Temporal and spatial drivers of elasmobranch diversity and relative abundance in Lhaviyani Atoll, Central Maldives

Abstract

The Maldives is widely recognised as a hotspot for shark and batoid diversity in the Indian Ocean, yet regional multispecies patterns of elasmobranch occurrence remain poorly characterised. This study analyses a seven-year (2017-2024) opportunistic dive-log dataset comprising 12,732 SCUBA surveys and 142,994 observation records of sharks and batoids collected across 94 dive sites in Lhaviyani Atoll, central Maldives. In total, 28 species (14 sharks and 14 batoids) were recorded, including 23 species listed as threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Elasmobranch relative abundance (sightings per hour of dive effort) and diversity peaked during the late southwest monsoon (August–September) and declined during the transitional period into the northeast monsoon (December–March), following the reversal of regional circulation and productivity fronts. Community composition shifted after 2021 towards greater diversity and evenness, while overall relative abundance declined. Effort-standardised relative abundance was modelled as a function of environmental and geomorphic variables using generalised additive models (GAMs). Results revealed that elasmobranch relative abundance was primarily driven by sea surface temperature, salinity, and surface current velocity (zonal and meridional components), with geomorphic complexity enhancing occurrence along reef slopes and sheltered slope habitats. For sharks, dissolved oxygen and chlorophyll a were also significant, whereas batoids’ relative abundance was influenced mainly by temperature, oxygen, and current velocity. Spatial kernel-density maps identified four persistent northern-rim elasmobranch activity hotspots, with sharks concentrated along exposed and semi-sheltered slopes and channels, and batoids distributed more broadly within lagoonal habitats. By characterising these spatial and environmental patterns, this study strengthens the scientific basis for targeted conservation and management at a time when national and international management frameworks for sharks and batoids are rapidly evolving.

1 Introduction

As apex and mesopredators, elasmobranchs play a vital role in marine food webs by exerting strong top-down control and shaping ecosystem structure and stability (Maioli et al., 2023). Despite this ecological significance, elasmobranch populations are undergoing unprecedented global declines, with more than one-third of species currently threatened with extinction (Jabado et al., 2023).

These declines result from a range of cumulative anthropogenic pressures, including habitat degradation, pollution, and climate change. Yet, overfishing (both targeted and incidental – bycatch) remains the principal driver, with exploitation rates in many regions exceeding the capacity of populations to recover (Ward-Paige, 2017; Dulvy et al., 2021; O’Keefe et al., 2023). Their vulnerability to depletion is further heightened by their K-selected life-history traits, characterised by slow growth, late maturity, and low reproductive output, which collectively constrain the resilience and recovery potential of populations (Dulvy et al., 2014; Sebastian et al., 2025). In parallel, climate-driven warming, acidification, and deoxygenation – processes already shown to impair aerobic performance, behaviour, and early survival in elasmobranchs (Rosa et al., 2016; Pegado et al., 2020; Santos et al., 2024, 2025) – are expected to intensify, adding further pressure to already depleted populations.

In recognition of both the ecological importance and economic value of sharks, several island nations have established shark sanctuaries as a conservation measure. Among them is the Republic of Maldives, comprising approximately 1,190 coral islands grouped into 26 atolls in the northern Indian Ocean (Robinson et al., 2022). Intensive shark overfishing during the 1980s and 1990s, including targeted reef fisheries and the expansion of longline operations, led to marked population declines, particularly around popular dive sites (Anderson and Ahmed, 1993; Ali and Sinan, 2014). This depletion led to reduced encounter rates at shark diving sites, diminished tourist interest, and significant economic losses for the shark-diving industry (Anderson and Ahmed, 1993). In response, the Maldivian government implemented a nationwide ban on shark fishing and exports and designated its entire exclusive economic zone as a shark sanctuary in 2010 (Zimmerhackel et al., 2019).

The Maldives is widely recognised as a regional hotspot for elasmobranchs, with a long history of high-value shark-diving tourism (Robinson et al., 2022). Lhaviyani Atoll, the atoll central to this study, is located in the northern Maldives and hosts two Important Shark and Ray Areas (ISRAs): Fushifaru Kandu in the northeast and Kuredhu–Huravalhi–Komandoo in the northwest, each supporting globally threatened species including the grey reef shark (Carcharhinus amblyrhynchos, EN), silvertip shark (C. albimarginatus, VU), blacktip reef shark (C. melanopterus, VU), ornate eagle ray (Aetomylaeus vespertilio, CR), reef manta ray (Mobula alfredi, VU) and blotched fantail ray (Taeniurops meyeni, VU) (IUCN SSC Shark Specialist Group, 2023a; 2023b). These areas encompass Mobula feeding sites, reproductive habitats for C. melanopterus, and aggregation areas of C. amblyrhynchos and C. albimarginatus, including some of the highest recorded encounter rates for reef-associated sharks in the archipelago. Additional neighbouring ISRAs in Ari and South Malé Atolls are associated with manta feeding corridors, high-current channel systems, and multi-species elasmobranch aggregations (IUCN SSC Shark Specialist Group, 2023c; 2023d). Despite their ecological importance, knowledge of elasmobranch distributions across these areas remains limited, because existing information is largely species-specific or site-specific, deriving from initiatives such as the government-led Sharkwatch programme (2009–2019), which quantified encounter rates of reef-associated sharks at selected sites, and long-term, taxon-focused monitoring efforts including manta ray surveys conducted by the Manta Trust across multiple Maldivian atolls (IUCN SSC Shark Specialist Group, 2023a). Long-term, multi-species datasets spanning multiple sites remain scarce, limiting our ability to quantify atoll-scale patterns in elasmobranch abundance, diversity, and environmental drivers across the Maldives, a uniquely monsoon-driven ecosystem in which seasonal reversals in circulation structure productivity, habitat use, and species distributions (Harris et al., 2020). In this context, citizen science datasets like those examined in this study can be a valuable resource for conservation ecology, helping to define 9.5% of ISRAs in the western Indian Ocean (Cochran et al., 2026).

While fishery closures and protective legislation represent critical steps forward, effective elasmobranch conservation ultimately depends on a deeper understanding of spatial ecology (Batista et al., 2024). Detailed knowledge of species distributions, habitat associations, and spatiotemporal dynamics is essential for identifying areas that support key life-history functions such as feeding, reproduction, and refuge (Reynolds et al., 2022). Equally important is understanding and quantifying how environmental drivers shape abundance and diversity patterns, as this provides crucial insight into the ecological processes underpinning species occurrence and forms the basis for spatially informed management and conservation strategies (Wang et al., 2024; Vizeu-Pinheiro et al., 2025).

