Two decades of citizen science reveal spatial biases and conservation gaps for elasmobranchs along the Mozambican coast

Abstract

Citizen science platforms play a crucial role in filling knowledge gaps and documenting global biodiversity trends, especially in under-sampled regions such as the Western Indian Ocean (WIO). Here, we assessed the contribution of citizen science data to elasmobranch records in Mozambique, examining species composition, spatio-temporal patterns, and conservation status. This study helps inform policy revision, targeted monitoring, and national reporting in Mozambique using existing citizen science datasets. Using 408 elasmobranch records from the iNaturalist platform collected between 2007 and 2025, we documented 44 species and noted that species records increased significantly over the last decade, particularly since 2019. Rays dominated the dataset, particularly the Dasyatiids and Mobuliids, whereas sharks were primarily represented by Carcharhiniids and Rhincodontiids. A high proportion of recorded species (71%) were classified as threatened on the IUCN Red List, with 10% listed as Critically endangered, 51% as Endangered, 38% as Vulnerable. Most records (82%) were classified as research grade, supporting the reliability of iNaturalist data for scientific applications. Overall, the iNaturalist dataset accounted for 32% of the 137 elasmobranch species previously reported from past studies in Mozambique. Observations were spatially biased toward southern areas of Mozambique, especially Inhambane and Maputo provinces, reflecting known inconsistencies in sampling effort in central and northern regions. Record density overlapped strongly with Important Shark and Ray Areas (ISRA), which accounted for over 90% of all records, whereas only 17% of records overlapped with Marine Protected Areas (MPAs), revealing a clear mismatch between priority areas and formal protection. Our findings demonstrate that citizen science provides a valuable and cost-effective complementary tool to traditional surveys and can meaningfully inform conservation planning, identify protection gaps, and support evidence-based management in data-limited contexts such as Mozambique and the WIO region.

1 Introduction

Documenting biodiversity is essential for monitoring species occurrence, composition, and distribution, as well as understanding interspecific interactions and discovering new species (Oliver et al., 2021; Li et al., 2025). These efforts provide the basis for the development of effective conservation strategies and policies (Webb et al., 2010; Pimm et al., 2014; Petersen et al., 2021). Despite their importance, biodiversity data are unevenly distributed across taxonomic groups and geographic regions, with many species remaining poorly documented (Troudet et al., 2017; White et al., 2023). The vastness of the oceans, the inaccessibility of many habitats, and the logistical, financial and technical constraints of underwater research have resulted in many marine environments remaining among the least-sampled on Earth, requiring substantial effort and resources to adequately document their biodiversity (Jones et al., 2018; Van De Putte et al., 2021). Sampling coverage is also highly uneven between regions, with some areas being frequently and repeatedly surveyed, often yielding limited new information, whereas others remain poorly explored (Troudet et al., 2017; White et al., 2023). As a result, countries with stronger research capacity and scientific infrastructure tend to have more complete biodiversity records than less resourced nations (Meyer et al., 2015; Oliver et al., 2021).

Citizen science platforms have emerged in recent years as important approaches to overcoming these challenges and improve knowledge about marine biodiversity, which is particularly important for data-limited regions (Kelly et al., 2020; Castejón-Silvo et al., 2023). While data quality can vary depending on observer expertise, citizen science platforms generate large volumes of observations that are widely distributed in space and time at relatively low cost. However, they may have limitations in taxonomic knowledge, as significant contributions come from the general public without requiring formal training in biology or related fields (Seltzer et al., 2020; Di Cecco et al., 2021).

Although still in its early stages in Mozambique, citizen science has already proven valuable elsewhere, revealing the occurrence of rare and endangered ornate eagle ray (Aetomylaeus vespertilio) (Araujo et al., 2020), documenting rare behaviors of the spotted eagle ray (Aetobatus ocellatus) (Araujo et al., 2024), providing baseline ecological data in data-poor regions (Blanco-Parra et al., 2022), and improved spatial coverage patterns that might otherwise remain undetected (Gibson et al., 2019; Tuya et al., 2021). These examples highlight the potential for citizen science to complement traditional research approaches in advancing biodiversity knowledge and informing conservation, including in regions where such datasets remain in early stages of development.

Among the citizen science initiatives, iNaturalist stands out as one of the most widely used platforms, bringing together millions of geo-referenced observations contributed by citizen scientists, researchers and research organizations worldwide (Seltzer, 2019; Seltzer et al., 2020; López-Guillén et al., 2024). Through collaborative species identification, iNaturalist generates an open-access biodiversity database (Seltzer et al., 2020; Di Cecco et al., 2021). The relevance of iNaturalist is especially evident in its ability to provide essential information on the occurrence, distribution, and behavior of marine species (Roberts et al., 2022; Mason et al., 2025). In Mozambique, knowledge of elasmobranch diversity and distribution remains limited and concentrated in a few localities (O’Connor and Cullain, 2021; Buschmann et al., 2024; Murie et al., 2024). iNaturalist-generated data therefore represent a unique opportunity to expand our understanding of species that are present along the Mozambican coast. Despite the growing number of iNaturalist observations along the coast of Mozambique, no study has systematically assessed the representativeness, reliability, spatio-temporal coverage, and conservation relevance of these records, particularly with regard to elasmobranchs, a taxonomic group that plays an important ecological role as top and meso-predators (Heithaus et al., 2008). Many of these species face increasing pressures due to overfishing, habitat degradation, bycatch, finning and a lack of up-to-date scientific data needed to support effective conservation policies (Asbury et al., 2021; Dulvy et al., 2021; Bennett et al., 2022; Daly et al., 2025; IUCN, 2025).

Here, we analyze iNaturalist records of elasmobranchs from Mozambique collected between 2007 and 2025 to (i) characterize species composition, (ii) assess spatio-temporal patterns of observations, and (iii) identify geographic and taxonomic gaps relevant to conservation strategies, management and future surveys. Beyond documenting species occurrence, this study evaluates how iNaturalist data intersect with conservation planning and national fisheries regulation; an analysis that has not previously been conducted for Mozambique or the Western Indian Ocean (WIO) region.

