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    Review of 'Improved bathymetry leads to 4000 new seamount predictions in the global ocean'

    Improved bathymetry leads to 4000 new seamount predictions in the global oceanCrossref
    This is a short paper to inform readers about an updated seamount prediction database now available
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    Improved bathymetry leads to 4000 new seamount predictions in the global ocean

    Seamounts are important marine habitats that are hotpots 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 10000 to more than 60000. Seamount locations can be estimated by extracting conical shaped features from bathymetry grids. 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 global bathymetry version 11. 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 4,437 from the previous predictions derived from an older global bathymetry grid (SRTM30 v. 6). 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 https://doi.pangaea.de/10.1594/PANGAEA.921688 .

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      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 www.scienceopen.com.

      Knolls,Environmental science,Bathymetry,Seamounts
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      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.


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