Elasmobranch distributions are shaped by the interaction of physical, chemical, and biological gradients operating across multiple spatial and temporal scales. Temperature, salinity, depth, and dissolved oxygen impose physiological constraints that influence metabolic performance, aerobic capacity, and vertical habitat use, with tropical species particularly sensitive to warming and hypoxic conditions due to their proximity to thermal and oxygen limits (Froeschke et al., 2010; Rosa et al., 2016; Pegado et al., 2020). Salinity gradients can further influence local distribution and abundance patterns; however, because most elasmobranchs are stenohaline, movements associated with salinity variation are generally interpreted as behavioural responses to avoid osmotic stress rather than reflecting broad physiological tolerance (Schlaff et al., 2014; Williamson et al., 2019). In parallel, biological proxies of ecosystem productivity structure elasmobranch assemblages through bottom-up processes. Chlorophyll-a concentration, together with measures of prey biomass, have been shown to correlate with elasmobranch occurrence and community composition, reflecting spatial variation in prey availability rather than direct physiological effects (Plumlee et al., 2018).

Surface current velocity can further influence shark and batoid habitat use by mediating energetic costs and access to foraging habitats. Movement aligned with tidal flow has been associated with reduced energy expenditure and expanded foraging areas (e.g. in juvenile sandbar sharks, more than half of net movements occur in the direction of tidal currents), and is particularly relevant for reef- and channel-associated species that exploit flow-driven dynamics (Schlaff et al., 2014).

Benthic substrate composition and reef geomorphology are also key determinants of elasmobranch habitat use. Depth, substrate type, and seafloor structure consistently emerge as strong predictors of elasmobranch distribution and diversity, with reef slopes and structurally complex habitats supporting higher shark abundance than reef flats (Rizzari et al., 2014; Ruiz-García et al., 2025). Across neighbouring western Indian Ocean reef systems, habitat substrate has been shown to strongly structure elasmobranch occurrence, with sharks most frequently associated with rock and rubble, while batoids are more commonly found on rock and sand (Lourie et al., 2025).

The aim of this study is to characterise the spatiotemporal patterns of elasmobranch assemblages in Lhaviyani Atoll, Maldives, between 2017 and 2024. Specifically, it examines how elasmobranch relative abundance, species richness, diversity, and evenness vary in relation to key environmental drivers, including sea surface temperature, salinity, chlorophyll-a concentration, dissolved oxygen, eastward and northward current regimes, substrate composition, and reef geomorphology. The study further identifies major aggregation sites and spatial hotspots of elasmobranch occurrence, and links environmental drivers to observed distribution patterns to provide an ecological basis for habitat-focused conservation and the effective management of shark and batoid populations within one of the Indian Ocean’s major elasmobranch hotspots.

2 Materials and methods

2.1 Study area and sampling

The study was conducted in Lhaviyani Atoll, also known as Faadhippolhu (5° 22′ 28.9″ N, 73° 30′ 44.3″ E; Figure 1), in the central Maldives, a strongly monsoon-driven archipelago shaped by the southwest monsoon (May–October) and northeast monsoon (December–March), which drive seasonal reversals in surface circulation, productivity, and habitat use across atolls (Harris et al., 2020). These monsoonal regimes are punctuated by transitional periods, typically occurring in April and November, although their timing and duration are highly variable and may extend into March or October (Aslam and Kench, 2017). In Lhaviyani Atoll, these seasonal dynamics influence current direction and strength within reef channels and lagoons, with direct implications for dive accessibility, detectability, and elasmobranch habitat use.

Figure 1

Dive-log data from 2017–2024 were provided by Prodivers, a PADI-certified diving operator headquartered on Kuredhu Island and operating five centres across the atoll. Sightings were recorded by certified, senior PADI instructors, with entries from trainees verified immediately after each dive to ensure reliability. All observations were collected opportunistically during recreational SCUBA dives and guided snorkelling excursions.

More than 700 original site names were consolidated into 94 unique locations in consultation with local dive guides, and coordinates were verified using Google Maps (Supplementary Table S2; Supplementary Figure S1). These sites encompassed sand, rubble, rock, seagrass, and coral–algal substrates distributed across reef crest, flat, slope, lagoon, and plateau habitats, as classified by the Allen Coral Atlas (2020) (see Supplementary Figures S3, S4). Site selection was non-random, shaped by operational, safety, and ecological factors. Dives often targeted sites with frequent megafauna sightings and were influenced by sea state, visibility, diver certification, and proximity to dive centres. Channel dives were conducted exclusively during incoming (flood) currents into the atoll.

All surveys followed a roving visual census approach, in which observers moved freely within each site and recorded elasmobranch sightings opportunistically during the survey. Although this approach lacks the spatial standardisation of fixed-transect methods, dives followed a single, directional route from entry to exit. A representative survey trajectory is shown in Supplementary Figure S2 for a dive near the Caves site (D06; Supplementary Table S2). This one-directional movement reduced the likelihood of re-encountering and re-recording the same individuals within a survey, as repeated back-and-forth movements across the same reef section did not occur.

Observations were recorded using a standardised MaxN approach, defined as the maximum number of animals visible simultaneously within the observer’s forward field of view (looking straight ahead), to minimise double-counting (Espinoza et al., 2020). Species counts were agreed immediately after each survey to ensure consistency. Because snorkelling surveys differ markedly from SCUBA surveys in depth, duration, and detectability (MacNeil et al., 2008; Dearden et al., 2010), and because mixing underwater visual census (UVC) methods can introduce heterogeneity that undermines temporal comparability (Pais and Cabral, 2018), snorkelling records were incorporated into diversity metrics (species richness, Shannon diversity, and evenness) but excluded from effort-standardised modelling analyses. Modelling was therefore restricted to SCUBA records to ensure methodological consistency across the 7-year dataset.

2.2 Data curation, effort standardization, and taxonomic validation

Data were first screened to exclude unsuitable entries. Records listed as “unknown activity” or “no dive” as well as all entries without dive duration (including all 2020 sightings and ~50% from 2021), were excluded as effort could not be standardised. Duplicate observations and records from outside Lhaviyani Atoll were also removed.

Sightings per unit of effort (SPUE) have been used in non-transect surveys to evaluate species relative abundance (Espino et al., 2019, using citizen-science SCUBA sightings standardised per hour of dive effort; Sherman et al., 2020, using elasmobranch counts from BRUVS standardised by hours of video footage, i.e., MaxN per hour). Accordingly, relative abundance was quantified in this study as the number of sightings per hour of dive effort (SPUE).

Visibility (horizontal underwater visibility; the approximate distance at which objects could be clearly seen ahead of the diver) was converted to midpoints, with open-ended categories (e.g., “30+ m”) set conservatively to 30 m. Inconsistent records for current direction and strength were discarded, and corresponding marine current velocity data were retrieved from Copernicus Marine Service products (Copernicus Marine Service, 2025a; 2025b). Surface currents were represented by their zonal (uₒ; east–west) and meridional (vₒ; north–south) velocity components (m s⁻¹), which together characterize the direction and magnitude of surface flow. When species counts were reported as ranges, the lower bound was used. Open-ended values such as “>15 individuals” were conservatively treated as 15.