2 Materials and methods

2.1 Data source and filtering

To obtain information on shark and ray records, we searched the iNaturalist platform in December 2025 (iNaturalist, 2025). Records were retrieved using the taxonomic filter Elasmobranchii (class), with the geographical boundary set to Mozambique and no temporal restrictions applied. For each observation, we extracted available metadata, including record identification number, date, location, quality grade (i.e., the iNaturalist classification of data reliability based on community verification: research grade, needs identification, or casual), taxonomic identification, and other relevant fields described in Table 1. Each record was manually reviewed to determine whether it corresponded to an observation in the marine environment or to a land-based sighting associated with fisheries activities (e.g., individuals landed or photographed post-capture).

We manually reviewed observer profiles to identify potential elasmobranch records that were not captured by the initial taxonomic filter. We also examined observer images to detect the presence of multiple individuals or species within a single observation. In cases where more than one individual was visible, we created separate records so that each row represented a single individual. When different species were present in the same image, we duplicated the observation once for each additional species, changing only the taxonomic identification field to account for the extra taxon. In all cases, duplicated rows retained identical metadata (date, location, observer, and observation identification number), indicating that they originated from the same observation event. This procedure ensured that both the number of individuals and the diversity of species were represented, without generating independent sampling events, therefore, the results should not be interpreted as abundance estimates.

2.2 Conservation status, legal protection and data quality

All records in the final dataset were carefully reviewed line-by-line to identify potential errors, inconsistencies, or omissions, ensuring that no relevant records were excluded. All observations were restricted to the Mozambican geographic boundary. Species identifications were reviewed by the authors, with uncertain photographs primarily verified by co-author David van Beuningen, who has extensive experience in elasmobranch identification in the Western Indian Ocean. We then compared the list of observed species with the International Union for Conservation of Nature (IUCN) Red List of Threatened Species to verify the conservation status of each species (IUCN, 2025). Species were initially classified according to their full Red List categories: critically endangered (CR), endangered (EN), vulnerable (VU), near threatened (NT), least concern (LC), data deficient (DD), and not evaluated (NE). Additionally, species were grouped into four categories: threatened, not threatened, data deficient and not evaluated. The threatened species group included those classified as critically endangered, endangered, and vulnerable, while the non-threatened species group included species listed as near threatened and least concern. Data deficient and not evaluated species were treated separately, as these taxa are typically rare or lack sufficient information for an accurate assessment of extinction risk (IUCN SPC, 2024). This dual classification allowed both a clear overall overview of conservation while retaining consistency with widely applied conservation frameworks among individual IUCN categories, which carry different implications for management and policy (IUCN SPC, 2024).

To assess national legal protection status, we followed Decree No. 89/2020, which approves theMaritime Fisheries Regulation (REPMAR) (Boletim da República, 2020). Under this regulation, elasmobranch species were grouped into three main categories: fully protected, protected by size, and non-protected. The fully protected category included species whose capture is strictly prohibited by REPMAR; the protected by size category included species whose capture is only prohibited until they reach a specified size; and the unprotected category comprised species not listed under the protection measures established by REPMAR. To assess species coverage, we calculated the proportion of species recorded in the iNaturalist dataset relative to the total number of species previously reported from past studies in Mozambique (n = 137; Supplementary Table 1) (Pierce et al., 2008; Boletim da República, 2020; Bennett and van Beuningen, 2023; IUCN, 2025). During manual inspection of observation photographs, we also refined the grade importance assigned to each record based on the level of verification and reliability, following the classification system used by iNaturalist: ‘casual’, ‘needs identification’, and ‘research grade’.

Casual observations included records lacking essential information such as date, location, or evidence of a wild organism and were therefore unsuitable for scientific analyses. Observations classified as needs identification met basic data requirements but lacked sufficient consensus among iNaturalist users on taxonomic identification. Research grade observations represented the highest level of reliability and were assigned when more than two-thirds of identifiers agreed on a taxon at the species level and when associated metadata were validated by community consensus (iNaturalist, 2025). Importantly, no records were removed from the dataset. Instead, we created an internal column to document cases of misidentification detected during manual review. In such cases, we retained the observation at the higher taxonomic level (i.e. shark or ray), ensuring that all data contributed to the analyses while acknowledging uncertainty at the species level.

Taxonomic nomenclature for each identified species was cross-checked against the World Register of Marine Species (WoRMS) (WoRMS, 2025). We further categorized sampling effort as marine−based, land−based, or unknown. Marine−based observations corresponded to individuals confirmed as alive and freely swimming in the marine environment, while land−based observations included individuals recorded outside the marine environment (e.g., landed or photographed on land). The unknown category was used when the observation context could not be reliably determined.

2.3 Spatial and statistical analyses

To visualize spatial patterns and identify sampling gaps, we generated a heat map of shark and ray observations across Mozambique, using QGIS version 3.20 (QGIS Development Team, 2021). To assess overlap between iNaturalist records and conservation priorities, we overlaid iNaturalist occurrence data with spatial layers of Important Shark and Ray Areas (ISRA). ISRA shapefiles were obtained through a formal data request to the IUCN Species Survival Commission Shark Specialist Group and downloaded from the ISRA e-Atlas in accordance with the ISRA User License Agreement (IUCN SSC Shark Specialist Group, 2022). Recognizing that ISRA designation is important because they highlight the overall ecological value of the region but does not necessarily imply legal protection.

We also conducted equivalent analyses for Mozambique’s Marine Protected Areas (MPAs). The Mozambican MPA shapefiles were obtained from the World Database on Protected Areas (WDPA), accessible through the Protected Planet platform (UNEP-WCMC and IUCN, 2025), by filtering for sites located in Mozambique. Spatial intersections were performed in QGIS to quantify observations inside and outside ISRA and MPA polygons.

