I moved to Canada in 2008 to start a postdoctoral fellowship with Prof. Subhash Lele at the stats department of the University of Alberta. Subhash at the time just published a paper about a statistical technique called data cloning. Data cloning is a way to use Bayesian MCMC algorithms to do frequentist inference. Yes, you read that right.
In a recent paper entitled “Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada” we developed improved, spatially explicit models for 81 land bird species in northern Alberta, Canada. We then compared these estimates of bird abundance to a commonly-used but non-spatially explicit estimate by Partners in Flight (PIF v 3.0) that’s based on the North American Breeding Bird Survey (BBS) data set. The publication is a result of years of collaboration between the ABMI, Boreal Avian Modelling (BAM) project, Canadian Wildlife Service (Environment and Climate Change Canada), and United States Geological Survey.
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.
EDMAinR: Euclidean distance matrix analysis in R
moosecounter: Adaptive moose surveys
clickrup: Interacting with the ClickUp v2 API from R
WildLift: A Tool to Guide Decisions for Wildlife Conservation
deps: Dependency Management with roxygen-style Comments
rconfig: Manage R configuration at the command line
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
Leston, L., Dénes, F. V., Docherty, T. D. S., Tremblay, J. A., Boulanger, Y., Van Wilgenburg, S. L., Stralberg, D., Sólymos, P., Haché, S., St Laurent, K., Weeber, R., Drolet, B., Westwood, A. R., Hope, D. P., Ball, J., Song, S. J., Cumming, S. G., Bayne, E., Schmiegelow, F. K. A., 2024. A framework to support the identification of critical habitat for wide-ranging species at risk under climate change. Biodiversity and Conservation, 33: 603–628. — journal website — fulltext PDF.
Sólymos, P., 2023. Agent-based simulations improve abundance estimation. Biologia Futura, 74: 377–392. —
journal website —
fulltext PDF —
bSims R package.
Edwards, B. P. M., Smith, A. C., Docherty, T. D. S., Gahbauer, M. A., Gillespie, C. R., Grinde, A. R., Harmer, T., Iles, D. T., Matsuoka, S. M., Michel, N. L., Murray, A., Niemi, G. J., Pasher, J., Pavlacky Jr, D. C., Robinson, B. G., Ryder, T. B., Sólymos, P., Stralberg, D., and Zlonis, E. J., 2023. Point count offsets for estimating population sizes of North American landbirds. Ibis, 165: 482–503. — journal website — NA-POPS GitHub organization.
Leston, L., Bayne, E. M., Toms, J. D., Mahon, C. L., Crosby, A., Sólymos, P., Ball, J., Song, S. J., Schmiegelow, F. K. A., Stralberg, D., and Docherty, T. D. S., 2023. Comparing alternative methods of modelling cumulative effects of oil and gas footprint on boreal bird abundance. Landscape Ecology, 38: 147–168. — journal website.
Crosby, A. D., Leston, L., Bayne, E. M., Sólymos, P., Mahon, C. L., Toms, J. D., Docherty, T. D. S., and Song, S. J., 2023. Domains of scale in cumulative effects of energy sector development on boreal birds. Landscape Ecology, 38: 147–168. — journal website.