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We, the participants of the ZAMBI 2026 Conference How-to Peer-Review Workshop, an extension of the CAN-AMR-Net eJournal Club under Theme 2: Transdisciplinary Training and Career Readiness, hereby submit a review of the following bioRxiv preprint:

Past and future phenology changes of zoonotic vector-borne diseases under climate and land-use change

Holle, V., Klitting, R., Kabisch, N., and Zurell, D. (2026). bioRxiv Doi: https://doi.org/10.64898/2026.06.07.730142

We will adhere to the Universal Principled (UP) Review guidelines proposed in:

Universal Principled Review: A Community-Driven Method to Improve Peer Review. Krummel M, Blish C, Kuhns M, Cadwell K, Oberst A, Goldrath A, Ansel KM, Chi H, O'Connell R, Wherry EJ, Pepper M; Future Immunology Consortium. Cell. 2019 Dec 12;179(7):1441-1445. doi: 10.1016/j.cell.2019.11.029

SUMMARY: Flaviviruses like West Nile virus (WNV) and Tick-borne Encephalitis virus (TBEV) are dependent on their vectors Culex pipiens and Ixodes ricinus respectively for transmission. Anthropogenic impacts such as changes to land-use and climate change can alter the ecological niche and distribution for these vectors, thus impacting the distribution of WNV and TBEV. Holle et al sought to thoroughly understand how climate change and land-use impacted the phenology (seasonal timing) and distribution of viruses and their vectors and how this is expected to change over time in Europe. Using historical data for confirmed infections, two vector databases, and environmental data from the Inter-Sectoral Impact Model Intercomparison Project Phase 3, the authors generated an ensemble species distribution model (SDM) to predict vector and virus suitability. Using this model, they show that both vectors and their viruses are expected to expand across the continent of Europe as we approach the 2050s although the simultaneous change in climate regions will impact the specific risk periods. They determined impacts of land-use and climate change using counterfactual and factual scenarios with their SDM. Finally, the authors showed that the risk period duration is expected to increase for both vectors and their respective viruses. Holle et al have created a useful model for studying the disease phenology of two important vector borne viruses. Additionally, their predictive data makes a strong case for increased and targeted public health interventions.

OVERALL ASSESSMENT: Overall, this study addresses the important question of how climate change and land use impact vector-borne viruses. Throughout the manuscript, Holle et al. demonstrated that their ensemble SDM to predict vector and virus suitability could predict similar trends for two different flaviviruses and their vectors. The authors made the methodology transparent and accessible to the reader. However, we identified several significant issues that detract from the impact-potential of this manuscript. Improvements to data presentation, main text methodology, and a clearly identified scope/narrative would greatly improve the quality of this paper. We are encouraging these improvements to make this article more accessible to a broad audience in the One Health field, thereby informing recommendations for public health measures.

STRENGTHS: We think a significant strength of this paper lies in the concrete recommendations for public health and surveillance of vector-borne illnesses. These recommendations are further strengthened by demonstrating that their model predicts similar trends for two flaviviruses that are reliant on two distinct vectors. This speaks to the extensibility of the findings, a criterion that is covered below in section 6. We also appreciated the transparency the authors provided with their methodology and data sources. The inclusion of counterfactual models bolstered the authors’ conclusions.

WEAKNESSES: We identified several weaknesses in the manuscript that can be attributed to an overwhelming amount of data and an insufficiently strong narrative. More specifically, while the inclusion of two vectors and two viruses strengthened the model, including both vector/virus comparisons in each figure limited the resolution and readability of the figures. We suggest that the authors either focus their narrative on the ensemble model or around specific vectors/viruses they are interested in. Considering the amount of valuable and necessary data in the supplemental materials, we propose the authors split this work into two separate publications to provide a strong narrative and source data for the model and overall recommendations for public health respectively. Reducing the amount of data in each manuscript would reduce the complexity of each figure and increase overall reading comprehension.

We appreciate the level of detail the authors include in their ODMAP protocol and the remainder of the supplemental materials; however, we would like to see the authors include more of this information in their main text. For example, their supplemental clearly defines the three climate scenarios they consider for their model, but they fail to explicitly state the key features of their main climate scenario SSP3-7 in the text. Not including this information makes the manuscript less accessible to a broader audience, reducing the impact. We also feel that while some limitations are stated in the discussion, more consideration of these limitations should be given when interpreting results.

