PONE-D-21-23994Batch-produced, GIS-informed range maps for birds based on provenanced, crowd-sourced data inform conservation assessmentsPLOS ONE

Dear Stuart,

 At long last, both reviews are in! Congratulations! Both the reviewers and I strongly feel this represents a very valuable (and useful) addition to the literature.  However, there were a few small suggestions that probably should be tended to. Hence the "minor revision". But these are largely cosmetic and should not cause too much grief.  Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The authors of this manuscript have produced a useful tool for generating provenanced range maps so that they can be used to inform conservation assessments. However, despite the apparent utility of the tool to batch process large numbers of species, the authors repeatedly mention the need for closer inspection of the data in order to filter out incorrect records. The title and the abstract both emphasise the batch processing aspects, but much of the main text refers to the need for careful inspection of the data, so it is not clear how much of the process is automated and how much additional time is needed to manually correct data. It would be good of the authors could provide some estimates of how much time it takes to collate the data sets, how long it takes the protocol to do the batch processing, and how much time has to be spent manually checking and correcting data.

The authors do admit to the concerns about the quality and reliability of the eBird data (lines 102-107) and explain that the protocol does allow users to check each data point to make decisions about its veracity. However, I feel that the authors have under-stated this issue and it may be a significant issue in other parts of the world with many amateur observers who use eBird as their personal listing software and who are not careful about recording the exact location of each observation and instead use it to record their observations over a much larger area than actually indicated. A stronger caveat on the reliability and quality of the data is required. It is not clear from the analysis to what extent the authors of the paper have inspected all the outlier records to check on their validity, as this is not stated I would assume not. Hence the results showing significant differences between the published ranges and the Areas of Habitat may be due to the inclusion of incorrect records and this needs to be clearly indicated. However, that said, it is clear from the results obtained that there are a number of cases where these differences warrant a re-evaluation of the published ranges. But it would help if the authors were a bit more up-front about the issues of using eBird (and other citizen science data – e.g. the iNaturalist data uploaded to GBIF may even be worse because of issues concerning how records are assigned ‘research grade’ status).

Applying the methodology to birds in other habitats clearly has its limitations as indicated by the authors (lines 384-416). It is even more limited for other taxonomic groups given the patchy nature of records held by other citizen science initiatives. The taxonomic issues (lines 429-444) will pose problems for all taxonomic groups and only the adoption of a common taxonomy would resolve this (see Garnett et. al. 2020 - https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000736). Despite the limitations it is still a useful proof of concept and hopefully it will be easy to modify the code to allow users to add additional data sources in the future (as indicated in lines 374-382).

A minor point – on line 73, “Extent of Occurrence” is used, however, the IUCN Red List Categories and Criteria (https://portals.iucn.org/library/node/10315) and the Guidelines for Using the IUCN Red List Categories and Criteria (https://nc.iucnredlist.org/redlist/content/attachment_files/RedListGuidelines.pdf) both use “extent of occurrence”.

The authors are also to be congratulated on creating a tool that produces interactive HTML maps allowing users to zoom in on details and the opportunity to check individual data points and their metadata, etc. This kind of functionality helps to increase user confidence and certainly adds to the transparency of the process. A clear advantage of the protocol is its transparency and that it is explicit about what we know and what we don’t know.

I support the publication of this manuscript with some minor corrections to expand on the underlying caveats concerning the use of the eBird data, how automated the process really is, and how easy it can be extended to other taxonomic groups and data sources.

Reviewer #2: # Summary to author

As someone who has recently been working a lot on refining Red List/BirdLife range maps to Area of Habitat, I think anything that makes the process easier and more repeatable is a good thing. Thus, I like the underlying tools of this paper, particularly the transparency of the map-making process and its ability to be refined. The paper is generally well written and the examples given are informative. I really like the integration of eBird data - this is a great idea, and a huge advance.

