Tipping the balance: Reef community shifts after a regional urchin population collapse

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Ryan James, Valentina Bautista, Madison Sandquist, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303485/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Coral Reefs → Version 1 posted 9 You are reading this latest preprint version Abstract Coral reef ecosystems are maintained by complex interactions among organisms, with herbivory playing a critical role in regulating benthic algae populations and preserving reef health. Herbivores such as the black spiny sea urchin prevent macroalgae from outcompeting corals by exerting top-down control, sustaining ecosystem stability and diversity. There is evidence that the mass mortality of D. antillarum in the 1980s contributed to phase shifts from coral-dominated to algal-dominated states across the Caribbean, demonstrating their critical role in reef dynamics. Certain areas of the Caribbean experienced a slow recovery of D. antillarum , until 2022, when another mass mortality event occurred. This study investigates the ecological impacts of the recent 2022 die-off using monitoring data collected from Culebra, Puerto Rico, between 2021 and 2024. High-resolution orthomosaics generated through photogrammetry and Structure-from-Motion (SfM) techniques were used to assess shifts in benthic community structure, focusing on algal and coral cover changes in response to D. antillarum abundance. In addition, we modeled coral recruitment dynamics and found that both urchin cluster density and mean cluster size at baseline were related to declines in coral recruitment, with larger and more numerous urchin clusters associated with reduced post-die-off recruitment. Our results also showed increases in macroalgal cover and decreases in sponge cover following the loss of D. antillarum , highlighting the potential community assembly regulative role of this herbivore. These findings underscore the urgency of protecting and restoring key herbivore species and highlight the need for targeted management strategies to mitigate further degradation of Caribbean reef systems. photogrammetry algae proliferation benthic community structure herbivore loss coral-algal competition reef degradation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coral reef ecosystems are shaped by complex interactions among organisms, with herbivory playing a crucial role in maintaining reef health (Adam et al., 2015 , 2022 ; Mumby et al., 2006 ). Herbivores, such as sea urchins, help regulate algal populations, preventing macroalgae from outcompeting corals for space and light (Adam et al., 2022 ). This regulation is essential for maintaining healthy competition dynamics and reef resilience, particularly in the face of stressors like increasing anthropogenic disturbances and climate change (Arnold et al., 2010 ; Barott et al., 2012 ; Burkepile & Hay, 2008 ; Cramer et al., 2021 ; Mumby et al., 2006 ). When herbivore populations decline, macroalgae can proliferate, potentially leading to phase shifts where reefs transition from coral-dominated to algal-dominated states (Dudgeon et al., 2010 ; Hughes, 1994 ; Hughes et al., 2007 ). These interactions are further complicated by feedback loops involving nutrient dynamics, predator-prey relationships, and species-specific functional roles (Adam et al., 2015 ; Alvarez-Filip et al., 2011 ; Arnold et al., 2010 ). The balance between herbivory and algal growth plays a significant role in determining reef community structure and function, highlighting the importance of protecting key herbivores to sustain ecosystem stability and biodiversity. In the 1980’s Caribbean reefs experienced a mass mortality event of the black spiny sea urchin, Diadema antillarum ( Diadema hereafter), a key herbivore on Caribbean reefs (Carpenter, 1990 ; Mumby et al., 2006 ). The die-off reduced Diadema densities across the western Atlantic by over 97%, contributing to, and further accelerating, phase shifts from coral-dominated to algal-dominated communities in many reefs (Lessios, 2016 ; Mumby et al., 2006 ). Without the grazing pressure of Diadema , macroalgae rapidly proliferated, reaching densities high enough to overgrow and smother even adult coral colonies (Lessios, 2016 ). Following the catastrophic mass mortality of Diadema in the 1980s, populations remained depressed for decades, with only isolated signs of recovery observed in parts of the Caribbean by the mid-2010s (Lessios, 2016 ). However, in 2022, another widespread mortality event of Diadema swept across the eastern Caribbean, marking the most significant die-off of this keystone herbivore since the catastrophic 1983 event (Hylkema et al., 2023 ). First reported in the Virgin Islands, the die-off spread rapidly across multiple reef systems, with Diadema exhibiting characteristic symptoms such as spine loss, disorientation, and rapid tissue degradation before succumbing to mortality by a scuticocillate (Hewson et al., 2023 ; Hylkema et al., 2023 ). Given the species’ critical role in reef herbivory, this event raises concerns about the potential for increased macroalgal dominance and further reef degradation, particularly in systems where Diadema had begun to recover (Burkepile & Hay, 2008 ; Lessios, 2016 ; Williams, 2022 ). The 2022 die-off underscores the vulnerability of Diadema populations and highlights the urgency of understanding the drivers and ecological consequences of such mortality events in present day reefs. Evaluating the impacts of the 2022 Diadema die-off is essential for understanding the importance of their slow recovery and the resilience of today’s coral reef ecosystems. As with the mass mortality event in 1983, contemporary reefs continue to face compounding stressors such as climate change, ocean acidification, and the decline of herbivorous species due to overfishing and disease (Alvarez-Filip et al., 2022 ; Barott et al., 2012 ; Cramer et al., 2021 ; Hughes et al., 2007 ). However, unlike in the past, Diadema now persists at relatively low densities, raising important questions about the extent of their current ecological role. While their loss is concerning, the influence of Diadema on reef dynamics may not be as pronounced as it once was. Nonetheless, their continued decline could further hinder coral recruitment and accelerate shifts in benthic community composition and structure (Adam et al., 2022 ; Arnold et al., 2010 ; Carpenter, 1990 ; Rodríguez-Barreras et al., 2018 ). Given that some Diadema populations had shown signs of recovery prior to this event, their sudden decline raises concerns about whether natural recolonization can still occur under present-day conditions (Rogers & Lorenzen, 2016 ). Investigating the ecological consequences of this die-off will provide insights into the role of Diadema in reef dynamics under current altered conditions, the potential for recovery, and the management strategies needed to mitigate further ecosystem decline. Monitoring efforts using large-area images via photogrammetry can provide detailed (i.e., high-resolution) and diverse ecological information critical to understanding the role of herbivores, such as Diadema , in regulating the community assembly on reefs. These large-area images allow us to quantify changes in algal and coral cover over time and assess how the loss of this key herbivore influenced benthic community structure. For this study, we used large-area images created as part of a monitoring effort in the municipal island of Culebra (Puerto Rico) to specifically ask: 1) Are there signs of Diadema recovery? 2) How does macroalgae cover relate to urchin abundance in modern-day reefs? and 3) How did the benthic community structure respond to the Diadema die-off? Given the strong herbivory pressure exerted by Diadema , we hypothesized that sites with previously high urchin abundance would experience a more pronounced shift toward algae-dominated communities than those where Diadema was already scarce or absent. In the absence of these grazers, macroalgae are expected to proliferate, potentially outcompeting corals and altering habitat structure. Modeling work by Mumby et al. ( 2006 ) identified the importance of high Diadema densities needed to prevent algal phase shifts. To better understand these transitions, we assessed not only coral cover but also recruitment, as reduced herbivory may limit the settlement and survival of juvenile corals. Consequently, we hypothesized a shift in benthic community structure following the die-off, characterized by increased macroalgal cover, reduced coral recruitment and cover. Together, these metrics provide insights into the mechanisms driving reef change and the potential for recovery in the face of mounting anthropogenic stressors and climate change. Methods Benthic Monitoring Data for this study was collected in Culebra, Puerto Rico, over 3 years (2021 to 2023). A total of 24 sites were established on the south-eastern end of the island (Fig. 1 ). Each site was composed of four 100 m 2 plots (n = 96 total plots) that were delineated with nails and paracord in 2021 for consistent monitoring. For this study, our time points consisted of a Pre-die-off (2021), Early-die-off (June/July 2022, right after the Diadema mortality event in Puerto Rico), Post-die-off (October 2022), and 1-year-post (May 2023) period. We used photogrammetry and Structure-from-Motion (SfM) techniques to generate high-resolution digital large-area images of benthic communities (Petrovic et al., 2014 ; Remmers et al., 2023 ). In 2021, images were collected using two GoPro Hero 8 cameras, while in subsequent years, we used two Nikon D7500 high-resolution cameras to follow monitoring protocols established by the National Oceanographic and Atmospheric Administration (Suka et al., 2019 ). Divers followed a non-disruptive lawn-mower survey pattern, maintaining an altitude of approximately 1.5 meters above the reef substrate to ensure consistent image acquisition and minimize distortion. Images were captured with 70–80% overlap to facilitate accurate reconstruction. Eight scale bars were strategically placed within the survey area, and corresponding depth measurements were recorded. The collected images were color corrected in Adobe Lightroom and processed in Agisoft Metashape Pro (v2.0), where they were aligned, key points were matched, and a dense point cloud was generated following Cook et al., (2023). This point cloud was then used to create a two-dimensional orthomosaic or large-area Image (LAI), which served as the basis for the benthic analysis of this study. Assessment of Urchin Abundance and Benthic Community Response Benthic community structure was quantified using percent cover estimates via the virtual point intercept method. LAIs were imported into ArcGIS, where they were overlaid with a 1 m² grid to divide the LAI into uniform sections. Each grid cell was extracted as a 1 m² image (photo-quadrat), resulting in a total of 95 to 165 photo-quadrats per reef plot, depending on the plot's true size. Photo-quadrats were then uploaded to CoralNet for semi-automated image annotation (I. D. Williams et al., 2019 ). CoralNet uses convolutional neural networks trained on manually annotated images to classify benthic features, improving its accuracy over time through repeated exposure to manually labeled data. The model was trained with 23% of the uploaded photo-quadrats being manually annotated using 35 random points to classify benthic cover into 6 principal functional groups: hard coral, soft coral, macroalgae, sponge, and sand., achieving an overall functional group accuracy of 80.2%. This accuracy supports the reliability of the automated annotations in capturing patterns of benthic cover, although potential biases may still exist, particularly in underrepresented classes. To assess shifts in Diadema abundance, we utilized the LAIs in place of direct field counts. Since urchins were not systematically counted during fieldwork, LAIs