Research article

Improved bathymetry leads to >4000 new seamount predictions in the global ocean – but beware of phantom seamounts!

  • Chris Yesson orcid logo (Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK)
  • Tom B. Letessier orcid logo (Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK)
  • Alex Nimmo-Smith orcid logo (School of Biological & Marine Science, University of Plymouth, Plymouth, Devon PL4 8AA, UK)
  • Phil Hosegood orcid logo (School of Biological & Marine Science, University of Plymouth, Plymouth, Devon PL4 8AA, UK)
  • Andrew S. Brierley orcid logo (Pelagic Ecology Research Group, Scottish Oceans Institute, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TS, UK)
  • Marie Hardouin orcid logo (Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK)
  • Roland Proud orcid logo (Pelagic Ecology Research Group, Scottish Oceans Institute, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TS, UK)


Seamounts are important marine habitats that are hotspots of species diversity. Relatively shallow peaks, increased productivity and offshore locations make seamounts vulnerable to human impact and difficult to protect. Present estimates of seamount numbers vary from anywhere between 10,000 to more than 60,000. Seamount locations can be estimated by extracting large, cone-like features from bathymetry grids (based on criteria of size and shape). These predicted seamounts are a useful reference for marine researchers and can help direct exploratory surveys. However, these predictions are dependent on the quality of the surveys underpinning the bathymetry. Historically, quality has been patchy, but is improving as mapping efforts step up towards the target of complete seabed coverage by 2030. This study presents an update of seamount predictions based on SRTM30 PLUS global bathymetry version 11 and examines a potential source of error in these predictions. This update was prompted by a seamount survey in the British Indian Ocean Territory in 2016, where locations of two putative seamounts were visited. These ‘seamounts’ were targeted based on previous predictions, but these features were not detected during echosounder surveys. An examination of UK hydrographic office navigational (Admiralty) charts for the area showed that the summits of these putative features had soundings reporting ‘no bottom detected at this depth’ where ‘this depth’ was similar to the seabed reported from the bathymetry grids: we suspect that these features likely resulted from an initial misreading of the charts. We show that 15 ‘phantom seamount’ features, derived from a misinterpretation of no bottom sounding data, persist in current global bathymetry grids and updated seamount predictions. Overall, we predict 37,889 seamounts, an increase of 4437 from the previous predictions derived from an older global bathymetry grid (SRTM30 PLUS v6). This increase is due to greater detail in newer bathymetry grids as acoustic mapping of the seabed expands. The new seamount predictions are available at

Keywords: seamounts, knolls, bathymetry, environmental science

How to Cite: Yesson, C., Letessier, T. B., Nimmo-Smith, A., Hosegood, P., Brierley, A. S., Hardouin, M., & Proud, R. (2021). Improved bathymetry leads to >4000 new seamount predictions in the global ocean – but beware of phantom seamounts! UCL Open Environment, 4.

Rights: © 2021 The Authors.



Published on
22 Dec 2021
Peer Reviewed


Seamounts are ‘undersea mountains’, and although many definitions of this term have been used, they are commonly described as conical features that rise more than 1000 m above the surrounding seabed [1]. Seamounts are important marine habitats, they provide a pathway for localised production [2], often increasing surrounding biomass and species diversity [3], they can be hotspots of predator biodiversity in the open ocean [4], home to habitat-engineering species such as cold water corals [5], important spawning grounds [6], and even act as refugia from ocean acidification for carbon-calcifying species [7].

The increased productivity associated with seamounts makes them attractive targets for fishing. Fishing gear can cause long-lasting damage to habitat forming organisms associated with some seamounts [8]. Other threats to seamounts include deep-sea mining and climate change, with shallower, more accessible seamounts at greater threat. Protection of seamount habitats is a priority for marine conservation [9], but our knowledge on these habitats remains limited, with estimates of only 0.4–4% of seamounts having been directly surveyed [10].

Direct surveys require significant investment of resources and planning, and fundamental to this is identification of locations of interest for the survey. However, we do not yet know how exactly many seamounts there are, with estimates ranging from the tens to hundreds of thousands [11]. This has led to the publication of many predictive maps and databases of potential seamount locations, commonly based on pattern recognition of underlying bathymetry data [1113], but also using satellite altimetry to detect larger features [14,15].

