid centered on Moorea Island from 1985 to 2024. These data were used to investigate the island-wide MHW pattern. Coral cover data Coral cover data were extracted from benthic community surveys, which recorded the abundance of Scleractinia corals (identified to genus), macroalgae, crustose coralline algae (CCA)/bare space, soft corals, algal meadows, and sand. These surveys have been conducted annually in April since 2005, with data updated yearly. For the fringing reef (3, 4, 6, 7 m) and forereef (10, 17 m), benthic covers were quantified at six sites. At each site, forty fixed 0.25 m² quadrats were positioned along a 50-meter transect. For the backreef (2m), considering its high spatial variability, the transect method was not used. Instead, five patches were randomly selected at a 50-meter spatial scale. At the center of each patch, a cross-axis design was employed: one 5-meter-long transect was laid out in each of the east, south, west, and north directions. Five 0.5×0.5m sample plots were randomly set on each transect, with plot locations randomly reassigned annually. These data were used for dynamic studies of coral cover. Detailed sampling protocols and data specifics regarding Moorea Island are available at: http://mcr.lternet.edu/data . Thermal stress analysis According to the definition by Hobday et al. ( 2016 ), marine heatwaves (MHWs) are identified as periods when daily sea surface temperatures exceed the 90th percentile of their climatological values for at least five consecutive days. Two consecutive events separated by two days or less are considered a single event (Hobday et al. 2016 ). We quantified and visualized the magnitude and spatial distribution of MHWs on Moorea Island from 2005 to 2024 using four metrics: frequency, average intensity, maximum intensity, and duration. Frequency is calculated by the number of MHW events occurring in a year; average intensity is the mean temperature anomaly above the climatological mean value during the event; maximum intensity is the highest temperature anomaly during the event; duration is the number of days between the event start and end dates. The methodology for identifying MHWs and their spatial patterns is based on https://github.com/ZijieZhaoMMHW/m_mhw1.0(Zhao and Marin 2019 )。 To quantify fine-scale thermal stress patterns across geomorphological units, we calculated annual maximum Degree Heating Weeks (hereafter max_DHW) and thermal stress duration using in situ daily sea temperature data from 2005 to 2024 for the forereef, backreef, and fringing reef. DHW represents the sum of daily Hotspot ≥ 1℃ over a rolling 84-day (12-week) window, expressed in ℃-weeks. Hotspot is defined as the difference between the daily sea temperature and the Maximum Monthly Mean (MMM) (Skirving et al. 2020 ). The MMM climatology was derived from 1°×1° daily satellite SST data over the 1985–2012 baseline period. It was computed pixel-by-pixel by first calculating the monthly mean (MM) for each calendar month over the baseline, and then selecting the maximum value from these twelve monthly means for each pixel (Skirving et al. 2020 ). Thermal stress duration was calculated as the annual cumulative number of days with Hotspot ≥ 1℃. Following the NOAA's coral bleaching forecast framework, DHW exposure of 4°C-weeks is associated with significant bleaching, while 8°C-weeks leads to severe bleaching and widespread mortality (Liu et al. 2014 )。 Additionally, to analyze the temperature differentiation characteristics across different coastal orientations (north, east, west) on Moorea Island, we performed a one-way analysis of variance (ANOVA) on in situ daily temperature data from a severe marine heatwave period (1 August 2018 to 31 July 2019) to test for significant differences in temperature among sites. Key temperature indicators were also calculated, including the daily mean temperature (mean_T), annual maximum and minimum temperatures (max_T and min_T), and the mean daily temperature range for both the warm and cold seasons (DRT_w and DRT_c). The warm season was defined as December to May, and the cold season as June to November. The DTR was calculated as the difference between the daily maximum and minimum temperatures. Sites with missing data were excluded from the analysis. Coral cover and statistical analysis This study focused on five dominant coral genera: Acropora, Pocillopora, Porites, Montipora, and Pavona. As coral cover data from the backreef were not taxonomically resolved to genus level, this habitat was excluded from genus-specific analyses, and only total coral cover was considered. We first examined the long-term trends (2005–2024) in total coral cover (for all three habitats) and genus-level cover (for the fringing reef and forereef only). We then employed generalized linear mixed-effects models (GLMMs) to assess the overall effects of geomorphology, depth, and thermal stress (represented by max_DHW) on coral cover. Geomorphology, depth, and max_DHW were set as fixed effects, while year and site served as random effects. To elucidate the habitat-specific role of thermal stress, we further constructed separate linear models for each geomorphic zone. Additionally, Tukey's post-hoc tests were used to evaluate the significance of pairwise differences in coral cover across geomorphic types and depth gradients. All analyses were performed using MATLAB (R2018a) and R-4.5.0. Results Island-wide MHW patterns The climatological baseline for Moorea Island, represented by the Maximum Monthly Mean (MMM) sea surface temperature derived from satellite data, was calculated to be 28.8°C. A total of five distinct MHW events was identified between 2005 and 2024, occurring in 2007, 2012, 2016, 2019, and 2024 (Fig. 2 a). Among these, the 2019 event exhibited the most acute thermal shock, characterized by SST exceeding 30°C and lasting 31 days starting on April 1; the 2024 event ranked second in intensity, persisting for 33 days beginning March 2 (Fig. 2 b, c). During 2005–2024, MHWs in the 1°×1° area around Moorea occurred with an average frequency of 2–2.6 yr − 1 . These events typically exhibited an average intensity of 0.86–0.9°C, reaching maximum intensities of 2–2.2°C, and lasted for 12–14 days on average (Fig. 3 ). The frequency and average intensity showed a pronounced west-to-east gradient, with the highest values of > 2.5 yr⁻¹ and > 0.88°C in the west and north shores, and the lowest in the east (Fig. 3 a, b). Trends in average intensity, maximum intensity, and duration also show the same spatial pattern (Fig. 3 f–h). In contrast, maximum intensity and duration were more uniformly distributed, with hotspots located primarily in the northeast and northwest, where MHWs reached > 2.2°C and persisted for > 14 days (Fig. 3 c, d). These two metrics showed positive trends, peaking at 0.1°C/decade and 4 days/decade, respectively. Conversely, the average intensity of MHWs exhibited a weakening trend, reaching up to -0.15°C/decade. Additionally, MHW frequency trend demonstrated a contrary pattern, increasing in the east of 0.05 yr⁻¹/decade but decreasing in the west of -1 yr⁻¹/decade (Fig. 3 e). Thermal stress across geomorphologies and depths Based on data averaged across six sites, we analyzed max_DHW and duration for three reef geomorphic zones. The fringing reef of 1–7 m exhibited the highest and most frequent thermal stress accumulation among all habitats (Fig. 4 b, e). A peak was recorded at 1m depth in 2019, where max_DHW reached 5.96°C-weeks and thermal stress persisted for 37 days (Fig. 4 b, e). In contrast, the backreef of 1–2 m and forereef of 10–40 m experienced significantly lower stress, with max_DHW below 4°C-weeks and durations under 21 days (Fig. 4 a, d and Fig. 4 c, f). Notably, the 2019 event propagated into deep forereef areas at 30 m and 40 m. Furthermore, thermal stress attenuated with increasing water depth on both the fringing reef of 1–7 m and the forereef of 10–40 m (Fig. 4 b, c). Temperature difference across shores and sites A fine-scale analysis of temperature differences among sites on Moorea Island was conducted during the extreme 2018–19 MHW. A clear spatial pattern emerged: both the 2 m backreef (Table 1 ) and the 3–7 m fringing reef (Table 2 ) exhibited temperature heterogeneity, with the warmest sites on the north shore (LTER2, LTER1) and the coolest on the east and west shores (LTER4, LTER6), respectively. Temperatures at LTER4 (P < 0.05; Table 1 ) and LTER6 (P < 0.05; Table 2 ) were significantly lower than at other sites, with average temperatures (mean_T) below 28°C. However, temperatures were more homogeneous at 10 m on the forereef, with the coolest mean also occurring at LTER4 (east shore; Table 3 ). Temperature differences among sites within the same geomorphic zone were smaller in the warm season (DRT_w) and larger in the cold season (DRT_c), which was pronounced in the shallow fringing reef (Table 2 ). Meanwhile, daily temperature ranges (DRT) were also larger in the backreef (0.6–0.75°C) and fringing reef (0.8–1.2°C) than on the forereef (0.3–0.4°C) (Table 1 – 3 )。 