MIReVTD, a Minimum Information Standard for Reporting Vector Trait Data
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Abstract
Vector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. Here, we present MIReVTD, a Minimum Information standard for Reporting Vector Trait Data. Our reporting checklist balances completeness and labor- intensiveness with the goal of making these important experimental data easier to find and reuse, without onerous effort for scientists generating the data. To illustrate the standard, we provide an example reproducing results from an Aedes aegypti mosquito study.
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AbstractVector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. …
AbstractVector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. Here, we present MIReVTD, a Minimum Information standard for Reporting Vector Trait Data. Our reporting checklist balances completeness and labor- intensiveness with the goal of making these important experimental data easier to find and reuse, without onerous effort for scientists generating the data. To illustrate the standard, we provide an example reproducing results from an Aedes aegypti mosquito study.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag020), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
Reviewer 2:
I read with interest the manuscript as I wholeheartedly agree there is a strong need for harmonization on reporting quantitative measurements of vector traits, especially for the subsequent development of mathematical models. The paper is well written, and examples are very helpful, particularly the one shown in Figure 1, advocating for the need for the sharing of individual (possibly raw) observations. I have some very minor comments and suggestions. Given the broad readership of the journal, I feel the Introduction would benefit from some definitions of what the authors mean by vector and vector-borne diseases, with some examples (WNV, DENV, … up to you). It's not very clear to me how the authors' current proposal aligns with what already proposed in Wu et al. 2022 (ref 21). It seems like some sort of extension? Could you please further elaborate on this? Regarding latitude and longitude, I think also the coordinate reference system should be standardized (WGS, no UTM or others). You might provide some examples of online repositories (line 187). Some (like GitHub) might not be perpetually available, differently from (hopefully) others like Zenodo or the Supplementary Materials accompanying the paper. The latter might be preferrable in my opinion. Figure 1. Please provide the equation of the TPC. Please note that Figure 2 currently does not seem to be cited in the main text (perhaps it should be on line 248?). What does "Dataset: 572" mean? As currently VecTraits seem the best (and only?) example of what the authors are proposing, perhaps it should be mentioned in the Abstract as well.
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AbstractVector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. …
AbstractVector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. Here, we present MIReVTD, a Minimum Information standard for Reporting Vector Trait Data. Our reporting checklist balances completeness and labor- intensiveness with the goal of making these important experimental data easier to find and reuse, without onerous effort for scientists generating the data. To illustrate the standard, we provide an example reproducing results from an Aedes aegypti mosquito study.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag020), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
Reviewer 1:
The authors propose MIReVTD, a concise minimum-information checklist for reporting vector trait data, motivated by the lack of consistent terminology and metadata that impedes reuse and synthesis across studies. The scope and intent are clearly stated in the Abstract and Introduction, including the emphasis on FAIR principles and the illustrative Aedes aegypti example and VecTraits implementation. Overall, this is a timely, valuable contribution that complements MIReAD (arthropod abundance) and the vector competence minimum data standard, and it will be highly useful to both experimentalists and modellers.
Major
- It would be highly beneficial to demonstrate the compatibility and added value of the MIReVTD and the VecTraits database to the existing initiatives aiming to collect and structure similar information. The authors mentioned ETS, MIAPPE, and MIReAD but and explicit mapping of the minimum information field alignment will help to place MIReVTD in context and facilitate adoption of this standard.
- The "Axes of Variation" section is strong, but it could be clearer about what constitutes a stressor or condition. It would help to list common confounders such as humidity, photoperiod, diet or food ration and quality, larval density, and light cycle, and to encourage recording fixed or background conditions in separate fields rather than only gradients. This would help avoid ambiguity between variables that are experimentally varied and those that simply describe the environment. In Figure 2, the second stressor appears to take a fixed value (0.1). This is somewhat confusing because it is not clear whether this field is meant for another gradient (e.g., temperature in the range of 20 to 40 °C in addition to food ration categories), or whether it lists fixed conditions under which the experiment was performed. If it is the latter, it might be more practical to include additional fields for stressors so that all relevant conditions, such as humidity and photoperiod, can be recorded. It would also help to clarify whether a third or further stressor can be added to the table, and how these would appear. It might in fact be preferable not to distinguish gradients from fixed conditions at all, and instead to treat them uniformly as conditions, each defined with its corresponding unit and uncertainty. This would simplify the structure and prevent confusion about whether a variable was held constant or systematically varied.
- It would highly improve usability and adoption if the standard also recommended ORCIDs for contributors, DOIs for datasets, and an explicit data license (e.g., CC BY/CC0). If this extension is possible, I recommend that the authors add a short "Data licensing & citation" paragraph to the Results section.
Minor
- Line 248: Fig. 2?
- Please update the citation of bayesTPC.
- If possible, please provide a code snippet with the data used (in Zenodo or as Supplementary Material) for Fig. 1.
- I believe the followings are also relevant to this study and should be mentioned appropriately:
- Adams B, Franz N, König-Ries B, et al. TraitBank: Practical semantics for organism attribute data. Semantic Web. 2015;7(6):577-588. doi:10.3233/SW-150190
- Kattge, J., Ogle, K., Bönisch, G., Díaz, S., Lavorel, S., Madin, J., Nadrowski, K., Nöllert, S., Sartor, K. and Wirth, C. (2011), A generic structure for plant trait databases. Methods in Ecology and Evolution, 2: 202-213. https://doi.org/10.1111/j.2041-210X.2010.00067.x
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