All analyses were conducted using R version 4.4.0 (R Core Team, 2024). Statistical significance was assessed at a 95% confidence level, with p-values < 0.05 considered statistically significant. Species accumulation curves were generated from iNaturalist records using the ‘specaccum’ function in the vegan package (Oksanen et al., 2022). Spatial analyses were conducted with the sf package (Pebesma, 2018), with data manipulation conducted in dplyr (Wickham et al., 2025). Chi-square (χ²) goodness-of-fit tests were used to compare observed frequencies of records across geographic regions (north, central, and southern Mozambique), as well as across ISRAs and MPAs, against expected frequencies derived from the proportional area of each spatial unit. Analyses were based on occurrence records, where each row represents a single individual of a given species within an observation. Observation effort was not standardized and could not be controlled, which is an inherent limitation of citizen science data. For the ISRA and MPA and analyses, the study area was defined as the minimum convex polygon encompassing all observations. Data visualization and additional data handling were carried out using ggplot2 (Wickham, 2016), RcolorBrewer (Neuwirth, 2022) and gridExtra (Auguie et al., 2017).

3 Results

3.1 iNaturalist observations, temporal trends and conservation status

Our search yielded 408 elasmobranch observations within the Mozambican coastal boundary in the iNaturalist database, retrieved without temporal restrictions up to December 2025. All records corresponded to sharks and rays, with no skate observations detected (Figures 1A,B). In the data preparation process, several categories of records were flagged, including records georeferenced outside marine environment, dead specimens, uncertain identifications and additional entries created to represent multiple individuals within the same observation (Supplementary Table 2).

Figure 1

Rays dominated the dataset, accounting for 65% (n = 266) of observations, while sharks comprised 35% (n = 142). In total, 53 elasmobranch species were recorded, of which 44 were reliably identified to species level, representing 17 families and 30 genera (Table 2). The species accumulation curve does not reach a clear asymptotes and additional species are likely to be recorded with further effort (Figure 2). The Dasyatiids was most frequently recorded (39% of observations), followed by Carcharhinids (19%), Mobulids (12%), and Rhincodontiids (7%) (Figure 3A). Several families were represented by only one or two records each (e.g. Lamniids, Ginglymostomatiids, Odontaspidiids, Myliobatiids, Rhinopteriids and Triakids) (Supplementary Figure 1A). The most frequently observed species were Rhincodon typus, Neotrygon indica, Pateobatis jenkinsii, and Taeniurops meyeni, while numerous species were recorded only once or twice (Figure 3B, Supplementary Figure 1B). Observation frequency increased over time (Figure 4), with the highest number of records occurring after 2019. Peak reporting years were 2023, 2025, 2019, and 2024 whereas early years showed very limited coverage.

Figure 2

Figure 3

Figure 4

In terms of conservation status, 71% of all observations corresponded to threatened species, with endangered and vulnerable taxa comprising the majority (51% and 38%, respectively). Threatened species were dominant in both groups. Among sharks, over 92% of the records involved threatened taxa, while in rays the proportion was 60% (Figure 5A). Across all observations, endangered species were most frequent, followed by vulnerable species, with data−deficient records representing only a small fraction (Supplementary Figure 2A). Endangered species accounted for 32% of ray records and 45% of shark records (Figure 5B). More than half of all observations (56%) involved species without legal protection under REPMAR (Supplementary Figure 2B). Among rays, 63% of records were unprotected, while in sharks the proportion was 42% (Figure 5C). Most of these unprotected records corresponded to species listed as threatened by the IUCN (Figure 5D).

Figure 5

3.2 Data quality

The majority of records were of high data quality, with 82% classified as research grade (Table 3). Most records (84%) originated from confirmed marine−based, with research grade classifications dominating both marine−based and land−based records. Misidentifications were concentrated in the needs identification category (over 50%), while rates were much lower in research grade (3%). All elasmobranch species recorded on iNaturalist were previously known from Mozambique. Overall, the platform captured 32% of the country’s documented elasmobranch diversity, indicating that approximately one-third of Mozambique’s recognized elasmobranch diversity is represented in iNaturalist data.

3.3 Important shark and ray areas and marine protected areas

Spatially, records were distributed unevenly along the Mozambican coastline (χ² = 220.25, df = 2, p < 0.001), with the majority of observations originating from southern Mozambique, particularly Inhambane (52%) and Maputo (37%) provinces (Figure 6A; Supplementary Table 2). Central and northern regions were comparatively under-represented.

Figure 6

Most records occurred within ISRAs, particularly in southern Mozambique and the Thongaland Transboundary Corridor, which together accounted for over 90% of all records (Figure 6B; Supplementary Table 3). ISRAs encompassed 30.5% of the study domain, defined by the minimum convex polygon encompassing all occurrence records, yet the proportion of records inside these areas was significantly higher than expected based on area alone (χ² = 215.16, df = 1, p < 0.001), reflecting the spatial overlap between ISRAs and regions of concentrated observation activity.

Marine protected areas covered approximately 10.2% of the study area, defined as the minimum convex polygon encompassing all occurrence records. Based on area alone, a similar proportion of records would be expected to be found within MPAs. However, 17% of all records (n = 68) were located inside MPAs, representing a significantly higher proportion than expected (χ² = 1282.2, df = 1, p < 0.001). Although most observations (83%) occurred outside legally designated protected areas, records were disproportionately concentrated within MPAs relative to their spatial extent. The majority of observations inside MPAs (96%, n = 65) were derived from marine−based, whereas only 4% (n=3) originated from land−based sources (Figure 6C; Supplementary Table 4).

4 Discussion

We demonstrate the potential of iNaturalist as a cost-effective and accessible tool for monitoring marine biodiversity in data-limited regions, while providing valuable information for the public, researchers, environmental managers, and policy makers, while encouraging the adoption of innovative and collaborative approaches to marine biodiversity conservation.

4.1 iNaturalist observations and temporal trends

Using information from 408 iNaturalist observations of sharks and rays collected between 2007 and 2025, this study provides the first comprehensive assessment of the contribution of iNaturalist to understanding the species composition, spatio-temporal patterns, and conservation status of elasmobranchs along the Mozambican coast. Our results demonstrate that iNaturalist data captured 32% of national elasmobranch diversity, while simultaneously revealing pronounced spatial, taxonomic, and conservation relevant gaps.