Species identifications were standardised to accepted scientific nomenclature. Ambiguous labels were resolved in consultation with the Prodivers team (e.g., ‘manta’ assigned to Mobula alfredi; ‘mobula’ recorded as Mobula spp. to reflect the occurrence of both M. mobular and the more commonly encountered M. kuhlii in Lhaviyani Atoll). Categories labelled as “unknown” were retained for analyses conducted at higher taxonomic levels. Rare, implausible or unexpected observations were cross-checked, with only sightings verified by senior, experienced guides retained.

2.3 Diversity metrics and statistical analyses

All analyses were performed in R 4.4.2 (R Core Team, 2024). Relative abundance was quantified as sightings per unit effort (SPUE; see Section 2.2), expressed as the number of individuals observed per hour of dive time, where “individuals” denotes observed counts and not uniquely identifiable individuals across dives. Species richness (S), Shannon diversity (H′), and Pielou’s evenness (J′) were calculated for elasmobranchs (sharks and batoids) at both spatial and temporal levels (Magurran, 2004). Environmental covariates were obtained as daily products from the Copernicus Marine Service (Copernicus Marine Service, 2025a; 2025b) and included sea surface temperature (SST), salinity (S), zonal (uₒ) and meridional (vₒ) currents, chlorophyll-a concentration (Chl), and dissolved oxygen (O₂). These variables were extracted at daily resolution and spatially matched to each dive record based on survey date and site location. Substrate type and geomorphic zones were retrieved from the Allen Coral Atlas (2020) and included in the ecological modelling analysis. Bathymetry was retrieved from NOAA and was initially included but was excluded from the final models due to low explanatory contribution and for causing worse model fit.

SPUE data were modelled using generalized additive models (GAMs) with a Tweedie distribution and log link (package mgcv; Wood, 2017), an approach appropriate for overdispersed ecological count data containing many zeros (Bolker et al., 2009). The response variables were SPUE (sightings per hour of dive effort) of sharks, batoids, and all elasmobranchs combined. The aforementioned continuous environmental covariates were modelled as smooth terms to capture their non-linear relationships with the response variables. Benthic habitat descriptors (substrate type and geomorphology) were included as categorical linear fixed effects. Substrate type consists of coral, rock, rubble and sand habitats; while the geomorphology encompasses back reef slopes, reef slopes, sheltered reef slopes, deep and shallow lagoons, outer reef flats and plateaus. To account for repeated sampling and spatio-temporal dependence, dive site, month, and year were incorporated as random effects. Smoothing parameters were estimated using restricted maximum likelihood (REML). A baseline basis dimension of k = 10 was used for the environmental smooths and adjusted where necessary to avoid both under and overfitting. Model fit was evaluated using standard mgcv diagnostics (k.check, gam.check), with full residual diagnostics (Q-Q plots, residuals vs fitted, response vs fitted), autocorrelation analyses (ACF/PACF), and concurvity checks provided in the (Supplementary Figures S6-S9; Supplementary Tables S7-S8). Spatial autocorrelation was tested with Moran’s I based on distance-defined neighbourhood structure; the threshold was defined at 17.5 km, the minimum distance to create a continuous neighbouring grid, with no sub-plots. Statistical significance was assessed at α = 0.05. Final model selection was based on Akaike’s Information Criterion (AIC) and R² (Supplementary Table S7).

To examine spatial distribution patterns and identify elasmobranch activity hotspots across Lhaviyani Atoll, kernel density estimation (KDE) was applied to effort-standardised site-level SPUE (ind. h⁻¹) to map relative spatial density. Spatial data were processed and visualized in R using the sf (Pebesma and Bivand, 2023), ggplot2 (Wickham, 2016), and spatstat (Baddeley et al., 2015) packages. KDE surfaces were generated using a Gaussian kernel, with densities normalised to a 0–1 scale. Maps were generated for the total elasmobranch assemblage, and separately for sharks, batoids, the five most abundant species, and the four IUCN Critically Endangered species.

3 Results

3.1 Elasmobranch species composition and conservation status

A total of 28 elasmobranch species were recorded across Lhaviyani Atoll between 2017 and 2024, 14 sharks and 14 batoids species (see Table 1). There was one unidentified shark, one guitarfish, and one stingray species recorded. All 25 identified species belonged to five orders: Carcharhiniformes, Orectolobiformes, Lamniformes, Rhinopristiformes, and Myliobatiformes, and nine families, dominated by Carcharhinidae, Mobulidae, and Dasyatidae. Carcharhiniformes was the most species-rich order (7 spp.), followed by Myliobatiformes (6 spp).

23 of all 25 identified species are listed as threatened under the IUCN Red List (2024), including four Critically Endangered (Sphyrna mokarran, Aetomylaeus vespertilio, Rhynchobatus djiddensis, and Rhina ancylostomus), twelve Endangered, and seven Vulnerable species (Figure 2).

Figure 2

Across 12,732 SCUBA dives (719,966 minutes of dive effort), a total of 142,989 elasmobranch counts/observations were recorded. Sharks accounted for approximately 73% of observations, while batoids represented 27% (Table 1). Approximately 80% of dives were conducted in the morning and 19% in the afternoon, with night dives representing less than 1%. Species-level observational counts and effort-standardised SPUE are summarised in Table 1. The elasmobranch assemblage was dominated by reef-associated and benthic species (see Supplementary Table S1). Community-level indices (Supplementary Table S3) indicated a total richness of 28 species (H′ = 1.6; J′ = 0.48). Sharks and batoids each contributed with 14 species, but batoids displayed higher Shannon diversity (H′ = 1.25) and evenness (J′ = 0.47) than sharks (H′ = 1.03; J′ = 0.39).

Elasmobranch diversity was low between 2017 and 2019 (H′ below 0.7), increased markedly in 2020 (H′ = 1.35), and remained above 1.3 from 2021 onwards, indicating an approximately twofold increase relative to the pre-2020 period (Supplementary Table S4).

Shark diversity was lowest in 2018–2019 (H′ = 0.42–0.43; J′ ≈ 0.21) (Figure 3C, Supplementary Table S5), coinciding with peak SPUE (Figure 4A) and reflecting dominance by a few highly abundant taxa. Diversity then rose steadily from 2020 onwards, reaching a maximum in 2024 (H′ = 0.87; J′ = 0.36) (Figure 3A; Supplementary Table S4), while SPUE stabilised at lower levels (Figure 4A).

