README.md

In pjbouchet/espresso: Bayesian Inference For Multi-Species Behavioural Dose-response Functions

Multi-species Bayesian dose-response models

espresso stands for Estimating Shared Patterns of RESponsiveness to Navy SOnar, and was designed as a toolkit for fitting and selecting among behavioural dose-response functions in cetaceans exposed to anthropogenic sound.

Rationale

This work builds upon previous research completed under the U.S. Navy-funded MOCHA project (Harris et al. 2016, 2018), in which Bayesian hierarchical models were developed to estimate the probabilities of noise-related behavioural impacts to individual marine mammals, whilst accounting for uncertainty and the effects of contextual covariates (Miller et al. 2014; Antunes et al. 2014). The current modelling framework is implemented in the Bayesian analysis software JAGS (https://mcmc-jags.sourceforge.io/), and relies on Gibbs Variable Selection (O’Hara and Sillanpää 2009) to identify groups of species exhibiting similar patterns of responsiveness to impulsive sound stimuli. However, this approach proves computationally intractable for more than a few species and/or covariates. espresso uses a bespoke dimension-jumping reversible-jump Markov chain Monte Carlo algorithm (rjMCMC, Green 1995; Hastie and Green 2012) to relax these constraints and allow species groupings to be identified in an objective, data-driven way. The package also accommodates: (1) the selection of any number of explanatory covariates (e.g., sonar frequency, previous history of exposure, feeding behaviour, source-whale range), (2) the comparison of dose-response functional forms (i.e., monophasic or biphasic [soon to be released]), and (3) the appropriate treatment of both left- and right-censored observations (i.e., animals which display either an immediate response on first exposure, or no signs of response across the array of doses received, respectively).

Getting started

If you are just getting started with espresso, we recommend reading the tutorial vignette, which provides a quick introduction to the package.

Installation

Install the GitHub development version to access the latest features and patches.

# install.packages("remotes")
remotes::install_github("pjbouchet/espresso") # OR

# install.packages("devtools")
devtools::install_github("pjbouchet/espresso")

The package relies on compiled code (C++) and functionalities provided by the Rcpp package. The Rtools software may be needed on Windows machines. Installation instructions can be found at https://cran.r-project.org/bin/windows/Rtools/rtools40.html.

References

Antunes, R., P. H. Kvadsheim, F. P. A. Lam, P. L. Tyack, L. Thomas, P. J. Wensveen, and P. J. O. Miller. 2014. “High thresholds for avoidance of sonar by free-ranging long-finned pilot whales (Globicephala melas).” *Mar. Pollut. Bull.* 83 (1): 165–80. .

Green, Peter J. 1995. “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination.” *Biometrika* 82 (4): 711–32. .

Harris, Catriona M., Len Thomas, Erin A. Falcone, John Hildebrand, Dorian Houser, Petter H. Kvadsheim, Frans-Peter A. Lam, et al. 2018. “Marine mammals and sonar: Dose-response studies, the risk-disturbance hypothesis and the role of exposure context.” *J. Appl. Ecol.* 55 (1): 396–404. .

Harris, Catriona M., Len Thomas, Dina Sadykova, Stacy L. DeRuiter, Peter L. Tyack, Brandon L. Southall, Andrew J. Read, and Patrick J. O. Miller. 2016. “The Challenges of Analyzing Behavioral Response Study Data: An Overview of the MOCHA (Multi-study OCean Acoustics Human Effects Analysis) Project.” In *The Effects of Noise on Aquatic Life II*, 399–407. New York, USA. .

Hastie, David I., and Peter J. Green. 2012. “Model choice using reversible jump Markov chain Monte Carlo.” *Stat. Neerl.* 66 (3): 309–38. .

Miller, Patrick J. O., Ricardo N. Antunes, Paul J. Wensveen, Filipa I. P. Samarra, Ana Catarina Alves, Peter L. Tyack, Petter H. Kvadsheim, et al. 2014. “Dose-response relationships for the onset of avoidance of sonar by free-ranging killer whales.” *J. Acoust. Soc. Am.* 135 (2): 975. .

O’Hara, Robert B, and Mikko J Sillanpää. 2009. “A Review of Bayesian Variable Selection Methods: What, How and Which.” *Bayesian Analysis* 4 (1): 85–117.

pjbouchet/espresso documentation built on July 27, 2024, 12:31 p.m.