The steady and continuous increase in observations over time, particularly after 2019, reflects both global and local trends driven by rising environmental awareness (Srisathan et al., 2024), growth in marine tourism and dive activities in southern Mozambique (Venables et al., 2016), increased access to smartphones and internet connectivity (Turner, 2025), and the growing visibility of iNaturalist as a biodiversity documentation tool (Mason et al., 2025), and increasing interest among the public to contribute to such initiatives (Roberts et al., 2022). The ecological and charismatic appeal of elasmobranchs likely further enhances reporting rates for this taxonomic group (Rohner et al., 2013). Together, these trends confirm the platform’s potential to generate long-term biodiversity datasets in regions where traditional monitoring remains limited.

Rays dominated the dataset, with Dasyatids and Mobulids being the most frequently observed, while sharks were primarily represented by Carcharhinids and Rhincodontids. This taxonomic pattern is consistent with previous studies from southern Mozambique, which report high occurrence and frequent encounters of rays and coastal sharks from these families (Murie et al., 2023; Buschmann et al., 2024; Murie et al., 2024).

The predominance of conspicuous and charismatic species such as R. typus, N.indica, M. alfredi, P. jenkinsii, T. lymma, and T. meyeni is consistent with previous studies documenting frequent encounters of these species in southern Mozambique (O’Connor and Cullain, 2021; Buschmann et al., 2024; Murie et al., 2024). Their prominence in iNaturalist records may additionally reflect their conspicuousness, detectability, body size, and frequent occurrence in areas associated with diving and ecotourism activities (Venables et al., 2016; Keeping et al., 2021). Conversely, several rare, cryptic, or less conspicuous species (e.g., Torpedo sinuspersici, Mobula mobular, Rhinoptera jayakari, and Aetomylaeus vespertilio) were each recorded only once, underscoring a key limitation of opportunistic observation approaches. All of these rare observations originated from marine−based observations, which reinforces their reliability despite the low frequency of occurrence. At the same time, these rare observations highlight the unique value of iNaturalist data in documenting species that are infrequently encountered and might otherwise remain under-represented in conventional survey methods (Blanco-Parra et al., 2022; Fontaine et al., 2022; Baker et al., 2024).

4.2 Spatial coverage, conservation and protection biases

Spatially, the pronounced geographic bias toward southern Mozambique likely reflects higher tourism intensity, greater presence of dive centers, and more developed research infrastructure in this region, rather than underlying species distributions. Similar spatial biases are well documented in global biodiversity databases and are frequently linked to disparities in infrastructure, site accessibility, security, connectivity, and political or logistical constraints (Foster and Briceño-Garmendia, 2010; Beegle et al., 2016; Troudet et al., 2017; Oliver et al., 2021; Fabre and Vertier, 2024). Although records were present in central and northern regions, their low numbers likely reflect uneven sampling effort rather than species absence, highlighting critical knowledge gaps and emphasizing the need for targeted initiatives to strengthen data collection, local capacity, and engagement with community-science platforms such as iNaturalist in less-studied areas (Fontaine et al., 2022; Baker et al., 2024; Mason et al., 2025).

Despite these spatial biases, a strong overlap was observed between areas of high observation density and designated ISRAs, particularly in southern Mozambique and the Thongaland Transboundary Corridor. This pattern indicates spatial congruence between recognized elasmobranch conservation priority areas and regions of concentrated observation effort, suggesting that iNaturalist data collection is largely focused within areas already identified as ecologically important. Although records outside ISRAs were comparatively sparse, this pattern likely reflects uneven sampling effort rather than true absence of elasmobranchs, indicating that additional areas of ecological importance may remain undocumented and could warrant future consideration for ISRA designation as data coverage improves (García-Rodríguez et al., 2025; Rohner et al., 2025). In contrast, the more limited overlap with legally designated MPAs highlights a potential mismatch between areas of frequent elasmobranch occurrence and formal spatial protection (Cochran et al., 2026). This mismatch suggests that current MPA coverage may not adequately encompass key elasmobranchs habitats, reinforcing calls for the expansion or improved management of protected areas (Klein et al., 2015; Venables et al., 2020; Cochran et al., 2026).

Beyond ecological patterns, this mismatch is likely also influenced by institutional and social factors, including the limited public availability of biodiversity reports generated within conservation areas, observer preferences for sites outside MPAs due to accessibility, and security constraints that restrict tourism and monitoring in some regions. In addition, the relatively small spatial extent of MPAs along the Mozambican coastline reduces the likelihood of opportunistic sightings within their boundaries (Villegas-Ríos et al., 2021; Davies et al., 2017). Taken together, these factors highlight the need to interpret iNaturalist data with caution, while underscoring its value in identifying protection gaps and supporting better alignment between ecological priority frameworks, such as ISRAs, and legally designated conservation measures (Cochran et al., 2026; Klein et al., 2015).

This study also found a high proportion of elasmobranch observations corresponding to species classified as threatened on the IUCN Red List. Endangered and vulnerable species together accounted for the majority of records, reflecting both the global conservation status of elasmobranchs and their heightened vulnerability in coastal and nearshore habitats (Leurs et al., 2023; Hopkins et al., 2025). This pattern was particularly striking for sharks, for which over 92% of observations involved threatened species, but was also evident among rays (60%), which also face intense pressure from overfishing (Dulvy et al., 2021), bycatch and habitat degradation (IUCN, 2025), finning (Asbury et al., 2021), pollution, and climate change (Dulvy et al., 2021). More than half (n = 227) of all records belonged to species not protected under REPMAR, and the majority of these unprotected species were classified as threatened (63%). This mismatch was especially pronounced for rays, which accounted for the largest proportion of unprotected threatened records, including a critically endangered species (A. vespertilio). These findings highlight important gaps in current management frameworks and emphasize the need for periodic updates to national protection lists that better reflect contemporary conservation assessments and the actual exposure of threatened species to fishing pressures, habitat degradation and other anthropogenic impacts. Updating and expanding the national list of protected elasmobranchs, together with strengthening awareness and engagement among fishers, coastal communities, and other local stakeholders, could help bridge this gap and ensure that threatened species receive adequate and enforceable protection.

