Author response:
The following is the authors’ response to the original reviews.
Reviewer 1:
Authors reject the substance of Reviewer 1’s feedback primarily due to clear lack of understanding of typical parameterization practices used to avoid overfitting. To ensure the Spearman-rank correlation accuracy, 70% of all data was withheld from the optimization process and used solely for testing to yield figure 6. Data was withheld prior to model parameterization and therefore avoids Reviewer 1’s charge of “artificially forcing the correlation”. Authors did appreciate the request for clarification of additional definitions and minor reorganization suggestions. Below we provide specific responses to each numbered point (note: multiple responses are provided for some of the reviewer points).
Point 1: Clarify Metrics Definition and Evaluation
Authors clarified the description of biodiversity metrics. The metrics associated with manual methods are detailed in the third paragraph of the Materials and Methods: Data Analysis section, while the sensor-based metric is described in the second paragraph, and summarized in its last sentence.
Text Additions:
Authors added clarification to the introduction’s first paragraph defining biodiversity metrics, including species richness.
Authors added detailed definitions of community metrics and their significance in community ecology in the Materials and Methods section (3rd paragraph of “Data Analysis” section). The discussion was updated to include a reference to community ecology and the benefits of big data, specifically highlighting the potential of autonomous optical sensors in entomology.
Methods Reorganization
We have reorganized the Methods section for clarity. Updated section clarifies metrics studied, location, dates, a description and methods around optical sensors, Malaise traps, and sweep netting.
Text Additions:
An overview paragraph was added to “Data analysis” (3rd paragraph) detailing key metrics used, specifying metrics such as abundance, richness, Shannon index, and Simpson index.
Visualization methods for sensor data to deliver analogous metrics of abundance, richness, and diversity indices was added to “Data analysis” section.
Supplementary Table 1 and the first paragraph of the Materials and Methods section cover location, dates, and other general information.
Detailed descriptions and methods for optical sensors, Malaise traps, and sweeping are provided.
Integration of Metrics
Authors integrated two paragraphs explaining the fundamental differences between conventional methods in the 3rd paragraph of the discussion and the presented method of biodiversity measurement.
Point 2: Body-to-Wing Ratio Calculation
The backscattered optical cross-section is now clearly defined as the value measured at the maximum point of the event. Specifically, we have added the word ‘maximum’ to our methods section for clarity.
Point 3: Ecosystem Services Paragraph
We have shortened and edited this paragraph for clarity. The revised text is now more straightforward and comprehensible.
Point 4: Results Section Structure
We believe restructuring the results section around each metric would result in redundancy. The value of our analysis is in the comparison of different methods; therefore, instead of talking about methods in isolation, we provide an integrated discussion and comparison of all three methods across all metrics. Instead, we have maintained our current structure but ensured that the metrics are consistently described and analyzed.
Point 5: Abundance Correlation
We agree that the lack of a correlation between methods for abundance remains an open question. However, we maintain that fitting a linear model would be inappropriate and potentially misleading in the absence of significant correlation. We have clarified this in our manuscript.
Point 6: Richness and Diversity Evaluations
The authors disagree with Reviewer 1's feedback, citing a clear misunderstanding of standard parameterization practices used to prevent overfitting. Specifically, authors implemented a 30/70 Training/Testing split. Therefore only 30% of the data was used to fit the model and 70% of the dataset was reserved for testing to ensure the validity and reliability of our clustering results. By validating with a 70% testing dataset, we ensure that the clustering model can accurately group new data points and is robust against overfitting. This process helps verify that the identified clusters are meaningful and consistent across different subsets of the data. Spearman's rho converts the data values into ranks and does not assume a linear relationship between the variables or require the data to follow a normal distribution. Spearman's rank correlation offers robustness against non-linearity and outliers by focusing on ranks. This approach is explained in the 4th paragraph of the “Data Analysis” section.
Point 7: Clustering Method Credibility
Authors acknowledge the variability in optical sensor features. However, the Law of Large Numbers supports increased insect measurement accuracy and stability occurs from optical insect sensors due to the increased number of observations made by the optical sensors compared to conventional methods. The manuscript now includes a detailed discussion of these aspects in the 3rd paragraph of discussion, emphasizing the correlation observed despite variability.
Reviewer 2:
Authors appreciate Reviewer 2’s feedback especially regarding contextualization. While authors disagree with the need for more specific experimental questions in a methods paper and the suggested need for more complex analysis, we agree with the essence of the review and added additional text regarding potential questions, method applications, and ecosystem processes for contextualization.
Point 1: Larger Question Framing
We present this article as a methodological paper rather than asking a specific experimental question. This approach is justified by the generalizable nature of methods papers, akin to those describing ImageJ or mass spectrometers. The method is widely applicable to a range of scientific questions.
We provided a discussion on how this technology could be applied in community ecology, conservation, and managed ecological systems like agriculture.
In the Conclusion section we provided elaboration on the potential research questions and applications.
Point 2: Complex Analyses
While complex analyses like NMDS are useful for specific questions, this paper aims to establish the method. Once established, this method can be applied to various research questions in future studies. Therefore, as we are not directly asking an experimental question, more complex analysis is unnecessary.
Point 3: Ecosystem Process (Granivory) Assay
We have improved the contextualization and explanation of the ecosystem process assay throughout the manuscript, ensuring it is well-integrated and clear to readers.