Seamount predictive maps are dependent on the underlying data to extract features. Global bathymetry grids such as GEBCO (General Bathymetric Chart of the Oceans) – [16] and SRTM (Shuttle Radar Topography Mission [17]) are models based on a combination of soundings (i.e., high resolution acoustic surveys) and satellite altimetry (lower resolution data from satellite sensors). Satellite altimetry provides global coverage and is the foundation of bathymetry models, but these sensors cannot determine small features (i.e., seamounts under 1.5 km height [14]). Acoustic surveys generate data best suited for determining seabed depth and these are utilised to constrain models used to create bathymetry grids [17]. Despite global efforts to improve coverage, such as the Nippon Foundation-GEBCO challenge to survey the ocean floor across the globe by 2030 [18], soundings in the latest bathymetry grids are limited to a small proportion of the ocean, and the majority of bathymetry grid data is derived from the underlying model rather than acoustically surveyed. For example, only 18% of current GEBCO grid cells (each 30 × 30 arc seconds ≈ 1 × 1 km at the equator) are directly supported by acoustic surveys [16]. As sounding data is limited, it is valuable to make use of all available data. Historical soundings based on weighted lines have been extracted from nautical charts to expand the data available [17].

This study describes issues with seamount predictions stemming from the use of historical sounding records, based on the findings of a seamount survey in the Indian Ocean. It presents an update of previous seamount predictions and examines whether this erroneous use of historical data persists.

BIOT seamount survey

The British Indian Ocean Territory (BIOT) is a region of the Indian Ocean encompassing a variety of undersea features, including the flat shallow banks of the Chagos Archipelago, and the high slopes of the Chagos–Laccadive ridge, and depths beyond 5000 m [19]. The area could be home to as many as 86 seamounts, based on estimates from an automated seamount-recognition algorithm applied to version 6 of the SRTM30 PLUS global bathymetry grid [11]. Two of these predicted seamounts, clearly discernible on the latest bathymetry grids, were targeted during a 2016 multidisciplinary survey around the Chagos Archipelago [20]. The seamount section of the survey moved around the Great Chagos bank spanning c.5–7°S and 71–73°E, between 5 and 24 February. Two seamounts of interest were ID 4050548 (latitude −5.354, longitude 71.292, summit depth 481 m) and ID 4060551 (lat. −5.733, long. 71.396, depth 141 m) from Yesson et al. [11]. The survey sought to visit these features for the purpose of establishing baseline monitoring sites for mobile oceanic predators [21]. Seamounts in BIOT have previously been shown to be important locations of bio-physical coupling between reef and pelagic ecosystems, and may therefore support elevated numbers of predators [2,22,23]. Acoustic data were collected using a Simrad (Bergen, Norway) EK60 echosounder operating at 38 kHz with a pulse length of 1.024 ms and ping rate of 2 s. At these settings, the seabed was detectable up to 1500 m below the surface. Seabed was detected at around this depth for seamount A (predicted depth 183 m), but no seabed was detected around the area of seamount B (predicted depth 491 m) despite circling (up to 5 km) around the supposed summits (Figs 1 and 2). We note that the source of the reading that accounts for seamount B was a digital nautical chart from the National Geospatial Agency and this erroneous point is removed from construction of more recent bathymetry grids (D. Sandwell, pers. comm.).

Figure 1
Figure 1

Location of survey conducted in 2016. Left shows depth contours based on the 2014 GEBCO bathymetry grid, right shows depth contours derived from SRTM30 PLUS v11. Both grids indicate the presence of a conical seamount (A) c.20 km NW of the Great Chagos Bank. No feature was detected by the 2016 survey. Around 40 km north of this, is another predicted seamount (B), again not detected on the 2016 survey. Feature B is predicted by the GEBCO grid but is not shown in the SRTM30 PLUS grid (although present in previous versions). Map projection UTM zone 43 south (epsg:32743).

Figure 2
Figure 2

Latitudinal transects across apparent positions of the two ‘phantom seamounts’. Black triangles are overlayed at the position and summit depth of the predicted seamounts. Colormap is volume backscattering strength (Sv). A deep scattering layer was observed at c.450 m for both sites. Seabed was observed at site A c.1500 m (red line). No seabed was detected for site B (i.e., seabed is deeper than the limit of the sensor).

