© Benoît Audet

NA-POPS: Point Count Offsets for Population Sizes of North America Landbirds

© Benoît Audet

BAM is a primary partner on a new project to generate open-source detectability offsets for all North American landbirds. These offsets will allow for the quantitative integration of observations from different programs and field protocols. 

This project is a collaborative effort with the Canadian Wildlife Service (CWS), Boreal Avian Modeling Project (BAM), Canadian Forest Service (CFS), Partners in Flight Science Committee (PIF), and more. 

Predicting current and future abundance of breeding waterfowl across Canada

Predicting current and future abundance of breeding waterfowl across Canada

 

Mapping the abundance and distribution of 18 species of waterfowl in Canada.

Canard pilet. Northern Pintail (Anas acuta). Photo Credit: Robin Besançon

Project Summary

The aim of this project was to develop predictive statistical models for mapping the abundance and distribution of waterfowl species across Canada. This project improves on the Canadian breeding waterfowl models constructed by Barker et al. (2014) by (a) developing new, more interpretable statistical models that (b) explicitly account for spatiotemporal variations in waterfowl abundance, while (c) testing for associations with an updated suite of habitat covariates. Furthermore, as breeding areas are becoming warmer and wetter, climatic changes are likely to affect the distributions of millions of waterfowl. This study also assessed the potential effects of climate change on the distribution and abundance of waterfowl species using a climate envelope modeling approach.

In 2019, a review of the literature was conducted that summarized environmental variables known to affect breeding duck distribution and abundance in northern North America. This review was published in ÉcoscienceFollowing this review, models were built for mapping the spatio-temporal abundance of 18 waterfowl species at a pan-Canadian level over a 25 year period. This work was published in Diversity & Distributions and is available as a data product.

Lastly, this project assessed the potential effects of climate change on the breeding distribution and abundance of 12 common waterfowl species in Eastern Canada. This work was published in Climatic Change in August 2020.

 

Applications

Possible applications of the resulting models and maps include the development and evaluation of biodiversity indicators and conservation planning strategies. See the data products below to use these models and maps. 

 

Project Partners

This project was led by Antoine Adde for his PhD. This research continues an 11-year collaboration between Steve Cumming and Marcel Darveau of Ducks Unlimited Canada to estimate the abundance of waterfowl across Canada and extends BAM’s taxonomic scope to waterfowl.  For more information please contact us. 

Publications

Adde, A., Darveau, M., Barker, N., Cumming, S., 2020. Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach. Divers Distrib ddi.13129. https://doi.org/10.1111/ddi.13129

Adde, A., Darveau, M., Barker, N., Imbeau, L., Cumming, S., 2020. Environmental covariates for modelling the distribution and abundance of breeding ducks in northern North America: a review. Écoscience 1–20. https://doi.org/10.1080/11956860.2020.1802933

Adde, A., Stralberg, D., Logan, T., Lepage, C., Cumming, S., Darveau, M., 2020. Projected effects of climate change on the distribution and abundance of breeding waterfowl in Eastern Canada. Climatic Change. https://doi.org/10.1007/s10584-020-02829-9

Data Products

Canada-wide density
Version 2 (2020): Current density of 18 breeding waterfowl species across Canada using hierarchical generalized linear models.
   
Regional future density
Eastern Canada (2020): Future density projections of the distribution and abundance of breeding waterfowl in Eastern Canada.

New Canada-wide land bird density estimates (version 4.0)

New Canada-wide land bird density estimates (version 4.0)

 

Population sizes, habitat associations, and distributions for 143 landbird species to support status assessment, regional planning, conservation prioritization, and recovery of species at risk.

Density map of Canada Warbler (average density, males/ha)

PROJECT SUMMARY

In 2020, BAM launched version 4.0 of our Canada-wide density models for 143 species of landbirds. 

The development of national-scale products is challenged by sparse data in remote regions, complex species' responses to environmental factors, regional variation in habitat selection and more. However, reliable information on species’ population sizes, trends, habitat associations, and distributions is important for conservation planning and management.