4.3 Data quality and iNaturalist potential

The high proportion of research grade observations supports the reliability of iNaturalist data for scientific and conservation applications. Nonetheless, our manual inspection confirms that the research grade designation does not fully eliminate misidentifications, with an error rate of approximately 3%, consistent with previous reports that citizen science data may contain a small but non-negligible error rate (Bird et al., 2014; Van Eupen et al., 2021). Misidentification rates were substantially higher in lower-confident categories, with 50% for needs identification and 13% for casual records, reinforcing that errors are not uncommon in open-access databases (Bird et al., 2014; Cruickshank et al., 2019). Levels of taxonomic expertise may vary across regions, being dependent on community validation and quality of photographic evidence, as these two tools can yield robust biodiversity information, particularly for well-known taxa (Bird et al., 2014; Cruickshank et al., 2019; Mason et al., 2025). These limitations highlight the need for continuous expert validation and targeted training in photographic documentation and species identification, particularly in biodiversity-rich but data-poor regions such as Mozambique (Koo et al., 2022; Roberts et al., 2022; Mason et al., 2025).

At the same time, the iNaturalist database played a crucial role in expanding the evidence based on the occurrence, distribution, and vulnerability of elasmobranchs in Mozambique. The patterns revealed by iNaturalist data draw attention to information gaps that may otherwise remain undocumented and highlight the value of periodically reviewing conservation policies and fisheries regulations in light of updated and locally grounded scientific evidence (McKinley et al., 2016; Fontaine et al., 2022; Baker et al., 2024).

The database also shows the potential of such platforms as an early warning system for range shifts or emerging threats, which may be due to a sudden increase in elasmobranch records in areas where they are not normally observed, or decreases in sightings in regions where they were previously common. Due to the cumulative nature of observations in such databases, these datasets also become important for long-term monitoring (Baker et al., 2024). Strengthening partnerships among scientists, dive centers, and citizen scientists will be essential to ensure curation and long-term utility of these records, based on the spatial biases and misidentification rates observed in our dataset, as well as evidence from previous studies showing that collaborative networks improve data quality and coverage (e.g., Araujo et al., 2020, Araujo et al., 2024; Fontaine et al., 2022; Baker et al., 2024).

Overall, the 44 species documented in this study represent approximately one-third of all elasmobranch species known from Mozambique. This proportion reflects the foundational role of previous surveys in establishing national species inventories, while highlighting that iNaturalist contributes a different, and still developing, component of biodiversity knowledge. Importantly, the species accumulation curve derived from iNaturalist records does not reach an asymptote, indicating that observations continue to add new species over time and that sampling remains incomplete. This suggests that, unlike traditional surveys which are often temporarily and spatially constrained, iNaturalist provides a continuous stream of that can progressively expand species records and capture temporal dynamics in biodiversity. While iNaturalist data are inherently influenced by observer bias and uneven sampling effort, they offer a cost-effective and scalable complement to traditional surveys, particularly in data-limited regions (McKinley et al., 2016; Chowdhury et al., 2023). In Mozambique, where systematic surveys are limited, platforms such as iNaturalist enable broader spatial and temporal data collection, including in areas or periods not covered by conventional research efforts (Mason et al., 2025). As such, citizen science does not replace traditional surveys but enhances them by contributing ongoing, distributed observations that can help update species lists and support the identification and monitoring of conservation priority areas. Future work should combine iNaturalist data with fisheries-dependent and fisheries-independent surveys, alongside targeted sampling in under-represented regions, to improve spatial coverage and strengthen ecological inference.

Based on our results, we recommend: (i) promoting training and awareness initiatives focused on iNaturalist data collection, including photographic documentation, species identification, and the use of platforms such as iNaturalist, among coastal communities and dive operators; (ii) prioritizing efforts to increase participation in iNaturalist biodiversity reporting in under-represented central and northern coastal regions through engagement with local communities, fishers, and conservation organizations; (iii) strengthening the involvement of taxonomic experts in species validation, for example by supporting new initiatives such as the iNaturalist ambassador program, which trains experienced community members to promote high-quality observations and foster local stewardship; and (iv) periodic revision of national fisheries regulations to better align species protection with IUCN Red List assessments and observed national occurrence patterns.

5 Conclusions

In conclusion, this study demonstrates that iNaturalist has significant potential to advance the understanding and conservation of elasmobranchs in Mozambique. While limitations remain, particularly regarding spatial representativeness and observer bias, iNaturalist data provide an unprecedented opportunity to support inclusive, participatory, and evidence-based conservation strategies, complementing traditional research methods. Continued engagement by citizens, researchers, environmental managers, non-governmental organizations, and decision-makers will be critical to fully realizing the value of these platforms for marine biodiversity conservation.

References

• 1

  AraujoG.Bateman-JohnR.ShackletonJ. L.GarrawayC.MarshN.WhicheloeD.et al. (2024). Citizen science highlights rare behaviours by the spotted eagle ray Aetobatus ocellatus Kuhl 1823. Front. Mar. Sci.11. doi: 10.3389/fmars.2024.1406803. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  AraujoG.LegaspiC.MatthewsK.PonzoA.ChinA.Manjaji‐MatsumotoB. M. (2020). Citizen science sheds light on the cryptic ornate eagle ray Aetomylaeus vespertilio. Aquat. Conserv. Mar. Freshw. Ecosyst.30, 2012–2018. doi: 10.1002/aqc.3457. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  AsburyT.BennettR.PriceA.Da SilvaC.BürgenerM.KleinJ.et al. (2021). Application of DNA mini-barcoding reveals illegal trade in endangered shark products in southern Africa. Afr. J. Mar. Sci.43, 511–520. doi: 10.2989/1814232x.2021.1996459. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  AuguieB.AntoveA.AuguieM. B. (2017). Package ‘gridExtra’. Miscel laneous functions for “Grid” Graphics.

  • Google Scholar
  • View reference in article

• 5

  Boletim da República. (2020). “ Decreto no. 89/2020,” in Aprova o Regulamento da Pesca Marítimo (REPMAR). (Maputo: República de Moçambique).

  • Google Scholar
  • View reference in article

• 6

  (2025). iNaturalist. Available online at: https://www.iNaturalist.org (Accessed December 27, 2025).