Table 1 Analysis of site variance and thermal characteristics on the backreef (2m). The temperature data of LTER1 is missing, so it is not analyzed Site (depth) ANOVA Thermal Characteristics (℃) 2 3 4 5 6 mean_T min_T max_T DRT_w DRT_c 2(2m) \ 0.08 0 *** 0.43 0.01 ** 28.18 25.44 30.43 0.75 1.34 3(2m) 0.08 \ 0.04 ** 0.87 0.94 28.09 25.97 30.30 0.71 0.67 4(2m) 0 *** 0.04 ** \ 0 *** 0.26 27.99 25.89 30.18 0.73 0.68 5(2m) 0.43 0.87 0 *** \ 0.39 28.13 25.45 30.56 0.68 0.72 6(2m) 0.01 ** 0.94 0.26 0.39 \ 28.06 24.59 31.03 1.23 1.20 Table 2 Analysis of site variance and thermal characteristics on the fringing reef (3–7m). Site (depth) ANOVA Thermal Characteristics (℃) 1 2 3 4 5 6 mean_T min_T max_T DRT_w DRT_c 1(6m) \ 1 0.99 0.34 0.98 0 *** 28.32 25.76 30.34 0.88 1.22 2(4m) 1 \ 1 0.50 0.99 0 *** 28.30 25.44 30.43 0.92 1.26 3(7m) 0.99 1 \ 0.69 1 0.01 ** 28.28 26.03 30.22 0.66 0.70 4(6m) 0.34 0.50 0.69 \ 0.88 0.22 28.15 25.97 30.09 0.87 0.83 5(3m) 0.98 0.99 1 0.88 \ 0.02 ** 28.25 25.45 30.32 0.94 1.04 6(4m) 0 *** 0 *** 0.01 ** 0.22 0.02 ** \ 27.94 24.59 30.19 0.91 0.79 Table 3 Analysis of site variance and thermal characteristics on the forereef (10m). The temperature data of LTER2 and LTER3 are missing, so they are not analyzed Site (depth) ANOVA Thermal Characteristics (℃) 1 4 5 6 mean_T min_T max_T DRT_w DRT_c 1(10m) \ 0.16 0.59 0.96 28.24 26.21 30.26 0.39 0.38 4(10m) 0.16 \ 0.87 0.60 28.07 26.01 30.15 0.38 0.31 5(10m) 0.59 0.87 \ 0.32 28.13 25.90 30.13 0.28 0.33 6(10m) 0.96 0.60 0.32 \ 28.28 25.85 30.16 0.40 0.39 Coral cover dynamics and thermal stress Coral cover from 2005 to 2024 exhibited distinct dynamic trajectories shaped by geomorphology. The backreef (2 m) and fringing reef (3–7 m) both underwent a degradation-recovery cycle. Before 2015–16, coral cover on backreef and the fringing reef suffered severe declines, with loss rates of 19.11–38.59% and 16.93–45.27%, respectively, and were most acute at LTER1 and LTER2 (north shore; Fig. 5 a, b). Notably, LTER5 was an exception, maintaining a high cover of 30–56% (Fig. 5 b). However, the coral recovery rate diverged after 2016. The backreef demonstrated robust recovery, with the highest rate of 19.33–25.42% at the west and east shores and the lowest rate of 2.92–6.3% at north shore (Fig. 5 a). In contrast, fringing reef showed weak recovery, with cover maintaining below 10% in most sites except LTER4 over decade (Fig. 5 b). Furthermore, fringing reef coral cover (3–7 m depth) exhibited no distinct depth-related gradient (Fig. 5 b). Coral cover at the forereef (10 m and 17 m) exhibited a pronounced "decline-increase-decline" trajectory from 2005 to 2024 (Fig. 5 c, d). An initial collapse in 2007 resulted in catastrophic losses, reaching 47% at 10 m and 48.57% at 17 m, and reducing cover to near-zero levels by 2009–2011. LTER1 exhibited the highest loss rate among all sites. During the subsequent rapid recovery phase from 2012 to 2019, recovery rates at 10 m depth varied from 14.38% to 80.85%, with the speed decreasing in the order: LTER1, LTER2, LTER6, LTER5, LTER4, LTER3 (Fig. 5 c). Recovery rate at 17 m was lower, ranging from 22.46% to 37.02% (Fig. 5 d). However, a sharp decline ensued after the 2019 MHW, culminating in near-complete mortality at both 10 m and 17 m depths by 2024 (Fig. 5 c, d). The fringing reef (3–7 m) community, comprised of Pocillopora , Acropora , Montipora , Porites , and Pavona , exhibited site-specific dominance patterns linked to resilience. The heat-tolerant Porites dominated LTER1, LTER2, and LTER5 (Fig. 6 a); however, only LTER5 maintained high cover, while LTER1 and LTER2 suffered high losses (Fig. 5 b). LTER3 was dominated by heat-sensitive Acropora , exhibited low cover. Coral community diversity was low on the forereef (10 m), dominated by Pocillopora , together with Montipora and Acropora accounted for 80–90% of the total coral population (Fig. 5 c). Temporal dynamics in community composition further shaped distinct recovery pathways. The community at LTER4 shifted from Montipora -dominated to a resilient Montipora-Porites symbiotic equilibrium by 2014 (Fig. 6 a). Similarly, LTER6 transitioned to a Pavona -dominated community between 2010 and 2015 from an initial mix dominated by Pocillopora and Pavona during 2005–2009 (Fig. 6 a). GLMM identified depth and geomorphology as significant drivers of coral cover (p < 0.01), with the fringing reef having the strongest negative effect and deeper forereef sites showing lower cover (Table S1 a). The random effect of year had greater variance than that of site, indicating that temporal variation influenced cover more than spatial differences among sites, though the absolute effect size was small (SD < 0.5). Although the overall effect of thermal stress (max_DHW) was non-significant (p = 0.21; Table S1 a), habitat-stratified analyses revealed countervailing relationships: a negative impact in the backreef (2m) and fringing reef (3–7 m, except LTER5) versus a positive one on the forereef (10–17m) (Fig. 7 , Table S1 b). Furthermore, pairwise comparisons confirmed that coral cover on the fringing reef differed significantly from both the backreef and the forereef (p < 0.01; Table S1 c). Within the same geomorphic zone, coral cover at 3 m depth on the fringing reef (site LTER5) was significantly higher than at 4, 6, and 7 m, and a significant difference also existed between 10 m and 17 m on the forereef (p < 0.001; Table S1 c). These findings collectively demonstrate that geomorphology and depth are primary determinants of coral cover spatial pattern variations. Discussion This study reveals pronounced spatial heterogeneity in MHW around Moorea Island. The west and north shores are experiencing higher intensity, frequency, and increasing trends of MHW than the east shore (Fig. 3 ). We propose that this spatial pattern is driven by a combination of interacting physical processes. First, the west and north shores lie in the lee of prevailing southeast trade winds (Leichter et al. 2013 ), where reduced wind mixing and stable stratification facilitate heat buildup at the surface. In contrast, the windward east shore experiences stronger wind-driven mixing and potential upwelling, which dissipates heat and suppresses peak temperatures. Second, anticyclonic (counter-clockwise) eddies, influenced by the westward South Equatorial Current (SEC), transport warm water past the island's north and west shores (Wyatt et al. 2023 ). These eddies elevate sea levels and suppress internal wave cooling (IWC), thereby enhancing heat accumulation in these regions. Third, orographic effects from the island's central peak (1207 m) may also contribute, leading to temporal variations in sunlight exposure. The west shore receives the strongest solar radiation in the afternoon, causing significant daytime warming. In contrast, the east shore is shaded by the mountains in the afternoon, resulting in low and scattered radiation levels. This may explain the strong daily temperature fluctuations of LTER6 at backreef on the west shore (Table 1 ). Furthermore, the semi-enclosed lagoon topography of LTER6 (Fig. 1 ) restricts water exchange, which dampens the influence of internal waves from the forereef and amplifies high-frequency temperature variability. This site-specific temperature profiling confirms fine-scale spatial heterogeneity in thermal stress, enabling more precise bleaching risk assessments and spatially explicit conservation strategies. Our study confirms that reef geomorphology and depth are key factors of both thermal stress spatial patterns and coral community responses. The fringing reef (3–7 m) experienced the most severe thermal stress (Fig. 4 ) and exhibited the lowest recovery rates (Fig. 5 ), a vulnerability likely linked to its physical setting. Its nearshore location allows for rapid solar heating of the entire shallow water column. Furthermore, this habitat is more susceptible to cumulative stressors from terrestrial inputs (e.g., sediments, nutrients) and anthropogenic disturbances, which may collectively impair coral resistance and resilience. In contrast, the forereef benefits from greater water exchange and a comparatively pristine environment, which supports higher coral recruitment rates. The attenuation of thermal stress with depth further explains tow patterns on the forereef: the high recovery rates at 10 m and 17 m, and the paradoxical positive correlation between max_DHW and coral cover (Fig. 7 ). The anomalously high coral cover on the forereef in the severe 2019 heatwave (Fig. 5 c, d), may result from a temporal mismatch between survey timing and the peak of heat stress. The subsequent significant drop in 2020 demonstrated the lag between bleaching and mortality (Roelfsema et al. 2021 ). Additionally, superior recovery at the 10 m forereef compared to 17 m (Fig. 5 c, d) may be attributed to more favorable light conditions, hydrodynamics, larval supply, or temperature range difference (Winslow et al. 2024 ). The absence of a clear depth gradient within the fringing reef of 3–7m (Fig. 5 b) is likely due to its uniformly shallow environment. Differences in coral community composition and bleaching susceptibility represent another core factor explaining variations in recovery rates between sites and geomorphic zones (Pratchett et al. 2013 ). Thermally tolerant genera (e.g., Porites , Pavona ) conferred resilience at sites like LTER5 and during certain periods (2010–2015) at LTER6 on the fringing reef (Fig. 5 b, 6 a). In contrast, communities dominated by heat-sensitive taxa (e.g., Acropora , Montipora , Pocillopora ) suffered catastrophic mortality, as seen at fringing reef LTER3 and most forereef sites after the 2019 heatwave (Fig. 5 b–d and Fig. 6 a, b). Despite shared dominance by the tolerant Porites with high cover until 2011 (Fig. 6 a), LTER1 and LTER2 subsequently crashed to near-zero cover, while LTER5 remained resilient (Fig. 5 b). This anomaly is likely explained by a localized crown-of-thorns starfish (COTS) outbreak recorded only at LTER1 and LTER2 during 2008–2011 (Table S2). Furthermore, coral structural complexity and herbivorous fish density are critical factors influencing reef recovery (Graham et al. 2015 ). The forereef's low-complexity, Pocillopora -dominated assemblage (Fig. 6 b) offered limited ecological resistance, resulting in its collapse after successive heatwaves (Fig. 5 c, d), consistent with known biodiversity-stability relationships (Graham et al. 2015 ). However, forereef supports greater biomass of key herbivorous fishes (Han et al. 2016 ), which may enhance its resilience relative to the lagoon by suppressing macroalgal proliferation. Post-disturbance reef recovery rates are intrinsically linked to coral recruitment and larval supply. The broader patterns of larval dispersal are governed by oceanic and local circulation. The westward South Equatorial Current (SEC) dominates