provided a critical ex-situ tool to approximate their presence and abundance across reef plots. Each LAI was examined for visible Diadema individuals, and observations were grouped into cluster categories to represent relative abundance. Each observed cluster of Diadema was recorded, and cluster size was categorized into four levels: (1) a single urchin, (2) small clusters of 2–5 individuals, (3) medium clusters of 5–10 individuals, and (4) large clusters exceeding 10 individuals. Plots with no observed urchins were assigned a 0. This approach allowed for standardized comparisons across plots and time points despite the absence of direct urchin surveys in the field. For coral recruitment estimates, point clouds generated during the LAI stitching process were imported into VISCORE, scaled, and exported as orthoprojections. These orthoprojections were then analyzed in TagLab, where coral colonies of Porites astreoides were identified and delineated. Porites astreoides was chosen due to its classification as a weedy coral, that is currently expected to be doing well under current conditions (Darling et al., 2012 ). Recruitment was assessed by tracking the appearance of new colonies between survey periods: only colonies not present in 2021 were counted as recruits in Early-die-off, and only colonies absent in 2022 were counted as recruits in 1-year-post. To standardize measurements, recruits were limited to a maximum size of 5 cm². Statistical Analysis All statistical analyses were conducted in R (v4.4.2; R Core Team, 2024) to examine temporal changes in Diadema abundance and the response of the benthic community to the die-off. We used generalized linear models (GLMs) to test whether macroalgal cover change, quantified as the log-ratio of Post to Pre-die-off values, was predicted by Pre-die-off Diadema abundance and size. As the response, we used the log-ratio of Pre-die-off macroalgae cover to Post-die-off macroalgae cover to assess how Pre-die-off urchin cluster counts and sizes influenced changes in macroalgae cover. We used generalized linear mixed models (GLMMs) to model the change in urchin cluster size and urchin cluster amount over time. We fit a GLMM for the urchin cluster amount model using a negative binomial error distribution with site as a random effect. The urchin cluster size model was ran using GLMM with a Gaussian error distribution and site as a random factor after fitting for the best model. Given the random distribution of clusters across the study sites, we focused exclusively on cluster size when clusters were present, considering only period as a predictor variable in our model. Additionally, the log-ratio of coral recruitment from Early-die-off to 1-year-post was modeled using a Gaussian error distribution, with initial Diadema cluster density and size as fixed effects, to explore the role of urchins in shaping post-die-off coral recruitment dynamics. Similarly, the log response ratio of sponge cover Pre-die-off and Post-die-off was modeled using a GLM with a Gaussian distribution, with Diadema cluster count and size as predictor variables to evaluate their influence on sponge cover change. We used GLMMs with beta error distribution (i.e., beta regressions) to assess changes in coral, macroalgae, and sponge percent cover across periods (Pre-die-off, Early-die-off, Post-die-off and 1-year-post), with period as a fixed effect and site as a random intercept to account for site-level variability. Individual GLMMs were performed to evaluate changes in the percentage cover of macroalgae, sponges, and corals across the periods relative to the Diadema die-off. Model diagnostics and simplification were conducted using the performance package (Lüdecke et al., 2021 ) to assess fit and assumptions, and model selection was refined through dredging with the MuMIn package (Bartoń, 2010 ) to identify the top four models based on AICc and then selected for the most most parsimonious model. The response of the benthic community structure to the Diadema die-off was assessed using a multivariate analysis framework at the level of functional groups, including corals, macroalgae, sponges, and seagrass. We conducted a Permutational Multivariate Analysis of Variance (PERMANOVA) using a Bray-Curtis dissimilarity matrix to evaluate differences in community structure across time periods (treated as a fixed effect). To account for spatial non-independence among samples, we specified site as a random effect in the adonis2 function. This approach constrained permutations within sites, helping control site-level variation while testing the main effect of period. Additionally, we conducted pairwise PERMANOVA comparisons among time periods to identify specific temporal shifts in community structure. Multivariate analyses were performed in R using the vegan package for adonis2 (Oksanen et al., 2001 ). Results Diadema die-off recovery Our study revealed a sharp decline in both the number and size of Diadema clusters across the study site following the die-off event, with limited indications of recovery (Fig. 2 a). Urchin presence declined by approximately 96% immediately after the mortality event (mean = 0.11, CI = 0.033–0.347; p < 0.001), with the lowest predicted amount observed Post-die-off (mean = 0.03, CI = 0.006–0.137). By 1-year-post, there was a slight increase in urchin presence (mean = 0.13, CI = 0.045–0.394), though values remained well below Pre-die-off levels (mean = 2.37, CI = 0.98–5.76). Pairwise comparisons confirmed that urchin presence was significantly lower across all periods compared to Pre-die-off (z ≥ 6.99, p < 0.0001, Tukey-adjusted), while differences among post-die-off periods were not significant (z ≤ 2.19, p ≥ 0.127, Tukey-adjusted), indicating limited recovery within one year (Tables 1 & S1, Fig. 2 a). Table 1 Fixed effects from generalized linear mixed models examining the effects of time (period) on urchin cluster counts and cluster sizes. Period estimates are relative to the Baseline period (intercept). The cluster count model used a negative binomial distribution, while the cluster size model used a Gaussian distribution with a log link. Model Term Estimate Std. Error z value p-value Cluster Counts (Intercept) 0.863 0.453 1.907 0.057 Cluster Counts Period Early-die-off -3.089 0.402 -7.683 < 0.001 Cluster Counts Period Post-die-off -4.357 0.623 -6.989 < 0.001 Cluster Counts Period 1-year-post -2.877 0.340 -8.452 < 0.001 Cluster Size (Intercept) 0.355 0.053 6.689 < 0.001 Cluster Size Period Early-die-off -0.349 0.101 -3.473 < 0.001 Cluster Size Period Post-die-off -0.496 0.200 -2.477 0.013 Cluster Size Period 1-year-post -0.374 0.075 -4.982 < 0.001 Urchin cluster size significantly decreased following the die-off and remained low throughout the study period. Urchin cluster size significantly declined following the die-off event and remained at low numbers (Table 1 , Fig. 2 b). Post-hoc comparisons confirmed a significant decline in cluster size between Pre-die-off and Early-die-off periods (t ≥ 2.48, p < 0.0056, Tukey-adjusted (p = 0.0056), and between Pre-die-off and 1-year-post periods (t ≥ 4.98, p < 0.0001, Tukey-adjusted). However, there were no significant differences in cluster size between the Early-die-off period and later time points (t ≤ 0.67, p ≥ 0.90, Tukey-adjusted). Benthic functional group response to die-off (univariate analysis) Univariate analyses from the benthic data extracted with CoralNet revealed temporal changes in the benthic cover of functional groups, often correlated with urchin cluster metrics. Coral percent cover increased modestly following the urchin die-off, with model-predicted values rising from 1.26% Pre-die-off to 1.48% Early-die-off (z = 2.69, p = 0.036; +17.5%) and 1.51% in 1-year-post (z = 3.04, p = 0.013; +19.8%) (Fig. 3 a). In contrast, the Post-die-off period showed a smaller, non-significant increase to 1.31% (z = 0.66, p = 0.911; +4.0%) relative to the Pre-die-off period (Table 2 , S2). However, given the relatively small effect sizes and the already low cover of coral, these differences likely fall within the uncertainty bounds of the CoralNet classification model, suggesting that they may reflect model sensitivity rather than true ecological shifts. Similarly, the percentage cover of macroalgae increased following the die-off event (Early-die-off) and remained elevated a year after the event (Fig. 3 b, Table 2 , S2). Specifically, at baseline, the mean predicted macroalgae cover was 25.2%, which increased to 31.6% in the Early-die-off period (z = 4.62, p < 0.0001). Cover remained elevated Post-die-off at 29.6% (z = 3.28, p = 0.0058) and in 1-year-post at 28.6%, though the latter was marginally non-significant (z = 2.56, p = 0.052) compared to Pre-die-off levels. In contrast, sponge percent cover decreased sharply following the die-off, from a model-predicted 5.0% Pre-die-off to 1.5% in Early-die-off (z = 11.85, p < 0.0001) and 1.6% in Post-die-off (z = 11.23, p < 0.0001). By 1-year-post, sponge cover showed a slight increase to 2.2%, but it remained significantly lower than baseline levels (z = 8.93, p < 0.0001) (Fig. 3 c, Table 2 , S2). Table 2 Fixed effects from beta regression models examining changes in percent cover of coral, algae, and sponge functional groups across time periods relative to the baseline. Positive values indicate an increase in cover relative to baseline, while negative values indicate a decline. All models include significant temporal effects, particularly for algae and sponge cover following the urchin die-off. Model Term Estimate Std. Error z value p-value Coral Cover (Intercept) -4.328 0.103 -42.067 < 0.0001 Coral Cover perioddieoff 0.190 0.066 2.881 0.0040 Coral Cover periodafter 6mo 0.063 0.068 0.934 0.3501 Coral Cover periodafter 12mo 0.207 0.065 3.201 0.0014 Algae Cover (Intercept) -1.074 0.104 -10.334 < 0.0001 Algae Cover perioddieoff 0.296 0.071 4.174 < 0.0001 Algae Cover periodafter 6mo 0.204 0.071 2.867 0.0041 Algae Cover periodafter 12mo 0.153 0.070 2.175 0.0296 Sponge Cover (Intercept) -2.943 0.089 -33.079 < 0.0001 Sponge Cover perioddieoff -1.208 0.105 -11.450 < 0.0001 Sponge Cover periodafter 6mo -1.148 0.106 -10.859 < 0.0001 Sponge Cover periodafter 12mo -0.817 0.096 -8.502 < 0.0001 Benthic community structure response to die-off (multivariate analysis) Our multivariate analysis confirmed that the benthic community structure shifted significantly following the die-off (Fig. 4 ). PERMANOVA results indicated a significant effect of period on community structure (R² = 0.0812, F = 10.607, p = 0.001), explaining 8.12% of the variation in the dataset (Table 3 , S3). Pairwise comparisons revealed that community structure changed significantly following the die-off, with the Pre-die-off period differing from all subsequent periods (Pre-die-off vs. Early-die-off: R² = 0.1159, F = 23.33, p = 0.001; Pre-die-off vs. Post-die-off: R² = 0.0924, F = 18.02, p = 0.001; Pre-die-off vs. 1-year-post: R² = 0.0611, F = 11.92, p = 0.001). However, there was no significant difference between Early-die-off and Post-die-off levels (p = 0.111), suggesting that community structure remained relatively stable during this period. Similarly, Post-die-off and 1-year-post were not significantly different (p = 0.254), indicating little change beyond the Post-die-off period. A small but significant difference between Early-die-off and 1-year-post (p = 0.026) suggests some gradual shifts in community structure over time. Table 3 PERMANOVA results testing for differences in community composition across four time periods. Term df Sum of Squares R² F-value p-value period 3 1.844 0.081 10.607 0.0010 Residual 360 20.861 0.919 Total 363 22.705 1.000 Precedent urchin conditions effects on macroalgae and coral recruitment (log Response ratio analysis) Preceding conditions in urchin cluster size and abundance influenced the magnitude of coral recruitment decline and of macroalgae cover. Our log-ratio response models, where the log-ratio represents the change in Porites astreoides recruits from