Partners in Flight (PIF) has been publishing population estimates for landbirds in Canada and the United States since 2004. These estimates have been widely used in support of species status assessments, conservation planning, and communicating the status of bird populations. However, broad uncertainty around many estimates and potential biases due to gaps in geographic and temporal coverage have partly limited their applicability. Determining the absolute size of wild bird populations requires adequate survey data and appropriately complex modeling of detection probability to transform field observations into estimates of density. We have developed an updated approach that integrates data-derived estimates of detectability for hundreds of species, range-wide relative abundance surfaces, and spatial information from the North American Breeding Bird Survey (BBS) into a formal Bayesian model. This improved method combines important elements of the existing PIF population estimates framework with a large database of structured observations that allow formal detectability estimates (NA-POPS), the hierarchical Bayesian count-based trend models applied to the BBS data, and high-resolution spatially explicit estimates of relative abundance from eBird. It calibrates the relative abundance surface of a species to represent its density on the landscape and thereby can generate population estimates, with associated uncertainties, for any custom area. This model also generates population estimates for a single year, improving on the previous estimates that were averaged over 10 years. This new method works well for territorial songbirds and other species effectively monitored using point count field methods and observations during the early morning hours. Other species such as colonially nesting birds, crepuscular or nocturnal species, and species with highly specific habitat requirements, will require further model refinement. PIF will use this revised model to publish estimates of population sizes for North American birds and will continue to improve the model in an open and reproducible way.

Bird population monitoring is often conducted using point-count surveys. Accounting for detection errors is a major challenge in analyzing these data. The commonly used methods for correcting detection errors in counts of organisms are distance sampling, removal sampling, N-mixture, and QPAD. These methods rely on multiple surveys or subdivisions within surveys (time/distance bins). The reliability of these approaches depends on the accurate estimation of distance, correct identification of individuals, and the closed population assumption. Errors in distance estimation, double counting, and mortality and migration of individuals within and between survey periods can lead to substantial biases in population density estimation. Furthermore, tracking individuals and estimating distances can be difficult in field conditions. We propose a simple modification of the QPAD method so that field observers are required to collect information only about either the occupancy status or count of individuals within a specified time interval and a specified spatial buffer, a “single bin,” around the observer’s location. We show that population density parameters are identifiable by changing the time interval and the radius of the spatial buffer for each survey location. We show that this variable-effort survey method is robust against errors in distance estimation and double counting. We also show that data collected under current protocols in North America can be analyzed using single bin QPAD. We illustrate our methodology with biologically realistic simulations and a reanalysis of some field data.

Breeding bird atlases play a crucial role in understanding bird species distribution and abundance during the breeding season. This information is essential for creating accurate species distribution maps, which are fundamental for understanding the geographic range of species and identifying areas of high conservation value. Our primary goal was to develop species distribution models (SDMs) for as many Minnesota breeding bird species as possible using data from the Minnesota Breeding Bird Atlas (MNBBA; 2009-2013). The MNBBA combined volunteer atlas observations with systematic point-count surveys, resulting in datasets that varied in structure and quality across species. Recognizing this variability, we applied multiple modeling approaches tailored to the available data, which also led to differing ecological interpretations. To maximize species coverage given heterogeneous data characteristics, we used three modeling strategies to maximize the number of species we modeled: (1) bootstrapped Poisson generalized linear models with a detectability offset to predict species' density and population size, (2) bootstrapped Poisson generalized linear models to predict a species' point count index of abundance, and (3) Maxent models to predict a species' index of environmental suitability. We applied the first strategy to 73 species, the second to 30, and the third to 33 species each (136 species in total). We also produced statewide population estimates for the 73 species using the first strategy. Our framework demonstrates that linking model choice to data structure significantly increases the number of species that can be modeled compared to a single-model approach. While these results serve as a foundation for broad-scale distribution and abundance hypotheses, we suggest this adaptable methodology be tested in other regions to maximize the utility of diverse atlas datasets.