the regional-scale flow near Moorea, forming a counter-clockwise circulation(Rougerie and Rancher 1994 ), which facilitates the reception of coral larvae from upstream Tahiti. The northern shore lies in the downstream region of the circulation, so coral recruitment should be highest there and decrease in a counterclockwise direction around the island. This larval depletion model explains why the reef-front recovery rate decreases sequentially from the north to west to east shores (Fig. 5 c). On a more local scale, broken surface waves on the reef crest are funneled into the backreef and lagoon (Hench et al. 2008 ). This increased wave flux likely augments larval transport into the backreef, thereby boosting coral recruitment and contributing to the higher recovery rates in the backreef compared to the fringing reef. The dynamic trajectory of Moorea's coral reefs over the past two decades profoundly reveals the complex effects of temporal heterogeneity and cumulative disturbances on resilience. Coral reefs do not respond in isolation to single heatwaves but are shaped collectively by multiple natural disturbances (e.g., the 2007–2009 starfish outbreak and the 2010 cyclone) and consecutive MHWs (e.g., those in 2012, 2016, 2019, 2024). This cycle of "collapse-recovery-collapse" led to near-complete mortality, underscoring the catastrophic consequences of frequent, high-intensity sequential disturbances. The ecological impact of MHW on coral reefs is not universally catastrophic but is highly dependent on both environmental setting and disturbance intensity (Shlesinger and Van Woesik 2023 ). For instance, forereef coral cover both at 10 m and 17 m increased following the 2016 event (Fig. 5 c, d), highlighting the role of these mid-to-deep forereef zones as temporary refugia, and revealing the differential response mechanisms across geomorphic zones. However, the extreme 2019 MHW caused widespread coral decline across the entire island, demonstrating that when environmental pressure exceeds a critical threshold, the protective capacity offered by geomorphic heterogeneity can be completely overridden. This illustrates a critical nonlinearity in reef responses to stress and highlights the systemic ecological crisis of increasing extreme events (Oliver et al. 2019 ). Our research in Moorea identified two categories of "winners": one represented by thermally tolerant, massive corals like Porites (Fig. 5 b, 6 a), and the other by fast-growing and persistently recruiting taxa like Pocillopora in the forereef (Fig. 5 c, d and 6 b). These findings align with the framework established by Van Woesik(Van Woesik et al. 2011 ), confirming the divergent survival strategies adopted by different coral taxa in the face of a changing climate. Conclusion Through a multi-scale (island-wide, geomorphic, depth, and site-specific) and multi-dimensional (thermal, ecological, and temporal) analysis, we revealed significant spatiotemporal heterogeneity in Moorea's coral reefs' response to MHWs. Key patterns include: (1) west > north > east shore gradient in thermal stress intensity, with the western reefs experiencing the most severe MHWs; (2) geomorphic and depth dependency, where shallow fringing reef exhibited the highest thermal stress, while the 10 m and 17 m forereef zone showed the strongest coral recovery; and (3) variable coral cover trajectories, reflecting differential resilience across habitats. This heterogeneity is driven by the interplay of physical conditions (e.g., localized cooling, depth-mediated buffering), community composition (e.g., heat-tolerant taxa like LTER5), and historical disturbance regimes. Ecologically, this mosaic created refugia—such as the cooler eastern shore, resilient forereef habitats, and high-recruitment zones—alongside vulnerability hotspots, particularly in shallow western/northern fringing reefs and areas dominated by sensitive taxa. Our findings provide a mechanistic framework for reef resilience, informing targeted conservation strategies. Future should integrate high-resolution physical models (e.g., eddies, internal waves), coral eco-physiology (e.g., symbiont plasticity), and long-term monitoring to refine predictions of reef trajectories under climate change. This work advances the scientific basis for precision management of Moorea’s reefs in an era of escalating thermal stress. Declarations Conflict of interest The authors declare no competing interests. Funding The National Natural Science Foundation of China under contract No. 42276182; the Major Talent Project of Guangxi Zhuang Autonomous Region No. GXR-2BGQ2525027; the Natural Science and Technology Innovation Development Doubling Program of Guangxi University under contract No. 2023BZRC019; the Guangxi Natural Science Foundation of China under contract No. 2022GXNSFAA035548. Author Contribution W.L. and X.Z. developed the idea and methodology; X.Z. provided funding for the project; W.L. collected the data and conducted analyses. W.L., X.Z. wrote the main manuscript text. All authors reviewed the manuscript. Acknowledgement The data analyzed in this study were collected from the Moorea Coral Reef Long-Term Ecological Research (MCR LTER). 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09:23:45","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103078,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/317939a360d8f448891339b0.png"},{"id":97448389,"identity":"c831b394-6e81-46f5-bcfc-ece4f10374ab","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90043,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/9c4a8b38b7f24a8293a7d228.png"},{"id":97669207,"identity":"d3588818-52ce-47da-bb5a-3d4bfd211392","added_by":"auto","created_at":"2025-12-08 09:27:34","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182306,"visible":true,"origin":"","legend":"","description":"","filename":"85c73244f9b147a08585a09dd6cf3df51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/7c3897c15708527bca1ac7b7.xml"},{"id":97448401,"identity":"5d96eb9e-efce-4196-8043-f9bd5d48d66b","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185538,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/069badeb6638ed22a451cefe.html"},{"id":97448373,"identity":"f188588e-2ae1-4bac-b637-104a6ff1ad87","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124131,"visible":true,"origin":"","legend":"\u003cp\u003eOverview map and reef cross-section of Moorea. The geomorphic classification was adapted from the Allen Coral Atlas (https://allencoralatlas.org), with shallow and deep lagoons merged into a single 'lagoon' class, and inner and outer reef flats merged into 'reef flat'\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/0bb74ac4c54f734490ceb763.png"},{"id":97669148,"identity":"c7041972-bd91-464c-82f2-4354917fa682","added_by":"auto","created_at":"2025-12-08 09:27:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112521,"visible":true,"origin":"","legend":"\u003cp\u003eMHW events in Moorea in 2005–2024 (a), 2019 (b), and 2024 (c). Climatology: the climatological mean, calculated over a reference period. Threshold: the seasonally varying temperature value that defines an MHW (e.g., the 90th percentile value). The climatology of this research was derived from 1°×1° daily satellite SST data around Moorea over the 1985–2012 baseline period\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/ce9e229958da8347edca3776.png"},{"id":97667427,"identity":"fc22cfea-c8f4-49e2-9c08-aaaf3093781a","added_by":"auto","created_at":"2025-12-08 09:23:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257873,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of marine heatwave (MHW) metrics around Moorea Island (2005–2024). (a–d) Multi-year means of (a) frequency (events yr⁻¹), (b) mean intensity (°C), (c) maximum intensity (°C), and (d) duration (days). (e–h) Decadal trends for the corresponding metrics\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/96c13e8b97aa41eba752c50a.png"},{"id":97667531,"identity":"a9dbd430-2877-4298-9a30-11968ca3c0eb","added_by":"auto","created_at":"2025-12-08 09:23:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110801,"visible":true,"origin":"","legend":"\u003cp\u003eThermal stress characteristics across geomorphic zones and depth gradients (2005–2024). (a–c) Annual maximum Degree Heating Weeks (max_DHW) and (d–f) thermal stress duration for the (a, d) backreef at 1–2 m depth, (b, e) fringing reef at 3–7 m depth, and (c, f) forereef at 10–40 m depth. Temperature data from 2016 and 2019 are missing in the backreef at 1 m depth\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/a6547fb6a0fbc374b4d2f21b.png"},{"id":97448376,"identity":"0a47eecd-c9e5-4e38-9300-f52e8cea880c","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110133,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual changes of coral cover at six stations in different geomorphic zones (2004–2024). (a) Backreef depths shown: 2 m (LTER1–6). (b) Fringing reef depths shown: 3m (LTER5), 4m (LTER2 and LTER6), 6m (LTER1 and LTER4), 7m (LTER3). (c) Forereef depths shown: 10m (LTER1–6). (d) Forereef depths shown: 17m (LTER1–6)\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/2772b32e508af0d08f13af83.png"},{"id":97448378,"identity":"e55e33fc-0dad-4dfb-8a7d-1dc0dcae0b34","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103078,"visible":true,"origin":"","legend":"\u003cp\u003eCover change of main coral genera from 2005 to 2024. (a) fringing reef at 3–7 m depth. (b) forereef at 10 m depth. Numbers 1-6 denote sites LTER1 to LTER6\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/5dc1cdc972ee7c089535a7d5.png"},{"id":97667691,"identity":"c9b52dd9-44c5-4372-8a12-22a883c2adf5","added_by":"auto","created_at":"2025-12-08 09:24:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90043,"visible":true,"origin":"","legend":"\u003cp\u003eCoral cover responded to thermal stress (max_DHW) across sites and depths. (a) Backreef depths shown: 2 m (LTER1–6). (b) Fringing reef depths shown: 3m (LTER5), 4m (LTER2 and LTER6), 6m (LTER1 and LTER4), 7m (LTER3). (c) Forereef depths shown: 10m, 17m (LTER1–6)\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/4a1f857d3cd49e8f6ed309b6.png"},{"id":101265764,"identity":"d2ad9d00-e81e-4134-b785-0a8aaa9c3e5f","added_by":"auto","created_at":"2026-01-27 23:24:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2117243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/b3c02129-83fa-473e-9a6f-d263e0f1eae4.pdf"},{"id":97448375,"identity":"6c65def9-bea5-4c1e-9baa-ce2343501930","added_by":"auto","created_at":"2025-12-04 13:11:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":167908,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8185935/v1/afda9b3074488dc0b9095136.