To support avian conservation in Canada, BAM developed a generalized analytical approach to model species densities in relation to environmental covariates. We used the BAM database and built models for 143 species.  Learn more about these methods and models.

DATA PRODUCTS

We provide data and maps of population sizes, habitat associations, and distributions for 143 landbird species. We provide our density results as 1 km² resolution raster layers, which are used to calculate population sizes and regional habitat associations.

WEBINAR

Watch a video to learn more about this modelling approach, how to discover the data products, and future applications of this work. 

Comparing spatially explicit models (PIX) and the Partners in Flight (PIF) approach to estimate population sizes

Comparing spatially explicit models (PIX) and the Partners in Flight (PIF) approaches to estimate population sizes

 

A collaborative project that developed spatially explicit boreal bird models in Alberta, Canada, to inform continental bird conservation.

 

Summary

For nearly two decades, BAM has worked to develop robust methods for estimating population sizes of North American boreal birds. In 2019-20, we continued to work on a project comparing population estimates of boreal birds in Alberta, derived from spatially explicit models and the Partners in Flight approach, which applies adjustments to North American Breeding Bird Survey (BBS) counts to get population estimates. We also quantified the effects of detectability, roadside bias, and other factors on these population estimates (see Box 1 for more details). In 2020, this work was published in The Condor.

Partners:

This work was a collaborative effort with the Alberta Biodiversity Monitoring Institute (ABMI), Canadian Wildlife Service (Environment and Climate Change Canada), and United States Geological Survey.

 

Publication: 

Sólymos et al. (2020). Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada The Condor 122. https://doi.org/10.1093/condor/duaa007

 

Blog Post: 

Made in Alberta models help continental bird conservation, ABMI, June 17, 2020

 

Media Coverage: 

CBC News: Survey estimates much higher Alberta bird populations than thought, Jun 21, 2020

Estimating Trends of Boreal Bird Populations

Estimating Trends of Boreal Bird Populations

 

Using the BAM database to advance methods for estimating population trends for landbird species in North America. 

Palm warbler, Paruline à couronne rousse (Dendroica palmarum)

Project Summary

Given poor coverage of Breeding Bird Survey (BBS) data in the boreal region, we have looked to other sources of data to augment the BBS for the purposes of trend estimation. Although the BAM dataset is ad-hoc and temporally sparse, it has grown substantially in extent and duration, allowing us to explore several hybrid methods for using it — in conjunction with BBS data — to estimate population trends. In the last year, with input from Environment Canada and Climate Change (ECCC) statisticians and biologists, we have identified several options for further exploration, one of which is a direct extension of our generalized national models. In regions not well represented by BBS, this relatively robust approach can provide an alternative to estimates based solely on repeated BBS counts. It is based on the development of spatio-temporal abundance models that combine data from multiple years to quantify habitat relationships, while considering inter-annual variation in abundance. These new generalized national models (see previous section, page 10) were separately constructed for each BCR sub-region within Canada (south of the Arctic) and hemi-boreal portions of the United States, thus ensuring that annual density estimates are regionally relevant.

To balance spatio-temporal coverage of input data and thereby limit the influence of sampling bias, we spatially and temporally stratified and subsampled the data for modeling purposes. We also controlled for the effects of spatial and temporal variation on abundance by including sources of temporal (sampling year) and spatial (climate, terrain, and vegetation) variation as direct covariates in our models. The boosted regression tree modelling approach that we used captures non-linear and interactive habitat relationships, thus resulting in relatively fine-scale (1-km resolution), spatially heterogeneous, annual predictions. These annual, pixel-level estimates can then be “rolled up” to estimate trends for a variety of different geographies and time periods. Trends are based on a combination of direct predicted changes as a function of changes in “habitat supply” (vegetation) over time, and unexplained (“residual”) variation in abundance that may be attributed to a variety of potential (unmapped) predictors, including wintering ground and migration conditions.

Further testing is still required, but preliminary results suggest that BAM data provide the best available trend estimates in areas not well-sampled by the BBS. We are working to identify the specific areas for which BAM trend estimates constitute an improvement over BBS-based trends. 

This work is led by Diana Stralberg and Péter Sólymos. For more information please contact us.