Canada has begun an ambitious project to build an observing system to monitor the changing state of its biodiversity and ecosystems. A Canada-wide Biodiversity Observation Network (CAN BON) can support the measurement, mapping, and modelling of biodiversity change—the losses and gains in the diversity of plant, animal, and microbial life—and ecosystem services. This initiative responds to eight challenges presently constraining Canada's capacity to deliver timely and robust knowledge to achieve its biodiversity goals. CAN BON is conceived as a network connecting diverse organizations to support sustained biodiversity monitoring by collaboration among universities, museums, governments, industries, NGOs, community groups, and Indigenous organizations. This inclusive network will “mobilize monitoring data” to (1) combine observation and computing infrastructures and traditional knowledge to track and understand biodiversity losses and gains across the country; and (2) link the accumulated data and knowledge to models to inform the detection and attribution of biodiversity change needed to support biodiversity policy with forecasts from local to national levels. We expect that CAN BON will foster the mainstreaming of biodiversity data and knowledge into other sectors of the economy and society, and thereby support the technical and social innovation in Canada's transition to a nature-positive future.

Spatially explicit estimates of species abundance and distribution are increasingly needed to support conservation planning and management across multiple spatial scales. We present a generalized modeling framework that bridges the gap between local studies and regional to national planning by compiling and harmonizing diverse datasets to predict avian abundance at fine resolution and broad extent. We applied detectability offsets to integrate point-count data from over 250,000 locations across subarctic Canada. Data were subsampled by two time periods and 16 geographic regions, and we used boosted regression trees to model the density of 143 boreal landbird species as a function of climate, vegetation composition (local [250 m] and landscape [similar to 1.5 km]), land cover, and topography. Bootstrapped regional predictions were combined to generate density maps, region- and habitat-specific estimates, and Canada-wide population totals. We estimated similar to 3.56 billion breeding males (7.13 billion individuals), with most occurring in boreal and hemi-boreal regions. Forest generalists accounted for nearly half the total (1.57 billion males), followed by boreal specialists (1.05 billion), habitat generalists (350 million), and species associated with eastern forests (274 million), grasslands (124 million), western forests (74.7 million), wetlands (63.5 million), and Arctic tundra (17.7 million). Introduced species totaled 48.9 million breeding males. Across species, landscape-level vegetation composition explained most variation in abundance, indicating that climate effects are primarily indirect, operating through vegetation. Landscape-scale variables were critical to capturing this variation. Model classification accuracy was highest for forest- and grassland-associated species (lowest for mountain and urban species), and for the families Regulidae and Phasianidae (lowest for Bombycillidae and Paridae). This work provides a standardized, updatable, and reproducible workflow for generating spatially explicit bird abundance estimates. These products can be revised as new data become available and used to support ongoing conservation and land-use decisions.

To recover species at risk, it is necessary to identify habitat critical to their recovery. Challenges for species with large ranges (thousands of square kilometres) include delineating management unit boundaries within which habitat use differs from other units, along with assessing any differences among units in amounts of and threats to habitat over time. We developed a reproducible framework to support identification of critical habitat for wide-ranging species at risk. The framework (i) reviews species distribution and life history; (ii) delineates management units across the range; (iii) evaluates and compares current and (iv) potential future habitat and population size and (v) prioritizes areas within management units based on current and future conditions under various scenarios of climate change and land-use. We used Canada Warbler ( Cardellina canadensis ) and Wood Thrush ( Hylocichla mustelina ) in Canada as case studies. Using geographically weighted regression models and cluster analysis to measure spatial variation in model coefficients, we found geographic differences in habitat association only for Canada Warbler. Using other models to predict current habitat amount for each species in different management units, then future habitat amount under land use and climate change, we projected that: (1) Canada Warbler populations would decrease in Alberta but increase in Nova Scotia and (2) Wood Thrush populations would increase under most scenarios run in Quebec, New Brunswick and Nova Scotia, but not in Ontario. By comparing results from future scenarios and spatial prioritization exercises, our framework supports identification of critical habitat in ways that incorporate climate and land-use projections.

Abundance is a fundamental characteristic of every biological population and is the focus of many research programs in ecology and conservation. In this paper I give an overview of the challenges of estimating abundance. I argue that truly understanding, validating, and refining the field techniques and quantitative methods used to estimate abundance can largely benefit from agent-based simulations. I illustrate this through the example of bird point counts and introduce the software bSims to test statistical and biological assumptions for estimating abundance and to aid survey design.