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Response of Moorea reef to marine heatwaves: spatiotemporal heterogeneity in geomorphology, water depth, and community dynamics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoral reefs support over a quarter of the world's fish species and biodiversity, making them one of Earth's most vital ecosystems. In recent years, due to the interference of natural factors and human factors, coral reefs have been rapidly deteriorating. Among them, the abnormal rise of sea surface temperature (SST) is the most serious and threatening factor affecting coral reefs (Glynn \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hughes et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kleinhaus et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Marine heatwaves (MHWs) are becoming more frequent, more intense, and longer (Hoegh-Guldberg et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Laufkotter et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bove et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, the recurring MHWs on the Great Barrier Reef from 2016 to 2022 (Hughes et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the multiple 2015\u0026ndash;2016 Pacific heatwaves (Sen Gupta et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the severe global MHWs of 2023\u0026ndash;2024 (Hoegh-Guldberg et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All of these events have led to frequent and widespread coral bleaching and mortality. Since 1998, four global coral bleaching events (1998, 2010, 2014\u0026ndash;2017, 2023\u0026ndash;2024) have affected 50\u0026ndash;70% of the world's coral reefs, each more severe than the last. In 2023\u0026ndash;2024, record-breaking DHW values were reported in many places (Hughes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Reimer et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Global warming of 1.5\u0026deg;C is projected to increase the likelihood of MHWs by an average of 16 times (Frolicher et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with 70\u0026ndash;90% of coral reefs disappearing. If the temperature rises by 2\u0026deg;C, 99% of the world's coral reefs will face the risk of death and disappearance (Schleussner et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Armstrong McKay et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dixon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, fully assessing the spatiotemporal variability of MHWs and their impacts on coral reefs is crucial for the protection and restoration of coral reef ecosystems.\u003c/p\u003e\u003cp\u003eThe intensity of MHWs that cause coral bleaching is spatially and temporally heterogeneous, and so too are the factors affecting coral recovery during and after these events. Consequently, coral community responses to thermal stress similarly demonstrate spatiotemporal differentiation (Asner et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Srednick et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the horizontal scale, MHWs' intensity can vary from meters to kilometers. For instance, at the geomorphological scale, fringing reef and backreef typically exhibit high-frequency temperature fluctuations (Craig et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sheppard \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Edmunds et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While the forereef may show more moderate yet persistent heat accumulation patterns, owing to its greater depth and offshore position. Roelfsema et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a site-to-subsite-level analysis of coral cover on Heron Island in the Great Barrier Reef to investigate these spatial variations on a fine scale. On the vertical scale, factors such as light penetration and water flow at different depths result in vertical differentiation of temperature and MHWs' intensity (Han et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Miyama et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On a temporal scale, MHWs' intensity can vary from hours to days, with SST fluctuations capable of reaching 5\u0026deg;C within 24 hours (Leichter et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Habitats experiencing greater thermal variability sometimes enhance coral acclimatization and heat tolerance (Oliver and Palumbi \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fox et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, corals inhabiting different thermal environments exhibit great variations in heat tolerance even at small spatiotemporal scales (Brown et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003eConnected habitats with taxonomically similar coral communities exhibiting asynchronous population dynamics can reduce the likelihood of local coral extinction (Srednick et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, forereef habitats demonstrate higher cover or faster recovery rates compared to lagoon habitats (Pratchett et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Holbrook et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Coral bleaching and cover vary across habitats, influenced by factors such as historical events, geographic zones, contemporary thermal conditions, and species composition (Donovan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gilmour et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shlesinger and Van Woesik \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Coral resilience and recovery potential are closely linked to the frequency, duration, and interactive effects of successive disturbances (Pratchett et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Coral recovery cycles typically span 7\u0026ndash;10 years (Diaz-Pulido et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Emslie et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, as marine heatwaves become more frequent, the recovery window narrows, leaving corals unable to withstand subsequent events and leading to collapse. One study indicates that 57% of corals experience at least one time Bleaching Alert Level 1 or higher within five years of recovery(Mul\u0026agrave; et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, temporal heterogeneity can provide a buffer period for species facing intense disturbances, enhancing adaptability through selection and acclimatization and promoting community succession(Stein et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Temporal variation in community composition has emerged as a key pattern of change in biodiversity research (Dornelas et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCurrently, most studies rely on satellite SST data to analyze heatwaves in coral reef regions. However, their relatively low spatial resolution fails to capture fine-scale thermal heterogeneity inside the reef and within distinct microhabitats, while also lacking detailed observations of MHWs within the vertical water column (Schlegel et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Diaz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, in situ temperature data collected across depths offer higher spatiotemporal resolution, enabling detection of temperature variation at hourly and meter scales. Nevertheless, such point-source measurements tend to underestimate MHWs, and obtaining long-term, three-dimensional in situ temperature datasets remains a challenge (Genevier et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Garrabou et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, studies show that high-resolution satellite-derived products can capture the spatial distribution patterns of temperature at the atoll and backreef, exhibiting minimal deviation from in situ measurements (Van Wynsberge et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Few studies have focused on integrating satellite SST with in situ temperature data across depths to enable a more comprehensive analysis of the three-dimensional characteristics of MHWs in coral reef regions.\u003c/p\u003e\u003cp\u003eMoorea Island in French Polynesia is an ideal location for our research, offering long-term in situ temperature data and ecological data across its three key geomorphic zones (forereef, backreef, fringing reef) and a depth gradient of 1\u0026ndash;40 meters. Significant spatiotemporal variation in coral cover on Moorea has been attributed to a multitude of factors, including coral recruitment differences (Edmunds \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Holbrook et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), internal wave mechanisms (Williams et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wyatt et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), herbivore assemblage variations (Han et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), hydrodynamic connectivity patterns (Lama et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), colony size (Speare et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Winslow et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the enigmatic \u003cem\u003ePocillopora\u003c/em\u003e species (Burgess et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) et al. Nevertheless, most of these studies have focused on a single shore aspect (north shore) or a single geomorphic zone (forereef), lacking a comprehensive and island-wide perspective across different geomorphic zones and shore aspects. Furthermore, the trajectory of coral recovery following the severe 2019 heatwave remains largely unexplored.