Figure 3

Figure 4

Seasonally, shark diversity, evenness, and SPUE were lowest during the transitional and northeast monsoon periods (lowest SPUE in October–December; lowest diversity and evenness in January–February) and highest during the southwest monsoon (July–September) (Figure 3B; Supplementary Table S5). In contrast, batoids showed steadier patterns, with annual diversity peaks in 2019 (H′ = 1.58; J′ = 0.69) and 2024 (H′ = 1.35; J′ = 0.54). Monthly batoid diversity peaked in late southwest monsoon months (August–September; H′ > 1.70) and was moderately reduced in May–June (Figure 3D; Supplementary Table S5). Across groups, diversity and evenness consistently peaked in September and declined during the early southwest (May–July) and northeast (January–March) monsoon periods. Overall, batoids maintained higher evenness than sharks, whereas sharks exhibited stronger inter- and intra-annual variability in diversity.

Temporal trends in SPUE (Figures 4A–D) showed a similar overall temporal pattern to the diversity patterns observed in Supplementary Table S2; Figure 3, but with an earlier peak, revealing a decoupling between community structure of the elasmobranch observations and total density. While richness and diversity increased after 2021, mean SPUE was highest in 2018–2019 and subsequently stabilised at lower values; a pattern that suggests a shift from earlier dominance by a few abundant taxa toward a more evenly structured yet less numerically dense assemblage in recent years. Seasonally, both SPUE and diversity increased toward the late southwest monsoon (July–September) and declined during the end of the northeast monsoon (January–March), reflecting concordant intra-annual dynamics between the two taxonomic groups.

3.2 Environmental patterns in elasmobranch occurrence

For the total elasmobranch assemblage, the Tweedie GAM explained 56.7% of the observed deviance. The smooth terms for temperature, salinity and both zonal and meridional currents proved to be significant predictors, with very low p-values (Table 2). Elasmobranch SPUE remained relatively constant across SST values, increasing when SST surpassed 31 °C. Salinity showed a negative effect, with lower values corresponding to higher elasmobranch SPUE, a plateau for the middle interval (~33.5-35.0 psu) and decreasing SPUE for higher salinity values. The opposite effect was noted for the zonal current, with lower current values predicting lower SPUE, a similar middle plateau, and higher current values to higher SPUEs, which had a positive effect overall. For the meridional current, a strong non-linear effect was observed, with SPUEs highest near zero current values and decreasing toward both low and high current extremes (Supplementary Figure S5). Regarding the linear predictors, no substrate type showed a differing effect from the baseline (Coral). For the geomorphology, only the reef slope and sheltered reef slope showed significant differences, both causing a positive effect in elasmobranch SPUEs (Table 2).

The Tweedie GAM for shark SPUEs explained 56.4% of the deviance. Temperature, salinity, both current components and dissolved oxygen were very strong non-linear predictors (with p-values lower than 0.01), while chlorophyll-a concentration showed a slight predictive effect (p-value smaller than 0.1) (Table 2; Figure 5A). Shark SPUEs increased at higher SST values, with relatively stable predicted rates at low–mid temperatures and a marked rise beyond 31 °C. Salinity had an overall negative effect, where lower salinity corresponded to higher shark SPUEs, followed by a midrange plateau and decreasing SPUEs at the highest salinity values. The zonal current showed a positive relationship, with low current speeds predicting lower shark SPUEs, close to no effect for the middle values, and stronger currents associated with higher rates. The meridional current displayed a strong non-linear pattern, with SPUEs peaking near zero current and declining toward both current extremes. Shark SPUEs were relatively unaffected by chlorophyll-a levels, with a slight decrease at high chlorophyll concentrations. DO showed an overall flat response, indicating little influence on shark SPUEs across 195–206 mmol m⁻³ DO range (Figure 5). For linear predictors, rubble and sand substrates exhibited significantly lower shark SPUEs than the baseline coral substrate. Regarding geomorphology, deep lagoon, reef slope and sheltered reef slope all had significant positive effects (Table 2; Figure 5A). The model for batoid SPUEs explained 56% of the deviance. Among the environmental smooth terms, temperature and the zonal current were significant predictors (with p-values below 0.05), whereas meridional current and dissolved oxygen showed weaker effects (with p-values below 0.1) (Table 2; Figure 5B). Ray SPUEs were highest at both low and high SST values. The zonal current displayed a positive non-linear effect, with higher ray SPUEs associated with stronger east–west current flow. In contrast, the meridional current suggested a negative response at its extremes, with SPUEs peaking at intermediate values. Even though dissolved oxygen showed a relatively low p-value, its relationship with ray SPUEs appeared relatively constant, with no obvious trend (Figure 5B). None of the substrate categories differed significantly from the coral baseline, and no geomorphological class presented a meaningful linear effect on ray SPUEs (Table 2).

Figure 5

Across all three models, all random effects (dive site, month and year) were highly significant (p < 0.001; Table 2; Figure 5B). No significant spatial or temporal autocorrelation was found in any model at the 0.05 significance level (see Supplementary Figure S6).

Kernel density estimation (KDE) maps revealed a clear concentration of elasmobranch activity along the exposed northern rim of Lhaviyani Atoll, which emerged as the principal aggregation zone (Figure 6A). A full list of all surveyed dive sites and their spatial layout is provided in Supplementary Table S2; Supplementary Figure S1, to aid interpretation of the site names referenced throughout this section. Four consistent density cores were identified. The primary hotspot (H₁) on the north-western margin, encompassing the Kahlifushifaru sites (D41–D42), Peak, Wall, and Hurawalhi House Reef (D83, D93, D36), exhibited the highest normalized densities (0.8–1.0). A second hotspot (H₂) occurred along the north-eastern rim, centred on the Fushivaru Complex (D22, D23, D25, D26), with intermediate to high densities (0.6–0.8). A third northern density core (H₃) was detected around the Kanifushi complex (D44–D46), with moderate densities (0.4–0.6). A fourth, smaller hotspot (H₄) followed southeastward on the eastern margin around the Meyyafushi and Faadhoo complexes (D71–D73, D14, D17), forming a weaker but persistent cluster (~0.4). In addition to these main aggregation areas, several lower-density (0.2–0.4) elasmobranch clusters were detected across the atoll. These included the Gavirifaru sites southwest of H₁ (D29–D30), the Madivaru sites (D65–D66), Vavvaru and Veyvah along the western margin (D90–D92), and Palm Beach to the southeast of H2 (D82). Numerous additional very low-density (0.01–0.2) clusters, largely driven by batoids, were scattered across both eastern and western margins, indicating diffuse background activity beyond the major hotspots.

Figure 6

When analyzed by taxonomic group, sharks were strongly concentrated along the northern rim, with H₁ and H₂ as the main aggregation zones and H₄ emerging as the third-ranked hotspot, whereas batoids showed a broader, more diffuse pattern extending east and south (Figures 6B, C). Their highest AR-standardised densities (0.8–1.0) occurred at H₃ (notably at D45), and secondary maxima were distributed between the other three hotspots (H₁, H_2, H₄). Additional moderate (0.2–0.4) batoid densities formed a near-continuous band along the eastern and western margins and southwards toward the atoll’s closing rim, contrasting with the more spatially restricted shark hotspots (Figure 6C).

