Philippa Kaur

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Sea urchinsSea urchins (~ 2 cm of test diameter) were transported from Dalian Haibao Fishery Company (121° 22′ E, 38° 77′ N) to the Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea, Ministry of Agriculture and Rural Affairs (121° 56′ E, 38° 87′ N) in November 2020 and maintained in the fiberglass tank of 139 L (750 × 430 × 430 mm) of the recirculating system (Huixin Co., China) at ~ 14 °C. During the experiment period, the salinity of the recirculated seawater was 30.07–30.25‰. Sea urchins were fed the kelp Saccharina japonica ad libitum with aeration before the experiments. The seawater was changed every three days to remove feces and algal debris.Conspecific alarm cuesConspecific alarm cues were made from crushing one M. nudus in 50 mL of fresh seawater and then filtered using a fine silk net (260 μm of mesh size)6. Fresh conspecific alarm cues were prepared before each behavioral experiment. Five mL of conspecific alarm cues was used to simulate the natural conditions, in which a fraction of body fluids from a sea urchin instantly released into the surrounding water6.Experiment 1: whether M. nudus are attracted by the kelp exposed to conspecific alarm cuesA piece of wild fresh S. japonica (~ 5 g) was placed on the side of the device with the raceways (70 × 6 × 5 cm) and 5 mL of conspecific alarm cues were subsequently added above it in the experimental group (Fig. 4A). Five mL of conspecific alarm cues were added in the control group, while the kelp was not involved (Fig. 4B). The sea urchin was individually placed 15 cm away from the kelp for each group (Fig. 4A,B). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The danger area (b area) refers to the position 15 cm away from the kelp and the escape area (a area) refers to the position 15 cm away from the sea urchin (Fig. 4A,B). The time of sea urchins spent in the danger (a area) and escape (b area) areas was calculated individually for each group. The experiment was repeated 20 times using different sea urchins for each group (N = 20). The seawater was changed and the experimental device was washed for each trial to avoid potential non-experimental effects.Figure 4Foraging devices for the experiments with Mesocentrotus nudus. Experiment 1: the kelp was attracted by sea urchins (A,B); the positions of a, c and f refer to the escape areas, while positions of b, d and e refer to the danger areas. Experiment 2: five mL of conspecific alarm cues were put in the middle of the device (C). Experiment 3 (D–F): foraging behavior of M. nudus at 70 cm in the first trial (D); five mL of conspecific alarm cues were put in the middle of the device in the second trial (E); the position of g refers to the danger area; different amounts of conspecific alarm cues (5 mL and 0.5 mL) were put in the left and right of the devices in the third trial (F); the positions of h and i refer to the safety areas close to the areas with more and less conspecific alarm cues, respectively. This figure was created using the Adobe Photoshop (version CS5) software.Full size imageExperiment 2: whether foraging behavior of fasted M. nudus is affected when they encountered conspecific alarm cuesThis experiment investigated the effects of conspecific alarm cues on the foraging behavior of fasted sea urchins. The experiment group was set to simulate that conspecific alarm cues appear on the way to kelp beds (Fig. 4C). Sea urchins were fasted for 7, 14 and 21 days in the experiment groups.An acrylic device with the raceways (70 × 6 × 5 cm) was designed according to the method of our previous study17 with some revisions. Five mL of conspecific alarm cues were added in the middle of the device, the wild fresh S. japonica (~ 10 g) was placed 15 cm away from the left side of the device, while the sea urchin was placed on 15 cm away from the right of the device (Fig. 4C). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). Arriving at the kelp within 20 min was defined as successfully foraging18. The danger area (e area) refers to the position between conspecific alarm cues and the sea urchin, while the escape area (f area) refers to the 15 cm position away from the right side of the sea urchin (Fig. 4C). The time of sea urchins spent in the e and f areas was calculated individually for all the groups. The individual experiment was repeated 20 times with different sea urchins for each group (N = 20).Experiment 3: whether foraging ability of M. nudus links to their responses to conspecific alarm cuesAn acrylic device with the raceways (70 × 6 × 5 cm) was designed for the measurement of foraging behavior of sea urchins. Experiment 3 included three trials.In the first trial, we measured the foraging ability of sea urchins. Ten grams of the kelp was placed on one side of the tank and one sea urchin was placed on the other side of the tank (Fig. 4D). Foraging behavior was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). Movement and velocity of sea urchins were calculated using the software ImageJ (version 1.51 n). The experiment was individually repeated 31 times using different sea urchins (N = 31). At the end of this foraging experiment, 31 sea urchins were recorded in individual cylindrical plastic cages in the tank. All the sea urchins were subsequently measured for the second and third trials (N = 31). To avoid potential fatigue influences, each behavioral experiment was carried out after another 24 h.The second trial was carried out to test the hypothesis that sea urchins with strong foraging ability are affected when conspecific alarm cues appear on the way to the kelp. A piece of wild fresh kelp (~ 10 g) was placed on the left side of the device (70 × 6 × 5 cm), while 5 mL of conspecific alarm cues were put in the middle of the device. The individual was placed on the right of the device (Fig. 4E). Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The danger area (g area) refers to the 15 cm position away from conspecific alarm cues. The time of the 31 sea urchins spent in the g area was individually calculated.We investigated whether different amounts of conspecific alarm cues around the kelp affected the sea urchins with strong foraging ability in the third trial. More conspecific alarm cues (5 mL) and a piece of wild fresh kelp (~ 10 g) were placed on the left side of the device (70 × 6 × 5 cm), while less conspecific alarm cues (0.5 mL) and wild fresh kelp (~ 10 g) were placed on the right side of the device (Fig. 4F). Sea urchins were individually placed in the middle of the device. Foraging behavior of sea urchins was recorded for 20 min using a digital video recorder (FDR-AXP55, SONY, Japan). The number of sea urchins that successfully foraged to areas with more and less conspecific alarm cues was recorded. The area h refers to the safety area that was 15 cm position away from the sea urchin, which was close to the side with more conspecific alarm cues. The area of i refers to the safety area that was 15 cm position away from the sea urchin, which was close to the side with less conspecifics alarm cues. The time spent in the h and i areas was individually calculated in 31 sea urchins.Statistical analysisNormal distribution and homogeneity of variance of the data were analyzed using the Kolmogorov–Smirnov test and Levene test, respectively. Independent-sample t-test was carried out to compare the differences of duration in danger (b and d) and escape (a and c) areas in experiment 1. One-way ANOVA was used to analyze the duration in danger (e) and escape (f) areas of the sea urchins fasted for 7, 14 and 21 days. Pairwise multiple comparisons were carried out using LSD test when significant differences were found in the ANOVAs. Correlations between foraging velocity and duration in the g, h and i areas were analyzed using Pearson correlation analysis. A probability level of P < 0.05 was considered significant. All data analyses were performed using SPSS 21.0 statistical software. Graphs 2−4 were performed using Origin 9.0 software (OriginLab, USA). More

Reef Cover was specifically developed to support the process used to produce and deliver globally applicable coral reef mapping products from remotely sensed data16. The typology acts as a key to bridge historic and contemporary knowledge, plot-scale and aerial viewpoints, and pixel data with natural history to convert pixel data into information in a form suitable for reef management decisions. Accompanying case-studies18,19 we use to demonstrate its application are also publicly available. The mapping products described in each case study were developed specifically using Reef Cover to support science and conservation of coral reef ecosystems.We sought to develop a robust system which balances the geomorphic complexity of reefs with the need to develop high accuracy maps of each class in the system. The result is a 17-class system that can be (i) applied to remote sensing datasets for future mapping, (ii) used to interpret coral reef maps (iii) effectively disseminated to users – mainly in coral reef ecology and conservation space – in a way that promotes research and conservation.Three steps were used in the development of the classification scheme.