Early-die-off to 1-year-post, revealed that sites with higher Pre-die-off urchin cluster counts experienced greater declines in recruitment (χ² = 8.15, p = 0.004) (ST 4, Fig. 5 a). Similarly, larger Pre-die-off cluster sizes were significantly associated with lower recruitment log-ratios (χ² = 8.47, p = 0.0036), indicating that plots initially dominated by large urchin clusters saw steeper declines in new coral settlers following the die-off (ST 4, Fig. 5 b). Our log-ratio response models for algae cover, where the log-ratio represents the change in macroalgae cover relative to Pre-die-off levels, demonstrated that the increase in macroalgae abundance Post-die-off was greater at plots with a higher Pre-die-off number of urchin clusters (χ² = 8.07, p = 0.004) (Fig. 6 a). In contrast, larger Pre-die-off cluster sizes were negatively associated with the log-ratio of macroalgae (χ² = 4.58, p = 0.032), indicating smaller relative increases in macroalgae at those plots (Fig. 6 b). Log-ratio response models used to assess whether sponge cover change was associated with Diadema cluster count and size prior Pre-die-off revealed neither the number of Diadema observed pre-die-off (z = 1.203, p = 0.229) nor their mean size (z = 0.482, p = 0.629) significantly predicted the change in sponge cover. The model intercept was significantly negative (Estimate = -1.537, p < 0.001), indicating an overall decline in sponge cover across sites, regardless of baseline urchin metrics (Table 4 ). Table 4 Fixed effects from gaussian model testing Pre-die-off Diadema abundance and size predict changes in sponge cover. effect component Term Estimate Std. Error z value p-value fixed cond (Intercept) -1.537 0.162 -9.513 < 0.001 fixed cond Counts Pre-die-off 0.049 0.041 1.203 0.229 fixed cond Mean size Pre-die-off 0.148 0.307 0.482 0.629 Discussion The loss of keystone herbivores can trigger profound changes in community structure across ecosystems, including coral reefs. In reef systems, Diadema historically played a critical role in maintaining competitive dynamics by grazing down fast-growing macroalgae (Carpenter, 1990 ; Lessios, 1988 ). When such herbivores are lost, there is a release from top-down control, allowing macroalgae to proliferate. In our study, the sharp decline in Diadema abundance was followed by a marked increase in macroalgal cover, and a shift in benthic community structure. This macroalgal expansion likely initiated the cascading effects on other benthic groups seen here through multiple mechanisms, including competitive exclusion and allelopathy (Rasher et al., 2011 ). As macroalgae physically overgrow corals and other sessile invertebrates, they compete for space and light, while also releasing chemical compounds that can inhibit coral recruitment and growth, as well as alter microbial community dynamics (McCook et al., 2001; Rasher & Hay, 2010). These changes may further reduce coral resilience and contribute to long-term shifts in community composition. Similar dynamics have been observed in terrestrial systems; for instance, in African savannas, exclusion of large herbivores can result in woody plant encroachment that suppresses understory diversity through both physical and chemical interference (Pringle et al., 2014; Young et al., 2013). These cross-ecosystem parallels underscore the broad ecological importance of herbivores in regulating community dynamics and maintaining ecosystem functions. In the context of our findings, the disproportionate influence of Diadema on benthic community structure highlights its role not only as a grazer, but as a regulator of broader feedback loops that shape reef trajectories. In our study, we assessed the response of the benthic community to the recent die-off of Diadema in Culebra, Puerto Rico. We documented a 96% decline in urchin abundance, comparable to the catastrophic 1980s die-off, when populations declined by approximately 98% (Carpenter, 1988 ; Lessios, 1988 ). A slight increase in urchin clusters was observed in later periods of our study, but we believe these may not indicate proper recruitment but rather the movement of individuals from other locations. As Lessios ( 2016 ) suggests, Diadema populations have been observed to migrate following die-off events, likely in search of suitable habitat. Given the slow population recovery rates documented in previous studies, it is unlikely that the observed clusters represent new recruitment events (Lessios, 2016 ; Rogers & Lorenzen, 2016 ). Instead, this may reflect redistribution from adjacent areas, temporarily increasing local densities. However, during the last sampling event, small juvenile individuals were observed (Santos and Rivas, unpublished observations), suggesting a potential slow recovery through recruitment. Our results indicate that initial urchin abundance patterns played a key role in determining coral recruitment and post-mortality algal responses. Areas with higher Pre-die-off cluster counts experienced greater decreases in coral recruitment and greater increases in macroalgae cover following the die-off, suggesting that these sites initially had strong grazing pressure (i.e., top-down control) from Diadema , which, when lost, led to a more pronounced algal expansion, potentially outcompeting corals for space (Edmunds & Carpenter, 2001 ; Williams, 2022 ). Sites with larger clusters exhibited smaller shifts in macroalgae abundance post-die-off, potentially indicating that some residual urchins from larger clusters survived; however, those sites still experienced a greater decrease in coral recruits. These findings highlight the importance of urchin density in reef benthic dynamics and suggest that future recovery efforts should consider not only the number of urchins but also their spatial distribution and local density patterns (Burkepile & Hay, 2008 ; Levitan et al., 2014 ; Williams, 2016 ). Previous studies have demonstrated that herbivore abundance and aggregation are critical for effective top-down regulation of macroalgal communities. Research on Diadema has shown that below certain density thresholds, grazing pressure is insufficient to prevent macroalgal dominance, whereas above those thresholds, algae are effectively suppressed (Manuel et al., 2021 ;Williams, 2016 , 2022 ). This highlights the existence of a functional threshold in herbivory-driven control, underscoring the need for targeted strategies that restore urchin populations to ecologically meaningful levels. Changes of macroalgae and coral recruitment associated with the recent mortality event resulted in a dramatic shift in reef community structure, with impacts persisting beyond the initial decline. By linking the sharp decline in urchin abundance to subsequent increases in macroalgal cover and shifts in benthic community structure, our analyses reinforce the critical ecological function of Diadema in suppressing algal-dominated reef states (Williams, 2022 ). While coral cover appeared to increase over time, this trend is likely an artifact of sampling bias rather than actual biological recovery. Coral cover was already extremely low and point count methods are known to underestimate true values when cover is sparse (Pante & Dustan, 2012 ; Rivas et al., in prep). Furthermore, given the ongoing degradation of coral reefs, decreasing larval supply, and the lack of observed recruitment that could drive coral expansion, it is unlikely that this increase reflects a true positive trajectory (Edmunds & Elahi, 2007; Hughes et al., 2010). In such sparse conditions, even small absolute changes can disproportionately influence percent cover estimates (Pante & Dustan, 2012 ). Instead, the apparent uptick in coral cover highlights the methodological and ecological challenges of tracking coral responses in systems that are already severely degraded. This underscores the importance of combining traditional survey methods with complementary approaches, such as high-resolution photogrammetry, long-term colony tracking, or recruitment assays, to better detect subtle trends in coral dynamics and avoid misinterpreting noise as recovery (Burns et al., 2015 ; P. Edmunds & Riegl, 2020 ). The community structure change captured by our analysis was driven mostly by the increase in macroalgae and sponge cover. Macroalgae responded rapidly to the loss of Diadema , increasing in cover following the die-off and remaining elevated throughout the study. The stabilization of macroalgae cover after Fall 2022 indicates a shift toward a sustained phase of elevated algal dominance, rather than a return to pre-die-off conditions, especially on the reefs that previously had a relatively higher abundance of urchins. Similar patterns have been documented elsewhere in the Caribbean following the 1983–84 Diadema collapse; reefs experienced long-term algal dominance that persisted for decades (Edmunds & Carpenter, 2001 ; Mumby et al., 2006 ). One of the most striking changes observed was the sharp decline in sponge cover, which decreased by 61% immediately following the Diadema die-off and remained low throughout all subsequent periods. Sponges play a significant role in reef function through water filtration, nutrient cycling, and providing structural complexity (Bell, 2008 ). Their sustained loss suggests long-term disruption to these functions. Our results did not reveal a significant relationship between the change in sponge cover and precedent urchin cluster counts and size, suggesting that other factors could be occurring during our study period. While the decline may be partly linked to increased macroalgal cover through space competition, shading, or allelopathic effects (Rasher & Hay, 2010; McCook et al., 2001), the possibility of confounding factors such as winter swells, storm impacts, or disease cannot be ruled out. Still, the timing of the decline, coupled with the absence of recovery, supports the hypothesis that sponges are vulnerable to cascading effects associated with the loss of herbivore functional roles. Our results show that Porites astreoides recruitment declined more severely at sites that had higher Diadema cluster counts and larger cluster sizes prior to the die-off. This pattern, based on the log response ratio of recruits one-year post-die-off relative to pre-die-off levels, indicates that sites previously supported by Diadema have been disproportionately impacted by their loss. Notably, this decline was observed even for P. astreoides , a species typically characterized as weedy and resilient (Darling et al., 2012 ). This suggests that the functional role of Diadema in maintaining recruitment conditions, likely by suppressing macroalgae and facilitating space availability, was not only critical, but also part of a broader set of ecological feedbacks that structure reef communities (Van De Leemput et al., 2016 ). The sudden absence of Diadema appears to have disrupted these positive feedback loops, leading to a cascade of ecological effects that impaired coral recruitment. Loss of this herbivory function can tip reefs into alternative stable states dominated by macroalgae, further inhibiting coral recovery via interference with recruits (Mumby et al., 2013 ; Van De Leemput et al., 2016 ). These findings highlight the vulnerability of seemingly resilient taxa when key ecosystem processes are lost (Hirota et al., 2011 ; Steneck et al., 2002 ). It’s important to note the use of large-area imagery (LAI) in this study, which offers a powerful and relatively novel approach for detecting spatially explicit ecological patterns across benthic communities. LAIs provide a high-resolution, permanent record of reef conditions that can be analyzed for multiple response variables including coral recruitment, macroalgal expansion, and herbivore (e.g., Diadema ) distribution within a consistent spatial framework (Burns et al., 2015 ; Remmers et al., 2023 ). This allows for repeatable, fine-scale tracking of individual colonies, algal patches, and herbivore clusters over time, which is particularly valuable in