Xcertainty R package is now available!

Download in R/R Studio via CRAN:

install.packages("Xcertainty")
library(Xcertainty)

GitHub: https://github.com/MMI-CODEX/Xcertainty 

CRAN: https://cran.r-project.org/web/packages/Xcertainty/index.html 

Take a look at the Xcertainty vignette to help get started and learn about the different functions.

 

All morphological measurements derived using drone-based photogrammetry are susceptible to uncertainty. This uncertainty often varies by the drone system used. Thus, it is critical to incorporate photogrammetric uncertainty associated with measurements collected using different drones so that results are robust and comparable across studies and over long-term datasets.

The Xcertainty R package makes this simple and easy by producing a predictive posterior distribution for each measurement. This posterior distribution can be summarized to describe the measurement (i.e., mean, median) and its associated uncertainty (i.e., standard deviation, credible intervals). The posterior distributions are also useful for making probabilistic statements, such as classifying maturity or diagnosing pregnancy if a proportion of the posterior distribution for a given measurement is greater than a specified threshold (e.g., if greater than 50% of posterior distribution for total body length is > 10 m, the individual is classified as mature).

Xcertainty is based off the Bayesian statistical model originally described in Bierlich et al., 2021a that was then later adapted to incorporate multiple measurements (body length and width) to estimate body condition with associated uncertainty Bierlich et al. (2021b), as well as combine body length with age information to construct growth curves Bierlich et al., 2023 and Pirotta and Bierlich et al., 2024. In essence, measurements of known-sized objects (‘calibration objects’) collected at various altitudes are used as training data to predict morphological measurements (e.g., body length) and associated uncertainty of unknown-sized objects (e.g., whales). 

Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A. S., & Johnston, D. W. (2021). Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Marine Ecology Progress Series, 673, 193–210. https://doi.org/10.3354/meps13814

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., Dale, J., Goldbogen, J., Read, A. J., Calambokidis, J., & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.749943 

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: gray whales using an alternative foraging ground have smaller morphology. Biology Letters, 19(8). https://doi.org/10.1098/rsbl.2023.0043

Pirotta, E. & Bierlich, K. C.  New, L., Hildebrand, L., Bird, C. N., Fernandez Ajó, A., & Torres, L. G. (2024). Modeling individual growth reveals decreasing gray whale body length and correlations with ocean climate indices at multiple scales. Global Change Biology, 30(6). https://doi.org/10.1111/gcb.17366