1.

Step 1. Review. Existing coral reef geomorphic classification schemes (expert-led classifications from Darwin’s 1842 reef classification20 to the Millennium Coral Reef Mapping Project classification21) were carefully reviewed22 to identify synergies in terminology and definitions for reef features, and evaluate how well common features can be described in terms of remote sensing biophysical data. The review allowed us to develop a set of classes that build constructively on previous foundational knowledge on coral reef geomorphology and are relatable to existing mapping and classification efforts, and addresses the challenge of relevance.

2.

Step 2. Development. Reef Cover classes were then derived from attributes data, building on established machine-led reef mapping theory13. Physical attributes datasets commonly available to remote sensing scientists were examined to refine a set of 17 meaningful internal reef classes that relate to broader interpretation from a natural history point of view, gathered in Step 1. A workshop was organised to gather feedback on classes. Clarity around how each class relates to attribute data addresses the challenge of transparency.

3.

Step 3. Dissemination. Reef Cover classes were then documented23 in a way to promote re-use and cross-walking, with a strong focus on needs of the users, to address the challenges of clarity and accessibility. Development of the Reef Cover document considers and details the 1) relevance e.g. rationale behind why it was important to map this class, but also broader global applicability of the class, 2) simplicity e.g., promoting user-uptake by employing plain language, not over-complicating descriptors and limiting the number of classes to manageable amount, 3) transparency supplying methodological basis behind each class, and exploring caveats and ambiguities in interpretation, 4) accessibility including discoverability, open access and language translations to support users, and 5) flexibility allowing for flexible use of the scheme depending on user needs, allowing for flexible interpretation of classes by providing cross-walk to other schemes and existing maps, and making the classification adaptable, and open to user feedback (versioning).

Finally, as a proof-of-concept the Reef Cover classification was tested in two large scale coral reef mapping exercises: one in the Great Barrier Reef 24 (Case Study 1) and one across Micronesia19 (Case Study 2, Technical Validation section). During this process, the Reef Cover dataset was reviewed to assess how useful it was for both a) producers using Reef Cover to map large coral reef areas from satellite data, and b) consumers using Reef Cover to interpret map products for application to real world problems.Step 1. Review. Building global classes on foundational reef mapping and classification workGlobal reef mapping: the need for a geomorphic classification to map coral reefs at scaleCoral reefs represent pockets of biodiversity that are widely dispersed, often remote/inaccessible and globally threatened2,3. Communities and economies are highly dependent on the ecosystem services they provide25,26,27. This combination of vulnerability, value and a broad and dispersed global distribution mean global strategies are needed for reef conservation, for which maps (and the classifications that underpin them) play a supporting role. Global coral reef maps have been fundamental to geo-political resource mapping and understanding inequalities28,29, the valuation of reef ecosystem services26, understanding the past30, present31, and future threats to reefs32, supporting more effective conservation33,34 and reef restoration strategies35,36, and facilitating scientific collaborations and research outcomes37. Reef conservation science and practice may particularly benefit from technological advancements that allow delivery of more appropriate map-based information, particularly across broader, more detailed spatial scales and in a consistent manner34,36,38.Existing expert-led reef classificationsTraditionally, coral reef features have been grouped based on observations of morphological structure, distributions of biota and theories on reef development, gleaned from aerial imagery, bathymetric surveys, geological cores and biological field censuses by natural scientists7,39 (Fig. 1). Natural scientists were struck by both the uniformity and predictability of much of the large-scale three-dimensional geomorphic structure of reefs and biological partitioning across that structure, and how consistent these characteristic geologic and ecological zones were across large biogeographic regions20,40). Technological developments of the 20th century, such as SCUBA demand regulators and compressed air tanks (commercially available in the 1940s41), acoustic imaging for determining seafloor bathymetry (e.g., side-scan sonar developed in the 1950s), light aircraft for aerial photography (first applied in the 1950s42) and lightweight submersible drilling rigs for coring (applied in the 1970s43), allowed reef structure to be viewed from fresh perspectives. New aerial, underwater and internal assessments of reef structure expanded the diversity of external and internal classes, with hundreds of new terms for features defined7,8,10 (Online-Only Table 1). However, the localised nature of most of these applications (Fig. 1) meant that many of the classes developed using these tools were region-specific, leading to experts warning against too heavy a reliance on “the imperfect and perhaps biased existing field knowledge on reefs” for developing global classifications44.Existing reef classifications derived from satellite dataShallow water tropical coral reefs are particularly amenable to global mapping from above12. They develop in clear, oligotrophic tropical waters, so many features are detectable from space45. Satellite technology has spawned a wealth of data on reefs, enabling large area coverage, with resolution of within reef variations. Initial approaches to reef mapping in the 1980s expanded our traditional viewpoint from single reef mapping and extent mapping to detailed habitat mapping of whole reef systems46. Through the 1990s and early 2000s evolving field survey techniques described above enabled more effective linkage of ecological surveys to remote sensing data47,48. Accessibility to higher spatial resolution images over larger areas in combination with detailed field data, physical attributes and object-based analysis resulted in large reef area mapping13,21,49,50. In the last five years, the increase in daily to weekly global coverage of this type of imagery, in combination with cloud-based processing capability has expanded to a global capability for reef mapping38. This is a new type of global information that requires a different approach to classification to make sense of complex natural systems at ocean scales.One of the first steps in creating the Reef Cover classification was reconciling existing classification schemes across the nomenclature driven by disciplinary, linguistic and regional biases. To do this we conducted a review of reef geomorphic classifications, looking for consistencies and usage of terms that transcended divides in discipline22 (see summary in Online-Only Table 1).Scaling and consistency: choosing an appropriate level for Reef CoverRemote sensing scientists have been developing automated methods to make sense of the increasing availability of earth observation data over coral reefs, yielding information on ecosystem zones derived from data sources such as spectral reflectance and bathymetry at increasingly larger scales12,51. As more data increasingly reveal the diversity and complexity of reefs, selecting an appropriate level at which to map reefs on the global scale requires balancing the need for a limited number of classes that can be mapped consistently based on available earth observation data, with user need for comparable information.Reef type classification
Morphological diversity can make global geomorphic classification – particularly between reefs (at the “reef type” level, e.g., Fringing, Atoll reefs) – challenging. Divergent regional morphologies (e.g., Pacific atolls vs Caribbean fringing reefs) and endemic local features (e.g., Bahamian shallow carbonate banks, Maldivian farus) are created by underlying tectonics, antecedent topography, eustatics, climate and reef accretion rates which can all vary geographically52. The diversity of reef types is reflected in the large number of classes defined in the impressive Millennium Coral Reef Mapping Project (68 classes at the between-reef geomorphic level L3), the most comprehensive globally applicable coral reef classification system to date21.