studying cascading ecological effects following a die-off event. Additionally, LAIs reduce diver subjectivity and increase sampling efficiency over broad spatial extents, which is often a limitation of traditional transect based methods (Curtis et al., 2023 ; Remmers et al., 2023 ). However, several caveats must be considered when interpreting these estimates. The detectability of Diadema in LAIs is influenced by the urchins’ position within the reef structure, particularly when individuals are partially obscured in crevices or overhangs. Additionally, this method captures only those urchins visible on the exposed reef surface at the time of image acquisition and may underestimate true abundance. Cluster categorizations are coarse and may miss nuanced shifts in urchin demographics or behavior (e.g., diurnal hiding). Despite these limitations, LAIs provide a valuable proxy for Diadema abundance that, when paired with standardized imagery protocols and known limitations, enables robust inference in long-term ecological studies where direct herbivore data are lacking. Overall, these findings emphasize the lasting impacts of the Diadema die-off, particularly the severe and persistent loss of sponge cover. While macroalgae responded predictably to the decline of herbivory, the lack of recovery in sponges and urchins suggests that ecosystem functions disrupted by the die-off may not readily return to their previous state. Furthermore, the apparent increase in coral cover is likely an artifact of methodological limitations rather than actual reef recovery. Taken together, these results reinforce the importance of key herbivores in structuring reef communities and highlight the potential for long-term ecosystem shifts following widespread mortality events. Declarations Statement of Declaration: The authors declare no relevant financial or non-financial interests that could be perceived as influencing the content of this article. They report no conflicts of interest and confirm that they have no affiliations with any organization or entity with financial or non-financial stakes in the subject matter or materials presented in this manuscript. Additionally, the authors hold no financial or proprietary interest in any material discussed herein. Author Contribution This work was conceptualized by NR, WRJ, and ROS. Data collection was carried out by NR, VB, MS, AM, and ROS, and the data were processed by NR, VB, MS, AFG, and SBA. Statistical analyses were conducted by NR, WRJ and ROS. The original draft was written by NR, WRJ and ROS, and all authors contributed to the review and editing of the final manuscript. Acknowledgement We thank Sociedad Ambiente Marino (SAM) for their support, as well as the volunteers and staff of the Santos Seascape Ecology Lab for their invaluable assistance in the field and data processing. This material is also based upon work supported by the U.S. National Science Foundation under Grant No. HRD-1547798 and Grant No. HRD-2111661. These NSF Grants were awarded to Florida International University as part of the Centers of Research Excellence in Science and Technology (CREST) Program. This work was funded by the National Fish and Wildlife Foundation (NFWF GRANT ID: 0318.19.066113, 2022-IC-071; O-VS-PEP13-SJ-00052-27102022) and the National Science Foundation (NSF RAPID: 2235138; 2023-EPE-024 O-VS-PVS15-SJ-01341-30112022), whose support made this research possible. This is contribution #X from the Institute of Environment at Florida International University. 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Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Coral Reefs → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 10 Aug, 2025 Editor assigned by journal 10 Aug, 2025 Submission checks completed at journal 09 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7303485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501536543,"identity":"3671c652-e35b-4bd6-86be-c7ea4f71bd04","order_by":0,"name":"Nicolas Rivas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYDACZgaGA0BKjr0ZxGMjQYsxz2GitUBBYs8BYrWYs/M+PMxTU5few85jwPCh7DBhLZbN7AaHeY4dzu1h5jFgnHGOCC0Gh9kYDs5gO5C7H6iFmbeNaC3/6tJ5QFr+EqvlwMc25gSwFkbitfQdNuxhZis42HMunQgt548xf0j4VifPw39444MfZdaEtaCAAySqHwWjYBSMglGACwAAmOY094lr0DcAAAAASUVORK5CYII=","orcid":"","institution":"Florida International University","correspondingAuthor":true,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Rivas","suffix":""},{"id":501536544,"identity":"b82f7e26-fe9e-43ac-944b-d0e52bad05d0","order_by":1,"name":"W. 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Sites were monitored across four time periods to assess temporal dynamics in reef conditions\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/550c579629f59f2a820e456a.jpeg"},{"id":89318014,"identity":"4b782614-c150-4b38-9295-7147d6f080d9","added_by":"auto","created_at":"2025-08-18 17:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1134353,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Diadema antillarum cluster dynamics before and after the mass mortality event. (a) Mean number of urchin clusters per plot and (b) mean cluster size across four time periods relative to the urchin die-off, with 95% confidence intervals. Different letters indicate significant pairwise differences between periods (Tukey-adjusted, p \u0026lt; 0.05). (c) Photograph taken during study period showing multiple Diadema husks\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/8b14f3e7643b3ddffebc334f.png"},{"id":89317750,"identity":"95d7a768-83c5-447d-a562-1b719082ab3c","added_by":"auto","created_at":"2025-08-18 17:26:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172717,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in percent cover of major benthic functional groups across four time periods relative to the Diadema die-off event. (a) Coral cover remained relatively stable. (b) Algae cover increased significantly after the die-off and remained elevated. (c) Sponge cover declined sharply post-die-off, followed by a gradual recovery. Points represent mean percent cover per period with 95% confidence intervals. Letters denote significant differences between time periods based on post hoc pairwise comparisons (Tukey-adjusted, p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/160b16b935db58b2d6707dda.png"},{"id":89318015,"identity":"8eec1898-4f57-4c4f-be47-b09a3c2e86a7","added_by":"auto","created_at":"2025-08-18 17:34:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289581,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) ordination of benthic community composition across four time periods relative to the Diadema die-off. Each point represents a plot in NMDS space based on Bray–Curtis dissimilarity. Colors indicate sampling periods. A stress value of 0.0749 indicates a good fit\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/b75172a8b3660e5a160080a3.png"},{"id":89317751,"identity":"df4e3452-9552-471b-9bca-a1cb1db931ff","added_by":"auto","created_at":"2025-08-18 17:26:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":207861,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between urchin cluster characteristics and changes in coral recruit abundance. (Left) Sites with higher baseline Diadema cluster counts experienced greater declines in coral recruitment following the die-off event. (Right) Similarly, sites with larger baseline cluster sizes were associated with stronger declines in recruit abundance. Shaded areas represent 95% confidence intervals for each linear model\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/62d7304c3d07dadf53767f31.png"},{"id":89318863,"identity":"497255d3-9a8e-4de7-ad4e-a4734c7e665b","added_by":"auto","created_at":"2025-08-18 17:50:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216684,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between baseline urchin cluster metrics and changes in macroalgae cover following the Diadema die-off. (Left) Sites with a greater number of urchin clusters at baseline exhibited significantly larger increases in algae cover (p = 0.0045). (Right) In contrast, sites with larger urchin clusters showed smaller increases in algae cover (p = 0.0323). Shaded areas represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/6cc5f586b33f32870462e517.png"},{"id":102785573,"identity":"8418f407-c511-436b-b5fa-38e2bcf0e7bc","added_by":"auto","created_at":"2026-02-16 16:08:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3259361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/8a4b38ca-9afa-474a-a597-37705d8ead8e.pdf"},{"id":89317747,"identity":"334f4c12-e60d-4e9d-be5c-3905ce00bf76","added_by":"auto","created_at":"2025-08-18 17:26:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33099,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7303485/v1/5e2d72fe75c84df6570f9065.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tipping the balance: Reef community shifts after a regional urchin population collapse","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoral reef ecosystems are shaped by complex interactions among organisms, with herbivory playing a crucial role in maintaining reef health (Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mumby et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Herbivores, such as sea urchins, help regulate algal populations, preventing macroalgae from outcompeting corals for space and light (Adam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This regulation is essential for maintaining healthy competition dynamics and reef resilience, particularly in the face of stressors like increasing anthropogenic disturbances and climate change (Arnold et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Barott et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Burkepile \u0026amp; Hay, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cramer et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mumby et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). When herbivore populations decline, macroalgae can proliferate, potentially leading to phase shifts where reefs transition from coral-dominated to algal-dominated states (Dudgeon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hughes, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Hughes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These interactions are further complicated by feedback loops involving nutrient dynamics, predator-prey relationships, and species-specific functional roles (Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alvarez-Filip et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Arnold et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The balance between herbivory and algal growth plays a significant role in determining reef community structure and function, highlighting the importance of protecting key herbivores to sustain ecosystem stability and biodiversity.