-->PONE-D-25-55790-->-->Sampling bias obscures biodiversity patterns, reveals data gaps in priority conservation areas: a call for improved documentation-->-->PLOS One-->--> -->-->

Dear Dr. Pitogo,

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Sincerely,

Juan C. Diaz-Ricaurte, PhD

Academic Editor

PLOS ONE

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Additional Editor Comments:

Decision: Major Revision

Manuscript: Sampling bias obscures biodiversity patterns, reveals data gaps in priority conservation areas: a call for improved documentation

Dear Dr. Pitogo,

I have now received two reviewer comments on your manuscript. Both reviewers recognize the study as well written, timely, and highly relevant for biodiversity research and conservation planning. The reviewers and I agree that the manuscript addresses an important and well-defined problem, and that the assembled dataset represents a valuable contribution. However, in its current form, the manuscript cannot be considered for publication.

Both reviewers identify a number of analytical and methodological issues that are central to the study’s main conclusions. I agree with the reviewers that these issues must be addressed before the manuscript can be evaluated further. Below, I synthesize their comments, indicating where each reviewer places particular emphasis, and outline the key expectations for revision.

A first overarching issue, raised primarily by Reviewer 1, concerns the alignment between research questions and analytical choices. Although the research questions are clearly stated, the links between each question, the statistical models used, and the corresponding results are not always explicit. I strongly suggest that the revised manuscript make these connections clear and systematic. Each main question should be directly associated with the analysis used to address it and with the relevant results, as this alignment is essential for transparency and interpretability.

A major methodological concern, discussed in detail by Reviewer 1 and reinforced by Reviewer 2, relates to the framework used to compare different categories of conservation areas. Much of the manuscript’s inference depends on comparing sampling effort and diversity patterns across conservation area types, yet the current analytical approach does not allow formal statistical testing of differences among these categories. I agree with both reviewers that this represents a substantive limitation. For the revision to be satisfactory, these comparisons should be conducted within a unified modeling framework that allows direct statistical evaluation of differences in effects across conservation area types.

Closely linked to this issue, Reviewer 1 emphasizes the importance of retaining model components that are justified by the study design, particularly grouping or hierarchical variables included to account for non-independence among observations. I concur with this point. Structural terms that reflect the sampling design should not be removed based solely on variance explained or model fit. I therefore recommend revisiting the model structure to ensure that non-independence is appropriately controlled, as this is critical for the validity of the inferences.

The spatial properties of the dataset constitute another key issue raised by both reviewers from complementary perspectives. While reviewer 1 focuses on spatial precision, including how coordinate uncertainty was handled and whether the chosen grid resolution is appropriate given the characteristics of the occurrence records, the reviewer 2 emphasizes the need to explicitly address spatial dependence among observations. Given the spatially explicit nature of the analyses, I agree that these issues cannot be treated as secondary. The revised manuscript should clearly justify the analytical scale, address positional uncertainty, and either incorporate spatial structure into the models or provide a clear and defensible rationale for not doing so.

Both reviewers also identify limitations in the biological interpretation of the results, particularly regarding the joint treatment of amphibians and reptiles. These groups differ in ecology, detectability, sampling intensity, and conservation context. I suggest that the authors either explicitly assess whether these taxa exhibit similar or contrasting patterns, or clearly justify their combined analysis. Without this clarification, the ecological interpretation of the results remains incomplete.

Further clarification is also required regarding the conceptual framing of conservation areas. Reviewer 2 raises questions about how different conservation categories are defined and compared, while Reviewer 1 highlights the difficulty of interpreting area-based comparisons under strong and uneven sampling biases. I agree with both assessments and recommend that the revised manuscript explicitly state the assumptions underlying these comparisons and clearly articulate how results should be interpreted in light of known data limitations.