An examination of the admiralty chart for the region provided some insight. Soundings on charts are recorded by displaying the depth reading over the location. A different class of sounding is also recorded. Soundings where no bottom was recorded are annotated with at the location of the sounding. These soundings are typically old, prior to the 19th century, dating from when soundings were conducted using handheld, weighted, lead lines, before the widespread use of sounding machines. It is easy to mistake these as bottom soundings, and this appears to be the root cause of the ‘phantom seamounts’. For site A (Fig. 1) there is a sounding in the chart at the summit of the mound seen on the bathymetry grids. The chart reports no bottom recorded at 183 m, while the GEBCO depth at this cell is 179 m and SRTM30 PLUS depth is 183 m.

However, the SRTM30 PLUS grid at site B does not show a seamount-like feature, in contrast to GEBCO, which shows an isolated point of markedly higher elevation, which is interpreted as a conical seamount-like peak by seamount detection algorithms. It is noted that previous versions of the SRTM30 PLUS grid showed a seamount-like feature at this location. The version history reports the removal of isolated and outlier ‘bad pings’ prior to the construction of version 11. The revision of SRTM has removed other seamount-like features from the revised bathymetry grid [i.e., northwest (NW) corner of Fig. 1]. It is apparent that bathymetry grids such as GEBCO and SRTM30 PLUS have mistakenly used these ‘no seabed detected’ observations as soundings indicating seabed depth, and in regions with sparse sounding data, these spatially isolated erroneously interpreted records are sufficient to create a local maximum that creates the appearance of a seamount in the final bathymetry grid.

This study aims to update the Yesson et al. [11] seamount predictions using the latest available bathymetry and assess the impact of no bottom sounding data on the prediction of seamounts.


Version 11 of the Shuttle Radar Topography Mission ‘SRTM30 PLUS’ global bathymetry ([17] – version 11 released 2014) was used to update the seamount prediction estimates of Yesson et al. [11]. The prediction algorithm of Yesson et al. [11], which identifies seamounts as cone-shaped features rising more than 1000 m above the surrounding seabed, was run on SRTM30 PLUS v11, creating a new set of seamount predictions based solely on the new bathymetry.

New seamount predictions were compared with the previous dataset ([11] – henceforward the ‘old’ dataset). Seamounts were defined as present in the old dataset if the base of a seamount in the new dataset spatially overlapped with a seamount summit in the old seamount dataset (i.e., both datasets have a predicted seamount in approximately the same location). Seamount bases are the area covered by the ‘cone’ of the seamount, and are delimited by 8 radii 45° apart, radiating from the seamount summit point, that extend outwards from this point until the downward slope levels off, up to a maximum distance of 20 km from the summit (thus the maximum base area is ∼1131 km2). These seamount bases can, and often do, encompass multiple seamount peaks in both the old and new datasets, but a new seamount has to overlap with just one seamount in the old dataset to count as being a consistent prediction.

A dataset of no bottom sounding observations was provided by OceanWise Ltd (Alton, UK), from a dataset of depth readings from digitised admiralty charts. These data include 1009 observations from charts covering the majority of the Atlantic and East Pacific, but with little data from the Southwest Indian Ocean and West Pacific. The depth readings of no bottom soundings that were spatially located within seamount bases were compared with the summit depths, seamounts with peak-depth similar to no bottom sounding depths (+/−50 m) were regarded as potential ‘phantom seamounts’.


The updated seamount predictions based on the SRTM30 PLUS v11 bathymetry gives a total of is 37,889 seamounts. A map of these is presented in Fig. 3. There are 32,340 ‘consistent’ seamounts in the new dataset that overlap with predictions from Yesson et al. [11] and 5549 ‘new seamounts’ (15%) that do not overlap with old predictions. Conversely, there are 3429 seamount predictions in the old dataset (=10% of old seamount predictions) that do not overlap with the seamount bases of the new dataset.

Figure 3
Figure 3

Map of predicted seamounts. New Seamounts are those in the new dataset that are not found in the Yesson et al. [11] dataset. ‘Consistent predictions’ are new predictions that spatially overlap with the old predictions of Yesson et al. [11], while those seamounts present in Yesson et al. [11] but with no overlapping feature in the updated dataset are classed ‘no longer considered seamounts’. Robinson map projection (EPSG:54030). Lat/Long grid lines at 30° intervals.