\u003c/p\u003e\u003cp\u003eIn the 1980\u0026rsquo;s Caribbean reefs experienced a mass mortality event of the black spiny sea urchin, \u003cem\u003eDiadema antillarum\u003c/em\u003e (\u003cem\u003eDiadema\u003c/em\u003e hereafter), a key herbivore on Caribbean reefs (Carpenter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Mumby et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The die-off reduced \u003cem\u003eDiadema\u003c/em\u003e densities across the western Atlantic by over 97%, contributing to, and further accelerating, phase shifts from coral-dominated to algal-dominated communities in many reefs (Lessios, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mumby et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Without the grazing pressure of \u003cem\u003eDiadema\u003c/em\u003e, macroalgae rapidly proliferated, reaching densities high enough to overgrow and smother even adult coral colonies (Lessios, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFollowing the catastrophic mass mortality of \u003cem\u003eDiadema\u003c/em\u003e in the 1980s, populations remained depressed for decades, with only isolated signs of recovery observed in parts of the Caribbean by the mid-2010s (Lessios, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, in 2022, another widespread mortality event of \u003cem\u003eDiadema\u003c/em\u003e swept across the eastern Caribbean, marking the most significant die-off of this keystone herbivore since the catastrophic 1983 event (Hylkema et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). First reported in the Virgin Islands, the die-off spread rapidly across multiple reef systems, with \u003cem\u003eDiadema\u003c/em\u003e exhibiting characteristic symptoms such as spine loss, disorientation, and rapid tissue degradation before succumbing to mortality by a scuticocillate (Hewson et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hylkema et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given the species\u0026rsquo; critical role in reef herbivory, this event raises concerns about the potential for increased macroalgal dominance and further reef degradation, particularly in systems where \u003cem\u003eDiadema\u003c/em\u003e had begun to recover (Burkepile \u0026amp; Hay, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lessios, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Williams, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The 2022 die-off underscores the vulnerability of \u003cem\u003eDiadema\u003c/em\u003e populations and highlights the urgency of understanding the drivers and ecological consequences of such mortality events in present day reefs.\u003c/p\u003e\u003cp\u003eEvaluating the impacts of the 2022 \u003cem\u003eDiadema\u003c/em\u003e die-off is essential for understanding the importance of their slow recovery and the resilience of today\u0026rsquo;s coral reef ecosystems. As with the mass mortality event in 1983, contemporary reefs continue to face compounding stressors such as climate change, ocean acidification, and the decline of herbivorous species due to overfishing and disease (Alvarez-Filip et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Barott et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cramer et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hughes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, unlike in the past, \u003cem\u003eDiadema\u003c/em\u003e now persists at relatively low densities, raising important questions about the extent of their current ecological role. While their loss is concerning, the influence of \u003cem\u003eDiadema\u003c/em\u003e on reef dynamics may not be as pronounced as it once was. Nonetheless, their continued decline could further hinder coral recruitment and accelerate shifts in benthic community composition and structure (Adam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Arnold et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Carpenter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Rodr\u0026iacute;guez-Barreras et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that some \u003cem\u003eDiadema\u003c/em\u003e populations had shown signs of recovery prior to this event, their sudden decline raises concerns about whether natural recolonization can still occur under present-day conditions (Rogers \u0026amp; Lorenzen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Investigating the ecological consequences of this die-off will provide insights into the role of \u003cem\u003eDiadema\u003c/em\u003e in reef dynamics under current altered conditions, the potential for recovery, and the management strategies needed to mitigate further ecosystem decline.\u003c/p\u003e\u003cp\u003eMonitoring efforts using large-area images via photogrammetry can provide detailed (i.e., high-resolution) and diverse ecological information critical to understanding the role of herbivores, such as \u003cem\u003eDiadema\u003c/em\u003e, in regulating the community assembly on reefs. These large-area images allow us to quantify changes in algal and coral cover over time and assess how the loss of this key herbivore influenced benthic community structure. For this study, we used large-area images created as part of a monitoring effort in the municipal island of Culebra (Puerto Rico) to specifically ask: 1) Are there signs of \u003cem\u003eDiadema\u003c/em\u003e recovery? 2) How does macroalgae cover relate to urchin abundance in modern-day reefs? and 3) How did the benthic community structure respond to the \u003cem\u003eDiadema\u003c/em\u003e die-off? Given the strong herbivory pressure exerted by \u003cem\u003eDiadema\u003c/em\u003e, we hypothesized that sites with previously high urchin abundance would experience a more pronounced shift toward algae-dominated communities than those where \u003cem\u003eDiadema\u003c/em\u003e was already scarce or absent. In the absence of these grazers, macroalgae are expected to proliferate, potentially outcompeting corals and altering habitat structure. Modeling work by Mumby et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) identified the importance of high \u003cem\u003eDiadema\u003c/em\u003e densities needed to prevent algal phase shifts. To better understand these transitions, we assessed not only coral cover but also recruitment, as reduced herbivory may limit the settlement and survival of juvenile corals. Consequently, we hypothesized a shift in benthic community structure following the die-off, characterized by increased macroalgal cover, reduced coral recruitment and cover. Together, these metrics provide insights into the mechanisms driving reef change and the potential for recovery in the face of mounting anthropogenic stressors and climate change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eBenthic Monitoring\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData for this study was collected in Culebra, Puerto Rico, over 3 years (2021 to 2023). A total of 24 sites were established on the south-eastern end of the island (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each site was composed of four 100 m\u003csup\u003e2\u003c/sup\u003e plots (n\u0026thinsp;=\u0026thinsp;96 total plots) that were delineated with nails and paracord in 2021 for consistent monitoring. For this study, our time points consisted of a Pre-die-off (2021), Early-die-off (June/July 2022, right after the \u003cem\u003eDiadema\u003c/em\u003e mortality event in Puerto Rico), Post-die-off (October 2022), and 1-year-post (May 2023) period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe used photogrammetry and Structure-from-Motion (SfM) techniques to generate high-resolution digital large-area images of benthic communities (Petrovic et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In 2021, images were collected using two GoPro Hero 8 cameras, while in subsequent years, we used two Nikon D7500 high-resolution cameras to follow monitoring protocols established by the National Oceanographic and Atmospheric Administration (Suka et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Divers followed a non-disruptive lawn-mower survey pattern, maintaining an altitude of approximately 1.5 meters above the reef substrate to ensure consistent image acquisition and minimize distortion. Images were captured with 70\u0026ndash;80% overlap to facilitate accurate reconstruction. Eight scale bars were strategically placed within the survey area, and corresponding depth measurements were recorded. The collected images were color corrected in Adobe Lightroom and processed in Agisoft Metashape Pro (v2.0), where they were aligned, key points were matched, and a dense point cloud was generated following Cook et al., (2023). This point cloud was then used to create a two-dimensional orthomosaic or large-area Image (LAI), which served as the basis for the benthic analysis of this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of Urchin Abundance and Benthic Community Response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBenthic community structure was quantified using percent cover estimates via the virtual point intercept method. LAIs were imported into ArcGIS, where they were overlaid with a 1 m\u0026sup2; grid to divide the LAI into uniform sections. Each grid cell was extracted as a 1 m\u0026sup2; image (photo-quadrat), resulting in a total of 95 to 165 photo-quadrats per reef plot, depending on the plot's true size. Photo-quadrats were then uploaded to CoralNet for semi-automated image annotation (I. D. Williams et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). CoralNet uses convolutional neural networks trained on manually annotated images to classify benthic features, improving its accuracy over time through repeated exposure to manually labeled data. The model was trained with 23% of the uploaded photo-quadrats being manually annotated using 35 random points to classify benthic cover into 6 principal functional groups: hard coral, soft coral, macroalgae, sponge, and sand., achieving an overall functional group accuracy of 80.2%. This accuracy supports the reliability of the automated annotations in capturing patterns of benthic cover, although potential biases may still exist, particularly in underrepresented classes.\u003c/p\u003e\u003cp\u003eTo assess shifts in \u003cem\u003eDiadema\u003c/em\u003e abundance, we utilized the LAIs in place of direct field counts. Since urchins were not systematically counted during fieldwork, LAIs provided a critical ex-situ tool to approximate their presence and abundance across reef plots. Each LAI was examined for visible Diadema individuals, and observations were grouped into cluster categories to represent relative abundance. Each observed cluster of \u003cem\u003eDiadema\u003c/em\u003e was recorded, and cluster size was categorized into four levels: (1) a single urchin, (2) small clusters of 2\u0026ndash;5 individuals, (3) medium clusters of 5\u0026ndash;10 individuals, and (4) large clusters exceeding 10 individuals. Plots with no observed urchins were assigned a 0. This approach allowed for standardized comparisons across plots and time points despite the absence of direct urchin surveys in the field.\u003c/p\u003e\u003cp\u003eFor coral recruitment estimates, point clouds generated during the LAI stitching process were imported into VISCORE, scaled, and exported as orthoprojections. These orthoprojections were then analyzed in TagLab, where coral colonies of \u003cem\u003ePorites astreoides\u003c/em\u003e were identified and delineated. \u003cem\u003ePorites astreoides\u003c/em\u003e was chosen due to its classification as a weedy coral, that is currently expected to be doing well under current conditions (Darling et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recruitment was assessed by tracking the appearance of new colonies between survey periods: only colonies not present in 2021 were counted as recruits in Early-die-off, and only colonies absent in 2022 were counted as recruits in 1-year-post. To standardize measurements, recruits were limited to a maximum size of 5 cm\u0026sup2;.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted in R (v4.4.2; R Core Team, 2024) to examine temporal changes in \u003cem\u003eDiadema\u003c/em\u003e abundance and the response of the benthic community to the die-off. We used generalized linear models (GLMs) to test whether macroalgal cover change, quantified as the log-ratio of Post to Pre-die-off values, was predicted by Pre-die-off \u003cem\u003eDiadema\u003c/em\u003e abundance and size. As the response, we used the log-ratio of Pre-die-off macroalgae cover to Post-die-off macroalgae cover to assess how Pre-die-off urchin cluster counts and sizes influenced changes in macroalgae cover.