Finally, both reviewers note that several figures (particularly those illustrating spatial patterns) are difficult to interpret due to low resolution and small text. This may be related to how figures are displayed in the submission system; however, I recommend you include all figures in the revised version at the highest resolution possible to facilitate careful evaluation by the reviewers. Thank you in advance for their attention to this point.

In light of these points, my editorial decision is Major Revision. Addressing them will require substantive re-analysis and careful revision of both methods and interpretation. Nevertheless, the reviewers and I agree that a thoroughly revised manuscript has strong potential, and we encourage you to engage fully with these comments as you prepare your revision.

We look forward to receiving a revised version of your manuscript.

Sincerely,

Juan C. Diaz-Ricaurte, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: I Don't Know

Reviewer #2: No

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: Dear Authors,

The study is well written and provides a comprehensive overview of the state of herpetofaunal knowledge in the Philippines, addressing important questions for species and territorial conservation.

The manuscript investigates a critical challenge in conservation biology: how sampling biases affect our understanding of biodiversity patterns, particularly within priority conservation areas (PCAs) in the Philippines.

This central question is clearly articulated through four specific inquiries:

1. How is observed herpetofaunal diversity (amphibians and squamate reptiles) spatially distributed across the Philippines?

2. How do these patterns differ across datasets from museum collections, citizen-science platforms, and peer-reviewed literature?

3. How well do observed diversity patterns coincide with priority conservation areas (PCAs)?

4. Which PCAs, and which types, are more thoroughly sampled?

To address these questions, a data-driven spatial analysis approach was adopted, compiling an impressive dataset of occurrence records from museum collections, citizen science, and literature. You then apply grid-based mapping, statistical modeling, and model selection procedures to explore species richness and sampling effort.

The overall approach is adequate. However, I would suggest that the authors more explicitly connect each research question to the specific model(s) used. Clarifying which models and results correspond to which of the four main questions would enhance the transparency and coherence of the study design.

Beyond this general comment, my primary concerns are methodological, and I believe addressing them will strengthen the statistical rigor and interpretability of this important work.

Thank you for the detailed description of the data curation procedures. All the steps were important to guarantee the quality of the input data. However, I would like to raise a question regarding the spatial precision of the occurrence records used.

While the manuscript mentions the exclusion of records outside known distributions and the removal of generic coordinates, I did not find any reference to the use of the “coordinate uncertainty in meters” field (available in GBIF records) or a similar assessment of the dataset's spatial accuracy. Given that the study employs a 10-km grid resolution, I wonder whether this scale is appropriate considering the potential variability in coordinate precision, especially for historical museum records which can lack fine-scale georeferencing.

Could the authors clarify whether any filtering based on coordinate uncertainty was applied? If not, do the authors consider the 10-km resolution robust to such imprecision, and how might this potentially affect the spatial allocation of records and the resulting diversity patterns?

The current analysis utilizes Negative Binomial GLMs fitted separately for Protected Areas (PAs) and Key Biodiversity Areas (KBAs). The methodological rationale for this decision is not fully clear in the manuscript. If this separation was a deliberate choice to address, for example, severe variance heterogeneity, it would be very helpful for the reader if this justification were made explicit.

My main concern with this approach is that a central objective of the study appears to be the comparison of predictor effects (e.g., area, topographic relief), and the comparison of richness and effort between PAs and KBAs. Adjusting separate models, while legitimate for describing the relationships within each group, impedes a formal statistical comparison of the coefficient estimates.

To formally test the hypothesis that a predictor's effect differs between PAs and KBAs, the standard and more robust statistical approach would be to analyze both groups within a single, unified model. This is typically achieved by including the group affiliation (PA vs. KBA) as a categorical predictor and, crucially, testing interaction terms between this group variable and the predictors of interest (e.g., PCA_Type * Area). The p-value for the interaction term provides the formal statistical test of the null hypothesis that there is no difference in the slopes between the groups.