There are only 15 seamounts in the new dataset that fit a ‘phantom seamount’ profile (i.e., near a no bottom sounding record with the seamount peak of similar depth to the sounding record), these are presented in Table 1. In contrast there are 14 seamounts from the ‘old’ 2011 dataset that fit this pattern. These ‘phantom seamounts’ are focused in the Indian Ocean (12/14 from 2011 data and 12/15 from the updated dataset), with four potential ‘phantom seamounts’ around Chagos Bank and six from the southern Mascarene Plateau (Fig. 4).

Table 1.

List of ‘phantom seamounts’ where inferred seamounts appear coincide with sites of no bottom soundings

Peak ID Depth (m) Height (m) Longitude Latitude
4509328 52 1732 59.42083 −8.68750
4523965 2 2015 60.79583 −9.22917
4525766 2 2051 60.65417 −9.30417
4515124 65 2114 60.70417 −8.90417
4475075 304 2267 71.12917 −7.47917
4408881 191 2354 72.78750 −5.15417
4521899 2 2409 60.90417 −9.15417
4414134 135 2481 72.64583 −5.33750
844462 166 2712 71.39583 −5.72917
3736711 2 2802 −65.93750 17.80417
888460 2 3068 43.92083 −12.38750
4495055 54 3676 60.36250 −8.16250
699884 133 3752 144.38750 12.77917
4499613 85 3762 60.61250 −8.32917
4264476 17 6361 −159.97917 −0.37917
Figure 4
Figure 4

Focus on seamounts of NW Indian Ocean. Most of the 15 predicted seamounts based on no bottom soundings are in the Indian Ocean. EEZ are exclusion economic zones (boundary of national jurisdiction – source Robinson map projection (EPSG:54030). Lat/long lines shown for reference.

The ‘phantom seamounts’ are all in shallow water (summit depth <1500 m) and most are in the southern hemisphere (Fig. 5). The majority of seamounts are at the smaller end of the size distribution and typically found at 2000–3000 m depth (Fig. 5). However, the ‘new’ seamounts from the 2019 data are overrepresented in the smaller and deeper categories, while the seamounts only seen in the 2011 dataset are greatly focused on the smallest size class.

Figure 5
Figure 5

Histograms showing distribution of seamounts by seamount height (top left), depth of seamount summit (top right), and geographic location of seamount (latitude – bottom left, longitude – bottom right). Numbers above the bars show the count of ‘phantom seamounts’ in the relevant grouping.


The 37,889 seamounts predicted from the latest SRTM30 PLUS bathymetry represents an increase in number (4437 = 13%) of seamounts predicted from the previous study (n = 33,452). The revised predictions are higher than other predictions that post-date Yesson et al. [11], such as 24,643 seamounts in the Kim and Wessel [15] dataset and 10,234 of Harris et al. [12], but are still lower than some other predictions, for example, 68,669 of Costello et al. [24]. It is worth noting that each of these studies uses different ways of detecting seamounts, for example, Harris et al. [12] have a stricter definition of seamount that excludes features along ridges, while the methodology used in this study (from Yesson et al. [11]) employs a distance-based filtering of adjacent features.

Regardless of the methodology used, it is important to keep prediction datasets up to date with the latest bathymetry grids. We note that a global 15 second bathymetry grid is available (SRTM15+ v2.1 [25]), and that this greater detail may assist with seamount identification, although may require adjustment of the current methodology to fully utilise [12]. We expect the expansion of multibeam echosounder data [18] to allow the detection of smaller (<1.5 km) features in regions where previously bathymetry grids relied on only coarse resolution satellite-derived data, which is why authors have extrapolated their ‘detected’ seamount numbers to higher global estimates (e.g. [15] detect 24,643 seamounts, but extrapolate this to a global total of 40,000–55,000). This pattern of increased seamount detection as more acoustic data becomes available fits our observation and we note that the majority of ‘new’ seamounts are in the smaller, deeper size and depth categories, which is consistent with greater acoustic data giving more detailed resolution. We also note that these totals are really counts of seamount peaks, some of which may be linked together into seamount chains which could be regarded as a single feature. This potential double-counting may become more prevalent as these features are mapped in greater detail and smaller peaks on larger structures are identified. It was to address this issue that Yesson et al. [11] introduced an optional filter to remove spatially adjacent features, and we recommend always examining the filtered and unfiltered predictions with this in mind.