Among the five most abundant species, grey reef sharks, silvertip sharks, and whitespotted eagle rays closely matched the overall elasmobranch pattern, showing their highest densities within H₁ and H₂ along the northern rim. Blacktip reef sharks also peaked in H₁, but instead of exhibiting a secondary maximum at H₂, they formed a distinct hotspot centred at Caves (D06) and across the Kuredu–House Reef–Lagoon–Outreef and Express Channel sites (D11, D56–D58), where densities exceeded 0.6. The marble stingray had the widest distribution, forming a low-to-moderate density band (0.2–0.4) along the eastern–southern corridor from Maa Giri (D61) to the Olhuvelifushi–Palm Beach sector (D80–D82). Within this range, a single high-density patch (> 0.6) occurred at Vavvaru–Veyvah (D90–D92), with additional moderate clusters (0.4–0.6) at Gavirifaru (D29–D30) and Madivaru (D65–D66).

Linking density and diversity patterns, H₁ and H₂ coincided with low-evenness sites (e.g., D41, D22, D25, D26), reflecting numerical dominance by few species, consistent with grey reef shark and schooling batoid aggregations. The highest Shannon diversity (H′ > 1.9, J′ > 0.7) occurred on the flanks of H₁–H₂ and along the eastern–southern corridor, particularly at Hudhufushi Beyru and Ethere (D34–D35), Tinga Giri (D87), Faadhoo Beyru (D14), and Bodu Giri (D05), where assemblages were more evenly structured. Notably, Hurawalhi House Reef (D36), immediately adjacent to H₁, supported high batoids diversity despite its proximity to the density core, marking a transition between aggregation-dominated and mixed assemblages. See Supplementary Table S7 for the top 10 site-level richness rankings for each group.

Overall, these results indicate that the northern rim concentrates elasmobranch activity, whereas species diversity peaks in adjacent, more heterogeneous reef–lagoon habitats, reflecting distinct habitat preferences between reef-associated sharks and batoids.

4 Discussion

4.1 Elasmobranch species richness and community structure

Elasmobranch diversity in the Maldives is among the highest in the Indian Ocean (Gallagher and Hammerschlag, 2011), yet, to our knowledge, no study has examined multispecies assemblages across the country at a comparable spatiotemporal scale (94 reef sites over seven years). Earlier work is either dated or limited to a single species. Anderson and Ahmed (1993) documented habitat partitioning, with Carcharhinus falciformis dominating offshore catches and reef-associated taxa such as C. amblyrhynchos and C. melanopterus prevailing nearshore. More recently, a six-week, four-site survey around Hurawalhi Island Resort (Lhaviyani Atoll) provided a community-level snapshot of local elasmobranchs, recording 10 species across 170 sightings and reporting low diversity (H′ = 0.44; J′ = 0.67) and SPUEs of 2.83 ind. h⁻¹ (1.68 sharks h⁻¹; 1.15 batoids h⁻¹). Time of day was the only significant predictor, with higher sightings during morning and evening dives (Waters, 2023).

Most subsequent efforts have focused on manta rays (such as M. alfredi) and whale sharks (Rhincodon typus) (Anderson et al., 2011; Harvey-Carroll et al., 2021). Across more than 12,700 dives and 719,000 minutes, this study recorded approximately 142,989 observations representing 28 elasmobranch species (14 sharks and 14 batoids) throughout Lhaviyani Atoll from 2017 to 2024. The elasmobranch assemblage was dominated by reef-associated species from the families Carcharhinidae, Dasyatidae, and Mobulidae, mirroring patterns described across the Indo-Pacific (Ward-Paige et al., 2010a; Espinoza et al., 2024). 23 of all 25 identified taxa are listed as threatened under the IUCN Red List (2024), ranging from Vulnerable to Critically Endangered. Community-level indices (H′ = 1.60; J′ = 0.48) revealed low evenness, driven by dominance of Carcharhinus amblyrhynchos (61% of all records). Mean SPUEs reached ~12 ind. h⁻¹ (~9 sharks h⁻¹, ~3 batoids h⁻¹), over four times higher than the 2.83 elasmobranch ind. h⁻¹ reported from short-term diver surveys at Hurawalhi (Waters, 2023). Beyond assemblage-level patterns, a few species stood out. The more than 50 sightings of A. vespertilio recorded here are striking, given that across its Indo–West Pacific range the species is generally described as rare and typically observed only as solitary individuals or in small groups (Araujo et al., 2020). Global databases likewise report only sporadic occurrences from Maldivian atolls – including isolated records from Lhaviyani, North Malé, and Ihavandhippolhu (FishBase, 2025). These comparatively high densities suggest that Lhaviyani Atoll may provide regionally important habitat for this Critically Endangered ornate eagle ray.

Diversity metrics (species richness, Shannon and Pielou’s evenness) were crucial in identifying important areas for elasmobranch occurrence. One thing that stands out is the importance of including snorkelling data. This is especially true for coastal shallow reef systems, with surface-dwelling filter feeders (like mantas and whale sharks) and shallow-reef residents (such as blacktip sharks). Including snorkelling data resulted in an increase in sightings of 142.49% for reef manta rays, 61.11% for whale sharks and 51.32% for blacktip reef sharks. Other significantly affected species were the endangered ornate eagle ray (+32.73%), porcupine ray (+32.64%) and the tawny nurse shark (+31.16%). Its exclusion would result in a significantly smaller sample size and uneven diversity estimation across different habitat types.

4.2 Environmental patterns in elasmobranch SPUEs

Observed spatiotemporal patterns in elasmobranch presence across Lhaviyani Atoll closely mirrored the monsoon-driven circulation of the northern Indian Ocean, where surface currents reverse seasonally (Schott & McCreary, 2001; Radice et al., 2019). SPUEs and diversity peaked during the late southwest (SW) monsoon (August–September) and declined during the northeast (NE) monsoon (December–March) (Figure 4; Supplementary Table S6), following regional productivity cycles. These seasonal reversals in monsoon winds and surface circulation generate spatial shifts in productivity and promote water exchange across the atoll system, enhancing plankton and small-nekton concentrations along reef slopes and channel habitats (Radice et al., 2019). Inter-annual shifts were also evident, with diversity increasing sharply after 2021 while overall SPUEs stabilised at lower levels.