It is plausible that adopting this unified model approach would yield substantive results similar to those already presented. However, its key advantage would be to make the comparative claims—which are central to the study's objectives—statistically explicit and defensible, thereby increasing the analysis's robustness and interpretive clarity.

My final methodological point relates to the model selection for control effects. The authors state that the random effect variable "PAIC" was removed from one of the models because it "explained less than 1% of the variance and did not improve model performance.” (Line 234).

A low number of levels (seven, in this case) can lead to imprecise variance estimates. However, the decision to include or exclude a structural random effect should be based on the study design, not on its statistical performance (such as variance explained or AIC).

As the 'PAIC' variable represents a grouping variable (e.g., island/region) dictated by the sampling design, its function in the model is not primarily to "explain variance" (as in a predictive model) , but rather to control for the non-independence of the data (i.e., pseudoreplication). Observations from the same PAIC group are likely to be more similar to each other, violating the assumption of independence. The literature strongly advocates for retaining the random effects structure justified by the design. Removing this structural term, even if its variance is low, can lead to "overoptimistic" results, such as artificially reduced standard errors and an inflated Type I error rate. (Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255-278. https://doi.org/10.1016/j.jml.2012.11.001)

I would, therefore, recommend that the authors retain the 'PAIC' random effect in the final models to ensure that potential non-independence in the data is appropriately controlled for, thereby strengthening the validity of the model's inferences.

The study is situated within a well-established body of literature that highlights the challenges posed by sampling bias in biodiversity data and its implications for conservation planning. By focusing on the Philippines—a recognized biodiversity hotspot—the authors contribute valuable insights to this broader discourse. Their integration of museum records, citizen science, and literature-based data reflects current best practices in biodiversity informatics.

The study's main findings and conclusions are clear and relevant. You found that biodiversity data is unevenly distributed, with most records concentrated in a few well-studied areas. A key conclusion is that many PCAs, especially in mountainous regions, lack sufficient documentation. Furthermore, the analysis suggests that some of the most species-rich areas may lie outside current PCAs, highlighting a potential need to expand conservation boundaries. I believe this discussion could be further strengthened by evidence following the adjustments to the model selection steps, as suggested above. The authors then conclude that integrating diverse data sources and improving documentation practices are essential to address these critical data gaps and enhance future conservation planning.

Regarding the presentation of these results, I found many of the figures, both in the supplementary material and - more significantly - in the main manuscript, difficult to interpret: the resolution appears to be low, and the font size for labels and legends is very small. I would recommend the authors provide higher-resolution figures with more legible text to ensure readers can fully interpret the study’s important spatial results.

Thank you for the opportunity to review this manuscript. I truly enjoyed reading it and found the research to be a very valuable contribution to conservation. I hope my comments are clear and provide a constructive path for improving the analysis and impact of this important work.

Kind Regards

Reviewer #2: Dear Authors

The manuscript is well-written, and the findings are important contributions to conservation planning in the Philippines. Nevertheless, there are some drawbacks in the MS. 1. There is confusion about conservation based on area figures, requiring justification for why to analyze KBA separately from OECM. 2. Models don´t account for spatial correlation, and since you’re using georeferenced records, it´s necessary to include the effect of dependence between observations. 3 The model relating species diversity to potential biases from topography and conservation area size is not appropriate, given the acknowledged data biases. 4. It’s not clear whether amphibians follow similar patterns of reptile species records. Both classes have differences in distribution, sampling, conservation and research effort. Therefore, presenting data analysis that discriminates between the two classes would be helpful to unveil shortfalls at a finer resolution.

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: PONE-D-25-55790.pdf

-->PONE-D-25-55790R1-->-->Sampling bias obscures biodiversity patterns, reveals data gaps in a megadiverse island archipelago hotspot-->-->PLOS One-->--> -->-->Dear Dr. Pitogo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Thank you for submitting the revised version of your manuscript. I have now received the reviewers’ comments and have carefully considered them together with the revised manuscript.