However, there is a competing pressure that may lead to a reduction of seamount numbers, as isolated no bottom soundings or erroneous readings, such as those identified in this study, are removed from bathymetry grid construction, so features defined by these mistakes should be removed as underlying grids are improved [16,17]. It is imperative that our predictions are as accurate as possible, as every erroneously identified feature could prove costly in terms of the investment required to conduct a research cruise to a ‘phantom seamount’ or the negative effects of taking protection measures for non-existent features. Fortunately, the scale of the problem directly identified in this study appears to be small and will likely reduce as methods improve and primary data collection expands. However, not all of these no bottom sounding records have been removed and there may be other causes of error not currently identified.

Finally, although these predictions are based on a global bathymetry grid, we note that seamount predictions based on the lat.–long. bathymetry grid perform poorly at high latitudes where there is a large spatial distortion. Seamount predictions for Arctic and Antarctic regions should be remade based on polar specific grids such as the International Bathymetric Chart of the Arctic Ocean (IBCAO [26]).


Bathymetry grids are continually improving [18], whether that is from new multibeam acquisition, such as that collected during the search for Malaysian Airlines flight MH370 [27], or improved satellite gravity data [28]. However, these bathymetry grids still rely on sparse sounding data for many regions, and thus have the capacity to mislead if invalid historical weighted line measurements are used in the construction of bathymetric models as isolated falsely interpreted records can lead to the appearance of ‘phantom seamounts’. Despite advances in data acquisition, modelling and prediction methods, these data will continue to contain errors. Therefore, it is important that we use all the information available, including multiple seamount predictions, multiple bathymetry models and printed charts to assess potential seamount distributions, particularly when planning surveys to unsampled seamounts, or in the arena of conservation planning, where seamount distributions can be used as proxies for endangered predator distributions [29].


We are grateful to the Bertarelli Foundation for supporting this research. Institute of Zoology staff are funded by Research England. Field activities were conducted under permit and with support from the Foreign and Commonwealth Office and the BIOT administration. We thank the Pacific Marlin, its Master, Chief, and crew for excellent assistance. We are grateful to OceanWise Ltd for sharing the dataset of no bottom sounding observations from the digitised admiralty charts. We thank Andrew Roy for helpful suggestions during this MSc thesis project. We thank David Sandwell and members of his lab for helpful reviewer comments.

Declarations and conflicts of interest

All other authors declare no conflicts of interest in connection to this article.

Research ethics statement

The authors conducted the research reported in this article in accordance with ZSL standards.

Consent for publication statement

The authors declare that research participants’ informed consent to publication of findings – including photos, videos and any personal or identifiable information – was secured prior to publication.

Conflicts of interest statement

The authors declare no conflict of interest with this work / The authors declare the following interests / The authors are a current Editor for this journal. All efforts to sufficiently blind the author during peer review of this article have been made. The authors declare no further conflicts with this article.

Open data and materials availability

Data availability: The datasets generated during and/or analysed during the current study are available in the repository:

Author contribution

CY and TL conceived the work. TL, ANS, PH, AB and RP planned and conducted fieldwork. CY, TL and MH assembled the data. CY performed the analysis. All authors contributed to writing the article.


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 Open peer review from Junjun Yang


Review information

DOI:: 10.14293/S2199-1006.1.SOR-GEO.AOD2B9.v1.RZJKAA
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at .

ScienceOpen disciplines: Geography
Keywords: Knolls , Environmental science , Bathymetry , Seamounts

Review text

This study produced a global seamount census using Becker et al. (2009)’s SRTM30_PLUS bathymetric model, and reported a very interesting finding, inappropriate use of the sparsely distributed seafloor depths in historical admiralty charts may lead to erroneous presence of seamounts in global bathymetric models. However, the two stories are not well combined whereas usually only one theme is expected in a paper. This is the largest problem of this manuscript, as evidenced by the disordered structure.

The introduction of this paper has a nice start, highlighting the importance of seamount census in fishery management/research. But, the motivation of this study is not clearly presented. What is the gap in our current knowledge? What are the objectives of this paper? For example, you could say improved global bathymetric models have been released and an updated seamounts chart need to be produced. The updated chart may benefit the fishery by ***. Or, you could focus on another topic by saying that digitized historical nautical charts are used to expand the data coverage, but may lead to erroneous presence of seamounts in bathymetric models. The mechanism needs to be identified and false seamounts need to be removed from the current census. But remember to stick to one theme in a paper.