Context Industrial development in Canada’s boreal forest creates cumulative environmental effects on biodiversity. Some effects may be scale-dependent, creating uncertainty in understanding and hindering effective management. Objectives We estimated cumulative effects of energy sector development on distributions of sixteen migratory songbird species at multiple spatial scales within the boreal region of Alberta, Canada, and evaluated evidence for scale domains in species responses. Methods We used a hierarchical, multi-scale sampling and modelling framework to compare effects of oil and gas footprint on songbirds at five spatial scales. We used Bayesian Lasso to facilitate direct comparison of parameter estimates across scales, and tested for differences in grouped parameter estimates among species. Results We found consistent scale-dependent patterns across species, showing variable responses to development occurring at the smallest scale, little effect at intermediate scales, and stronger, mainly positive effects at the largest scales. Differences in grouped parameter estimates across scales showed strong evidence for scale domains in the response of songbirds to energy sector development. Conclusions We concluded that variable effects at the smallest scale represented individual habitat selection, while larger scale positive effects reflected expanding distributions of open habitat- and disturbance-associated species in areas of high oil and gas footprint. Our results show that single-scale analyses do not reflect population processes occurring at other scales. Future research on linking patterns at different scales is required to fully understand cumulative effects of land use change on wildlife populations.

Bird monitoring in North America over several decades has generated many open databases, housing millions of structured and semi-structured bird observations. These provide the opportunity to estimate bird densities and population sizes, once variation in factors such as underlying field methods, timing, land cover, proximity to roads, and uneven spatial coverage are accounted for. To facilitate integration across databases, we introduce NA-POPS: Point Count Offsets for Population Sizes of North American Landbirds. NA-POPS is a large-scale, multi-agency project providing an open-source database of detectability functions for all North American landbirds. These detectability functions allow the integration of data from across disparate survey methods using the QPAD approach, which considers the probability of detection (q) and availability (p) of birds in relation to area (a) and density (d). To date, NA-POPS has compiled over 7.1 million data points spanning 292 projects from across North America, and produced detectability functions for 338 landbird species. Here, we describe the methods used to curate these data and generate these detectability functions, as well as the open-access nature of the resulting database.

Context Oil and gas activity is increasing in the western boreal forest of North America. To manage cumulative effects of this industry, a better quantification of footprint effects on wildlife is needed. Objectives We used point-count surveys to evaluate how well footprint amounts within 150 m, and proximity to seismic lines, pipelines, well sites, roads, and energy facilities predicted the abundance of 48 bird species. Methods We developed models for each species, evaluating the best functional forms for different footprint effects, then predicted how different model structures influenced estimates of regional population size. Results Most species exhibited at least one nonlinear response to footprint amount (79% of species) or distance (88%). Species associated with older coniferous forests decreased more often with increased footprint amount and closer proximity to footprint, while species associated with open lands and young forests increased with greater footprint and closer proximity. In one-third of species, bird abundance versus distance changed from a positive to negative relationship (or reverse) at a threshold distance from at least one footprint. Models based on footprint proximity had better fit than those based on footprint amount for 29 of 48 species, but both model types produced similar population estimates. Conclusions Both footprint amount and proximity models were useful for assessing cumulative effects on wildlife and mechanisms causing change. Models of distance to footprint can provide evidence of positive or negative edge effects for developing management buffers. Models of footprint amount provide important information on functional changes in habitat.

Context Movement is one of the proposed explanations for the scale at which a species responds most strongly to its environment, or the "scale of effect". Scarcity of empirical evidence for this hypothesis may be because studies determine scale of effect for individual environmental variables; however, seasonal movement is the product of reactions to multiple variables. Objectives We predicted scale of effect should correspond to movement range for the most predictive single scale habitat model with multiple predictors ("overall scale of effect"), but not for individual predictors. Methods We used passive acoustic monitoring and machine learning to model home range and territorial (i.e., defended area) habitat for the common nighthawk. We modeled extents from 0.1 to 12.8 km to determine the overall scale of effect. We used the scale of effect for each predictor to build optimized models and evaluated their spatial predictive performance. Results The overall scale of effect was 0.2 km for territorial habitat and 1.6 km for home range habitat, which roughly equate to territory and home range size. The scale of effect for individual predictors that explained the most deviance did not correspond to overall scale of effect. Models with each predictor at its scale of effect offered no substantial improvements in predictive performance relative to overall scale of effect models. Conclusions Our new perspective on scale of effect suggests that different mechanisms drive overall scale of effect and scale of effect of individual variables. Further research should revisit the relationship between movement and scale of effect in pursuit of a mechanistic framework for prediction.