  • Google Scholar
  • View reference in article

• 7

  BakerP. M.SamuelsB.HainT. J. A. (2024). Using citizen science to document biodiversity on a university campus: A year-long case study. Conservation4, 533–546. doi: 10.3390/conservation4030032. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  BeegleK.ChristiaensenL.DabalenA.GaddisI. (2016). Poverty in a rising africa (Washington, DC: The World Bank). doi: 10.13140/RG.2.1.2111.8167

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  BennettR.van BeuningenD. (2023). “ Sharks & Rays,” in Marine fauna of Mozambique. Eds. TibiriçáY.SilvaM. I.(Pemba, Mozambique), 1–19. e-print.

  • Google Scholar
  • View reference in article

• 10

  BennettR. H.van BeuningenD.BräutigamA.BürgenerM. C. R.BladonA.KiszkaJ. J.et al. (2022). Chondrichthyans of the western Indian ocean: biodiversity, fisheries and trade, management and conservation. A status report prepared by the wildlife conservation society for the nairobi convention for the protection, management and development of the marine and coastal environment of the eastern african region (New York: Wildlife Conservation Society), 339. 2 appendices. doi: 10.19121/2022.Report.44805

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  BirdT. J.BatesA. E.LefcheckJ. S.HillN. A.ThomsonR. J.EdgarG. J.et al. (2014). Statistical solutions for error and bias in global citizen science datasets. Biol. Conserv.173, 144–154. doi: 10.1016/j.biocon.2013.07.037. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  Blanco-ParraM.GascaA. A.RincónC. R.MartínezN. H. G.Niño-TorresC. A. (2022). Citizen science as a tool to get baseline ecological and biological data on sharks and rays in a data-poor region. Sustainability14, 6490. doi: 10.3390/su14116490. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  BuschmannJ.RoquesK. G.DaviesJ. S.DissanayakeA.KeepingJ. A. (2024). Novel approach to studying marine fauna: Using long-life remote underwater video cameras to assess occurrence and behaviour of threatened and data-deficient elasmobranch species in southern Mozambique. Front. Mar. Sci.11. doi: 10.3389/fmars.2024.1518710. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  Castejón-SilvoI.TerradosJ.Morales-NinB. (2023). Citizen science in the study of marine biodiversity: The case of iconic and cryptic syngnathids. Thalassas An. Int. J. Mar. Sci.39, 679–686. doi: 10.1007/s41208-023-00590-1. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  ChowdhuryS.FullerR. A.RokonuzzamanM.AlamS.DasP.SiddikaA.et al. (2023). Insights from citizen science reveal priority areas for conserving biodiversity in Bangladesh. One Earth6, 1315–1325. doi: 10.1016/j.oneear.2023.08.025. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  CochranJ. E. M.CharlesR.TempleA. J.KyneP. M.García‐RodríguezE.Gonzalez‐PestanaA.et al. (2026). Only one percent of important shark and ray areas in the Western Indian Ocean are fully protected from fishing pressure. Ecol. Evol.16, e72690. doi: 10.1002/ece3.72690. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  CruickshankS. S.BühlerC.SchmidtB. R. (2019). Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends. Conserv. Sci. Pract.1. doi: 10.1111/csp2.54. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  DalyR.MurrayT. S.RobertsM. J.SchoemanD. S.LubitzN.BarnettA.et al. (2025). Breaking barriers: Transoceanic movement by a bull shark. Ecology106. doi: 10.1002/ecy.70096. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  DaviesT. E.MaxwellS. M.KaschnerK.GarilaoC.BanN. C. (2017). Large marine protected areas represent biodiversity now and under climate change. Sci. Rep.7, 9569. doi: 10.1038/s41598-017-08758-5. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  Di CeccoG. J.BarveV.BelitzM. W.StuckyB. J.GuralnickR. P.HurlbertA. H. (2021). Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. BioScience71, 1179–1188. doi: 10.1093/biosci/biab093. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  DulvyN. K.PacoureauN.RigbyC. L.PollomR. A.JabadoR. W.EbertD. A.et al. (2021). Overfishing drives over one-third of all sharks and rays toward a global extinction crisis. Curr. Biol.31, 4773–4787.e8. doi: 10.1016/j.cub.2021.08.062. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  FabreC.VertierP. (2024). The economic stakes of biodiversity loss in Africa and measures implemented to limit it. hal-04760200f.

  • Google Scholar
  • View reference in article

• 23

  FontaineA.SimardA.BrunetN.ElliottK. H. (2022). Scientific contributions of citizen science applied to rare or threatened animals. Conserv. Biol.36, e13976. doi: 10.1111/cobi.13976. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  FosterV.Briceño-GarmendiaC. (2010). Africa's infrastructure: a time for transformation (Washington, DC: The World Bank).

  • Google Scholar
  • View reference in article

• 25

  FroeseR.PaulyD. (2025). FishBase. World Wide Web electronicpublication. Available online at: www.fishbase.org (Accessed December 27, 2025).

  • Google Scholar
  • View reference in article

• 26

  García-RodríguezE.Gonzalez-PestanaA.CharlesR.PalaciosM. D.Di SciaraG. N.Alfaro-ShiguetoJ.et al. (2025). Mapping important shark and ray areas (ISRAs) in the Central and South American Pacific: Existing knowledge and data needs. PloS One20, e0322445. doi: 10.1371/journal.pone.0322445. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  GibsonK. J.StreichM. K.ToppingT. S.StunzG. W. (2019). Utility of citizen science data: A case study in land-based shark fishing. PloS One14, e0226782. doi: 10.1371/journal.pone.0226782. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  HeithausM. R.FridA.WirsingA. J.WormB. (2008). Predicting ecological consequences of marine top predator declines. Trends Ecol. Evol.23, 202–210. doi: 10.1016/j.tree.2008.01.003. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  HopkinsC. R.CullenG.FlattR. L.BrookerE. E.BaileyD. M.BurnsN. M. (2025). Conservation relevant fine‐scale distribution and habitat associations of threatened elasmobranchs in temperate nearshore waters. Aquat. Conserv. Mar. Freshw. Ecosyst.35. doi: 10.1002/aqc.70276. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  IUCN (2025). The IUCN Red List of Threatened Species. Version 2025. Available online at: http://www.iucnredlist.org (Accessed December 28, 2025).

