Lines 667–670: The procedure described in the Materials and Methods section demonstrates that the BEAST program was appropriately applied in this study to generate the results. However, a significant limitation of this type of analysis is that the outcomes are highly dependent on the dataset used (e.g., sampling imbalance). The dataset employed in this study does not represent a comprehensive sample of all OROV lineages circulating across their entire geographic range—particularly as the authors have not provided a complete list of the genomic data used in their analyses. Consequently, the results are influenced by biases inherent in the dataset, especially those arising from missing data. This limitation is particularly critical when reconstructing the dispersal history and dynamics of OROV lineages. I strongly recommend that the authors explicitly discuss the potential effects of incomplete OROV sampling on the reliability and interpretation of their findings. Additionally, the authors should include a complete list of the genomic data used, along with their accession numbers and associated metadata (e.g., how many samples come out of each site in Brazil), in the supporting information to enhance transparency and reproducibility.
Line 421-423: The manuscript does not address the investigation of reassortment patterns within the dataset, which is a notable gap. Detecting segment reassortment events could provide valuable insights into the evolutionary processes shaping Oropouche virus (OROV) dynamics and may better inform the interpretation of phylogenetic and epidemiological findings. Reassortment is a critical mechanism in the evolution of segmented viruses, and its omission leaves an important aspect unexplored. Please clarify why reassortment events were not investigated in your dataset. If possible, consider incorporating analyses to detect reassortment events, as this could significantly enhance the study’s contribution to understanding OROV evolution. Additionally, I recommend including a paragraph in the Discussion section to detail this limitation and its potential implications for the study’s findings. This addition would provide a more balanced and transparent interpretation of the results.
Figure 1: The manuscript would be greatly enhanced by the inclusion of additional visualizations to complement the current analyses. Specifically, a detailed phylogenetic tree displaying the diversity of Oropouche virus (OROV) lineages, annotated with posterior probabilities, would provide critical insights into the confidence levels of the evolutionary relationships. In addition, integrating maps that illustrate the dispersal history and dynamics of OROV lineages for the three minigenomic segments within Brazil would offer a comprehensive view of both evolutionary and spatial patterns.
The phylogeographic analysis presented in the manuscript, based on a Bayesian approach, is separately conducted for the three minigenomic segments (S, M, and L). While this approach provides valuable insights, analyzing the segments together for samples with complete genetic information from all three minigenomic sequences could yield more robust results. Such a combined analysis might also offer additional support to the findings derived from each single genomic segment. I suggest conducting a joint phylogeographic analysis that integrates data from the S, M, and L segments for samples where all three sequences are available. Thus, it will provide stronger statistical support by leveraging the combined genetic information, and Offer a comparative perspective to validate the findings from the segment-specific analyses.
Line 164-243: The difference in spatial resolution between the genomic data and the raster data used to analyze the environmental conditions associated with Oropouche virus (OROV) lineage dispersal over time may introduce additional limitations. Specifically, if the genomic data were not sampled at the exact geographic coordinates used for the environmental data, this discrepancy could add an extra layer of uncertainty to the analysis. The spatial mismatch between these datasets may affect the precision of the inferred relationships between environmental factors and OROV lineage dispersal. I recommend discussing the potential impact of this spatial resolution discrepancy on the results.
Line 633-645: The manuscript would benefit from additional details regarding how the occurrence records used in the ecological niche modeling (ENM) were collected. It is essential to specify the methodology employed to gather these records and how each occurrence was assigned to its corresponding geographic coordinates. This information will help clarify the accuracy and reliability of the data used in the analysis. Clearly describe how the occurrence records were collected and assigned to its coordinate, with providing additional details including sources (e.g., field surveys, public databases, literature, or previous studies) and the time frame of data collection. For example, the link provided on line 636 didn't provide except general information on OROV transmission and didn't show any geographic references for OROV cases.
To enhance the transparency and reproducibility of the study, I strongly recommend that the authors provide the complete datasets used in the analysis—specifically the genomic data, occurrence records, and any scripts used for data processing and analysis. Since the results and conclusions are heavily dependent on the quality and accuracy of these datasets, making them available for review would allow for a more thorough evaluation of the study's methodology and findings. Including these datasets and resources as supplementary materials would significantly strengthen the manuscript by enhancing its transparency and reproducibility, and providing reviewers with the necessary tools to thoroughly assess the study’s validity.
Line 168-169: The decision to select Aedes aegypti as the sole vector species for this study may not be sufficiently justified, particularly considering the well-established roles of other vector species in the transmission of arboviruses. Aedes aegypti has been widely studied and modeled in numerous other studies, and I found at least a few of these that critically evaluate the models used in this study. Given this background, it remains unclear why the authors chose to use Aedes aegypti alone as a covariate in their analysis.
Line 177: The authors mention the use of "high resolution," but do not specify the spatial resolution of the covariates used in the analysis. It is essential to clearly define the spatial resolution of these covariates so that readers can assess the suitability of the data for the modeling approach. Similarly, the manuscript does not address the temporal resolution of the data or how it correlates with the time of Oropouche virus (OROV) sampling. This aspect is important for understanding how the temporal dynamics of OROV transmission are represented in the models.
Line 218: Remove "human". The word "human" is duplicated.
Line 252: The occurrence data used in the analysis appears to be limited to Brazil; however, the map presented in the manuscript shows the entire South American region. This discrepancy is notable, especially considering that Oropouche virus (OROV) has been sampled from other countries in South America. The limitation of occurrence data to Brazil raises questions about the representativeness of the analysis, particularly in the context of broader regional patterns of OROV transmission and dispersal.
Line 258: Specify the diameter of the radius implemented here.
Line 289: Why 0.4 is used as a cutoff for thresholding the model. Please justify.
Figure S3 as presented, does not provide meaningful insight and could be improved. Instead of this figure, I recommend replacing it with a reference to a link that provides the layers in GeoTIFF format. This would allow for greater transparency and reproducibility of the analyses, enabling other researchers to access and use the data for further study.
Supplementary Table S3: Give a reference to the same link above.