Emulating natural disturbance (END) patterns to conserve biodiversity is often a goal of forest management but the impact of forest cutting is still controversial. We examined effects on avian assemblages of partially-cut forest stands of 1-10, 11-20 and > 45 years (cut classes) post-harvest differing in the % basal area retained as wildlife species' habitat in 85 shade-tolerant hardwood stands in Algonquin Park, Ontario. Permutational multivariate analysis of variance indicated significant compositional differences among cut classes, after controlling for year and township (spatial variation in surficial geology and topography), both of which were also significant. Pairwise comparisons indicated significant compositional differences only between young and old stands. The difference in beta diversity (homogenization/differentiation) among cut classes was marginally significant (P = 0.066). Pairwise differences were significant between old versus mid-aged cut classes, marginally significant for young versus old cuts, and non-significant for young versus mid-aged cut classes. Six species and cavity users as a group were more abundant in > 45 years post-harvest stands than in the other two cut classes. Six species had higher abundances in younger cuts than > 45 years post-harvest cuts. Red-eyed vireo (Vireo olivaceus) showed a nonlinear response and was least abundant in 11-20 year cuts. Blackburnian warblers Setophaga fusca, black-throated green warblers S. virens and pileated woodpeckers Dryocopus pileatus increased with the density of supercanopy trees (>= 60 cm diameter at breast height - dbh) and in the case of pileated woodpeckers more large snags (>= 40 cm dbh). Brown creeper Certhia americana abundance increased with greater basal area of trees >= 25 cm dbh and ovenbird numbers were significantly related to overall basal area. Chestnut-sided warbler S. castanea abundance decreased with an increase in overall basal area and was higher in the 1-10 year cuts and American redstarts S. ruticilla decreased with an increase in basal area. Our modelling suggests that effects of selection cutting systems on the avian community may be reduced as tolerant hardwood stands become regulated and have higher residual basal areas. Current guidelines in Ontario for the retention of large, scattered conifer trees appear to meet the requirements for most, but not all, species; however, guidelines for supercanopy trees are too low. These results indicate that the END paradigm for single-tree selection cutting in tolerant hardwoods may be partially effective but requires modifications, such as retaining higher densities of supercanopy trees that would be beneficial for birds.

Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and timeof-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America.

Aim Climate change is expected to influence boreal bird communities significantly, notably through changes in forest habitat (composition and age structure), in the coming decades. How these changes will accumulate and interact with anthropogenic disturbances remains an open question for most species. Location Northeastern Alberta, Canada. Methods We used the LANDIS-II forest landscape model to project changes in forest landscapes, and associated bird populations (72 passerine species), according to three climatic scenarios (baseline, RCP 4.5 and RCP 8.5) and three forest harvesting scenarios of differing intensity. Results Both forest harvesting and climate-related drivers were projected to have large impacts on bird communities in this region. As a result of climate-induced increases in fire activity as well as decreased conifer productivity, our simulations projected that an important proportion of Alberta's boreal forests would transition to treeless habitat (i.e. grass- or shrub-dominated vegetation) while many conifer-dominated stands would likely be replaced by broadleaf tree cover. Consequently, the abundance of bird species associated with open and deciduous habitats were projected to increase. With a strong anthropogenic climate-forcing scenario (RCP 8.5), sharp declines in abundance of coniferous trees were also projected, particularly in mature and old forest stands, triggering major declines for bird species associated with coniferous and mixedwood forest types. Main conclusions As the most comprehensive simulation of climate change and harvesting impacts on avian habitats in the North American boreal region to date, our study stresses the importance of considering key habitat characteristics like forest age structure and composition through forest landscape modelling and identifies 18 bird species particularly sensitive to climate change. Our simulations suggest that a change in forest management practices could play an important role in the conservation of boreal bird species vulnerable to climate change. The intensive forest harvesting simulated accelerated declines in bird abundance compared to a "no harvesting" scenario.