  • Google Scholar
  • View reference in article

• 31

  IUCN SPC (2024). Guidelines for using the IUCN red list categories and criteria. Version 16. Prepared by the standards and petitions committee. Available online at: https://www.iucnredlist.org/ (Accessed April 3, 2026).

  • Google Scholar
  • View reference in article

• 32

  IUCN SSC Shark Specialist Group (2022). Dataset of Important Shark and Ray Areas (IUCN SSC SSG-ISRA). Made available under a User License Agreement by the IUCN SSC Shark Specialist Group and accessible via the ISRA e-Atlas. Available online at: https://sharkrayareas.org/e-atlas/ (Accessed September 11, 2025).

  • Google Scholar
  • View reference in article

• 33

  JabadoR. W.KyneP. M.García-RodríguezE.CharlesR.ArmstrongA. O.MoutonT. L.et al. (2023). Western Indian ocean: A regional compendium of important shark and ray areas (Dubai: IUCN SSC Shark Specialist Group). doi: 10.59216/ssg.isra.2023.r7

  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  JonesK. R.KleinC. J.HalpernB. S.VenterO.GranthamH.KuempelC. D.et al. (2018). The location and protection status of Earth’s diminishing marine wilderness. Curr. Biol.28, 2506–2512.e3. doi: 10.1016/j.cub.2018.06.010. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  KeepingJ.MilliganR. J.Reeve-ArnoldK.BaileyD. (2021). Trends in sightings of the stingrays of southern Mozambique. Mar. Freshw. Res.72, 1824–1837. doi: 10.1071/mf21013. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  KellyR.FlemingA.PeclG. T.von GönnerJ.BonnA. (2020). Citizen science and marine conservation: A global review. Philos. Trans. R. Soc Lond. B. Biol. Sci.21, 20190461. doi: 10.1098/rstb.2019.0461. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  KleinC. J.BrownC. J.HalpernB. S.SeganD. B.McGowanJ.BegerM.et al. (2015). Shortfalls in the global protected area network at representing marine biodiversity. Sci. Rep.5, 17539. doi: 10.1038/srep17539. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  KooK.OhJ.ParkS.ImJ. (2022). Accessing the accuracy of citizen science data based on iNaturalist data. Diversity14, 316. doi: 10.3390/d14050316. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  LeursG.NieuwenhuisB. O.ZuidewindT. J.HijnerN.OlffH.GoversL. L. (2023). Where land meets sea: Intertidal areas as key‐habitats for sharks and rays. Fish Fish.24, 407–426. doi: 10.1111/faf.12735. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  LiX.YangD.WangL.WiensJ. J. (2025). The past and future of known biodiversity: Rates, patterns, and projections of new species over time. Sci. Adv.11, eadz3071. doi: 10.1126/sciadv.adz3071. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  López-GuillénE.HerreraI.BensidB.Gómez-BellverC.IbáñezN.Jiménez-MejíasP.et al. (2024). Strengths and challenges of using iNaturalist in plant research with focus on data quality. Diversity16, 42. doi: 10.3390/d16010042. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  MasonB. M.MesaglioT.HeitmannJ. B.ChandlerM.ChowdhuryS.GortaS. B. Z.et al. (2025). iNaturalist accelerates biodiversity research. BioScience. 75, 953–965. doi: 10.1093/biosci/biaf104. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43

  McKinleyD. C.Miller-RushingA. J.BallardH. L.BonneyR.BrownH.Cook-PattonS. C.et al. (2016). Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Conserv.208, 15–28. doi: 10.1016/j.biocon.2016.05.015. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 44

  MeyerC.KreftH.GuralnickR.JetzW. (2015). Global priorities for an effective information basis of biodiversity distributions. Nat. Commun.6, 8221. doi: 10.1038/ncomms9221. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 45

  MurieC. J. G.LebratoM.LawrenceA.BrownJ.GavardL.BowlesK. R.et al. (2023). A Mozambican marine protected area provides important habitat for vulnerable pelagic sharks. Sci. Rep.13. doi: 10.1038/s41598-023-32407-9. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 46

  MurieC. J. G.OliverS. P.GavardL.LebratoM.BrownJ.LawrenceA. (2024). Environmental factors modulate the distribution of elasmobranchs in southern Mozambique. Front. Mar. Sci.11. doi: 10.3389/fmars.2024.1408727. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 47

  NeuwirthE. (2022). RColorBrewer: ColorBrewer Palettes. R package version 1. 1–3. Available online at: https://CRAN.R-project.org/package=RColorBrewer (Accessed February 21, 2026).

  • Google Scholar
  • View reference in article

• 48

  OksanenJ.SimpsonG. L.BlanchetF. G.FriendlyM.KindtR.LegendreP.et al. (2022). Package ‘vegan’. Community Ecology Package, Version 2.6-4. Available online at: https://cran.r-project.org/web/packages/vegan/vegan.pdf.

  • Google Scholar
  • View reference in article

• 49

  O’ConnorB.CullainN. (2021). Distribution and community structure of at-risk and data deficient elasmobranchs in Zavora Bay, Mozambique. Afr. J. Mar. Sci.43, 521–532. doi: 10.2989/1814232x.2021.1997814. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 50

  OliverR. Y.MeyerC.RanipetaA.WinnerK.JetzW. (2021). Global and national trends, gaps, and opportunities in documenting and monitoring species distributions. PloS Biol.19, e3001336. doi: 10.1371/journal.pbio.3001336. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 51

  PebesmaE. (2018). Simple features for R: standardized support for spatial vector data. R J.10, 439–446. doi: 10.32614/rj-2018-009. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 52

  PetersenT. K.SpeedJ. D. M.GrøtanV.AustrheimG. (2021). Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. Ecol. Solutions Evidence2. doi: 10.1002/2688-8319.12048. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 53

  PierceS. J.TrerupM.WilliamsC.TilleyA.MarshallA. D.RabaN. (2008). “ Shark fishing in Mozambique: a preliminary assessment of artisanal fisheries,” (Maputo: Eyes on the Horizon).

  • Google Scholar
  • View reference in article

• 54

  PimmS. L.JenkinsC. N.AbellR.BrooksT. M.GittlemanJ. L.JoppaL. N.et al. (2014). The biodiversity of species and their rates of extinction, distribution, and protection. Science344, 1246752. doi: 10.1126/science.1246752. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 55

  QGIS (2021). A free and open source geographic information system. Available online at: http://www.qgis.org (Accessed September 30, 2025).

