Paper 35: Incorporating evaporative water loss into bioenergetic models of hibernation to test for relative influence of host and pathogen traits on white-nose syndrome

Author

Lee Jones - Senior Biostatistician - Statistical Review

Published

April 5, 2026

Reference

Haase CG, Fuller NW, Hranac CR, Hayman DTS, McGuire LP, Norquay KJO, et al. (2019) Incorporating evaporative water loss into bioenergetic models of hibernation to test for relative influence of host and pathogen traits on white-nose syndrome. PLoS ONE 14(10):e0222311. https://doi.org/10.1371/journal.pone.0222311

Haase, Catherine et al. (2019). Incorporating evaporative water loss into bioenergetic models of hibernation to test for relative influence of host and pathogen traits on white-nose syndrome [Dataset]. Dryad. https://doi.org/10.5061/dryad.2280gb5n2

Disclosure

This reproducibility project was conducted to the best of our ability, with careful attention to statistical methods and assumptions. The research team comprises four senior biostatisticians (three of whom are accredited), with 20 to 30 years of experience in statistical modelling and analysis of healthcare data. While statistical assumptions play a crucial role in analysis, their evaluation is inherently subjective, and contextual knowledge can influence judgements about the importance of assumption violations. Differences in interpretation may arise among statisticians and researchers, leading to reasonable disagreements about methodological choices.

Our approach aimed to reproduce published analyses as faithfully as possible, using the details provided in the original papers. We acknowledge that other statisticians may have differing success in reproducing results due to variations in data handling and implicit methodological choices not fully described in publications. However, we maintain that research articles should contain sufficient detail for any qualified statistician to reproduce the analyses independently.

Methods used in our reproducibility analyses

There were two parts to our study. First, 100 articles published in PLOS ONE were randomly selected from the health domain and sent for post-publication peer review by statisticians. Of these, 95 included linear regression analyses and were therefore assessed for reporting quality. The statisticians evaluated what was reported, including regression coefficients, 95% confidence intervals, and p-values, as well as whether model assumptions were described and how those assumptions were evaluated. This report provides a brief summary of the initial statistical review.

The second part of the study involved reproducing linear regression analyses for papers with available data to assess both computational and inferential reproducibility. All papers were initially assessed for data availability and the statistical software used. From those with accessible data, the first 20 papers (from the original random sample) were evaluated for computational reproducibility. Within each paper, individual linear regression models were identified and assigned a unique number. A maximum of three models per paper were selected for assessment. When more than three models were reported, priority was given to the final model or the primary models of interest as identified by the authors; any remaining models were selected at random.

To assess computational reproducibility, differences between the original and reproduced results were evaluated using absolute discrepancies and rounding error thresholds, tailored to the number of decimal places reported in each paper. Results for each reported statistic, e.g., regression coefficient, were categorised as Reproduced, Incorrect Rounding, or Not Reproduced, depending on how closely they matched the original values. Each paper was then classified as Reproduced, Mostly Reproduced, Partially Reproduced, or Not Reproduced. The mostly reproduced category included cases with minor rounding or typographical errors, whereas partially reproduced indicated substantial errors were observed, but some results were reproduced.

For models deemed at least partially computationally reproducible, inferential reproducibility was further assessed by examining whether statistical assumptions were met and by conducting sensitivity analyses, including bootstrapping where appropriate. We examined changes in standardized regression coefficients, which reflect the change in the outcome (in standard deviation units) for a one standard deviation increase in the predictor. Meaningful differences were defined as a relative change of 10% or more, or absolute differences of 0.1 (moderate) and 0.2 (substantial). When non-linear relationships were identified, inferential reproducibility was assessed by comparing model fit measures, including R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). When the Gaussian distribution was not appropriate for the dependent variable, alternative distributions were considered, and model fit was evaluated using AIC and BIC.

Summary from statistical review

This study incorporates evaporative water loss into bioenergetic models to understand its effects on white-nose syndrome. This disease impacts hibernating bats and increases the frequency of arousals during hibernation. The methods section is long and complicated and reads like a verbatim list. The results section is easier to follow with clear linear reporting in figures and text of linear regression models, including degrees of freedom and F values. The purpose of the linear models was validation, so their interpretation makes sense in context.

Data availability and software used

The authors provided an Excel file and accompanying data dictionary in the Dryad data repository. However, these materials contained only bat morphometric and physiological data and did not include the full dataset required to reproduce the reported linear regression analyses, which relied on both observed bat measurements and values derived from a predictive model. Although some components of the dataset may, in principle, be derivable from the 14 equations described in the methods section, it is unclear whether all required inputs were reported. Even if reconstruction were possible with substantial effort, this does not meet PLOS ONE’s Data Availability requirements, which require that all data underlying the findings and used in the statistical analyses be made publicly available in a form that enables replication of the results, rather than relying solely on reported equations, summaries, or derived values.

Regression sample

The data to reproduce the linear regression models were not available.

Computational reproducibility results

The data to reproduce the linear regression models were not available.

Inferential reproducibility results

The data to reproduce the linear regression models were not available.