Geomorphic zone classification
Internally, reef morphology becomes a lot more consistent. Physical boundaries in the depth, slope angle and exposure of the reef surface create partitioning into “geomorphic zones” (e.g., Reef Flat, Reef Crest), developed in parallel to the reef edge and coastlines and generally with a distinct ecology17,39,53. These internal patterns of three-dimensional geomorphic structure can be remarkably predictable, even between oceans. This makes geomorphic zonation a good basis for consistent and comparable mapping at regional to global scales54. Moreover, congruence between geomorphic zones and ecological partitioning means that ecological understanding can be derived from geomorphic habitat classes, making geomorphic mapping valuable to conservation practitioners55.

Benthic classification
Many classifications developed for reef mapping (e.g., Living Oceans Foundation49, NOAA Biogeography Reef Mapping Program50), monitoring (Atlantic and Gulf Rapid Reef Assessment56, Reef Cover Classification System57, Reef Check58,59) and management (Marine Ecosystem Benthic Classification60) have included an ecological component. Classifying reef benthos is important as associated metrics, such as abundance of living coral and algae, are widely used indicators of ecosystem change. However, most classifications that consider benthic cover are operational at reef61 to regional scales, due to the need for very high-resolution remote sensing data11 (e.g. from UAVs and CASI62, or high-resolution satellites like QuickBird and WorldView 1 m) to be able to reliably determine classes such as coral cover and type, soft coral, turf, coralline algae, rubble and sand. A comprehensive benthic coral reef classification23 that met the Reef Cover objective of being globally scalable (both in terms of remote sensing biophysical data availability and processing capabilities) but that also fully recognises and includes the rich benthic detail required to address ecological questions at sub-metre scales is beyond the scope of this classification. In the coming years it is likely that further advancements in technology – both downscaling of remote-sensing and up-scaling of field observations63 – will enable us to address this spatial mismatch.
The challenge of creating the Reef Cover classification was to create a set of classes that related to natural science observations, despite using data pulled from remote sensing. Intra-reef zones defined by natural scientists often represent different biophysical /ecological communities that in turn reflect environmental gradients (e.g., in light, water flow) and geo-ecological processes (sediment deposition, reef vertical accretion) below the water that led to the arrangement17. However, these classes frequently also can be related to biophysical information on slope, depth and aspect that can be determined remotely. A thoughtfully prepared classification – that adheres to Stoddart’s (1978) classification principles, which state that classes should be explicit, unique, comprehensible, and should follow the language of prior schemes – can support production of maps and other science (monitoring, management) that are still relevant to historic work but that can go forward with consistent definitions21.
Step 2. Development. Creator requirements – relating Reef Cover classes to remote sensing dataDevelopment of appropriate mapping classes requires a sensitive trade-off between the needs of users (in terms of the level of detail needed, appropriate for scaling, consistent across regions, simple enough to be manageable but detailed enough to be understandable), and the input data available and quality of the globally repeatable mapping methods of the map producers.While vast in terms of scalability, data producers are more constrained in terms of sensor capabilities such as spatial resolution (limited to pixels) and depth detection limits (limited by light penetration), and processing power (high numbers of map classes becoming more computationally expensive). Physical conditions and colour derived from remote sensing, along with their textural and spatial relationships, can be linked to reef zonation13, with depth and wave exposure being particularly important information to explaining geomorphology64.To select a set of Reef Cover classes that could be defined by attributes available from most commonly available public access or commercial satellite data, but that also corresponded to common classes found in the classification literature, and relevant from a user perspective, we looked for intersectionality between physical attribute data that can be derived from satellites but also help shape and define reef morphology.Physical attributesThe physical environment – light, waves and depth – plays a deterministic role in reef structural development and the ecological patterning across zones39. Underlying geomorphic structural features can almost always be characterised in terms of three core characteristics: i) depth, ii) slope angle and iii) exposure to waves (Fig. 2).Fig. 2Physical attributes derived from remote sensing data such as depth, slope angle and exposure are sufficient to delineate some of the key geomorphic reef zones in the classic literature. The coral reef classifier for global scale analyses of shallow water tropical coral reefs shows how relative measures can characterise reef zones.Full size image
Depth
Depth is a useful attribute for bridging human and machine-led classifications. Bathymetry can be derived from spectral information from satellites since the absorption of light at specific wavelengths also has known relationships with water column depth65 but also relates to reef geomorphology (due to role of primary production in powering biogenic calcification)66. Bathymetric data also provides the basis for other critical depth-derived products, slope and aspect, which are used to distinguish geomorphic classes and reef environmental parameters, e.g., exposure to breaking wave energy (Fig. 2).
Reef Crest, for example, is often described as the shallowest part of the reef 62, while Lagoon represents a deep depression in the reef structure57,67. Depth thresholds are sometimes defined: a threshold of 10 m was suggested to differentiate true lagoons from shallow water areas68, and an 18 m threshold has been used to distinguish Reef Front from Reef Slope17. In the Reef Cover classification, depth was particularly important for distinguishing Fore Reef classes (e.g. Reef Slope, Terrace) from Reef Crest and Reef Flat classes (Table 1, Fig. 2). Generally, tides and variability in water clarity and regional eustatic discrepancies in reef top depth (e.g., Reef Flat in Atlantic systems generally lie much lower with respect to tides than in the Indo-Pacific67) mean relative depths are more appropriate, which is why absolute numbers were not used in Reef Cover definitions.Table 1 Attributes of reef zones that help support classification.Full size table

Slope
Slope angle, either absolute angle or discontinuities in angle acting as a break between zones, is an important differentiator of reef zones. Reef Flats are defined as being horizontal ‘flattened”69 “flat-topped”70; Fore Reef slope zones often include references to slope angle (e.g., in one classification Fore Reef has been defined as “any area of the reef with an incline of between 0 and 45 degrees”62), and Walls– common on atolls – are “near vertical” features. Variability in slope continuity can also be an important way to demarcate zones71. Montaggioni illustrated a range of representative profiles across atolls and barrier reefs, with convoluted profiles allowing subdivisions of reef slope, particularly across fringing reefs which are less likely to show a uniform reef slope than an atoll53, and Reef Crest is sometimes defined as a demarcation point separating the Fore Reef from the Reef Flat53,62,72. Where water depth can be derived from remotely sensed spectral data, bathymetry can be used to directly calculate slope (i.e., by calculating the slope angle between a pixel and its neighbours) or by considering the local variance in depth (e.g., the standard deviation in depth values within some radius of each pixel).
In the Reef Cover classification, slope angle data were used to distinguishing Fore Reef classes such as Reef Slope and Reef Front, from horizontal classes such as Outer and Inner Reef Flat and Lagoons (Table 1, Fig. 2).

