The bSims R package is a highly scientific and utterly addictive bird point count simulator. Highly scientific, because it implements a spatially explicit mechanistic simulation that is based on statistical models widely used in bird point count analysis (i.e. removal models, distance sampling), and utterly addictive because the implementation is designed to allow rapid interactive exploration (via shiny apps) and efficient simulation (supporting various parallel backends), thus elevating the user experience.
In a paper recently published in the Condor, titled Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity, we assessed different ways of controlling for point-count duration in bird counts using data from the Boreal Avian Modelling Project. As the title indicates, the paper describes a cost-benefit analysis to make recommendations about when to use different types of the removal model. The paper is open access, so feel free to read the whole paper here.
The intrval R package is lightweight (~11K), standalone (apart from importing from graphics, has exactly 0 non-base dependency), and it has a very narrow scope: it implements relational operators for intervals — very well aligned with the tiny manifesto. In this post we will explore the use of the package in two shiny apps with sliders.
It all started with this paper in Methods in Ecol. Evol. where we looked at detectability of many species. So we wanted to use life history traits to validate our results. But we had to cut the manuscript, and there was this leftover with some neat patterns, but without much focus. It took a few years, and the most positive peer-review experience ever, and the paper is now early view in Ecography. This post is a quick summary of the goodies stuffed inside the lhreg R package that makes the whole analysis reproducible, and provides some functions for similar PGLMM models.
A friend and colleague of mine, Péter Batáry has circulated news from Nature magazine about the EU freezing innovation funds to Bulgaria. The article had a figure about publication trends for Bulgaria, compared with Romania and Hungary. As I have blogged about such trends in ecology before (here and here), I felt the need to update my PVA models with two years worth of data from WoS.
The pbapply R package that adds progress bar to vectorized functions has been know to accumulate overhead when calling
parallel::mclapply with forking (see this post for more background on the issue). Strangely enough, a GitHub issue held the key to the solution that I am going to outline below. Long story short: forking is no longer expensive with pbapply, and as it turns out, it never was.
bSims: Bird point count simulator
opticut: Likelihood based optimal partitioning for indicator species analysis
intrval: Relational operators for intervals
pbapply: Adding progress bar to '*apply' functions
vegan: Community ecology package
ResourceSelection: Resource selection (probability) functions for use-availability data
mefa4: Multivariate data handling with S4 classes and sparse matrices
detect: Analyzing wildlife data with detection error
dclone: Data cloning and MCMC tools for maximum likelihood methods
dcmle: Hierarchical models made easy with data cloning
PVAClone: Population viability analysis with data cloning
sharx: Models and data sets for the study of species-area relationships
mefa: Multivariate data handling in ecology and biogeography
Knight, E. C, Sólymos, P., Scott, C., and Bayne, E. M., 2020. Validation prediction: a flexible protocol to increase efficiency of automated acoustic processing for wildlife research. Ecological Applications, xx: xx–xx..
Tremblay, J. A., Boulanger, Y., Cadieux, P., Cyr, D., Taylor, A. R., Price, D. T., Stralberg, D., and Sólymos, P., 2020. Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada. Diversity and Distributions, xx: xx–xx. — journal website — fulltext PDF.
Sólymos, P., Toms, J. D., Matsuoka, S. M., Cumming, S. G., Barker, N. K. S., Thogmartin, W. E., Stralberg, D., Crosby, A. D., Dénes, F. V., Haché, S., Mahon, C. L., Schmiegelow, F. K. A., and Bayne, E. M., 2020. Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada. Condor, xx: xx–xx. — supporting material.
Roy, C., Michel, N., Handel, C., Van Wilgenburg, S., Burkhalter, J., Gurney, K., Messmer, D., Princé, K., Rushing, C., Saracco, J., Schuster, R., Smith, A. C., Smith, P. A., Sólymos, P., Venier, L., and Zuckerberg, B., 2019. Monitoring boreal avian populations: how can we estimate trends and trajectories from noisy data? Avian Conservation and Ecology, 14(2): 8. — journal website — fulltext PDF.
Yip, D. A., Knight, E. C., Haave-Audet, E., Wilson, S. J., Charchuk, C., Scott, C. D., Sólymos, P., and Bayne, E. M., 2019. Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations. Remote Sensing in Ecology and Conservation, xx: xx–xx. — journal website — fulltext PDF.