This course is for researchers who analyze field observations. These data are often inconsistent because sampling protocols can change across projects, over time, or when older data are combined with new recordings.
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.
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
tryr: Client/Server Error Handling for HTTP API Frameworks
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
Walton, N. G., Zlonis, E. J., Sólymos, P., Grinde, A. R., and Niemi, G. J., 2026. Species distribution models and abundance estimates enhance breeding bird atlas data. Ecology and Evolution, 16:e73808. — fulltext PDF.
Gahbauer, M. A., Smith, A. C., Edwards, B., Iles, D. T., Knight, E. C., Robinson, B. G., Rosenberg, K. V., Sólymos, P., Toms, J. D., and Van Wilgenburg, S. L., 2026. Refining population estimates for North American birds: reducing bias from imperfect detection, spatial sampling, and temporal change. Avian Conservation and Ecology, 21(1):25. — fulltext PDF.
Van Wilgenburg, S. L., Ball, J. R., Barker, N. K. S., Casey, B. G., Crosby, A., Cumming, S. G., Docherty, T. D. S., Drake, A. V., Fontaine, T., Haché, S., Houle, M., Leston, L. F. V., Mahon, C. L., Schmiegelow, F. K. A., Sólymos, P., Song, S. J., Stralberg, D., Toms, J. D., Tremblay, J. A., Yip, D. A., Bayne, E. M., and Knight, E. C., 2026. Twenty years of progress in boreal avian conservation: A history and path forward for the Boreal Avian Modelling project. Avian Conservation and Ecology, xx: xx–xx..
Lele, S. R., Sólymos, P., 2026. Single bin QPAD (SQPAD) approach for robust analysis of point count data with detection error. Ornithological Applications, 128: 1–14. —
fulltext PDF —
bSims and detect R packages —
GitHub repository.
Gonzalez, A., O'Connor, M. I., Bates, A. E., Bobiwash, K., Burton, A. C., van Dam-Bates, P., Eckert, I., Gravel, D., Idrobo, C. J., Pollock, L., Simon, A. D. F., Slein, M. A., Sólymos, P., Starzomski, B. M., Sunday, J., Tekwa, E., 2025. A Biodiversity Observation Network to support conservation action and mainstream knowledge in Canada. FACETS, 10: 1–9. — fulltext PDF.
Stralberg, D., Sólymos, P., Docherty, T. D. S., Crosby, A. D., Van Wilgenburg, S. L., Knight, E. C., Drake, A., Boehm, M. M. A., Haché, S., Leston, L., Toms, J. D., Ball, J. R., Song, S. J., Schmiegelow, F. K. A., Cumming, S. C., Bayne, E. M., 2025. A generalized modeling framework for spatially extensive species abundance prediction and population estimation. Ecosphere, e70405. — fulltext PDF — GitHub repository.
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.
See all publications here.