Where I think the paper currently falls down is the justification for certain limitations of the study. In particular, I don’t follow the restriction to forest-dwelling species. I understand that forest cover data is widely available globally, at a high resolution, but why is that strictly necessary? Why didn’t the authors include all habitat types using, for example, the 2020 Jung et al. IUCN habitat types map? This would of course only give habitat presence/absence in a cell rather that any continuous measure, but then it could be applied at a much larger scale, to all species. I am not requesting that the authors completely re-do this analysis, but rather that they better justify in the text why the focus is on forest species, and how this limitation can/will be overcome in the future. There is some discussion of this point (e.g. lines 89-91), but I think it is insufficient at present.

My other comments are relatively minor, please see below. I think this is a very useful contribution to the field!

# Methods

Lines 114-118: I’m not entirely following this. You identified open habitat species using BirdLife habitat descriptions? And had made the decision a priori to exclude them because they occupy low forest cover habitat? Or you identified that you should exclude these species based on the discovery that they occupy low forest cover habitat?

Lines 129-131: this doesn’t make it clear what you ultimately did with these 99 species

Lines 138-139: can you add a citation for eBird’s best practices?

Line 144: I think more upfront explanation on what an alpha hull actually is (and why you used it) would be useful. Probably just move lines 224-227 up to here.

Line 167: be explicit in what these four values are (your min & max based on GLOBE, plus the Red List min and max?)

Lines 172-173: why 75%? I presume this is an arbitrary value that can be changed by the user?

Lines 178-179: this is crucial, so I think it should be more detailed. Or maybe have a flowchart? If I am following this correctly, the process is: (1) the alpha hull is created based on the range and occurrence points? (N.B. I don’t know what “median inter-presence distance” as the alpha actually means). (2) Min/max elevation and forest cover are identified based on distribution of occurrence points. (3) Alpha is refined to forest cover and elevation that falls within the range identified by (2)? (4) Within this AOH, you identify cells that are potentially occupied/unoccupied based solely on their proximity to presence/absence points, not anything to do with the environmental characteristics of that cell?

Line 184: what does “previously calculated” mean? By whom? You or the Red List? Or both, as in step (2) above?

Lines 185-186: I’m not sure what this means. Species would not be included on the checklist in cells where the habitat is thought to be unsuitable? Is the point that presences outside of range polygon can be included, while absences cannot (because they wouldn’t appear on the checklist)?

# Results

Line 230: many of the circle points also show a wide scatter

Lines 239-241: perhaps you should be more careful in the way the “AOH 12% larger than published ranges” finding is presented, since it includes the potentially unsuitable areas as well. It seems to me that the “potentially suitable AOH” is more accurate, since it is more strongly informed by actual observations. Probably (?) a lot of the “potentially unsuitable AOH” is indeed unsuitable but for reasons we can’t pick up through the environmental layers we currently have, only through repeatedly not observing the species there.

Lines 304-315: very interesting examples. Really exemplifies the utility of this approach.

# Discussion

Lines 3421-343: as above, I think more caution should be added to this point.

Lines 391-393: this starts to get at my concern discussed above re focus on forest species, but I’m not quite convinced. Surely you could limit by habitat, it would just be binary (habitat is present/absent in that cell) rather than a continuous measure like forest cover? But that will surely be true for a long time to come, because any continuous measure of non-forest “quality” is hard to measure remotely? Basically, I am concerned that we will be neglecting many non-forest species globally and for a long time if we ignore them until we have data as good as we have for forest species.

Lines 434-438: I would suggest adding some kind of text ref here before the numeric “[1]”, so that it is easier to read

Lines 457-458: could you add a very brief summary of the supplementary findings here for the lazy/busy reader?

# Figures and tables

As a general point, the figures are a bit low res at present. Please ensure they are provided at a higher resolution for publication.

Figure 1: in the legend it refers to blue lines, but I can’t really make them out in the figure.

Figure 3: please explain the solid black dots in more detail, in both the figure legend and its caption.

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Reviewer #1: No

Reviewer #2: No

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Batch-produced, GIS-informed range maps for birds based on provenanced, crowd-sourced data inform conservation assessments

PONE-D-21-23994R1

Dear Stuart,

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