The findings reported in the “BIOT Seamount Survey” section is very interesting, but its connection with the last and next paragraphs is not well established, making it seems abrupt and standalone. How does these findings contribute to your seamount census? You can produce two seamount censuses, one produced with the “no bottom soundings” and one without, and then find a way to evaluate the improvement obtained by removing the “no bottom soundings”.

The method section is too simple, lacking of formulas and step-by-step description of how you produce the seamount census. This makes readers hard to duplicate your results. Besides, some sentences (Lines 120-122) in this section should belong to a separate section named “Data”.

You could consider adding more text to the discussion section, e.g., histogram of the seamount heights, evaluation of the reliability of results, known insufficiency in the present method that needs to be improved in the future, etc.; please refer to Wessel (2001) and Harris et al. (2014) for ways of extending the discussion. Besides, adding something specific about how your seamount census improves fishery will be helpful.

Minor problems I noticed are listed in the following.

Line 1: 4000 is not compatible with the number in the abstract.

Line 51, Line 67: Do you mean SRTM30_PLUS? Note that SRTM30 and SRTM_PLUS are two different models. Only the land and ice topography of SRTM30_PLUS comes from SRTM30.

Line 82: GEBCO 2020 has been released. Why do not you use the latest version?

Line 84: Add the text “Great Chagos Bank” onto Figure 1 to improve the readability.

Lines 117-118: This sentence is hard to understand. Please consider rewriting it.

Line 135: In the legend, the marker for “New Seamounts” is hardly visible.

Line 139: Define the EEZ shown in the legend.

Line 147: I did not find (Costello et al., 2010) in the reference list.

Line 152: Do you mean SRTM15+? Please use the name given by the author.

Line 162: Substitute “that be” by “it is”.

This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from Xiaoyun Wan


Review information

DOI:: 10.14293/S2199-1006.1.SOR-GEO.AKXABB.v1.RVEAQH
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at .

ScienceOpen disciplines: Geography
Keywords: Knolls , Environmental science , Bathymetry , Seamounts

Review text

Seamount information, including position, depth of the summit, is of great importance for different kinds of applications. The manuscript predicted new seamounts using newer bathymetry grids, i.e. SRTM30 global bathymetry version 11. The findings are important and meaningful for the applications of current bathymetry models. For example, they found some seamounts predicted by the old bathymetry grids are not detected by sounding data. This means some phantom seamounts exist in the old version of bathymetry grids. Also, some seamounts would not be detected by the old bathymetry grids. In general, I think the result of this manuscript is very important. Here are my comments.

  1. Although this study found new seamounts, however, there are also phantom seamounts. Hence, I think the author should point out that some erroneous predictions may exist in the new predictions. If the new results can be verified by sounding data, it would be better. However, the sounding data may be not enough. Maybe the authors can select some local regions to do an evaluation. For example, in the areas near South China Sea and the Philippines, as there is high densification of seamounts according to Figure 3.
  2. It would be more helpful for the international research community if the authors could use mathematical equations to break down the method used in addition to the content of lines 115 to 119. It can be added as an appendix.
  3. Line 64, give the geographic extent (corner coordinates) of BIOT Seamount Survey.
  4. Line 117, insert ‘where’ after ‘terminate at the point’.
  5. Line 118, “km2”->”km 2
  6. It would be better to give the units of the data in the inset table of Figure 4.
  7. Line 162, change ‘removed’ to ‘resolved’.

This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from Cherisse Du Preez


Review information

DOI:: 10.14293/S2199-1006.1.SOR-GEO.ACTAJL.v1.RYVRIC
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at .

ScienceOpen disciplines: Geography
Keywords: Knolls , Environmental science , Bathymetry , Seamounts

Review text

This is a short paper to inform readers about an updated seamount prediction database now available online. The new database was created using an “old” method (Yesson et al. 2011)  to analyze new and improved bathymetric data (false data points removed, higher resolution). There are two main findings:  an increased number of predicted seamounts and the identification of phantom seamounts. Seamount models are incredibly useful for marine spatial planning, and it is fantastic that the authors update model outputs as better bathymetry data become available.

Flow: The main story of the title and the discussion is “new seamounts identified,” which is different than the “phantom seamounts” focus of the abstract, methods, and results.