First, I would like to acknowledge the work that has gone into this revision. Both reviewers recognize that the manuscript is clearer and that several of the previous concerns have been addressed. I also think that the study has considerable potential. The topic is relevant, the dataset is valuable, and the manuscript addresses questions that are important for understanding biodiversity patterns and sampling limitations.

However, there are still some important issues that need to be resolved before the manuscript can be considered further. For this reason, my decision is to invite a further revision.

At this stage, I would like to be very clear about the main editorial concern. The remaining issues are not simply matters of wording or presentation. They affect how the results are interpreted and how strongly the manuscript can support its conclusions. In particular, the reviewers continue to raise concerns about the treatment of sampling bias, the structure of the statistical models, spatial dependence, and the interpretation of observed richness patterns.

Please pay particular attention to the comments from Reviewer 1. Their points regarding model complexity, the use of occurrence records as a proxy for sampling effort, and the apparent inconsistency in the interpretation of sampling bias are especially important. These issues are central to the manuscript’s argument and should be addressed directly in the revised version.

In particular, the revised manuscript should clarify whether the current model structure is the most appropriate one for the questions being asked. The inclusion of several variables and interaction terms may make it difficult to identify the main patterns and may obscure the role of unequal sampling effort, which is one of the key issues the manuscript aims to address. If the authors retain the current model structure, a clearer justification is needed. Alternatively, some form of model simplification, model comparison, or sensitivity analysis may help make the results easier to interpret.

The use of total occurrence records as a proxy for sampling effort also needs to be treated with more caution. As noted by the reviewers, this variable may partly reflect observer effort, but it may also reflect true biological abundance or detectability. This creates a potential circularity that should either be addressed analytically, if possible, or discussed more explicitly as a limitation. The interpretation of the results should be adjusted accordingly.

There is also an apparent inconsistency in the way the manuscript discusses sampling bias. In some sections, the text suggests that sampling effort does not drive the observed patterns, whereas in others it suggests that spatial sampling bias is a major driver of observed richness. This point should be resolved carefully, because it is central to the manuscript. The revised version should present a clear and internally consistent interpretation of what the analyses can and cannot show.

I also ask the authors to address Reviewer 2’s concerns regarding spatial dependence and the combined analysis of amphibians and reptiles. If spatial autocorrelation cannot be explicitly modeled or tested, this should be clearly justified and incorporated into the limitations of the study. Similarly, the decision to analyze amphibians and reptiles within a single framework should be better justified, or the interpretation should be made more cautious and, where appropriate, group-specific.

Finally, please revise the Discussion to make it more focused and less repetitive. The manuscript would benefit from a clearer distinction between ecological signal, sampling artifacts, and the limitations imposed by the available data. Please also improve figure readability, especially the font size, as noted by Reviewer 1.

Because this is already a further round of revision, I strongly encourage the authors to address the reviewers’ comments as fully and carefully as possible. From an editorial perspective, the next version should allow a clear final assessment of whether the main concerns have been resolved. In that sense, this may be the last opportunity for substantive revision before a final decision is made.

I remain interested in the manuscript and believe it could make a valuable contribution if these issues are addressed convincingly. Please submit a detailed response letter explaining how each point raised by the reviewers has been handled. Where you decide not to make a suggested change, please provide a clear and specific justification.

I look forward to receiving your revised manuscript.-->--> -->-->Kind regards,-->-->

Juan C. Diaz-Ricaurte, Ph.D

Academic Editor

PLOS One

Please submit your revised manuscript by Jun 26 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Partly

Reviewer #2: Partly

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: I Don't Know

Reviewer #2: No

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The manuscript is noticeably clearer and have addressed all previous questions. That said, I still have very few concerns I would appreciate clarification.