In addition to our suggestion of reducing some data in a single manuscript, we would like the authors to adopt a more conventional figure format. For example, labeling each panel A, B, C, etc. would make it easier to reference these figures in the text. Building on this point, we would also like the authors to refer to their figures more frequently in the text, so it is clear to the reader which data set supports the authors’ conclusions.

Finally, we also would have liked to see the authors include model validation, showing how their historical predictions compare to the known number of vectors and positive infections for a certain period such as 2008-2018. Providing this validation would also allow the authors to generally reduce the complexity of some of their data sets. For example, while the counterfactual data is useful for drawing conclusions about the impact of land use and climate change, it may not be necessary to show 1970s, 1990s, and 2010s as historical predictions but rather use one as an example once the model is validated with true historical data.

DETAILED U.P. ASSESSMENT:

OBJECTIVE CRITERIA (QUALITY)

1. Quality: Experiments 

· Figure by figure, do experiments, as performed, have the proper controls? [note: we use this ‘figure-by-figure' section for broader detailed critiques, rather than only focusing on controls]

Fig. 1: We appreciate the amount of effort the authors put in to produce and analyze the data in Figure 1 and we feel that the overview level of data they provide is a good choice for their first figure. However, reader accessibility could be improved by reducing the amount of data in the figure or by separating data. To enhance understanding without reducing data, we recommend displaying the data like an immunofluorescence microscopy image where each channel (colour) is displayed in a separate frame alongside a merged image. We suggest the authors take this approach to display the vector suitability and viral suitability in different panels, followed by a merged image, so that the reader can view all the data without it being obscured. We also recommend a bar graph displaying percent overlap of each virus and vector would be a valuable addition to this figure. To understand this figure and the subsequent figures, we would like the authors to provide a clear definition of what “suitability” means in this manuscript. The definition is not obvious to readers outside of the field, leaving it up to interpretation.

Fig. 2: Figure 2 contains a lot of data. To improve reader comprehension, we recommend several adjustments:

a. We recommend the authors include the actual number of reported vectors/viruses and how their predictive model compares to this. We think this would be more informative than continuously showing the historical prediction data. Additionally, including a panel that compares true historical numbers to the predictive model could reduce the number of panels and/or lines required for some graphs, improving readability.

b. The similarity between the colours on the graph make the data difficult to interpret. We suggest the authors employ a colour scheme with more contrast. Different line styles would also be beneficial as it is difficult to tell the difference between the dash+dot lines vs. dot lines.

Fig. 3: We struggled to understand the climate classification data presented in Figure 3. The terminology referring to these different geographical/climate areas is not consistent throughout the manuscript. The authors use the terms “climate regions”, “climate zones”, and “climate classifications” making it difficult to determine what is being referred to. We also suggest using the same map format from Figure 1 for consistency (i.e. remove Russia/Asia); maps should be harmonized throughout the manuscript. The black colouration over some regions in Europe is also not explained or included in the figure legend. Additionally, the authors have used inappropriate titles to model the decades they are studying; for example, 1961-1990 should not be classified as the “1970s”. Further, they use these decade titles in other figures but do not define the year range in the figure caption. We suggest including the actual year range (e.g. 1961-1990) in their figures rather than an arbitrary decade assignment.

Fig. 4: We appreciate the amount of information being presented and appreciate the value of data describing the duration length for the 2010s and change in duration length in the future. However, the authors may consider representing this data in a different way. For example, showing the true numerical values associated with the data in the left-hand panels may be easier to interpret, especially when the authors refer to these precise numbers in the text. Increasing the resolution of the figure and changing the colour scheme of the panels on the right-hand side (using yellow as a background/zero value made it difficult to visualize) would improve readability. Based on the data in Figure 4, it also appears that some sub-arctic and polar regions are predicted to have 12-month risk periods for both vectors, which does not align with data presented in previous figures or the climate of these regions. We would like the authors to address these striking risk periods in the main body of their text.

· Are specific analyses performed using methods that are consistent with answering the specific question? Is there appropriate technical expertise in the collection and analysis of data presented?

We would like to see the authors represent their methods in a schematic or flow chart style to better understand the variables considered for calculating the predicted vector and virus suitability.

· Do analyses use the best-possible (most unambiguous) available methods quantified via appropriate statistical comparisons?

While the authors present a convincing predictive model, inclusion of some important controls/benchmarks would further strengthen the credibility of the model. For example, could the authors provide the true historical data for viral infections and vector detection and show how that compares to historical predictions generated by the model? Including this data would also alleviate the need to continue to show historical prediction data in some of their denser figures such as Figure 3.