\u003c/p\u003e\u003cp\u003eThis study provides a comprehensive assessment of MHWs around Moorea Island. First, we use coarse-resolution satellite data (CoralReefWatch SST) to identify all MHW events from 2005 to 2024, characterizing their island-wide attributes and spatial patterns. Second, high-resolution in situ temperature data were utilized to compare thermal stress across three geomorphic zones. Meanwhile, we further dissect thermal heterogeneity across habitats, depths, and sites during the representative 2019 heatwave. Finally, statistical analyses and generalized linear mixed models (GLMMs) were applied to quantify the effects of geomorphic type, depth, thermal stress, and coral taxa on coral cover. Our work aims to uncover the three-dimensional spatiotemporal dynamics of MHWs and coral responses at a fine scale and contribute to global coral conservation and restoration efforts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy site\u003c/h2\u003e\u003cp\u003eMoorea Island (17\u0026deg;30\u0026acute;S, 149\u0026deg;50\u0026acute;W) is a volcanic island with a circumference of approximately 60 kilometers, including multiple geomorphic types such as fringing reef, lagoon, reef flat, backreef, and forereef. The fringing reef is defined as a coral reef adjacent to the coast and within about 50m of the land. The Moorea Coral Reef Long-Term Ecological Research (MCR-LTER) project has established two research sites each along the northern (LTER1 and LTER2), eastern (LTER3 and LTER4), and western (LTER5 and LTER6) coasts of the island, totaling six sites. These sites collect long-term in situ SST data and conduct annual ecological surveys to update data in real time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIn-site temperature data\u003c/h3\u003e\n\u003cp\u003eWe obtained in situ seawater temperature time series data for three geomorphic zones from 2005 to 2025: (1) Forereef: 10m, 20m, 30m, and 40m (LTER1 to LTER6 sites); (2) Backreef: 1m (LTER1), 2m (LTER2 to LTER6); (3) Fringing reef: 1m (LTER1 to LTER6), 3m (LTER5), 4m (LTER2/LTER6), 6m (LTER1/LTER4), 7m (LTER3). Before 2021, SST sampling intervals were 20 minutes. After July 2021, SB39 and SB56 instruments sampled every 2 minutes, while Hobo instruments sampled every 8 minutes. These data were mainly used for the analysis of heatwave characteristics and temperature variations across geomorphology, depth, and site. Data obtained from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mcr.lternet.edu/data\u003c/span\u003e\u003cspan address=\"http://mcr.lternet.edu/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eRemote sensing temperature data\u003c/h3\u003e\n\u003cp\u003eRemote sensing temperature data were obtained from the 5km CoralTemp product provided by the Coral Reef Observatory Program at the National Oceanic and Atmospheric Administration (NOAA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coralreefwatch.noaa.gov\u003c/span\u003e\u003cspan address=\"https://coralreefwatch.noaa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We obtained daily satellite-derived sea surface temperature (SST) data for the 1\u0026deg;\u0026times;1\u0026deg; grid centered on Moorea Island from 1985 to 2024. These data were used to investigate the island-wide MHW pattern.\u003c/p\u003e\n\u003ch3\u003eCoral cover data\u003c/h3\u003e\n\u003cp\u003eCoral cover data were extracted from benthic community surveys, which recorded the abundance of Scleractinia corals (identified to genus), macroalgae, crustose coralline algae (CCA)/bare space, soft corals, algal meadows, and sand. These surveys have been conducted annually in April since 2005, with data updated yearly. For the fringing reef (3, 4, 6, 7 m) and forereef (10, 17 m), benthic covers were quantified at six sites. At each site, forty fixed 0.25 m\u0026sup2; quadrats were positioned along a 50-meter transect. For the backreef (2m), considering its high spatial variability, the transect method was not used. Instead, five patches were randomly selected at a 50-meter spatial scale. At the center of each patch, a cross-axis design was employed: one 5-meter-long transect was laid out in each of the east, south, west, and north directions. Five 0.5\u0026times;0.5m sample plots were randomly set on each transect, with plot locations randomly reassigned annually. These data were used for dynamic studies of coral cover. Detailed sampling protocols and data specifics regarding Moorea Island are available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mcr.lternet.edu/data\u003c/span\u003e\u003cspan address=\"http://mcr.lternet.edu/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eThermal stress analysis\u003c/h3\u003e\n\u003cp\u003eAccording to the definition by Hobday et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), marine heatwaves (MHWs) are identified as periods when daily sea surface temperatures exceed the 90th percentile of their climatological values for at least five consecutive days. Two consecutive events separated by two days or less are considered a single event (Hobday et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We quantified and visualized the magnitude and spatial distribution of MHWs on Moorea Island from 2005 to 2024 using four metrics: frequency, average intensity, maximum intensity, and duration.\u003c/p\u003e\u003cp\u003eFrequency is calculated by the number of MHW events occurring in a year; average intensity is the mean temperature anomaly above the climatological mean value during the event; maximum intensity is the highest temperature anomaly during the event; duration is the number of days between the event start and end dates. The methodology for identifying MHWs and their spatial patterns is based on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ZijieZhaoMMHW/m_mhw1.0(Zhao and Marin 2019\u003c/span\u003e\u003cspan address=\"https://github.com/ZijieZhaoMMHW/m_mhw1.0(Zhao and Marin 2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003eTo quantify fine-scale thermal stress patterns across geomorphological units, we calculated annual maximum Degree Heating Weeks (hereafter max_DHW) and thermal stress duration using in situ daily sea temperature data from 2005 to 2024 for the forereef, backreef, and fringing reef. DHW represents the sum of daily Hotspot\u0026thinsp;\u0026ge;\u0026thinsp;1℃ over a rolling 84-day (12-week) window, expressed in ℃-weeks. Hotspot is defined as the difference between the daily sea temperature and the Maximum Monthly Mean (MMM) (Skirving et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MMM climatology was derived from 1\u0026deg;\u0026times;1\u0026deg; daily satellite SST data over the 1985\u0026ndash;2012 baseline period. It was computed pixel-by-pixel by first calculating the monthly mean (MM) for each calendar month over the baseline, and then selecting the maximum value from these twelve monthly means for each pixel (Skirving et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thermal stress duration was calculated as the annual cumulative number of days with Hotspot\u0026thinsp;\u0026ge;\u0026thinsp;1℃. Following the NOAA's coral bleaching forecast framework, DHW exposure of 4\u0026deg;C-weeks is associated with significant bleaching, while 8\u0026deg;C-weeks leads to severe bleaching and widespread mortality (Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003eAdditionally, to analyze the temperature differentiation characteristics across different coastal orientations (north, east, west) on Moorea Island, we performed a one-way analysis of variance (ANOVA) on in situ daily temperature data from a severe marine heatwave period (1 August 2018 to 31 July 2019) to test for significant differences in temperature among sites. Key temperature indicators were also calculated, including the daily mean temperature (mean_T), annual maximum and minimum temperatures (max_T and min_T), and the mean daily temperature range for both the warm and cold seasons (DRT_w and DRT_c). The warm season was defined as December to May, and the cold season as June to November. The DTR was calculated as the difference between the daily maximum and minimum temperatures. Sites with missing data were excluded from the analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCoral cover and statistical analysis\u003c/h2\u003e\u003cp\u003eThis study focused on five dominant coral genera: Acropora, Pocillopora, Porites, Montipora, and Pavona. As coral cover data from the backreef were not taxonomically resolved to genus level, this habitat was excluded from genus-specific analyses, and only total coral cover was considered. We first examined the long-term trends (2005\u0026ndash;2024) in total coral cover (for all three habitats) and genus-level cover (for the fringing reef and forereef only). We then employed generalized linear mixed-effects models (GLMMs) to assess the overall effects of geomorphology, depth, and thermal stress (represented by max_DHW) on coral cover. Geomorphology, depth, and max_DHW were set as fixed effects, while year and site served as random effects. To elucidate the habitat-specific role of thermal stress, we further constructed separate linear models for each geomorphic zone. Additionally, Tukey's post-hoc tests were used to evaluate the significance of pairwise differences in coral cover across geomorphic types and depth gradients. All analyses were performed using MATLAB (R2018a) and R-4.5.