This pattern could suggest a structural reorganisation of the community – from early dominance by a few abundant taxa (notably C. amblyrhynchos in 2018–2019) toward a more even but less numerically dense assemblage in recent years. Similar decoupling between diversity and abundance has been observed across Indo-Pacific reefs undergoing recovery or redistribution following conservation interventions (MacNeil et al., 2020; Espinoza et al., 2024). The increase in species richness and diversity observed after 2020 likely reflects a combination of conservation measures, ecological and observational drivers. The COVID-19 “anthropause” led to a temporary reduction in human activity across reef systems, which has been shown to influence the spatial use and detectability of reef-associated elasmobranchs (Séguigne et al., 2022). Notably, sampling effort in 2020 was markedly reduced, with only 3 sites surveyed compared to more than 28 in all other years, likely constraining the range of community structures captured. The subsequent recovery of tourism in the Maldives, rapid and, in some cases, exceeding pre-pandemic levels by 2022 (Jaroensutasinee et al., 2025), likely increased sampling effort and encounter rates in this opportunistic dataset.

At the same time, these patterns may also reflect underlying ecological processes. Maldivian coral reefs have demonstrated capacity for recovery following major disturbance events such as the 2016 bleaching, with gradual improvements in reef condition and associated fish communities (Noo Raajje Program, 2021). Such recovery can enhance habitat complexity and prey availability, potentially supporting higher elasmobranch diversity and abundance. In addition, temporal variability in environmental conditions, including sea surface temperature and productivity, may influence prey distributions and predator aggregation dynamics, further contributing to the observed post-2020 trends.

Nonetheless, the relatively short seven-year time series limits the strength of inter-annual inferences.

The significant effects of year, month, and site obtained in the GAMs further reinforce that elasmobranch occurrence in the atoll is shaped by both temporal and spatial oceanographic processes. SPUEs increased at sea surface temperatures above 31 °C, coinciding with late southwest monsoon warming, and declined with higher salinity, consistent with rainfall-driven freshening and enhanced frontal retention at reef passes (Harris, 2018; Harris and Stevens, 2021; Bhavan et al., 2023). Comparable monsoon-linked mechanisms have been reported elsewhere in the western Indian Ocean, where productivity fronts generated by seasonal upwelling concentrate prey and attract sharks (Lopetegui-Eguren et al., 2022). Recent syntheses indicate that ocean warming can be associated with short-term increases in shark presence or encounter rates on coral reefs, even as longer-term ecosystem degradation progresses. Dedman et al. (2024) show that climate-driven coral bleaching and reef flattening reduce structural complexity and prey refugia, increasing prey vulnerability and modifying predator–prey encounter dynamics. Such habitat-mediated processes provide a plausible explanation for the positive association between elasmobranch SPUEs and higher sea surface temperatures observed here, particularly under late-SW-monsoon conditions when productivity is elevated. Importantly, this pattern is best interpreted as a transient ecological response rather than evidence of sustained population recovery. As reef degradation advances and prey availability declines, warming-driven increases in metabolic demand and altered foraging efficiency are expected to erode the stability of shark aggregations, ultimately constraining persistence under ongoing climate change (Dedman et al., 2024).

The zonal current component (uₒ) showed a positive effect on SPUEs, whereas the meridional component (vₒ) displayed a near-neutral response around 0 m s⁻¹, suggesting that east–west flow dynamics, characteristic of monsoon reversals moderate shear, may have enhanced the transport of prey and sensory cues, thereby facilitating predator foraging along reef slopes and channel entrances.

(Ryan et al., 2017; Papastamatiou et al., 2018). Dissolved oxygen was retained as a significant predictor in the shark GAM, but the response was nearly flat across the well-oxygenated range sampled (195–206 mmol m⁻³). This suggests that while minor nonlinear variation was detected statistically, DO is unlikely to exert a strong ecological influence on shark SPUEs under these reef conditions. All sampled values were well above hypoxia thresholds (~60 µmol kg⁻¹; equivalent to ~61–62 mmol m⁻³; Paulmier and Ruiz-Pino, 2009), confirming that oxygen was not a limiting factor within the study area. Chlorophyll-a concentration exhibited only a marginal, slightly negative effect on shark SPUEs (Table 2; Figure 5B), consistent with observations from other oligotrophic regions where sharks occur in low surface chlorophyll-a waters (Birkmanis et al., 2020). This pattern likely reflects the deep chlorophyll maximum typical of the Maldives (between 60–100 m), where subsurface productivity supports prey assemblages not captured by satellite-derived surface chlorophyll (Su et al., 2021).

Finally, our modelling results indicate that shark SPUEs in Lhaviyani Atoll is strongly and positively associated with structurally complex geomorphologies, specifically sheltered reef slopes, exposed reef slopes, and deep lagoon zones, and negatively associated with low-complexity substrates such as sand and rubble. This is consistent with evidence that sharks’ occurrence increases with habitat structural complexity. Reef slopes typically provide greater coral cover, prey biomass, and refuges from strong hydrodynamics – conditions known to sustain larger piscivorous fishes and promote schooling and residency of reef sharks (Skinner et al., 2020; Pikitch et al., 2005; Martins et al., 2018). They also provide shelter from swell and serve as important resting refuges for species such as Nebrius ferrugineus, which in Lhaviyani Atoll has been observed resting exclusively within lagoon habitats (Waters, 2023). Further aligning with findings from Glover’s Reef, Belize, where long-term sampling recorded higher elasmobranch CPUE in deep lagoons than in outer-reef sites (Pikitch et al., 2005), and from the neighbouring Seychelles, where elasmobranchs showed a preference for rocky substrates over sand and rubble (Lourie et al., 2025).

4.3 Spatial hotspots and conservation

KDE maps revealed four main elasmobranch hotspots within Lhaviyani Atoll (Figures 6–8). The primary northern hotspot (H₁ ), centred around KahliFushivaru outreef (D42) at the junction between an exposed and a sheltered reef slope, overlaps with the Kuredhu–Huravalhi–Komandoo ISRA, a multi-species aggregation zone where grey reef sharks, blacktip reef sharks, reef mantas, and several Rhinopristiform batoids routinely use outer-reef slopes and corners for foraging, cleaning, and social behaviour (IUCN SSC Shark Specialist Group, 2023b). Positioned directly over a reef channel (kandu), this slope–channel junction concentrates flow and prey, underscoring the model-inferred importance of current-exposed habitats as key drivers of elasmobranch aggregation.

Figure 7

Figure 8

A similar pattern characterized the second hotspot (H₂), spanning the semi-sheltered Fushivaru channel complex (D22–D25). This region overlaps with the Fushifaru Kandu ISRA, a nationally important manta and shark aggregation site where grey reef sharks, spotted eagle rays, reef mantas, and blotched fantail rays regularly occur (IUCN SSC Shark Specialist Group, 2023a), again highlighting the tendency of structurally complex and hydrodynamically active zones to support higher predator densities (Figure 6). This mirrors patterns reported by Vianna et al. (2013), who found that C. amblyrhynchos aggregates at current-exposed promontories along deep-lagoon edges, precisely the geomorphological setting of H₁ and H₂.