Exposure
Physical exposure of reefs is a key driver of zonation. Reef Crests – linked to wave breaking – are often described as “an area of maximum wave shoaling”, i.e. a zone that absorbs the greatest wave energy62,69. Fore Reefs are frequently sub-divided based on relative exposure (e.g. exposed vs sheltered slope, or windward vs leeward57). Exposure influences profile shape and importantly the communities growing in the zone, so that slopes with identical profiles could have very different communities57,73. Sometimes these zones are related to the communities found there. Meanwhile, exposure across the reef means back-reef zones contain sheltered water bodies. Together with data on water depth and bathymetry, wave energy data was key for distinguishing key Reef Cover classes74,75.

Colour and texture
Sub-surface spectral reflectance data can provide measurements of reef colour and texture over large areas. Concentrations of photosynthetic pigments in coral, algae and seagrass as well as light scattering by inorganic materials means spectral reflectance can also be used to determine biophysical properties of the reef 65. Colour and texture information derived from satellites can be used to manually draw polygons around similar geomorphologic units or habitats but provide the basis to drive image-based thematic mapping (such as digital number, radiance, reflectance) and texture, through spectral processing64. Texture measures are also used to improve classification by allowing spectrally similar substrates like corals and macroalgae to be distinguished. Reef Flats, for example, having a single driver of zonation, in contrast to several drivers on most other zones, makes benthic zonation particularly distinct39, and easily detectable as coloured bands in aerial images of reef flats. This allows colour and texture to be used to distinguish Outer Reef Flats, which have a greater component of photosynthetically active corals and algae, from Inner Reef Flats which appear brighter due to a higher proportion of sand build up in this depositional area (Fig. 3).Fig. 3Satellite-derived colour and texture can be informative in distinguishing Reef Cover classes of relevance to ecologists and managers, since spectral reflectance mirrors the benthos which in depositional areas may be dominated by reef-derived sediments, or on hard substrate may reflect benthic communities. Not all zones can be distinguished by colour alone (e.g., walls and steep slopes), but examples of zones with clear colour/texture differences are outlined in red.Full size image
Spatial relationships
Size and shape
The size and shape of reef features can help determine Reef Cover class. Many large-scale reef structural features appear elongate as the shelf constrains shape – and reef morphology can even help predict shape as they constrain accommodation space and influence deposition76. Reef Flats, for example, boast the broadest horizontal extent of any geomorphic zone, typically 500 to 1000 m across, but reaching several kilometres in width across some Pacific atolls71. Lagoons also tend to be broad in width although width and shape can be variable depending on reef type. Understanding some of these characteristics can help determine classes, although these are usually defined relationally rather than by application of size thresholds.

Neighbourhood and enclosure
Natural scientists agree that reefs feature three major geomorphic elements: a Fore Reef, a Reef Crest and a Back Reef (although subdivisions and complexities exist around these). Because of the influence of large-scale processes on reef development, these zones occur in order17,39,53. Reef Crest is arguably the most defining characteristic of any reef – the break point at which a sharply defined edge divides the shallower platform from a more steeply shelving reef front71, around which other geomorphic zones arranged in parallel77. As a result, spatial arrangement of zones can be informative for mapping (Table 2). For example, Back Reef is often defined as being contiguous to the Reef Crest (Back Reef is often defined as any reef feature found landward of the crest).Table 2 Relational characteristics of Reef Cover classes displaying neighbourhood (including adjacency and enclosure rules) used to distinguish internal coral reef geomorphological zones.Full size table
Enclosure to semi-enclosure within a bordering reef construction (e.g., in lagoons68) is another feature used classically to define reef zones, but that could also be derived from satellite imagery.
The Reef Cover typology presented is derived from earth observation data, but attempts to link classes to genetic process, social, ecological and geological importance23. By focussing on the attributes of depth, light, exposure, colour and texture and spatial relationships that are common to both domains, our traditional biophysical knowledge of reefs can be integrated with remote-sensing capabilities. Attributes can be combined to make decision trees (Fig. 4) to help use satellite data to map reefs at the global scale. The Reef Cover list of classes can all be distinguished from these physical attributes alone, supporting production of maps that are still relevant to existing work but that can allow computationally inexpensive determination of mapping classes to beyond what was previously possible38.Fig. 4Example decision tree for classification of intra-reef zonation using Reef Cover. The decision tree for use by mappers is based on information that would typically be available at the global scale, and related to the physical attributes (depth, slope angle and exposure), colour and texture, and spatial relationships. Here a mix of a priori logical or philosophical grounds taken from a review of literature, tailored to fit a methodology limited by the data.Full size image
Step 3. Dissemination. Providing user friendly Reef Cover class descriptors that facilitate uptake and useComputers have revolutionised our ability to classify multidimensional data sources, which allows mapping and modelling at far larger scales for the same effort compared to a human taxonomist. However, without proper consideration of the needs of the end user, classified data may not be effectively applied to conservation challenges4. The Reef Cover classification was developed with five user-needs in mind: relevance, simplicity, transparency, accessibility and flexibility.RelevanceDifferent habitats within reefs contribute differently to biological and physical processes. For example, Reef Crests play a disproportionate role in coastal protection, dissipating on average 85% of the incoming wave energy and 70% of the swell energy78,79; Reef Slopes supply an order of magnitude more material to maintain island stability61,80; shallow Reef Front areas often host more coral biodiversity81; Reef Flats support herbivorous fish biomass82 and accessibility of Lagoons often affords them cultural importance as places important for artisanal harvesting83. A classification that effectively captures the appropriate diversity of these habitats can therefore better inform social, biological and physical studies36, such as global conservation planning to safeguard reefs, for example, in order to meet the Convention on Biodiversity Aichi targets34. Map classes need to reflect differences of interest to a wide range of reef scientists, from oceanographers to paleoecologists and fisheries scientists – so careful consideration of natural history is important. Global mapping is usually to enable spatial comparisons, so a classification that is globally applicable was also important.To explore relevancy, a crosswalk was performed between Reef Cover and a selection of major regional to global coral reef classification10,60,84, mapping22,36,85 and monitoring efforts7,8,56, to make sure important classes from established classifications had not been missed23 (Online-Only Table 2).SimplicitySimplicity was achieved by (1) choosing an appropriate mapping scale (internal geomorphic classes), (2) limiting the number of geomorphic classes (17 classes), and 3) providing clear (1 line) descriptors with additional information to address issues of semantic interoperability.