2. Quality: Completeness 

· Does the collection of experiments and associated analysis of data support the proposed title- and abstract-level conclusions? Typically, the major (title- or abstract-level) conclusions are expected to be supported by at least two experimental systems.

The major conclusions are supported by the data in the manuscript. However, the title of this paper is not a conclusive statement of a single result. Their data supports the other claims of the abstract if they are true, however, we suggest an additional control to ensure the credibility of the model (see next section).

· Are there experiments or analyses that have not been performed but if ‘‘true’’ would disprove the conclusion (sometimes considered a fatal flaw in the study)? In some cases, a reviewer may propose an alternative conclusion and abstract that is clearly defensible with the experiments as presented, and one solution to ‘‘completeness’’ here should always be to temper an abstract or remove a conclusion and to discuss this alternative in the discussion section.

Yes, the authors have not shown the raw historical data and how their predictive model aligns with this. If the predictive model does not align with true historical data, then this would disprove conclusions.

3. Quality: Reproducibility

· Figure by figure, were experiments repeated per a standard of 3 repeats or 5 mice per cohort, etc.?

N/A this does not apply for this manuscript.

· Is there sufficient raw data presented to assess the rigor of the analysis?

No, as mentioned in previous comments, we would like to see the true historical data. Additionally, we would like to see the true values of the data presented in Figure 4. For example, in lines 499 to 500 they spell out specific percent differences of West Nile virus and Culex pipiens, but it is unclear where these numbers are coming from.

Are methods for experimentation and analysis adequately outlined to permit reproducibility?

The ODMAP provided by the authors is very detailed; however, finding a way to summarize and include more of this information in the main text would be beneficial.

· If a ‘‘discovery’ dataset is used, has a ‘‘validation’ cohort been assessed and/or has the issue of false discovery been addressed?

N/A

4. Quality: Scholarship 

· Has the author cited and discussed the merits of the relevant data that would argue against their conclusion?

The authors posit some explanations for why they see x or y in their data, but don’t present many alternative explanations. A more thorough exploration of alternative interpretations in the Discussion section would greatly benefit the reader.

· Has the author cited and/or discussed the important works that are consistent with their conclusion and that a reader should be especially familiar when considering the work?

While it is acceptable to cite pre-prints, it may be worth reconsidering the reference to McElwee et al., (has no other paper ever made a similar statement?).

· Specific (helpful) comments on grammar, diction, paper structure, or data presentation (e.g., change a graph style or color scheme) go in this section, but scores in this area should not be significant basis for decisions.

Culex pipiens has two major biomes, above ground pipiens and below ground molestus. We suggest the authors consider these biomes or ecotypes when assessing vector distribution across the continent. If they used “Culex pipiens” as their taxa ID, the lack of distinction may be why WNV was lagging behind Culex pipiens expansion. After first introducing the species, the authors should abbreviate Culex to Cx and Ix for Ixodes. We noted numerous instances of references being mentioned in the text that are not present in the references section. Additionally, some references involving weblinks are presented oddly in the manuscript and an alternative presentation (e.g. presenting the title of the webpage in text instead of the weblink) should be considered.

MORE SUBJECTIVE CRITERIA (IMPACT):

Impact: Novelty/Fundamental and Broad Interest 

How big of an advance would you consider the findings to be if fully supported but not extended? Has an initial result (e.g., of a paradigm in a cell line) been extended to be shown (or implicated) to be important in a bigger scheme (e.g., in animals or in a human cohort)? The extent to which this is necessary for a result to be considered of value is important. It should be explicitly discussed by a reviewer why it would be required. What work (scope and expected time) and/or discussion would improve this score, and what would this improvement add to the conclusions of the study? Care should be taken to avoid casually suggesting experiments of great cost (e.g., ‘‘repeat a mouse-based experiment in humans’’) and difficulty that merely confirm but do not extend (see Bad Behaviors, Box 2)

We think this study has the potential for substantial impact on global public health policy, especially given the ability of the authors to make concrete public health recommendations and show similar results for two different virus/vector relationships. However, the current manuscript requires improvements in readability and validation of historical data to gain acceptance. We would like the authors to consider the depth of each question they outline and define a narrower scope to allow for sufficient explanation of results and methods as well as improved data presentation. As a group we discussed whether the authors might have enough data to split this manuscript into two companion publications to maximize the number of data/methods included while maintaining a clear narrative.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.