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eIsland-wide MHW patterns\u003c/h2\u003e\u003cp\u003eThe climatological baseline for Moorea Island, represented by the Maximum Monthly Mean (MMM) sea surface temperature derived from satellite data, was calculated to be 28.8\u0026deg;C. A total of five distinct MHW events was identified between 2005 and 2024, occurring in 2007, 2012, 2016, 2019, and 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among these, the 2019 event exhibited the most acute thermal shock, characterized by SST exceeding 30\u0026deg;C and lasting 31 days starting on April 1; the 2024 event ranked second in intensity, persisting for 33 days beginning March 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring 2005\u0026ndash;2024, MHWs in the 1\u0026deg;\u0026times;1\u0026deg; area around Moorea occurred with an average frequency of 2\u0026ndash;2.6 yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These events typically exhibited an average intensity of 0.86\u0026ndash;0.9\u0026deg;C, reaching maximum intensities of 2\u0026ndash;2.2\u0026deg;C, and lasted for 12\u0026ndash;14 days on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The frequency and average intensity showed a pronounced west-to-east gradient, with the highest values of \u0026gt;\u0026thinsp;2.5 yr⁻\u0026sup1; and \u0026gt;\u0026thinsp;0.88\u0026deg;C in the west and north shores, and the lowest in the east (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Trends in average intensity, maximum intensity, and duration also show the same spatial pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef\u0026ndash;h). In contrast, maximum intensity and duration were more uniformly distributed, with hotspots located primarily in the northeast and northwest, where MHWs reached\u0026thinsp;\u0026gt;\u0026thinsp;2.2\u0026deg;C and persisted for \u0026gt;\u0026thinsp;14 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d). These two metrics showed positive trends, peaking at 0.1\u0026deg;C/decade and 4 days/decade, respectively. Conversely, the average intensity of MHWs exhibited a weakening trend, reaching up to -0.15\u0026deg;C/decade. Additionally, MHW frequency trend demonstrated a contrary pattern, increasing in the east of 0.05 yr⁻\u0026sup1;/decade but decreasing in the west of -1 yr⁻\u0026sup1;/decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThermal stress across geomorphologies and depths\u003c/h2\u003e\u003cp\u003eBased on data averaged across six sites, we analyzed max_DHW and duration for three reef geomorphic zones. The fringing reef of 1\u0026ndash;7 m exhibited the highest and most frequent thermal stress accumulation among all habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, e). A peak was recorded at 1m depth in 2019, where max_DHW reached 5.96\u0026deg;C-weeks and thermal stress persisted for 37 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, e). In contrast, the backreef of 1\u0026ndash;2 m and forereef of 10\u0026ndash;40 m experienced significantly lower stress, with max_DHW below 4\u0026deg;C-weeks and durations under 21 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, d and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, f). Notably, the 2019 event propagated into deep forereef areas at 30 m and 40 m. Furthermore, thermal stress attenuated with increasing water depth on both the fringing reef of 1\u0026ndash;7 m and the forereef of 10\u0026ndash;40 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTemperature difference across shores and sites\u003c/h2\u003e\u003cp\u003eA fine-scale analysis of temperature differences among sites on Moorea Island was conducted during the extreme 2018\u0026ndash;19 MHW. A clear spatial pattern emerged: both the 2 m backreef (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the 3\u0026ndash;7 m fringing reef (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) exhibited temperature heterogeneity, with the warmest sites on the north shore (LTER2, LTER1) and the coolest on the east and west shores (LTER4, LTER6), respectively. Temperatures at LTER4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and LTER6 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were significantly lower than at other sites, with average temperatures (mean_T) below 28\u0026deg;C. However, temperatures were more homogeneous at 10 m on the forereef, with the coolest mean also occurring at LTER4 (east shore; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemperature differences among sites within the same geomorphic zone were smaller in the warm season (DRT_w) and larger in the cold season (DRT_c), which was pronounced in the shallow fringing reef (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Meanwhile, daily temperature ranges (DRT) were also larger in the backreef (0.6\u0026ndash;0.75\u0026deg;C) and fringing reef (0.8\u0026ndash;1.2\u0026deg;C) than on the forereef (0.3\u0026ndash;0.4\u0026deg;C) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of site variance and thermal characteristics on the backreef (2m). The temperature data of LTER1 is missing, so it is not analyzed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003cp\u003e(depth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eANOVA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eThermal Characteristics (℃)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003emean_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003emin_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003emax_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDRT_w\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDRT_c\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2(2m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e28.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3(2m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e28.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4(2m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5(2m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e28.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6(2m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e28.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e24.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e31.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of site variance and thermal characteristics on the fringing reef (3\u0026ndash;7m).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003cp\u003e(depth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eANOVA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eThermal Characteristics (℃)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003emean_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003emin_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003emax_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDRT_w\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eDRT_c\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1(6m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e25.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2(4m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e25.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3(7m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e26.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4(6m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e25.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5(3m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e28.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e25.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6(4m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e24.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of site variance and thermal characteristics on the forereef (10m). The temperature data of LTER2 and LTER3 are missing, so they are not analyzed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003cp\u003e(depth)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eANOVA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eThermal Characteristics (℃)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003emean_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003emin_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003emax_T\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDRT_w\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDRT_c\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1(10m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e26.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4(10m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e26.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5(10m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e25.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6(10m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\\\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e25.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e30.