\u003c/p\u003e\u003cp\u003eWe used generalized linear mixed models (GLMMs) to model the change in urchin cluster size and urchin cluster amount over time. We fit a GLMM for the urchin cluster amount model using a negative binomial error distribution with site as a random effect. The urchin cluster size model was ran using GLMM with a Gaussian error distribution and site as a random factor after fitting for the best model. Given the random distribution of clusters across the study sites, we focused exclusively on cluster size when clusters were present, considering only period as a predictor variable in our model. Additionally, the log-ratio of coral recruitment from Early-die-off to 1-year-post was modeled using a Gaussian error distribution, with initial \u003cem\u003eDiadema\u003c/em\u003e cluster density and size as fixed effects, to explore the role of urchins in shaping post-die-off coral recruitment dynamics. Similarly, the log response ratio of sponge cover Pre-die-off and Post-die-off was modeled using a GLM with a Gaussian distribution, with \u003cem\u003eDiadema\u003c/em\u003e cluster count and size as predictor variables to evaluate their influence on sponge cover change.\u003c/p\u003e\u003cp\u003eWe used GLMMs with beta error distribution (i.e., beta regressions) to assess changes in coral, macroalgae, and sponge percent cover across periods (Pre-die-off, Early-die-off, Post-die-off and 1-year-post), with period as a fixed effect and site as a random intercept to account for site-level variability. Individual GLMMs were performed to evaluate changes in the percentage cover of macroalgae, sponges, and corals across the periods relative to the \u003cem\u003eDiadema\u003c/em\u003e die-off. Model diagnostics and simplification were conducted using the performance package (L\u0026uuml;decke et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to assess fit and assumptions, and model selection was refined through dredging with the MuMIn package (Bartoń, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to identify the top four models based on AICc and then selected for the most most parsimonious model.\u003c/p\u003e\u003cp\u003eThe response of the benthic community structure to the \u003cem\u003eDiadema\u003c/em\u003e die-off was assessed using a multivariate analysis framework at the level of functional groups, including corals, macroalgae, sponges, and seagrass. We conducted a Permutational Multivariate Analysis of Variance (PERMANOVA) using a Bray-Curtis dissimilarity matrix to evaluate differences in community structure across time periods (treated as a fixed effect). To account for spatial non-independence among samples, we specified site as a random effect in the adonis2 function. This approach constrained permutations within sites, helping control site-level variation while testing the main effect of period. Additionally, we conducted pairwise PERMANOVA comparisons among time periods to identify specific temporal shifts in community structure. Multivariate analyses were performed in R using the vegan package for adonis2 (Oksanen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDiadema die-off recovery\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study revealed a sharp decline in both the number and size of \u003cem\u003eDiadema\u003c/em\u003e clusters across the study site following the die-off event, with limited indications of recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Urchin presence declined by approximately 96% immediately after the mortality event (mean\u0026thinsp;=\u0026thinsp;0.11, CI\u0026thinsp;=\u0026thinsp;0.033\u0026ndash;0.347; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the lowest predicted amount observed Post-die-off (mean\u0026thinsp;=\u0026thinsp;0.03, CI\u0026thinsp;=\u0026thinsp;0.006\u0026ndash;0.137). By 1-year-post, there was a slight increase in urchin presence (mean\u0026thinsp;=\u0026thinsp;0.13, CI\u0026thinsp;=\u0026thinsp;0.045\u0026ndash;0.394), though values remained well below Pre-die-off levels (mean\u0026thinsp;=\u0026thinsp;2.37, CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;5.76). Pairwise comparisons confirmed that urchin presence was significantly lower across all periods compared to Pre-die-off (z\u0026thinsp;\u0026ge;\u0026thinsp;6.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Tukey-adjusted), while differences among post-die-off periods were not significant (z\u0026thinsp;\u0026le;\u0026thinsp;2.19, p\u0026thinsp;\u0026ge;\u0026thinsp;0.127, Tukey-adjusted), indicating limited recovery within one year (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; S1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\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\u003eFixed effects from generalized linear mixed models examining the effects of time (period) on urchin cluster counts and cluster sizes. Period estimates are relative to the Baseline period (intercept). The cluster count model used a negative binomial distribution, while the cluster size model used a Gaussian distribution with a log link.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod Early-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod Post-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod 1-year-post\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod Early-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod Post-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeriod 1-year-post\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eUrchin cluster size significantly decreased following the die-off and remained low throughout the study period. Urchin cluster size significantly declined following the die-off event and remained at low numbers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Post-hoc comparisons confirmed a significant decline in cluster size between Pre-die-off and Early-die-off periods (t\u0026thinsp;\u0026ge;\u0026thinsp;2.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0056, Tukey-adjusted (p\u0026thinsp;=\u0026thinsp;0.0056), and between Pre-die-off and 1-year-post periods (t\u0026thinsp;\u0026ge;\u0026thinsp;4.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Tukey-adjusted). However, there were no significant differences in cluster size between the Early-die-off period and later time points (t\u0026thinsp;\u0026le;\u0026thinsp;0.67, p\u0026thinsp;\u0026ge;\u0026thinsp;0.90, Tukey-adjusted).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBenthic functional group response to die-off (univariate analysis)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate analyses from the benthic data extracted with CoralNet revealed temporal changes in the benthic cover of functional groups, often correlated with urchin cluster metrics. Coral percent cover increased modestly following the urchin die-off, with model-predicted values rising from 1.26% Pre-die-off to 1.48% Early-die-off (z\u0026thinsp;=\u0026thinsp;2.69, p\u0026thinsp;=\u0026thinsp;0.036; +17.5%) and 1.51% in 1-year-post (z\u0026thinsp;=\u0026thinsp;3.04, p\u0026thinsp;=\u0026thinsp;0.013; +19.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In contrast, the Post-die-off period showed a smaller, non-significant increase to 1.31% (z\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;=\u0026thinsp;0.911; +4.0%) relative to the Pre-die-off period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S2). However, given the relatively small effect sizes and the already low cover of coral, these differences likely fall within the uncertainty bounds of the CoralNet classification model, suggesting that they may reflect model sensitivity rather than true ecological shifts. Similarly, the percentage cover of macroalgae increased following the die-off event (Early-die-off) and remained elevated a year after the event (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S2). Specifically, at baseline, the mean predicted macroalgae cover was 25.2%, which increased to 31.6% in the Early-die-off period (z\u0026thinsp;=\u0026thinsp;4.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Cover remained elevated Post-die-off at 29.6% (z\u0026thinsp;=\u0026thinsp;3.28, p\u0026thinsp;=\u0026thinsp;0.0058) and in 1-year-post at 28.6%, though the latter was marginally non-significant (z\u0026thinsp;=\u0026thinsp;2.56, p\u0026thinsp;=\u0026thinsp;0.052) compared to Pre-die-off levels. In contrast, sponge percent cover decreased sharply following the die-off, from a model-predicted 5.0% Pre-die-off to 1.5% in Early-die-off (z\u0026thinsp;=\u0026thinsp;11.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 1.6% in Post-die-off (z\u0026thinsp;=\u0026thinsp;11.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). By 1-year-post, sponge cover showed a slight increase to 2.2%, but it remained significantly lower than baseline levels (z\u0026thinsp;=\u0026thinsp;8.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S2).\u003c/p\u003e\u003cp\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\u003eFixed effects from beta regression models examining changes in percent cover of coral, algae, and sponge functional groups across time periods relative to the baseline. Positive values indicate an increase in cover relative to baseline, while negative values indicate a decline. All models include significant temporal effects, particularly for algae and sponge cover following the urchin die-off.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoral Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-42.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoral Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperioddieoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoral Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 6mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoral Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 12mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlgae Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlgae Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperioddieoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlgae Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 6mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlgae Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 12mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSponge Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-33.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSponge Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperioddieoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-11.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSponge Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 6mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSponge Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperiodafter 12mo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003cb\u003eBenthic community structure response to die-off (multivariate analysis)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur multivariate analysis confirmed that the benthic community structure shifted significantly following the die-off (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PERMANOVA results indicated a significant effect of period on community structure (R\u0026sup2; = 0.0812, F\u0026thinsp;=\u0026thinsp;10.607, p\u0026thinsp;=\u0026thinsp;0.001), explaining 8.12% of the variation in the dataset (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S3). Pairwise comparisons revealed that community structure changed significantly following the die-off, with the Pre-die-off period differing from all subsequent periods (Pre-die-off vs. Early-die-off: R\u0026sup2; = 0.1159, F\u0026thinsp;=\u0026thinsp;23.33, p\u0026thinsp;=\u0026thinsp;0.001; Pre-die-off vs. Post-die-off: R\u0026sup2; = 0.0924, F\u0026thinsp;=\u0026thinsp;18.02, p\u0026thinsp;=\u0026thinsp;0.001; Pre-die-off vs. 1-year-post: R\u0026sup2; = 0.0611, F\u0026thinsp;=\u0026thinsp;11.92, p\u0026thinsp;=\u0026thinsp;0.001). However, there was no significant difference between Early-die-off and Post-die-off levels (p\u0026thinsp;=\u0026thinsp;0.111), suggesting that community structure remained relatively stable during this period. Similarly, Post-die-off and 1-year-post were not significantly different (p\u0026thinsp;=\u0026thinsp;0.254), indicating little change beyond the Post-die-off period. A small but significant difference between Early-die-off and 1-year-post (p\u0026thinsp;=\u0026thinsp;0.026) suggests some gradual shifts in community structure over time.