Line 11: While “hotpots” made me chuckle, I think it’d be better to change the word to “hotspots.”

Line 13: In large numbers, help the reader by using commas. “10,000 to more than 60,000”—consider changing throughout the article (used sometimes).

Line 13-14: Provide more detail, “Seamount locations can be estimated by extracting conical shaped features [that meet other criteria (e.g., elevation)] from bathymetry grids.”

Line 41: Mention deep-sea mining here. This data was used at the first ISA REMP workshop for deep-sea mining on seamounts (a marine spatial planning meeting for the International Seabed Authority; report still in progress) to start to inventory seamounts in the North Pacific Area, to identify seamounts that are (i) contracted for exploration and (ii) could/should be considered for protection. Please consider mentioning mining or that fishing isn’t the only threat (e.g., climate change impacts too).

Line 49: If complete list, add: Kitchingman, A., and Lai, S. 2004. Inferences on potential seamount locations from mid-resolution bathymetric data. Seamounts: biodiversity and fisheries 12: 7-12.

Line 54: 1.5km diameter, right? Or height?

Figure 1 / Line 82-87: Is the grey “sub-figure profile” line meant to show on the map where the inset profile data is from? The lines on the map are so close, and the colour difference so subtle that I can’t tell—poor quality. Extract profile and present separately to avoid cluttered and help with figure readability.

Figure 1 / Line 82-87: Not sure what is meant by “Chart symbol: No bottom detected at 183 m.” Delete since the line below clearly states, “No bottom detected on 2016 survey.”

Lines 83-87 (Figure 1): Include/explain figure labels A & B when describing the location of each site in the caption: “...NW of the Great Chagos Bank (site A)…Area 40km north of this (site B)…”

Lines 89-91 (Figure 2): Text jumps between “Seamount A…B” and “site A…B”. Go with one.

Line 95: “were” not “where”

Line 119: I’m happy to see the authors mention that new bases can encompass multiple “old dataset” peaks, but it makes it sound like the new dataset doesn’t suffer the same issue of individually identifying multiple peaks on the same seamount—in reviewing the shapefiles I see this is still the case. Please see the comment for lines 144-155 below.

Figure 4 / Line 139: Remove table inset and present separately to avoid clutter. I am having a really hard time with readibility of the figures (poor quality).

Lines 144-155: In my experience, these models are incredibly helpful in marine spatial planning--especially when assessed altogether--but I have witnessed the pitfall/danger in counting the predictions as the “number of seamounts” instead of the number of peaks (e.g., justification for allowing harmful activities on dozens of seamounts because models illustrate there are supposedly hundreds within the region--when in actual fact more than half of the predicted points are just peaks on the same seamount). It’d be beneficial for the authors to provide this word of warning regarding peaks vs. counts. I don’t think the high number of replicate predictions is unique to my study regions, but if the authors want to review overlapping bases and replicate predictions, I would suggest the NW and NE Pacific seamounts.

This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from David Sandwell


Review information

DOI:: 10.14293/S2199-1006.1.SOR-GEO.AMRD2Q.v1.RMVCQF
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at .

Keywords: Knolls , Seamounts , Bathymetry , Environmental science

Review text

We discussed this paper as a group in our weekly lab meeting on March 8, 2021.  Three of us have provided comments below.

David Sandwell

This is an interesting paper that has two main conclusions.  The first is that some data were incorrectly harvested from old Admiralty charts and then used in global grids.  The second is an improved analysis of global seamounts from the SRTM30 V11 global grid which is based on predicted depth and measured depth.  For each of these grids there is a matching source identification (SID) grid that enables one to determine the source of every sounding.

I have a few comments about versions of SRTM and GEBCO grids.  First the, latest SRTM and GEBCO grids have moved to 15 arcseconds.  The latest SRTM 15 V2.1 grid is described in a recent publication (Tozer, B., Sandwell, D.T., Smith, W.H., Olson, C., Beale, J.R. and Wessel, P., 2019. Global bathymetry and topography at 15 arc sec: SRTM15+. Earth and Space Science, 6(10), pp.1847-1864).  Table 1 of that paper explains that the GEBCO grids are a copy of the SRTM grids with soundings added.  It is interesting that the GEBCO 2014 grid has two false seamounts while the SRTM V11 has only one.  Seamount A is in all versions of SRTM30 V7, 8, 9, 10, and 11.  However Seamount B is not in any of these versions.  The archive of all the old versions is at . Look in the topo30 folder for the SID grid in netcdf format.  Therefore, this B data point was somehow added to the GEBCO 2014.  I also see the B-pont it is not in the latest GEBCO 2020 grid which is mostly a copy of SRTM15_V2.0.