1.

Reef Cover was developed to provide consistent mapping of reefs across very large areas: classification of geologic and ecological zones is much more amenable to mapping using remote sensing, given greater consistency in geomorphology across large biogeographic regions32. Satellite data has supported the development of several detailed regional “reef type” classifications, such as nine reef classes for the Great Barrier Reef from Landsat imagery73, six reef classes from the Torres Straight74 and 16 classes for the Red Sea from Quickbird6. However, local reef type classifications are not always applicable globally due to large regional discrepancies in Reef Type. As a result, detailed reef type typologies are more suitable for local to regional classifications6,35. For global mapping, an internal geomorphic approach is better. Finer spatial scale classifications from satellite data are also challenging, due to differences in the spatial scale at which spectral data can be generated (metres) and which benthic assemblages display heterogeneity (sub-metres)32. Medium spatial resolution multispectral data (5 to 30 m) is the most commonly used satellite information used for coral reef habitat mapping42, and classification of internal geomorphic structures may be best suited to this kind of data.

2.

Reviews of habitat mapping from remote sensing found the number of map classes averages 18 at continental and global scales13. More than this can become overwhelming for users and computationally expensive for developers at this point in time. Many coral reef classifications contain four or five hierarchical levels and high numbers of classes: the Millennium Coral Reef Mapping Project (MCRMP) was ambitious in developing a standardised typology that captured much of the reef type diversity, but despite defining over 800 reef classes defined at the finest (level 5, essential for local reef mapping) scale36, level 3 (68 classes) continue to be more popularly adopted in publications using this dataset. To keep the classification simple, Reef Cover was limited to 17 geomorphic classes, with simple one line definition provided. A limited class was needed 1) to make it manageable for users, 2) to make it computationally manageable for very large (regional and global) data processing and 3) reduction in classes compared to MCRMP allowed for consistent automated mapping at the global scale – so that whole regions could be directly compared for monitoring and management.

3.

Short definitions were provided in plain language for simplicity. To address additional uses issues of semantic interoperability each Reef Cover class definition also outlines other commonly used terms for concepts (synonymy) and explains different interpretations of the same meanings and understanding of the relations between concepts.

TransparencyOne barrier to the use of analysing and interpreting big data is user-friendliness. Of 79 coral reef mapping attempts reviewed (62 benthic coral reef maps, 6 geomorphic coral reef maps and 11 mixed), only 13% were accompanied by a clear classification that defined the meaning of map classes14. Describing how the classification relates to data (Step 2) and producing a detailed descriptor (Step 3) along with a diagram allows classification to be understood and also adopted for different projects. We also attempted to address transparency by relating Reef Cover classes to other major global mapping and monitoring efforts (Online-Only Table 2) and providing a decision support tree for users (Fig. 4)69.AccessibilityAnother barrier to the use of analysing and interpreting big data is access75. Much information remains locked behind paywalls, and additional barriers exist including discoverability. To promote accessibility and encourage use, all data were made publicly available (see Data Records section for access). Terms were translated into different languages, as science published in just one language has been shown to hinder knowledge transfer and new findings getting through to practitioners in the field86.FlexibilityOne criticism of thematic habitat maps derived from remote sensing is a lack of flexibility: categorical descriptions of habitats are by design a discrete simplification of the ecological continua, thus classifications limit the interpretation and questions that can be asked76. Flexibility issues were addressed by 1) not prescribing absolute thresholds to each class, instead providing information on how classes relate to each other (Tables 1–3) allowing a) map producers to adapt application of Reef Cover to their own needs, and b) users to interpret with flexibility, 2) providing additional information (Standard Descriptors) including main features, exceptions to rules and broadness as to provide users with a broader understanding of hidden complexities when interpreting class meaning, 3) remaining open to feedback, we hope this Reef Cover version 1 can be improved upon with feedback from the community.Table 3 Table detailing how Reef Cover classes were used in each Case Study, and confidence of producers in determining each class (scored from 1 to 10, with 1 being very low confidence and 10 being very high) from satellite information in Case Study 2.Full size table More

These authors contributed equally: Zhongru Gu, Shengkai Pan, Zhenzhen LinKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long & Xiangjiang ZhanCardiff University–Institute of Zoology Joint Laboratory for Biocomplexity Research, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long, Michael W. Bruford, Andrew Dixon & Xiangjiang ZhanUniversity of the Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Li Hu, Han Su, Juan Long, Mengru Sun & Xiangjiang ZhanSchool of Biological Sciences, University of Bristol, Bristol, UKXiaoyang DaiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaJiang ChangKey Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, ChinaYuanchao Xue & Mengru SunWild Animal Rescue Centre, Moscow, RussiaSergey GanusevichInstitute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Ekaterinburg, RussiaVasiliy SokolovArctic Research Station of the Institute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Labytnangi, RussiaAleksandr Sokolov & Ivan PokrovskyDepartment of Migration, Max Planck Institute of Animal Behavior, Radolfzell, GermanyIvan PokrovskyLaboratory of Ornithology, Institute of Biological Problems of the North FEB RAS, Magadan, RussiaIvan PokrovskyState Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaFen JiSchool of Biosciences and Sustainable Places Institute, Cardiff University, Cardiff, UKMichael W. BrufordEmirates Falconers’ Club, Abu Dhabi, United Arab EmiratesAndrew DixonReneco International Wildlife Consultants, Abu Dhabi, United Arab EmiratesAndrew DixonInternational Wildlife Consultants, Carmarthen, UKAndrew DixonCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, ChinaXiangjiang Zhan More