\u003c/p\u003e\u003cp\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\u003ePERMANOVA results testing for differences in community composition across four time periods.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eperiod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrecedent urchin conditions effects on macroalgae and coral recruitment (log Response ratio analysis)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreceding conditions in urchin cluster size and abundance influenced the magnitude of coral recruitment decline and of macroalgae cover. Our log-ratio response models, where the log-ratio represents the change in Porites astreoides recruits from Early-die-off to 1-year-post, revealed that sites with higher Pre-die-off urchin cluster counts experienced greater declines in recruitment (χ\u0026sup2; = 8.15, p\u0026thinsp;=\u0026thinsp;0.004) (ST 4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Similarly, larger Pre-die-off cluster sizes were significantly associated with lower recruitment log-ratios (χ\u0026sup2; = 8.47, p\u0026thinsp;=\u0026thinsp;0.0036), indicating that plots initially dominated by large urchin clusters saw steeper declines in new coral settlers following the die-off (ST 4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur log-ratio response models for algae cover, where the log-ratio represents the change in macroalgae cover relative to Pre-die-off levels, demonstrated that the increase in macroalgae abundance Post-die-off was greater at plots with a higher Pre-die-off number of urchin clusters (χ\u0026sup2; = 8.07, p\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In contrast, larger Pre-die-off cluster sizes were negatively associated with the log-ratio of macroalgae (χ\u0026sup2; = 4.58, p\u0026thinsp;=\u0026thinsp;0.032), indicating smaller relative increases in macroalgae at those plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLog-ratio response models used to assess whether sponge cover change was associated with Diadema cluster count and size prior Pre-die-off revealed neither the number of Diadema observed pre-die-off (z\u0026thinsp;=\u0026thinsp;1.203, p\u0026thinsp;=\u0026thinsp;0.229) nor their mean size (z\u0026thinsp;=\u0026thinsp;0.482, p\u0026thinsp;=\u0026thinsp;0.629) significantly predicted the change in sponge cover. The model intercept was significantly negative (Estimate = -1.537, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating an overall decline in sponge cover across sites, regardless of baseline urchin metrics (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFixed effects from gaussian model testing Pre-die-off Diadema abundance and size predict changes in sponge cover.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecomponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-9.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCounts Pre-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003econd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean size Pre-die-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe loss of keystone herbivores can trigger profound changes in community structure across ecosystems, including coral reefs. In reef systems, \u003cem\u003eDiadema\u003c/em\u003e historically played a critical role in maintaining competitive dynamics by grazing down fast-growing macroalgae (Carpenter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Lessios, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). When such herbivores are lost, there is a release from top-down control, allowing macroalgae to proliferate. In our study, the sharp decline in \u003cem\u003eDiadema\u003c/em\u003e abundance was followed by a marked increase in macroalgal cover, and a shift in benthic community structure. This macroalgal expansion likely initiated the cascading effects on other benthic groups seen here through multiple mechanisms, including competitive exclusion and allelopathy (Rasher et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As macroalgae physically overgrow corals and other sessile invertebrates, they compete for space and light, while also releasing chemical compounds that can inhibit coral recruitment and growth, as well as alter microbial community dynamics (McCook et al., 2001; Rasher \u0026amp; Hay, 2010). These changes may further reduce coral resilience and contribute to long-term shifts in community composition. Similar dynamics have been observed in terrestrial systems; for instance, in African savannas, exclusion of large herbivores can result in woody plant encroachment that suppresses understory diversity through both physical and chemical interference (Pringle et al., 2014; Young et al., 2013). These cross-ecosystem parallels underscore the broad ecological importance of herbivores in regulating community dynamics and maintaining ecosystem functions. In the context of our findings, the disproportionate influence of \u003cem\u003eDiadema\u003c/em\u003e on benthic community structure highlights its role not only as a grazer, but as a regulator of broader feedback loops that shape reef trajectories.\u003c/p\u003e\u003cp\u003eIn our study, we assessed the response of the benthic community to the recent die-off of \u003cem\u003eDiadema\u003c/em\u003e in Culebra, Puerto Rico. We documented a 96% decline in urchin abundance, comparable to the catastrophic 1980s die-off, when populations declined by approximately 98% (Carpenter, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Lessios, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). A slight increase in urchin clusters was observed in later periods of our study, but we believe these may not indicate proper recruitment but rather the movement of individuals from other locations. As Lessios (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggests, \u003cem\u003eDiadema\u003c/em\u003e populations have been observed to migrate following die-off events, likely in search of suitable habitat. Given the slow population recovery rates documented in previous studies, it is unlikely that the observed clusters represent new recruitment events (Lessios, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rogers \u0026amp; Lorenzen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Instead, this may reflect redistribution from adjacent areas, temporarily increasing local densities. However, during the last sampling event, small juvenile individuals were observed (Santos and Rivas, unpublished observations), suggesting a potential slow recovery through recruitment.\u003c/p\u003e\u003cp\u003eOur results indicate that initial urchin abundance patterns played a key role in determining coral recruitment and post-mortality algal responses. Areas with higher Pre-die-off cluster counts experienced greater decreases in coral recruitment and greater increases in macroalgae cover following the die-off, suggesting that these sites initially had strong grazing pressure (i.e., top-down control) from \u003cem\u003eDiadema\u003c/em\u003e, which, when lost, led to a more pronounced algal expansion, potentially outcompeting corals for space (Edmunds \u0026amp; Carpenter, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Williams, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sites with larger clusters exhibited smaller shifts in macroalgae abundance post-die-off, potentially indicating that some residual urchins from larger clusters survived; however, those sites still experienced a greater decrease in coral recruits. These findings highlight the importance of urchin density in reef benthic dynamics and suggest that future recovery efforts should consider not only the number of urchins but also their spatial distribution and local density patterns (Burkepile \u0026amp; Hay, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Levitan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Williams, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous studies have demonstrated that herbivore abundance and aggregation are critical for effective top-down regulation of macroalgal communities. Research on \u003cem\u003eDiadema\u003c/em\u003e has shown that below certain density thresholds, grazing pressure is insufficient to prevent macroalgal dominance, whereas above those thresholds, algae are effectively suppressed (Manuel et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;Williams, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This highlights the existence of a functional threshold in herbivory-driven control, underscoring the need for targeted strategies that restore urchin populations to ecologically meaningful levels.\u003c/p\u003e\u003cp\u003eChanges of macroalgae and coral recruitment associated with the recent mortality event resulted in a dramatic shift in reef community structure, with impacts persisting beyond the initial decline. By linking the sharp decline in urchin abundance to subsequent increases in macroalgal cover and shifts in benthic community structure, our analyses reinforce the critical ecological function of \u003cem\u003eDiadema\u003c/em\u003e in suppressing algal-dominated reef states (Williams, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While coral cover appeared to increase over time, this trend is likely an artifact of sampling bias rather than actual biological recovery. Coral cover was already extremely low and point count methods are known to underestimate true values when cover is sparse (Pante \u0026amp; Dustan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rivas et al., in prep). Furthermore, given the ongoing degradation of coral reefs, decreasing larval supply, and the lack of observed recruitment that could drive coral expansion, it is unlikely that this increase reflects a true positive trajectory (Edmunds \u0026amp; Elahi, 2007; Hughes et al., 2010). In such sparse conditions, even small absolute changes can disproportionately influence percent cover estimates (Pante \u0026amp; Dustan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Instead, the apparent uptick in coral cover highlights the methodological and ecological challenges of tracking coral responses in systems that are already severely degraded. This underscores the importance of combining traditional survey methods with complementary approaches, such as high-resolution photogrammetry, long-term colony tracking, or recruitment assays, to better detect subtle trends in coral dynamics and avoid misinterpreting noise as recovery (Burns et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; P. Edmunds \u0026amp; Riegl, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe community structure change captured by our analysis was driven mostly by the increase in macroalgae and sponge cover. Macroalgae responded rapidly to the loss of \u003cem\u003eDiadema\u003c/em\u003e, increasing in cover following the die-off and remaining elevated throughout the study. The stabilization of macroalgae cover after Fall 2022 indicates a shift toward a sustained phase of elevated algal dominance, rather than a return to pre-die-off conditions, especially on the reefs that previously had a relatively higher abundance of urchins. Similar patterns have been documented elsewhere in the Caribbean following the 1983\u0026ndash;84 \u003cem\u003eDiadema\u003c/em\u003e collapse; reefs experienced long-term algal dominance that persisted for decades (Edmunds \u0026amp; Carpenter, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mumby et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the most striking changes observed was the sharp decline in sponge cover, which decreased by 61% immediately following the \u003cem\u003eDiadema\u003c/em\u003e die-off and remained low throughout all subsequent periods. Sponges play a significant role in reef function through water filtration, nutrient cycling, and providing structural complexity (Bell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Their sustained loss suggests long-term disruption to these functions. Our results did not reveal a significant relationship between the change in sponge cover and precedent urchin cluster counts and size, suggesting that other factors could be occurring during our study period. While the decline may be partly linked to increased macroalgal cover through space competition, shading, or allelopathic effects (Rasher \u0026amp; Hay, 2010; McCook et al., 2001), the possibility of confounding factors such as winter swells, storm impacts, or disease cannot be ruled out. Still, the timing of the decline, coupled with the absence of recovery, supports the hypothesis that sponges are vulnerable to cascading effects associated with the loss of herbivore functional roles.