Consistent with patterns of interspecific competition and hydrodynamic preferences reported in Palmyra (Papastamatiou et al., 2018) and in recent observations from Lhaviyani Atoll (Waters, 2023), C. amblyrhynchos and C. melanopterus exhibited clear habitat partitioning. Both species aggregated within the main elasmobranch hotspot (H₁ ), but their secondary concentrations occurred in distinct geomorphic zones. C. amblyrhynchos showed a secondary hotspot at H₂ (the Fushivaru complex, D22–D26), a slope–channel convergence zone characterised by strong currents and high prey biomass – conditions known to favour this larger, competitively dominant species (Papastamatiou et al., 2021). In contrast, C. melanopterus occurred mainly around sites north of H₁ (within the Kuredu complex, Caves, and Express Channel; D56, D57, and D06), spanning inner and outer reef flats and shallow-to-deep lagoon transitions (Figure 7; see Supplementary Figure S4 for geomorphology mapping).

Beyond foraging and refuge, habitat selection also reflects behavioural needs such as cleaning and social interaction (Perryman et al., 2019; Nicholson-Jack et al., 2021). The northern reef slopes provide multiple cleaning sites that offer both ecological and social functions likely contributing to the manta aggregations observed in these areas (Figures 6–8) (Perryman et al., 2019). In the Maldives, cleaning stations are typically found on shallow reefs or within lagoons, where mantas frequently visit for parasite removal and social interaction (Nicholson-Jack et al., 2021). M. alfredi preferentially selects hard-coral cleaning sites with cleaner-wrasse assemblages, forming spatial networks of “quality” stations within its home range (Newsome et al., 2024).

Batoids showed a more dispersed distribution than sharks, forming hotspots not only in the north but also in the south of the atoll. This spatial pattern agrees with previous findings that M. alfredi distribution in the Maldives is shaped by seasonally reversing monsoon currents, with individuals occurring predominantly on the downstream side of atolls and switching sides biannually as current direction changes. Alternating winds drive nutrient-rich upwelling and mixing that stimulate phytoplankton blooms, indirectly supporting zooplankton, the primary prey of mantas (Anderson et al., 2011). The four Critically Endangered species – S. mokarran, R. djiddensis, R. ancylostoma, and A. vespertilio – showed similar spatial preferences to the overall elasmobranch distribution, with additional nuances. S. mokarran exhibited its strongest aggregation at Faadhoo Beyru within H₄, where it reached its local maximum (D14; 0.8–1.0). A secondary concentration occurred in the north-western section of H₁ (D33, D42, D83), where individuals occupied deep outer-slope habitats and reached high densities (0.6–0.8).

Among the batoids, A. vespertilio and R. djiddensis also exhibited H₁ as their primary hotspot, with their highest densities (> 0.6) concentrated in the northern sector. In contrast, R. ancylostomus showed a distinct pattern, favouring the north-eastern sector and selecting the semi-sheltered channels and back-reef slopes of the H₂ Fushifaru complex (e.g., D23–D26) as its primary hotspot. Of these species, A. vespertilio displayed the widest spatial distribution, extending into low-to-moderate density areas along the southern and western margins of the atoll (e.g., D92 on the western side; D82 at the southern edge of H₂; and D14 within H₄) (Figure 8). Although H₃ around Kanifushi and H₄ along the Faadhoo–Meyyafushi sector fall outside current ISRA boundaries, both support substantial densities of CE species, including the highest recorded local density of S. mokarran, indicating that key elasmobranch habitats extend beyond existing priority areas and warrant parallel conservation attention. These spatial patterns should nevertheless be interpreted with caution. Although KDEs were derived from effort-standardised SPUEs, dive effort remained non-random and biased towards commonly visited sites, so part of the hotspot structure may reflect sampling effort and site accessibility rather than biological aggregation alone.

A recent regional assessment further underscores this spatial mismatch. Across the western Indian Ocean, 125 ISRAs have been delineated, covering approximately 10% of the region and encompassing 39% of reported chondrichthyan species, 76% of which are threatened. However, only 7.1% of these ISRAs overlap with marine protected areas, and just 1.2% fall within fully protected no-take zones. This contrast suggests that many recognised priority areas remain only partially protected in practice, despite the growing integration of unpublished records and local datasets that have played a key role in ISRA delineation and refinement (Cochran et al., 2026). In this context, the present study contributes new spatial evidence from Lhaviyani Atoll, identifying several species hotspots, notably the Critically Endangered ornate eagle ray. These spatial patterns take on additional importance in light of recent policy developments affecting elasmobranch conservation in the Maldives. In November 2025, the Maldivian government authorised the reopening of a targeted gulper shark fishery, despite survey evidence indicating a 97% population decline between 1982 and 2002 and widespread public opposition; this decision is now being challenged in the High Court (Blue Marine Foundation, 2025; Le Monde with AFP, 2025; Zalif, 2025). Over the same period, Parties to CITES adopted strengthened protections for elasmobranchs, listing gulper sharks (Centrophorus spp.) on Appendix II and transferring all mobulid rays to Appendix I (CITES CoP20 Prop. 30, 2025). Together, these developments reveal a growing tension between national fisheries policy and international conservation commitments, reinforcing the need for contemporary, site-specific evidence on elasmobranch distributions and habitat use.

Within this context, the more than one thousand Mobula alfredi individuals recorded through SCUBA surveys alone represent a substantial aggregation for a species experiencing regional declines across much of its Indo-Pacific range. These conservative estimates underscore the importance of Lhaviyani Atoll for M. alfredi and provide a quantitative baseline against which emerging changes in habitat use can be interpreted. Notably, recent field observations shared by the Prodivers regional manager, drawing on over 32 years of continuous experience in Lhaviyani Atoll, indicate an unprecedented absence of manta rays at traditional cleaning stations during the most recent season. No manta rays were observed at historically reliable sites such as Fushifaru, with only occasional sightings at Hurawalhi sandbank and sporadic observations at Dhanifaru. Although these observations fall outside the temporal scope of the present dataset and were therefore not included in the analyses, their lack of historical precedent warrants note in a conservation context and highlights both the dynamic nature of manta habitat use and the importance of sustained, site-based monitoring to detect emerging changes.

Beyond species-level outcomes, growing evidence indicates that large elasmobranchs can contribute to ecosystem resilience under climate change by mediating trophic interactions that support sedimentary and vegetated carbon stores (“blue carbon”), thereby reducing the likelihood of phase shifts following disturbance (Dedman et al., 2024). Predator presence on coral reefs has been associated with increased carbon deposition in sediments, while in adjacent systems such as seagrass meadows, shark-mediated trophic effects may facilitate recovery following marine heatwaves. Although the magnitude of these climate-mitigation services remains uncertain, safeguarding elasmobranch hotspots may yield benefits that extend beyond biodiversity conservation, strengthening their role within climate-resilient marine management frameworks.