This incorrect sounding at location A came from the following source.  This is a dataset from the NGA which was harvested from digital nautical charts (DNC).  It has a depth of -140 m.

16393 private DNC YEAR NGA

Basically I completely agree with this part of the paper and the analysis of the phantom seamounts.  The authors should add the locations of the 14 additional “phantom seamounts” so they can be removed from the next versions of the SRTM and then the GEBCO data sets.

Regarding the claim of the discovery of 4000 new seamounts, this is probably also correct although I think the better way to locate seamounts is to use the vertical gravity gradient data following the Kim and Wessel 2011 study.   I see that none of the phantom seamounts described in this paper on Figure 4 were identified in the VGG by Kim and Wessel (2011).

Here are some general comments:

  1. The data file on the locations and characteristics of the old and new seamounts needs to be available.

  2. There should be a more complete discussion about the overlaps of the various seamount data sets.  Also in areas of rough topography, associated with abyssal hills and fracture zones, how do the authors discriminate between features created by seafloor spreading and off-axis volcanoes.

  3. The quality of Figures 1, 3, and 4 is very poor.  The latitude/longitude labels on Figure 1 should be decimal degrees to match the text.

#20210308: From Katherine -- I mainly read this with an eye for editorial stuff, so I will just list some of the issues I had (I want to underline David’s point #3 above about the figures being quite poor too). Please feel free to rewrite/include whatever though!

Line 12 - 13: Delete “barely”, change to  “...vary from anywhere between 10000…”; Also, delete extraneous parentheses after “60000”

Line 13 - 14: If they are going to say “can be difficult” say/explain why (e.g. locating them can be difficult because of x, y, z reasons..)

Line 18 - 21: The second sentence of this paragraph needs a little help -- it’s very choppy/confusing. Why not instead say, from sentence one of that paragraph: “based on the most recent SRTM30 (is that the most recent?) global bathymetry, which was prompted by a recent (what year?) survey in the …. Two of the seamounts visited did not display any echosounder readings, despite having been previously identified as seamounts in earlier seamount maps/databases.”

Line 21: I’m not in this field and do not know what Admiralty charts are -- but maybe the audience of this paper does know what they are?

Line 23: “perhaps 15” features? Did they not just do an analysis to identify these? Maybe clarify, or don’t use the word perhaps (are they sure or not sure about their analysis?)

Line 26: Specify which “older bathymetry grid” they are comparing to.

Line 44 - 45: Does this not contradict the sentence in the abstract where they specify a seamount number estimate of 10000 - 60000 ?

Line 49 - 50: Probably should define those acronyms for completeness sake (GEBCO and SRTM)

Line 104 - 105: I feel like this sentence should have been stated earlier than now. This is the clearest thing I have read so far that describes what they are doing/trying to do, and I like it.

Line 108: First time telling the reader what SRTM is, but they don’t mention the acronym (stay consistent if possible)

Line 141 - 143: They mention that datasets vary, in particular with how they identify any seamounts along ridges, but the authors don’t appear to specify what they do when it comes to ridges and possible seamount features there?

Line 162: I think they mean “invalid” instead of “in-valid”?

Julie Gevorgian Review: I mostly looked for grammatical errors and the overall flow of the paper.

Line 50: Remove “themselves”

Line 51: Remove the dash between “Satellite- altimetry”

Line 54: Change to “these are utilized to constrain” - the word “used” is used twice in the same sentence, might be better to avoid repetition.

Line 59: remove “so”

Line 59-60: Might be worth rewording this sentence to make it flow better: “Since sounding data is limited, it is valuable to make use of all available data. Historical soundings based on weighted lines have been extracted from nautical charts to expand the data (Becker et al.,2009).”

Line 72: change “were” to “was” - important to be consistent with past/present tense

Line 115: change to “can and oftentimes do encompass”

Line 163: change to “phantom seamounts.” - the period always goes inside the quotation

This review refers to round of peer review and may pertain to an earlier version of the document.