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCoral cover dynamics and thermal stress\u003c/h2\u003e\u003cp\u003eCoral cover from 2005 to 2024 exhibited distinct dynamic trajectories shaped by geomorphology. The backreef (2 m) and fringing reef (3\u0026ndash;7 m) both underwent a degradation-recovery cycle. Before 2015\u0026ndash;16, coral cover on backreef and the fringing reef suffered severe declines, with loss rates of 19.11\u0026ndash;38.59% and 16.93\u0026ndash;45.27%, respectively, and were most acute at LTER1 and LTER2 (north shore; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Notably, LTER5 was an exception, maintaining a high cover of 30\u0026ndash;56% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). However, the coral recovery rate diverged after 2016. The backreef demonstrated robust recovery, with the highest rate of 19.33\u0026ndash;25.42% at the west and east shores and the lowest rate of 2.92\u0026ndash;6.3% at north shore (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In contrast, fringing reef showed weak recovery, with cover maintaining below 10% in most sites except LTER4 over decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Furthermore, fringing reef coral cover (3\u0026ndash;7 m depth) exhibited no distinct depth-related gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eCoral cover at the forereef (10 m and 17 m) exhibited a pronounced \"decline-increase-decline\" trajectory from 2005 to 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d). An initial collapse in 2007 resulted in catastrophic losses, reaching 47% at 10 m and 48.57% at 17 m, and reducing cover to near-zero levels by 2009\u0026ndash;2011. LTER1 exhibited the highest loss rate among all sites. During the subsequent rapid recovery phase from 2012 to 2019, recovery rates at 10 m depth varied from 14.38% to 80.85%, with the speed decreasing in the order: LTER1, LTER2, LTER6, LTER5, LTER4, LTER3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Recovery rate at 17 m was lower, ranging from 22.46% to 37.02% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). However, a sharp decline ensued after the 2019 MHW, culminating in near-complete mortality at both 10 m and 17 m depths by 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe fringing reef (3\u0026ndash;7 m) community, comprised of \u003cem\u003ePocillopora\u003c/em\u003e, \u003cem\u003eAcropora\u003c/em\u003e, \u003cem\u003eMontipora\u003c/em\u003e, \u003cem\u003ePorites\u003c/em\u003e, and \u003cem\u003ePavona\u003c/em\u003e, exhibited site-specific dominance patterns linked to resilience. The heat-tolerant Porites dominated LTER1, LTER2, and LTER5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea); however, only LTER5 maintained high cover, while LTER1 and LTER2 suffered high losses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). LTER3 was dominated by heat-sensitive \u003cem\u003eAcropora\u003c/em\u003e, exhibited low cover. Coral community diversity was low on the forereef (10 m), dominated by \u003cem\u003ePocillopora\u003c/em\u003e, together with \u003cem\u003eMontipora\u003c/em\u003e and \u003cem\u003eAcropora\u003c/em\u003e accounted for 80\u0026ndash;90% of the total coral population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eTemporal dynamics in community composition further shaped distinct recovery pathways. The community at LTER4 shifted from \u003cem\u003eMontipora\u003c/em\u003e-dominated to a resilient \u003cem\u003eMontipora-Porites\u003c/em\u003e symbiotic equilibrium by 2014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Similarly, LTER6 transitioned to a \u003cem\u003ePavona\u003c/em\u003e-dominated community between 2010 and 2015 from an initial mix dominated by \u003cem\u003ePocillopora\u003c/em\u003e and \u003cem\u003ePavona\u003c/em\u003e during 2005\u0026ndash;2009 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGLMM identified depth and geomorphology as significant drivers of coral cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the fringing reef having the strongest negative effect and deeper forereef sites showing lower cover (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). The random effect of year had greater variance than that of site, indicating that temporal variation influenced cover more than spatial differences among sites, though the absolute effect size was small (SD\u0026thinsp;\u0026lt;\u0026thinsp;0.5). Although the overall effect of thermal stress (max_DHW) was non-significant (p\u0026thinsp;=\u0026thinsp;0.21; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea), habitat-stratified analyses revealed countervailing relationships: a negative impact in the backreef (2m) and fringing reef (3\u0026ndash;7 m, except LTER5) versus a positive one on the forereef (10\u0026ndash;17m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Furthermore, pairwise comparisons confirmed that coral cover on the fringing reef differed significantly from both the backreef and the forereef (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Within the same geomorphic zone, coral cover at 3 m depth on the fringing reef (site LTER5) was significantly higher than at 4, 6, and 7 m, and a significant difference also existed between 10 m and 17 m on the forereef (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). These findings collectively demonstrate that geomorphology and depth are primary determinants of coral cover spatial pattern variations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reveals pronounced spatial heterogeneity in MHW around Moorea Island. The west and north shores are experiencing higher intensity, frequency, and increasing trends of MHW than the east shore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We propose that this spatial pattern is driven by a combination of interacting physical processes. First, the west and north shores lie in the lee of prevailing southeast trade winds (Leichter et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), where reduced wind mixing and stable stratification facilitate heat buildup at the surface. In contrast, the windward east shore experiences stronger wind-driven mixing and potential upwelling, which dissipates heat and suppresses peak temperatures. Second, anticyclonic (counter-clockwise) eddies, influenced by the westward South Equatorial Current (SEC), transport warm water past the island's north and west shores (Wyatt et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These eddies elevate sea levels and suppress internal wave cooling (IWC), thereby enhancing heat accumulation in these regions. Third, orographic effects from the island's central peak (1207 m) may also contribute, leading to temporal variations in sunlight exposure. The west shore receives the strongest solar radiation in the afternoon, causing significant daytime warming. In contrast, the east shore is shaded by the mountains in the afternoon, resulting in low and scattered radiation levels. This may explain the strong daily temperature fluctuations of LTER6 at backreef on the west shore (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the semi-enclosed lagoon topography of LTER6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) restricts water exchange, which dampens the influence of internal waves from the forereef and amplifies high-frequency temperature variability. This site-specific temperature profiling confirms fine-scale spatial heterogeneity in thermal stress, enabling more precise bleaching risk assessments and spatially explicit conservation strategies.\u003c/p\u003e\u003cp\u003eOur study confirms that reef geomorphology and depth are key factors of both thermal stress spatial patterns and coral community responses. The fringing reef (3\u0026ndash;7 m) experienced the most severe thermal stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and exhibited the lowest recovery rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), a vulnerability likely linked to its physical setting. Its nearshore location allows for rapid solar heating of the entire shallow water column. Furthermore, this habitat is more susceptible to cumulative stressors from terrestrial inputs (e.g., sediments, nutrients) and anthropogenic disturbances, which may collectively impair coral resistance and resilience. In contrast, the forereef benefits from greater water exchange and a comparatively pristine environment, which supports higher coral recruitment rates. The attenuation of thermal stress with depth further explains tow patterns on the forereef: the high recovery rates at 10 m and 17 m, and the paradoxical positive correlation between max_DHW and coral cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The anomalously high coral cover on the forereef in the severe 2019 heatwave (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d), may result from a temporal mismatch between survey timing and the peak of heat stress. The subsequent significant drop in 2020 demonstrated the lag between bleaching and mortality (Roelfsema et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, superior recovery at the 10 m forereef compared to 17 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d) may be attributed to more favorable light conditions, hydrodynamics, larval supply, or temperature range difference (Winslow et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The absence of a clear depth gradient within the fringing reef of 3\u0026ndash;7m (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) is likely due to its uniformly shallow environment.\u003c/p\u003e\u003cp\u003eDifferences in coral community composition and bleaching susceptibility represent another core factor explaining variations in recovery rates between sites and geomorphic zones (Pratchett et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thermally tolerant genera (e.g., \u003cem\u003ePorites\u003c/em\u003e, \u003cem\u003ePavona\u003c/em\u003e) conferred resilience at sites like LTER5 and during certain periods (2010\u0026ndash;2015) at LTER6 on the fringing reef (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In contrast, communities dominated by heat-sensitive taxa (e.g., \u003cem\u003eAcropora\u003c/em\u003e, \u003cem\u003eMontipora\u003c/em\u003e, \u003cem\u003ePocillopora\u003c/em\u003e) suffered catastrophic mortality, as seen at fringing reef LTER3 and most forereef sites after the 2019 heatwave (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u0026ndash;d and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b). Despite shared dominance by the tolerant \u003cem\u003ePorites\u003c/em\u003e with high cover until 2011 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), LTER1 and LTER2 subsequently crashed to near-zero cover, while LTER5 remained resilient (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). This anomaly is likely explained by a localized crown-of-thorns starfish (COTS) outbreak recorded only at LTER1 and LTER2 during 2008\u0026ndash;2011 (Table S2). Furthermore, coral structural complexity and herbivorous fish density are critical factors influencing reef recovery (Graham et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The forereef's low-complexity, \u003cem\u003ePocillopora\u003c/em\u003e-dominated assemblage (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) offered limited ecological resistance, resulting in its collapse after successive heatwaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d), consistent with known biodiversity-stability relationships (Graham et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, forereef supports greater biomass of key herbivorous fishes (Han et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which may enhance its resilience relative to the lagoon by suppressing macroalgal proliferation.