A recurring challenge for resource managers and decision makers is quantifying the trade-offs associated with alternative recovery actions for threatened species. Structured decision-making approaches can help evaluate such complex problems by formalizing objectives and constraints into functions that quantify the benefits and costs associated with each action. Yet, many of the scientific tools necessary to implement structured decision making require extensive literature review and often involve complex algorithms that make them inaccessible to managers. To address these issues, we integrated available information and developed a decision-support tool that managers can readily use to compare costs and benefits associated with alternative recovery actions for threatened species. Our software can be used to quantitatively estimate and compare the costs and demographic benefits of recovery actions for an iconic threatened species, woodland caribou (Rangifer tarandus caribou). While we use caribou as a case study, our approach to developing this management tool is transferable to other threatened taxa. The tool consists of a generalized matrix population model that is parametrized based on information from the published literature or ongoing experiments. Users can input population parameters (e.g., population size and survival rates) or choose from pre-set caribou subpopulations to estimate changes to populations from implementing recovery actions. The tool estimates the trade-offs associated with seven alternative recovery actions: linear feature restoration (LFR), linear feature deactivation (LFD), maternal penning (MP), conservation breeding (CB), predator exclosure (PE), wolf reduction (WR), and moose reduction (MR). We demonstrate our software by comparing recovery actions for the East Side Athabasca River caribou subpopulation and discuss how this tool can be used under a structured decision-making framework. This case study suggests that our open-source tool can be useful to guide wildlife conservation decisions by explicitly estimating costs and benefits associated with recovery actions, which ultimately helps to bridge the gap between management and science via increased accessible application of current knowledge.

There is interest in linking outputs from land use simulators to bird species distribution models to project how boreal birds will respond to cumulative effects of caribou (Rangifer tarandus) conservation, harvest, fire, and energy-sector development in Alberta. Our hypotheses were: (1) species associated with older mixed-wood stands would decline more if harvest was shifted away from areas used by caribou to areas with more mixed-wood; and (2) species associated with older forests would be more negatively affected by the combined effects of harvest, fire, and non-forestry footprint than by harvest alone. We used vegetation data from two harvest scenarios produced in Patchworks as inputs for density models of 20 boreal forest songbird and woodpecker species in Alberta. We projected abundance of these species over 50 years under: 1) two scenarios created in Patchworks, without fire but with and without deferral of timber harvest within a caribou conservation zone on lands tenured to Alberta-Pacific Forest Industries Inc.; (2) a scenario with fire but no human footprint; and (3) five scenarios in ALCES Online, in which habitat was affected by Patchworks harvest locations, fire (1–2 × current rate), and energy sector development (present or absent; with or without seismic line reclamation to improve caribou habitat). In the Patchworks scenarios, we found similar projected numbers of each bird species over time, whether harvest deferral occurred or not. Both harvest plans increased habitat and numbers for most species associated with older forests over 50 years, while most species associated with younger forests declined in both harvest plans, because average projected forest age increased over 50 years. Fire and other footprint generally reduced relative amount of habitat for species associated with older forests, which still increased over time, while other species responded positively or less negatively to fire. Seismic restoration created habitat for three-quarters of species that responded negatively to energy sector development over 50 years. As projections depended on whether just harvest, fire or all footprints were analyzed, multiple human impacts over time beyond harvest should be considered in conservation and land use planning based on long-term predictions about wildlife in anthropogenic landscapes.

Reliable information on species' population sizes, trends, habitat associations, and distributions is important for conservation and land-use planning, as well as status assessment and recovery planning for species at risk. However, the development of such estimates at a national scale is challenged by a variety of factors, including sparse data coverage in remote regions, differential habitat selection across large geographies, and variation in survey protocols. With these factors in mind, we developed a generalized analytical approach to model species density in relation to environmental covariates, using the Boreal Avian Modelling Project database of point-count surveys (through 2018) and widely available spatial predictors. We developed separate models for each geographic region (bird conservation regions intersected by jurisdiction boundaries) based on covariates such as tree species biomass (local and landscape scale), forest age, topography, land use, and climate. We used machine learning to allow for variable interactions and non-linear responses while avoiding time-consuming species-by-species parameterization. We applied cross-validation to avoid overfitting and bootstrap resampling to estimate uncertainty associated with our density estimates. Results available at https://borealbirds.github.io/