Modelling microbenthic O2 exportWe explored how microbial processes and export fluxes of their metabolic substrates and products from ancient benthic photosynthetic ecosystems were influenced by daylength, environmental conditions and various regulatory mechanisms of photosynthetic production and respiration using an in silico microbenthic model. Model scenarios were constructed and simulated using MicroBenthos software12. MicroBenthos model definitions and parameters for the described scenarios are provided with this article. The software and usage instructions are available at https://microbenthos.readthedocs.io.The modelling framework is an adaptation of de Wit et al.61. Briefly, benthic systems are constructed as a diffusive–reactive system in a 1D computational domain, with discrete cells used to represent the spatial distribution of the state and parameter variables. While the study by de Wit et al.61 focused on biomass growth running over long simulation times, our interest was to study the dynamics of process rates and solute fluxes over diel timescales. Therefore, we set a fixed biomass for the microbial groups, added a water subdomain on top of the sediment as a diffusive boundary layer and ran simulations until a diel steady state was reached (5 days). Our model domain used 5 µm cells, with an 8 mm sedimentary subdomain and 1 mm diffusive boundary layer of water on top. O2 and sulfide concentrations were the state variables that we solved for. Photosynthetically active radiation (PAR) was expressed as a percent of the maximum intensity at the diel zenith, and followed a cosinusoidal pattern similar to that of diel insolation dynamics.Raero and SOX were formulated to occur throughout the sediment. Microbial groups (cyanobacteria and SRB) were represented as biomass distributions in the sediment subdomain, and biomass-dependent metabolism kinetics were expressed as multiplications of the response functions of salient environmental and state variables. Coupled partial differential equations of the state variables (O2 and H2S) were composed from the reaction terms that accounted for sediment porosity and were solved with finite-volume numerical approximations62.Our in silico mat allowed us to explore how diffusive mass transfer shapes the interplay between illumination dynamics, gross production and consumption rates, and diel O2 export. The effect of daylength was studied by varying the period of the illumination from 12 h to 24 h, the range of estimated daylengths over Earth’s history after the earliest estimates for the origin of OP63. We report the calculated average diel net export and process rates in units of mmol m−2 h−1 because the hour is the largest temporal unit unaffected by changes in the Earth’s rotation and thus allows for comparison across daylengths.First, we explored the simplest case of O2 production, which is with light availability. Two microbial processes were considered: OP performed by cyanobacteria and Raero. The parameters for the biotic reactions were re-expressed as a biomass-specific maximal yield (Qmax). A fundamental assumption is that the photosynthesis rate is strictly correlated to the instantaneous photon flux:$${rm{OP}} = Q_{{rm{max}}}times {rm{biomass}}times {rm{sat}}left( {{rm{PAR}},,K_{{rm{PAR}}}} right),$$
(1)
where sat is a Michaelis–Menten function with KPAR = 15% and the cyanobacterial biomass with a log-normal distribution with a peak value of 12 mg cm−3 at 0.5 mm depth (Supplementary Video 1). The only source of O2 is OP, and the sinks are aerobic (sedimentary) respiration (Raero). For the production and consumption rates of Corg, we assumed a stoichiometry of:$${mathrm{H}}_2{mathrm{O}} + {mathrm{CO}}_2 to {mathrm{O}}_2 + {mathrm{CH}}_2{mathrm{O}}$$
(2)
with respect to O2 cycling rates, where CH2O refers to one Corg equivalent. Assuming that Corg is predominantly particulate, with negligible diffusional transport, diel Corg burial was thus calculated as:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(3)
where ∫OP and ∫Raero are the diel depth-integrated rates of O2 production and consumption and are equivalent to Corg production and consumption according to equation (2). Thus, diel burial can also be represented through the export flux of O2 at the top and bottom interfaces of the sedimentary domain:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = {mathrm{O}}_{2} {mathrm{export}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(4)
which allowed us to assess the dynamic steady state of the diel model when the average diel depth-integrated rates equalled the export fluxes.To calibrate the O2 productivity for unitless PAR intensities, we determined the Qmax that caused a maximum O2 export that corresponded to the median maximal flux from illuminated benthic photosynthetic systems13. A Qmax of 4.0022 mmol g−1 h−1 produced the target export flux of 5.76 mmol m−2 h−1 under a sedimentary respiration load of 0.1 mM h−1. Note that by calibrating the productivity to the maximum diel illumination, the model represents a ‘mean solar day’ of a given Earth year59. This allowed us to disentangle the effect of daylength from geological-scale changes in the insolation intensity, such as in the ‘faint young Sun’ paradigm reviewed thoroughly by Feulner23, or changes in the solar spectrum related to atmospheric composition64.Next, we explored the effect of Ranaero on the daylength dependency of the process rates and export fluxes. We used the example of sulfate reduction performed by SRB with a log-normal biomass distribution with a peak value of 2 mg cm−3 (Supplementary Video 1). The Ranaero rate was either defined as a constant rate process for the scenario ‘OP SRB constant’ as:$${mathrm{R}}_{{rm{anaero}}} = Q_{{rm{max}}}times{rm{biomass}}$$
(5)
or as an O2- and H2S-sensitive process as:$$begin{array}{rcl}{mathrm{R}}_{{rm{anaero}}} & = & Q_{{rm{max}}} times {rm{biomass}}times {rm{inhibition}}([{rm{O}}_2],,K_{{rm{max}},{{rm{O}}_2}},,K{_{{rm{half}},{{rm{O}}_2}})}\ && times {rm{inhibition}}left( [{{rm{H}}_2{rm{S}}],,K_{{rm{max}},{rm{H}}_2{rm{S}}},,K_{{rm{half}},{rm{H}}_2{rm{S}}}} right)end{array}$$
(6)
where inhibition is a function of the local H2S and O2 concentration (x) of the form:$$frac{{K_{{rm{max}}} – x}}{{2times K_{{rm{max}}} – K_{{rm{half}}} – x}}$$
(7)
when x  More

