Modeling Trends from North American Breeding Bird Survey Data: A Spatially Explicit Approach

Abstract

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

“…Because sample allocation within the BBS is currently based on pseudorandom sampling of latitude/longitude degree blocks (Bled et al 2013), we quantified the total area of forest loss, forest fire, and anthropogenic disturbance in polygons representing BBS degree blocks using the same zonal statistics (methods as described above), where zones were represented by degree blocks. We also conducted the same analyses within a 400-m buffer around the entire road network from road-segment shapefiles from the CanVec database (Natural Resources Canada 2011), as well as within polygons defining individual BBS strata that are defined by the intersection of provincial boundaries and BCRs (see Fig.…”

Distribution of priority grassland bird habitats in the Prairie Pothole Region of Canada

“…Observers follow set routes that are 39.4 km and conduct 3-minute point counts at each of 50 stops (i.e., one stop every 0.8 km) following standardized protocols. These data are used extensively to assess population trends (e.g., Amano et al 2012, Bled et al 2013, Schipper et al 2016) and for the development of species distribution models (e.g., BarbetMassin and Jetz 2014, Goetz et al 2014, Steen et al 2014. We obtained Breeding Bird Survey route and stop locations by request from the Canadian Wildlife Service (CWS).…”

The Relative Importance of Three Specific Climatic Factors on North American Breeding Bird Species Richness

“…Understanding of the interactions between various bird species and their environments is increasingly highlighted (e.g., Sauer et al, 1994;Link and Sauer, 1998;Bled et al, 2013;Goetz et al, 2014), especially in the context of global change. The relevant studies have been conducted in many fields concerning avian ecology, since the scientific inferences facilitate enhancing many studies and managements, e.g., guiding more effective policymaking for biodiversity conservation (Bellard et al, 2012).…”

“…Now, progress in remote sensing and ecological modelling can support a timely and robust analysis of more complicated ecological variables, such as 3D habitat structures that underlie large-scale patterns of breeding bird species richness (e.g., Bled et al, 2013;Goetz et al, 2014). However, the effects of the hydrology-typed climatic factors like precipitation, evapotranspiration and vapor pressure on bird species richness have not been fully examined.…”