\u003c/p\u003e\u003cp\u003eOur results show that \u003cem\u003ePorites astreoides\u003c/em\u003e recruitment declined more severely at sites that had higher \u003cem\u003eDiadema\u003c/em\u003e cluster counts and larger cluster sizes prior to the die-off. This pattern, based on the log response ratio of recruits one-year post-die-off relative to pre-die-off levels, indicates that sites previously supported by \u003cem\u003eDiadema\u003c/em\u003e have been disproportionately impacted by their loss. Notably, this decline was observed even for \u003cem\u003eP. astreoides\u003c/em\u003e, a species typically characterized as weedy and resilient (Darling et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This suggests that the functional role of \u003cem\u003eDiadema\u003c/em\u003e in maintaining recruitment conditions, likely by suppressing macroalgae and facilitating space availability, was not only critical, but also part of a broader set of ecological feedbacks that structure reef communities (Van De Leemput et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The sudden absence of \u003cem\u003eDiadema\u003c/em\u003e appears to have disrupted these positive feedback loops, leading to a cascade of ecological effects that impaired coral recruitment. Loss of this herbivory function can tip reefs into alternative stable states dominated by macroalgae, further inhibiting coral recovery via interference with recruits (Mumby et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Van De Leemput et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These findings highlight the vulnerability of seemingly resilient taxa when key ecosystem processes are lost (Hirota et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Steneck et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt\u0026rsquo;s important to note the use of large-area imagery (LAI) in this study, which offers a powerful and relatively novel approach for detecting spatially explicit ecological patterns across benthic communities. LAIs provide a high-resolution, permanent record of reef conditions that can be analyzed for multiple response variables including coral recruitment, macroalgal expansion, and herbivore (e.g., \u003cem\u003eDiadema\u003c/em\u003e) distribution within a consistent spatial framework (Burns et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This allows for repeatable, fine-scale tracking of individual colonies, algal patches, and herbivore clusters over time, which is particularly valuable in studying cascading ecological effects following a die-off event. Additionally, LAIs reduce diver subjectivity and increase sampling efficiency over broad spatial extents, which is often a limitation of traditional transect based methods (Curtis et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, several caveats must be considered when interpreting these estimates. The detectability of \u003cem\u003eDiadema\u003c/em\u003e in LAIs is influenced by the urchins\u0026rsquo; position within the reef structure, particularly when individuals are partially obscured in crevices or overhangs. Additionally, this method captures only those urchins visible on the exposed reef surface at the time of image acquisition and may underestimate true abundance. Cluster categorizations are coarse and may miss nuanced shifts in urchin demographics or behavior (e.g., diurnal hiding). Despite these limitations, LAIs provide a valuable proxy for \u003cem\u003eDiadema\u003c/em\u003e abundance that, when paired with standardized imagery protocols and known limitations, enables robust inference in long-term ecological studies where direct herbivore data are lacking.\u003c/p\u003e\u003cp\u003eOverall, these findings emphasize the lasting impacts of the \u003cem\u003eDiadema\u003c/em\u003e die-off, particularly the severe and persistent loss of sponge cover. While macroalgae responded predictably to the decline of herbivory, the lack of recovery in sponges and urchins suggests that ecosystem functions disrupted by the die-off may not readily return to their previous state. Furthermore, the apparent increase in coral cover is likely an artifact of methodological limitations rather than actual reef recovery. Taken together, these results reinforce the importance of key herbivores in structuring reef communities and highlight the potential for long-term ecosystem shifts following widespread mortality events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eStatement of Declaration:\u003c/h2\u003e\u003cp\u003eThe authors declare no relevant financial or non-financial interests that could be perceived as influencing the content of this article. They report no conflicts of interest and confirm that they have no affiliations with any organization or entity with financial or non-financial stakes in the subject matter or materials presented in this manuscript. Additionally, the authors hold no financial or proprietary interest in any material discussed herein.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis work was conceptualized by NR, WRJ, and ROS. Data collection was carried out by NR, VB, MS, AM, and ROS, and the data were processed by NR, VB, MS, AFG, and SBA. Statistical analyses were conducted by NR, WRJ and ROS. The original draft was written by NR, WRJ and ROS, and all authors contributed to the review and editing of the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Sociedad Ambiente Marino (SAM) for their support, as well as the volunteers and staff of the Santos Seascape Ecology Lab for their invaluable assistance in the field and data processing. This material is also based upon work supported by the U.S. National Science Foundation under Grant No. HRD-1547798 and Grant No. HRD-2111661. These NSF Grants were awarded to Florida International University as part of the Centers of Research Excellence in Science and Technology (CREST) Program. This work was funded by the National Fish and Wildlife Foundation (NFWF GRANT ID: 0318.19.066113, 2022-IC-071; O-VS-PEP13-SJ-00052-27102022) and the National Science Foundation (NSF RAPID: 2235138; 2023-EPE-024 O-VS-PVS15-SJ-01341-30112022), whose support made this research possible. This is contribution #X from the Institute of Environment at Florida International University.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files and published in Zenodo at TBD\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdam, T. C., Burkepile, D., Ruttenberg, B., \u0026amp; Paddack, M. (2015). Herbivory and the resilience of Caribbean coral reefs: Knowledge gaps and implications for management. \u003cem\u003eMarine Ecology Progress Series\u003c/em\u003e, \u003cem\u003e520\u003c/em\u003e, 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3354/meps11170\u003c/span\u003e\u003cspan address=\"10.3354/meps11170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdam, T. C., Holbrook, S. J., Burkepile, D. E., Speare, K. E., Brooks, A. J., Ladd, M. C., Shantz, A. A., Vega Thurber, R., \u0026amp; Schmitt, R. J. (2022). 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The reduction of harmful algae on Caribbean coral reefs through the reintroduction of a keystone herbivore, the long-spined sea urchin \u003cem\u003eDiadema antillarum\u003c/em\u003e. \u003cem\u003eRestoration Ecology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), e13475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/rec.13475\u003c/span\u003e\u003cspan address=\"10.1111/rec.13475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"photogrammetry, algae proliferation, benthic community structure, herbivore loss, coral-algal competition, reef degradation","lastPublishedDoi":"10.21203/rs.3.rs-7303485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoral reef ecosystems are maintained by complex interactions among organisms, with herbivory playing a critical role in regulating benthic algae populations and preserving reef health. Herbivores such as the black spiny sea urchin prevent macroalgae from outcompeting corals by exerting top-down control, sustaining ecosystem stability and diversity. There is evidence that the mass mortality of \u003cem\u003eD. antillarum\u003c/em\u003e in the 1980s contributed to phase shifts from coral-dominated to algal-dominated states across the Caribbean, demonstrating their critical role in reef dynamics. Certain areas of the Caribbean experienced a slow recovery of \u003cem\u003eD. antillarum\u003c/em\u003e, until 2022, when another mass mortality event occurred. This study investigates the ecological impacts of the recent 2022 die-off using monitoring data collected from Culebra, Puerto Rico, between 2021 and 2024. High-resolution orthomosaics generated through photogrammetry and Structure-from-Motion (SfM) techniques were used to assess shifts in benthic community structure, focusing on algal and coral cover changes in response to \u003cem\u003eD. antillarum\u003c/em\u003e abundance. In addition, we modeled coral recruitment dynamics and found that both urchin cluster density and mean cluster size at baseline were related to declines in coral recruitment, with larger and more numerous urchin clusters associated with reduced post-die-off recruitment. Our results also showed increases in macroalgal cover and decreases in sponge cover following the loss of \u003cem\u003eD. antillarum\u003c/em\u003e, highlighting the potential community assembly regulative role of this herbivore. These findings underscore the urgency of protecting and restoring key herbivore species and highlight the need for targeted management strategies to mitigate further degradation of Caribbean reef systems.\u003c/p\u003e","manuscriptTitle":"Tipping the balance: Reef community shifts after a regional urchin population collapse","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 17:26:41","doi":"10.21203/rs.3.rs-7303485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-25T06:34:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T13:19:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T18:26:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300155316072888799975256104488425205022","date":"2025-08-17T17:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80921331768184596726457280023262872989","date":"2025-08-14T20:02:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T01:31:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-10T21:48:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-09T13:44:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Coral Reefs","date":"2025-08-05T18:50:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d3fcb153-8416-4628-b730-224b4a6c8631","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:05:33+00:00","versionOfRecord":{"articleIdentity":"rs-7303485","link":"https://doi.org/10.1007/s00338-026-02822-1","journal":{"identity":"coral-reefs","isVorOnly":false,"title":"Coral Reefs"},"publishedOn":"2026-02-09 15:58:28","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-08-18 17:26:41","video":"","vorDoi":"10.1007/s00338-026-02822-1","vorDoiUrl":"https://doi.org/10.1007/s00338-026-02822-1","workflowStages":[]},"version":"v1","identity":"rs-7303485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7303485","identity":"rs-7303485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}