The previous manuscript version perhaps removed too many variables; this one may have went in the opposite direction. A model with many interaction terms can make it harder to identify the main effects—and might inadvertently blur the very patterns related to unequal sampling effort that the paper aims to highlight. Some form of model selection or simplification could help. The previous comments were about retaining the random variable in order to control the dependency among the occurrences. This version do not mentioned any model selection steps that could help interpretation.

I am a bit uncertain about using total occurrence records as a proxy for sampling effort. There's a risk of circularity here: high record counts could reflect genuine biological abundance rather than observer effort, which would make it difficult to isolate sampling bias as the driver. Would it be possible to use a more independent effort metric, or at least discuss this limitation more explicitly?

An internal contradiction regarding sampling bias seems to arise from the issues mentioned above. In some places, the text argues that sampling effort does not drive the observed patterns (e.g., Lines 455–458 and 529–534), while in others it suggests that spatial sampling biases are the main driver of observed richness (e.g., Lines 476–482 and 489–491). Since this point is central to the paper's argument, I think it is worth addressing explicitly.

The images are much better, but as they might go in the middle of the text I would recommend increasing the font size. It was possible to read the text only in full size screen.

I appreciate the work already put into the revision.

Reviewer #2: I appreciate the authors’ efforts to revise the manuscript and address previous comments, the manuscript has substantially improved. However, some key conceptual and methodological issues remain unresolved, which continue to limit the robustness of the analyses and the strength of the conclusions.

In particular, important concerns related to sampling bias, spatial dependence, and the interpretation of richness patterns are still not adequately addressed. Although the manuscript acknowledges data gaps and uneven sampling, the interpretation of analyses could lead to confusion over observed richness patterns as output of the ecological process.

Additionally, while the inclusion of biogeographic regions (PAIC) as a random effect is a useful step to account for broad-scale structure, spatial autocorrelation among observations is not explicitly modeled or tested. This limitation may affect statistical inference, as spatial dependence is expected in ecological datasets and can bias parameter estimates and significance tests.

The manuscript also continues to combine amphibians and reptiles within a single analytical framework without providing sufficient evidence that both groups share comparable spatial or ecological patterns. Given their known ecological differences, this approach may introduce bias and obscure meaningful patterns.

Moreover, the Discussion section would benefit from revision, as it is currently too long and somewhat repetitive, which reduces clarity and focus.

Overall, although the revision improves some aspects of the manuscript, the remaining issues are structural and affect the validity of the results and conclusions. Additional work would be required to adequately address these concerns.

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: PONE-D-25-55790_R1.pdf

Sampling bias and uneven data coverage shape observed biodiversity patterns in a megadiverse island archipelago hotspot

PONE-D-25-55790R2

Dear Dr. Pitogo,

Thank you for submitting the revised version of your manuscript, “Sampling bias and uneven data coverage shape observed biodiversity patterns in a megadiverse island archipelago hotspot,” to PLOS ONE.

I am pleased to let you know that, from an editorial and scientific perspective, your manuscript is now suitable for publication. The journal office may still request technical or formatting adjustments before formal acceptance and production, but no further scientific revision is required from my side.

Before production, I would only encourage you to take one final look at the figure files, particularly the maps, to ensure that key elements such as axis labels, titles, legends, and other visual information remain legible in the final PDF and online versions.

Congratulations to you and your co-authors on reaching this stage.

Kind regards,

Juan Diaz-Ricaurte, Ph.D

Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

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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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The authors have properly addressed my comments and suggestions from the previous review rounds. Concerns regarding the modeling steps and the authors' decisions are now well described in the text and supplementary material. Additionally, the previously highlighted contradictions have been corrected.

The maps are readable in high-resolution, and the authors stated that they prefer to keep them as they are. However, I consider that images should be legible on small screens (like tablets), in printed PDFs, and directly on the PLOS ONE website. Therefore, at least important information such as chart axis labels and titles might be readable for comparing even when zoomed-out.

Despite this, I would recommend that my revision finishes here and the work is ready to publication.

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: No

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