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The volatiles from fresh feces of Przewalski’s horse at the pre-oviposition, oviposition, and post-oviposition stages of G. pecorum
Throughout the stages of pre-oviposition (PREO), oviposition (OVIP), and post-oviposition (POSO) of G. pecorum, 70 volatiles were identified in fresh feces of Przewalski’s horse. Among them, 46, 48, and 52 volatiles were identified at PREO, OVIP, and POSO, respectively, and 29 volatiles were common at all three stages. In addition, 4, 5, and 9 volatiles were common between PREO and OVIP, OVIP and POSO, as well as PREO and POSO, whereas 4, 10, and 9 volatiles were unique at the single stage of PREO, OVIP, and POSO, respectively (Table 1; Fig. S1). According to relative content, the two main chemical classes of volatiles were aromatic hydrocarbons and alkenes, that is, their respective contents in a sample were both more than 25% of the total content. Except alcohols which exhibited significant difference between PREO and POSO (One-way ANOVA, F = 8.400, df = 2, P = 0.018), there was no significant difference in all other pairwise comparisons among the nine chemical classes at three stages (One-way ANOVA or Kruskal–Wallis test: P  > 0.05) (Fig. 1). Non-metric multidimensional scaling (NMDS) analysis revealed certain extent of overlap (Fig. 2), while one-way analysis of similarity (ANOSIM) indicated that there were significant differences among the three stages (R = 0.5391, P = 0.008).Table 1 The volatiles from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum.Full size tableFigure 1Volatile classes detected from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum. PREO, OVIP, and POSO represent fresh feces at the stages of pre-oviposition, oviposition, and post-oviposition of Gasterophilus pecorum, respectively. Data are mean (n = 3) ± SE. Different letters indicate significant differences at P  0.05). Furthermore, acetic acid was common to PREO and POSO, but no difference was observed between them (Independent t test, t = 0.137, df = 4, P = 0.897) (Table 1).Of particular concern among the eight volatiles mentioned above, ammonium acetate and butanoic acid were unique to OVIP, the critical stage of oviposition. Although not one of the five most abundant volatiles, another nine volatiles were also specific to OVIP, of which hexanoic acid, cyclopentasiloxane,decamethyl- and cyclohexene,3-methyl-6-(1-methylethyl)- were higher than 1% in relative content (Table 1).Among the 47 volatiles common to two or three stages, only six volatiles were significantly different in relative contents. Of which, D-limonene was higher at PREO than at OVIP (One-way ANOVA: F = 11.936, df = 2, P = 0.012) or POSO (P = 0.012), and 1-butanol was higher at OVIP than at PREO (One-way ANOVA: F = 8.175, df = 2, P = 0.024) or POSO (P = 0.04). Relative contents of the other four volatiles were less than 1% (Table 1).The volatiles from feces of Przewalski’s horse with different freshness states at the OVIP stage of G. pecorum
Totally, 83 volatiles were detected from fresh feces (Fresh), semi-fresh feces (Semi-fresh), and dry feces (Dry) at the OVIP stage of G. pecorum. Of which, 48, 41 and 28 volatiles were identified in Fresh, Semi-fresh and Dry, and 7 volatiles were common to all three feces with different freshness states. In addition, 14, 3 and 3, were common between Fresh and Semi-fresh, Semi-fresh and Dry, as well as Fresh and Dry, whereas 24, 17, and 15 were unique to Fresh, Semi-fresh, and Dry, respectively (Table 2; Fig. S2). Aromatic hydrocarbons and alkenes, acids and ketones, as well as alcohols and aldehydes were the two main chemical classes of Fresh, Semi-fresh, and Dry in respective. Except esters and ‘others’ which showed no significant difference in the feces, there were significant differences among other seven classes in at least one pairwise comparison of the three freshness states (One-way ANOVA, Independent t-test or Kruskal–Wallis test: P  More