\u003c/p\u003e\u003cp\u003ePost-disturbance reef recovery rates are intrinsically linked to coral recruitment and larval supply. The broader patterns of larval dispersal are governed by oceanic and local circulation. The westward South Equatorial Current (SEC) dominates the regional-scale flow near Moorea, forming a counter-clockwise circulation(Rougerie and Rancher \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), which facilitates the reception of coral larvae from upstream Tahiti. The northern shore lies in the downstream region of the circulation, so coral recruitment should be highest there and decrease in a counterclockwise direction around the island. This larval depletion model explains why the reef-front recovery rate decreases sequentially from the north to west to east shores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). On a more local scale, broken surface waves on the reef crest are funneled into the backreef and lagoon (Hench et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This increased wave flux likely augments larval transport into the backreef, thereby boosting coral recruitment and contributing to the higher recovery rates in the backreef compared to the fringing reef.\u003c/p\u003e\u003cp\u003eThe dynamic trajectory of Moorea's coral reefs over the past two decades profoundly reveals the complex effects of temporal heterogeneity and cumulative disturbances on resilience. Coral reefs do not respond in isolation to single heatwaves but are shaped collectively by multiple natural disturbances (e.g., the 2007\u0026ndash;2009 starfish outbreak and the 2010 cyclone) and consecutive MHWs (e.g., those in 2012, 2016, 2019, 2024). This cycle of \"collapse-recovery-collapse\" led to near-complete mortality, underscoring the catastrophic consequences of frequent, high-intensity sequential disturbances. The ecological impact of MHW on coral reefs is not universally catastrophic but is highly dependent on both environmental setting and disturbance intensity (Shlesinger and Van Woesik \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, forereef coral cover both at 10 m and 17 m increased following the 2016 event (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d), highlighting the role of these mid-to-deep forereef zones as temporary refugia, and revealing the differential response mechanisms across geomorphic zones. However, the extreme 2019 MHW caused widespread coral decline across the entire island, demonstrating that when environmental pressure exceeds a critical threshold, the protective capacity offered by geomorphic heterogeneity can be completely overridden. This illustrates a critical nonlinearity in reef responses to stress and highlights the systemic ecological crisis of increasing extreme events (Oliver et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our research in Moorea identified two categories of \"winners\": one represented by thermally tolerant, massive corals like \u003cem\u003ePorites\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), and the other by fast-growing and persistently recruiting taxa like \u003cem\u003ePocillopora\u003c/em\u003e in the forereef (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). These findings align with the framework established by Van Woesik(Van Woesik et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), confirming the divergent survival strategies adopted by different coral taxa in the face of a changing climate.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough a multi-scale (island-wide, geomorphic, depth, and site-specific) and multi-dimensional (thermal, ecological, and temporal) analysis, we revealed significant spatiotemporal heterogeneity in Moorea's coral reefs' response to MHWs. Key patterns include: (1) west\u0026thinsp;\u0026gt;\u0026thinsp;north\u0026thinsp;\u0026gt;\u0026thinsp;east shore gradient in thermal stress intensity, with the western reefs experiencing the most severe MHWs; (2) geomorphic and depth dependency, where shallow fringing reef exhibited the highest thermal stress, while the 10 m and 17 m forereef zone showed the strongest coral recovery; and (3) variable coral cover trajectories, reflecting differential resilience across habitats. This heterogeneity is driven by the interplay of physical conditions (e.g., localized cooling, depth-mediated buffering), community composition (e.g., heat-tolerant taxa like LTER5), and historical disturbance regimes. Ecologically, this mosaic created refugia\u0026mdash;such as the cooler eastern shore, resilient forereef habitats, and high-recruitment zones\u0026mdash;alongside vulnerability hotspots, particularly in shallow western/northern fringing reefs and areas dominated by sensitive taxa.\u003c/p\u003e\u003cp\u003eOur findings provide a mechanistic framework for reef resilience, informing targeted conservation strategies. Future should integrate high-resolution physical models (e.g., eddies, internal waves), coral eco-physiology (e.g., symbiont plasticity), and long-term monitoring to refine predictions of reef trajectories under climate change. This work advances the scientific basis for precision management of Moorea\u0026rsquo;s reefs in an era of escalating thermal stress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe National Natural Science Foundation of China under contract No. 42276182; the Major Talent Project of Guangxi Zhuang Autonomous Region No. GXR-2BGQ2525027; the Natural Science and Technology Innovation Development Doubling Program of Guangxi University under contract No. 2023BZRC019; the Guangxi Natural Science Foundation of China under contract No. 2022GXNSFAA035548.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.L. and X.Z. developed the idea and methodology; X.Z. provided funding for the project; W.L. collected the data and conducted analyses. W.L., X.Z. wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe data analyzed in this study were collected from the Moorea Coral Reef Long-Term Ecological Research (MCR LTER). We extend our gratitude to the staff of the Moorea Coral Reef Long-Term Ecological Research (MCR LTER) site for their long-term commitment to data collection and maintenance. We would also like to thank the Coral Reef Observatory Program at the National Oceanic and Atmospheric Administration (NOAA) for providing remote sensing temperature data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmstrong McKay DI, Staal A, Abrams JF, Winkelmann R, Sakschewski B, Loriani S, Fetzer I, Cornell SE, Rockstr\u0026ouml;m J, Lenton TM (2022) Exceeding 1.5\u0026deg;C global warming could trigger multiple climate tipping points. Science 377:eabn7950 \u003c/li\u003e\n\u003cli\u003eAsner GP, Vaughn NR, Heckler J, Knapp DE, Balzotti C, Shafron E, Martin RE, Neilson BJ, Gove JM (2020) Large-scale mapping of live corals to guide reef conservation. 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JOSS 4:1124 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Marine heatwaves, Geomorphic gradient, Spatiotemporal heterogeneity, Coral recovery","lastPublishedDoi":"10.21203/rs.3.rs-8185935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8185935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMost studies rely on course-resolution satellite-derived sea surface temperature (SST) data to analyze Marine Heatwaves (MHW) in coral reef regions, limiting their ability to capture fine-scale thermal variability. Our study integrated satellite SST with high-resolution in situ temperature and coral cover data from six sites across three geomorphic reef types (fringing reef, backreef, forereef) in Moorea reef. We examined the spatiotemporal patterns of MHW and coral cover dynamics from 2005 to 2024 and identified key drivers of coral variation. Results reveal a west-north-east gradient in MHW intensity around Moorea, with thermal stress being most severe in shallow fringing reef and attenuating with depth. Coral cover decline and recovery exhibited strong spatiotemporal heterogeneity, with lower mortality and recovery rates in shallow fringing reef and backreef compared to the forereef (10\u0026ndash;17 m). Over the past decade, fringing reef recovery rates remained below 10%, while forereef recovery was higher at 10 m than at 17 m. Notably, no clear depth-dependent recovery pattern was observed in the fringing reef. Generalized linear mixed models confirmed coral cover correlates with thermal stress and water depth, with variation across geomorphic zones. By elucidating the interactions among reef geomorphology, depth, thermal stress, and coral cover, our findings provide a scientific foundation for targeted coral conservation strategies.\u003c/p\u003e","manuscriptTitle":"Response of Moorea reef to marine heatwaves: spatiotemporal heterogeneity in geomorphology, water depth, and community dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 13:11:39","doi":"10.21203/rs.3.rs-8185935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30bc124a-5dc9-44b6-9c10-2c54599c14a2","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T23:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 13:11:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8185935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8185935","identity":"rs-8185935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}