4.4 Limitations

Across the Indo-Pacific and Atlantic, diver-led monitoring networks have demonstrated that trained recreational divers can collect data comparable in accuracy and consistency to professional surveys (Goffredo et al., 2010; Gonzáles-Mantilla et al., 2021; Blanco-Parra et al., 2022; Séguigne et al., 2023). Such programmes provide cost-efficient, large-scale coverage rarely achievable through traditional methods and have helped bridge key knowledge gaps in elasmobranch occurrence and habitat use, including species-specific redistributions under ocean warming over multi-decadal timescales. In some cases, climate-driven redistribution of mobile species has been shown to confound site-based abundance trends (Osgood et al., 2021) and potentially erode the effectiveness of fixed marine protected areas without complementary fisheries regulation beyond their boundaries.

Despite their value, citizen-science data carry inherent limitations (Séguigne et al., 2023). In this study, site selection was non-random and effort-biased, as dives were scheduled according to guest demand and sea conditions, resulting in oversampling of more accessible or frequently visited reef slopes and channels. To mitigate potential exaggeration of aggregation patterns, all KDEs were generated using effort-standardised SPUEs (counts per hour of dive time), rather than raw counts.

A further source of uncertainty relates to species identification. Species identification relied on in-house guide training rather than photo-verified records, which may introduce taxonomic uncertainty, particularly among morphologically similar species (Kohler et al., 2025). This is especially relevant for hammerheads, as distinguishing Sphyrna lewini from Sphyrna mokarran underwater is challenging even for experienced observers, and diver-based surveys are known to under-detect S. lewini in particular (Budd et al., 2021). Accordingly, hammerhead sightings were assigned to S. mokarran based on Prodivers’ long-term field experience in Lhaviyani, although both species occur in the Indian Ocean (Lin et al., 2024). Additionally, cryptic, nocturnal, or deeper-dwelling species beyond typical diving depths are likely underrepresented (Ward-Paige et al., 2010a).

To reduce the risk of within-survey double counting, a MaxN protocol was used rather than cumulative tallies; nevertheless, detections remain influenced by visibility, diver position, current direction, and the rapid movement of individuals—particularly pronounced at aggregation sites, introducing the non-instantaneous UVC bias typical of highly mobile taxa (Ward-Paige et al., 2010b; Pais and Cabral, 2018). While comparable dive-centre studies have incorporated random effects for site and observer to account for such variability (Osgood et al., 2021), this was not feasible here given the participation of more than 1,000 divers. Instead, analyses prioritised effort standardisation and the inclusion of environmental covariates known to influence detectability. An additional limitation of dive-centre datasets based on spatially fixed sites is that apparent changes in SPUEs may reflect short- or long-term shifts in habitat use rather than true demographic trends, as mobile elasmobranchs can redistribute in response to changes in temperature, productivity, or circulation, reducing detections at routinely surveyed locations despite regional persistence (Osgood et al., 2021).

A separate limitation applies to manta rays, such as Mobula alfredi, a major draw for marine tourism and among the species most sought after by visitors (Moloney et al., 2025). In the Maldives, mantas commonly form surface-feeding aggregations at shallow sites that are more readily detected by snorkellers rather than during SCUBA dives (Stevens, 2016; Stevens et al., 2018). Consequently, manta SPUEs in this study – comprising less than 3% of all batoid observations recorded on SCUBA – should be interpreted as conservative, reflecting methodological constraints rather than ecological absence.

In the absence of in situ dive measurements, environmental covariates were extracted from Copernicus Marine Service gridded datasets (Copernicus Marine Service, 2025a; 2025b). SST, salinity, and current layers (uₒ and vₒ) have a spatial resolution of 0.083°, while chlorophyll-a and dissolved oxygen grids are coarser at 0.25°. As a result, extracted values correspond to the nearest grid cell rather than the exact dive coordinates. Because these products reflect daily means, they may not capture short-term current reversals, updraft pulses, or tide-driven flow shifts that can occur on hourly scales in Maldivian channels and strongly influence shark movement and detectability (Papastamatiou et al., 2021). Depth was not included as a predictor variable, as it was not consistently recorded across dives. Assigning depth from bathymetric data was considered unreliable given the steep reef geomorphology, where small spatial errors can translate into large depth inaccuracies, potentially introducing noise into the analyses. Finally, inter-annual patterns should be interpreted with caution, as most populations require more than a decade of monitoring for reliable trend detection (White, 2019).

Although roving-diver surveys lack the spatial standardisation of fixed transects and do not yield density estimates, they have been shown to perform comparably to belt-transect and stationary point-count UVC methods for detecting conspicuous and highly mobile taxa, and are particularly effective for documenting low-density species such as elasmobranchs (Ward-Paige and Lotze, 2011). Given the mobility and aggregation behaviour characteristic of many sharks and batoids, the survey design employed here is well suited to capturing broad-scale patterns of occurrence and relative abundance across this reef system, providing consistent long-term insight despite the inherent constraints of citizen-science datasets.

5 Conclusion

This study suggests that elasmobranch diversity and spatiotemporal patterns of habitat use in Lhaviyani Atoll are shaped by the interaction between monsoon-driven oceanography and reef geomorphology, with slope-channel-lagoon mosaics sustaining higher predator densities through enhanced productivity and the availability of diverse functional habitats. The identification of spatial hotspots hosting four Critically Endangered IUCN species, including two Rhinopristiform batoids, highlights the exceptional conservation importance of this atoll. Populations of R. australiae and R. djiddensis have declined by more than 80% over the past 45 years (CITES CoP18 Fact Sheet, 2019), mirroring global chondrichthyan declines. Beyond their ecological significance, elasmobranchs represent vital economic assets. In the Maldives, shark-diving tourism generates substantial revenue - over 76,000 shark-observing dives were recorded in a single year, contributing an estimated US $2.3 million to the local economy in 1993 alone, with a single grey reef shark valued at up to US $35,000 annually at popular dive sites (Gallagher and Hammerschlag, 2011). By 2010, shark-based ecotourism accounted for over 30% of national GDP, prompting the nationwide shark-fishing ban introduced that same year. Within this heavily dived atoll, our findings underline the urgent need to prioritize the protection of all identified elasmobranch hotspots, particularly slope–channel junctions, deep lagoons, and known batoid cleaning-station zones. Two core hotspots (H₁ and H₂) already overlap with internationally recognised Important Shark and Ray Areas (ISRs), underscoring their global conservation relevance, while H₃ and H₄, although currently outside ISRA boundaries, also support high densities of critically endangered species, including the highest recorded local concentrations of S. mokarran. As the Maldives revisits its shark and ray governance framework, the ecological and spatial patterns identified here provide a critical evidence base for policies that safeguard the habitats and processes most important for threatened species. Integrating these insights into marine spatial planning and climate-resilient management can help ensure that both the biodiversity and the economic value of elasmobranchs continue to endure within one of the world’s most important marine sanctuaries.

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