Physiochemical characteristics of water in the blowout regionThe physiochemical properties of water were measured in the laboratory. The analyzed properties are shown in Table 3.Table 3 The analyzed physiochemical properties of water.Full size tableThe average value of pH is 6.529 indicates water of the study area is slightly acidic in nature. The average value of CO2 (6.5) complied with the lowering tendency of pH. The average ORP value 36 also reflecting the sign of acidic water in the study region. According to WHO standards (2011), the value of conductivity within range 0–800, Total dissolved solids less than 500 ppm, alkalinity 120 ppm, and total hardness less than 300 mg/L are allowable for drinking and domestic purpose37. The average value of conductivity 76.7 µs/cm, total dissolved solids 44.2 ppm, alkalinity 109.1 concurred with the dirking water standard by WHO (2011). The average value of TH is 49 ppm points out that the properties of water are soft.Spatial distribution of trace elements derived from water bodies around the blowout areaThe primary purpose of this study is to understand the concentration level of different Trace metals in the area. In this study, Pb, Ni, Cu, Cd, and Zn were examined (Fig. 3). Besides the toxic metal spatial distribution map is constructed using the inverse distance weighting (IWD) method in Arc GIS (version 10.5). The map (Fig. 4) shows common patterns of hotspots near the Syl-1 blowout area for every metal. This scenario indicates that these metals are originated from the same source46.The contiguous area near Syl-4 well also exhibited a similar pattern to Syl-1 for all metals except Cd. A high concentration of Cd was found closed to the Syl-1 area. The elevated concentration of toxic metals like Ni, Pb, and Cd are found in the adjacent areas of blowout points (Fig. 4). Continuous gas escaping from these abandoned wells might stimulate the trace metal accumulation, especially Pb would be more toxic when it will come to a contact with gasoline (Syl-1 and Syl-4)7. The non-essential toxic metals like Ni, Cd, and Pb in water can pose a serious health threat inthesite47. In addition, these toxic elements can contribute to acute or chronic health issues like high blood pressure, kidney failure, headache, abdominal pain, cancer, nerve damage, and so on for the long-term consumption of such water48.Standard value of Pb in water is 0.01 mg/L, Ni is 0.02 mg/L, Cu is 2 mg/L, Cd is 0.003 mg/L in water37. In this analysis, the average value of Pb = 0.04, Cd = 0.05, Ni = 0.16, Cu = 0.03 mg/L, respectively. The TMs like Zn concentration is about zero or below the detection level for water samples in the study location. The values of Pb, Cd and Ni were higher than the standards level indicates that the water should not be used for any purpose49.Figure 3The concentration of trace elements in the study area.Full size imageFigure 4The spatial distribution map of toxic metals in the area.Full size imageCorrelation coefficient (R) matrix of water quality parameter presented in the blowout areaA Correlation matrix represents the relationship among several variables. It is generated based on the correlation coefficient, which ranges from − 1 to 1. The value of correlation coefficient (1, − 1) indicates perfect correlation, (− 0.9 to − 0.7 or 0.9–0.7) shows strong correlation, (0.4–0.6 either positive or negative) represents moderate correlation, (0.1–0.3 or − 0.1 to − 0.3) displays as weak and 0 indicates no relationship between variables50. The mathematical expressions are described in the article by MacMillan et al.51 to evaluate the correlation coefficient (r).The correlation matrix is shown in Table 4. From the Table 4, it is clear that the pH shows a moderate to strong correlation with CO2 (0.63) and alkalinity (0.69). Whereas, it shows a very strong positive correlation with Total Hardness (TH) and Ca2+ (0.88), respectively. The moderately positive correlation reflects with EC (0.41) and trace elements Ni (0.62). The rest of parameters show a negative correlation. The CO2 exhibits a good correlation with EC (0.72), TH and Ca2+ (0.52). Alkalinity states a good correlation with TH and Calcium ions (0.76). EC shows a maximum correlation with TDS (1.00); maximum correlation also found in the case of TH and Ca2+. Turbidity has a positive correlation for all of the parameters except EC and TDS. TDS shows a strong positive correlation with all of the trace elements, in the case of Pb (0.54), Cd (0.88), Ni (0.68) and for Cu the value is 0.64. All trace elements have a strong correlation with each other. Pb represents a good correlation with Cd (0.65), Ni (0.54) and Cd (0.35). Ni has a strong correlation with Cd (0.61), Cu (0.43). Cu also implies a good correlation with Cd (0.58). In the end, it can be mentioned that a strong positive correlation can be detected among all of the trace elements and also for most of the relative parameters. CO2 established the equilibrium state in the water with ions might be lowering the oxidation. The trace metals Cu and Cd were positively correlated with the turbidity. The washed turbid water from the blow out areas might stimulate these trace metals. The inverse association with oxidation and total hardness indicates the less vegetated areas have higher influx rate of soil materials. It implies the result of the correlation matrix indicated that all of the trace elements and also relevant ions presented in the water of blowout area resultant from the same source46.Table 4 Correlation coefficient matrix of water parameters.Full size tableFactor loading of water parametersThe interrelationship within a set of variables or objects is represented by factor analysis. The factors contain all of the basic information about a wider set of variables or observed objects. It shows how the variables are strongly correlated with the determined factor. Factor analysis is also known as a multivariate approach to reducing data33. Among different types of factor analysis, Principal component analysis account for the maximum variance of observed variables. So, it can be called variance-oriented33. Factor loading shows how certain variables strongly correlate for a given factor. Factor loading varies from − 1 to + 1 where the value of factor loading below − 0.5 or above 0.5 suggested good correlations and value closed to − 1 or + 1, suggesting a more robust correlation32. The Table 5 represented the principal component analysis result of factor solution.Table 5 Principal components analysis results of water parameters.Full size tableFrom analysis (Table 5), it can be realized that the water quality parameters such as Turbidity, TH, Ca2+, Cd, Ni and Cu have a stronger correlation with each other’s reflecting their source of origin might be from the same area14. Factor loading also suggested that more robust interconnection exists among CO2, EC and TDS. In this analysis, the two-factor solution explained approximately 80.6% of the variance. The eigenvalue, total variance explained are represented in Supplementary Table S1. That percentage is high enough to accept the results. It can also be added that the red and yellow colored loading represented strong correlation with each other46,52.Water quality index (WQI)The WQI is one of the best tools for monitoring the surface-groundwater contamination and can be used for water quality improvement programs. The WQI is determined from various  physicochemical parameters like pH, EC, TDS, TH, EC, and so forth. Higher estimation of WQI indicates poor water quality and lower estimation of WQI shows better water quality. During this examination, WQI esteems a range from 0.02633 to 5144.37 and are characterized into five water types shown in Table 6. The noteworthy WQI is recorded in case of (sample-1) which demonstrates an elevated level of contamination. Water sample 2, 5, 8 and 10 are grouped under class-1 which demonstrates there is a lower degree of pollution in water. In addition, WQI calculation for sample 2, 5, 8 and 10 excluded trace elements value and WQI evaluation for sample 1, 3, 4, 6, 7 and 9 included the heavy metals value in water. These results also clarify the association of heavy metals on water quality degradation of the study area.Table 6 Classification of the water quality index for individual parameter of water.Full size tableThe situation of contamination in the areaThe level of contamination has been demonstrated in terms of the CFi, PLI, and also PI analysis of water samples around the blowout area. The values of CFi are indicated the degree of contamination. The intensity of CFi has been determined with some numerical values like 1, 3, and 6. The CFi value is less than 1, which implies low contamination, as the value is > 6 indicated a high degree of contamination36. The Table 7 elucidates that the degree of contamination in the case of trace elements Pb, Cd, and Ni are very high for most of the locations of the research sides. Besides Cu and Zn exhibit that level contamination is low in the area. In other cases, the PLI can be evaluated by using the CFi value. The value of PLI greater than 1 symbolizes polluted and less than 1 represents the unpolluted status36,39. The pollution load index rate of Pb, Cd, and Ni are 2.3, 2.87, and 2.56, respectively (Fig. 5).This result indicates the pollution of water bodies in the sampling sites. The other elements such as Cu and Zn are within the allowable limit are shown in Fig. 5. Moreover, the PI indicates similar results as CFi and PLI.Table 7 Contamination factor of water samples.Full size tableFigure 5Pollution load index of the study area.Full size imageThe state of potential ecological threat in the areaThe ecological potential risk index has been appealed to detect the possible threat to the ecological system in the adjoining area. The calculated RI value provided the risk factor of water for understanding the ecological threat. When the RI value is more than 600, it is considered a polluted case11,14,36. The computed RI value of the study for Pb, Cd, Ni, and Cu are 123.5, 2770, 235, and 0.23, respectively (Table 8). The value of Cd is high enough (RI  > 600). So, the Cd values indicated that the potential threat to the ecological system. Besides, the TMs like Ni and Cu are specified medium to low ecological pollution in the area are shown in Table 8. Moreover, the spatial distribution has been presented to outlook the potential ecological threats around the blow out location of the gas field is shown in Fig. 6.Table 8 Ecological risk index (RI) of the study area.Full size tableFigure 6A map of the spatial distribution of potential ecological risk threats in the study area.Full size imageThe spatial distribution map of RI also pointed out the high ecological risk closed to the blowout areas (Fig. 6). From these results, it can be implied that the use of  this water for domestic or drinking purposes, can be harmful for living beings. Moreover, it can be distressed the ecological system in the site. Hence, the use of the water from this site should be avoided by dwellers near the blowout areas of the gas field.Assessment of noncarcinogenic health risksNoncarcinogenic risk is one of the vital categories of human health risk assessment. It is known that a polluted environment is highly liable for causing a health risk. Toxic metal presents in water also very harmful for public health, including child and adult both. The health risks may be extended through ingestion and skin absorption of water. To know the harmful impacts of trace elements of water on the human body, noncarcinogenic risk evaluation is more important. For that, the value of CDI for ingestion and dermal absorption was evaluated at the beginning to identify such risk index (Supplementary Table S2 and S3). Then the CDI has been divided with the RfD value. From where, the HQ can be acquired separately for ingestion and dermal absorption. The summation of HQingestion and HQdermal expressed the HQtotal. And the HQtotal entirety was used to achieve the HI are shown in Table 9.Table 9 The HQ and hazard index (HI) value of noncarcinogenic analysis of the area.Full size tableThe results elucidate that case of adult, the mean value of CDITotal for Pb is 1.29E-03, Cd is 1.45E-03, Ni is 4.93E-03 and Cu is 4.83E-04, respectively. For the child, the mean value of CDITotal in the case of Pb, Cd, Ni, and CU is4.7544E-03, 5.33E-03, 1.81E-03 and 1.77E-04, correspondingly. Additionally, the order of CDITotal for adults are Ni  > Cd  > Pb  > Cu (Supplementary Table S2) whereas, for child, it is quite different. In the case of children, the order  are Cd  > Pb  > Ni  > Cu are presented in Supplementary Table S3.The mean values of HQTotal of Pb, Cd, Cu, and Ni are ranging from 4.46E−05 to 1.45E−02 for child. Besides, these values for adults are extending from 1.21E−05 to 4.66E−03. These values suggest that the trace elements in the water of the study area are quite harmful to the child than an adult. The children’s HQTotal has been ordered as Cd  > Pb  > Ni  > Cu and for the adult Cu  More