  • Google Scholar
  • View reference in article

• 56

  R Core Team (2024). R: a language and environment for statistical computing. Version 4.4.0. (Vienna, Austria: R Foundation for Statistical Computing). Available online at: https://www.R-project.org/ (Accessed September 30, 2025).

  • Google Scholar
  • View reference in article

• 57

  RobertsC. J.VergésA.CallaghanC. T.PooreA. G. B. (2022). Many cameras make light work: opportunistic photographs of rare species in iNaturalist complement structured surveys of reef fish to better understand species richness. Biodivers. Conserv.31, 1407–1425. doi: 10.1007/s10531-022-02398-6. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 58

  RohnerC. A.PierceS. J.MarshallA. D.WeeksS. J.BennettM. B.RichardsonA. J. (2013). Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Mar. Ecol. Prog. Ser.482, 153–168. doi: 10.3354/meps10290. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 59

  RohnerC. A.RichardsonA. J.García-RodríguezE.CharlesR.Batlle-MoreraA.BortoluzziJ. R.et al. (2025). Important Shark and Ray Areas can inform conservation planning in the Mediterranean and Black Seas. iScience28, 113192. doi: 10.1016/j.isci.2025.113192. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 60

  SeltzerC. (2019). Making Biodiversity Data Social, Shareable, and Scalable: Reflections on iNaturalist & citizen science. Biodivers. Inf. Sci. Stand.3. doi: 10.3897/biss.3.46670. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 61

  SeltzerC.IwaneT.MisrarajA.LoarieS. (2020). 50 million observations on iNaturalist! Available online at: https://www.iNaturalist.org/blog/40699-50-million-observations-on-iNaturalist/ (Accessed December 10, 2025).

  • Google Scholar
  • View reference in article

• 62

  SrisathanW. A.MalaiK.NarathawarananN.CoochampooK.NaruetharadholP. (2024). The impact of citizen science on environmental attitudes, environmental knowledge, environmental awareness to pro–environmental citizenship behaviour. Int. J. Sustain. Eng.17, 1–19. doi: 10.1080/19397038.2024.2354269. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 63

  TroudetJ.GrandcolasP.BlinA.Vignes-LebbeR.LegendreF. (2017). Taxonomic bias in biodiversity data and societal preferences. Sci. Rep.7, 9132. doi: 10.1038/s41598-017-09084-6. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 64

  TurnerA. (2025). How many smarthphones are in the world. Available online at: https://www.bankmycell.com/blog/how-many-phones-are-in-the-world (Accessed July 22, 2025).

  • Google Scholar
  • View reference in article

• 65

  TuyaF.AguilarR.EspinoF.BoschN. E.MeyersE. K. M.Jiménez‐AlvaradoD.et al. (2021). Differences in the occurrence and abundance of batoids across an oceanic archipelago using complementary data sources: Implications for conservation. Ecol. Evol.11, 16704–16715. doi: 10.1002/ece3.8290. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 66

  UNEP-WCMC and IUCN (2025). Protected planet: the world database on protected areas(WDPA) (Cambridge, UK: UNEP-WCMC andIUCN). Available online at: www.protectedplanet.net (Accessed December 20, 2025).

  • Google Scholar
  • View reference in article

• 67

  Van De PutteA. P.GriffithsH. J.BrooksC.BricherP.SweetloveM.HalfterS.et al. (2021). From data to Marine Ecosystem Assessments of the Southern Ocean: achievements, challenges, and lessons for the future. Front. Mar. Sci.8. doi: 10.3389/fmars.2021.637063. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 68

  Van EupenC.MaesD.HerremansM.SwinnenK. R.SomersB.LucaS. (2021). The impact of data quality filtering of opportunistic citizen science data on species distribution model performance. Ecol. Modell.444, 109453. doi: 10.1016/j.ecolmodel.2021.109453. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  VenablesS.Van DuinkerkenD.RohnerC.MarshallA. (2020). Habitat use and movement patterns of reef manta rays Mobula alfredi in southern Mozambique. Mar. Ecol. Prog. Ser.634, 99–114. doi: 10.3354/meps13178. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  VenablesS. K.WinstanleyG.BowlesL.MarshallA. D. (2016). A giant opportunity: the economic impact of manta rays on the Mozambican Tourism Industry—An incentive for increased management and protection. Tourism Mar. Environments12, 51–68. doi: 10.3727/154427316x693225. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  Villegas-RíosD.ClaudetJ.FreitasC.MolandE.ThorbjørnsenS. H.Alonso-FernándezA.et al. (2021). Time at risk: Individual spatial behaviour drives effectiveness of marine protected areas and fitness. Biol. Conserv.263, 109333. doi: 10.1016/j.biocon.2021.109333. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  WarnellL.DarrinH.PierceS. J. (2013). “ Threatened marine species in Mozambique: A summary of the conservation and legal status,” (Maputo: Eyes on the Horizon).

  • Google Scholar

• 73

  WebbC. O.SlikJ. W. F.TrionoT. (2010). Biodiversity inventory and informatics in Southeast Asia. Biodivers. Conserv.19, 955–972. doi: 10.1007/s10531-010-9817-x. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  WhiteH. J.McKeonC. M.PakemanR. J.BuckleyY. M. (2023). The contribution of geographically common and rare species to the spatial distribution of biodiversity. Global Ecol. Biogeogr.32, 1730–1747. doi: 10.1111/geb.13734. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  WickhamH. (2016). ggplot2: elegant graphics for data analysis (New York: Springer-Verlag).

  • Google Scholar

• 76

  WickhamH.FrançoisR.HenryL.MüllerK.VaughanD. (2025). dplyr: a grammar of data manipulation. Available online at: https://github.com/tidyverse/dplyr (Accessed January 31, 2026).

  • Google Scholar

• 77

  WoRMS (2025). World register of marine species: statistics. Available online at: https://www.marinespecies.org/aphia.php?p=stats (Accessed December 28, 2025).

  • Google Scholar