Use of North American Breeding Bird Survey Data to Estimate Population Change for Bird Conservation Regions

2003 ◽  
Vol 67 (2) ◽  
pp. 372 ◽  
Author(s):  
John R. Sauer ◽  
Jane E. Fallon ◽  
Rex Johnson
Keyword(s):  
Survey Data ◽  
North American ◽  
Population Change ◽  
Bird Conservation ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Bird Survey

scholarly journals Use of North American Breeding Bird Survey data in avian conservation assessments

The Condor ◽  
2017 ◽  
Vol 119 (3) ◽  
pp. 594-606 ◽  
Author(s):  
Kenneth V. Rosenberg ◽  
Peter J. Blancher ◽  
Jessica C. Stanton ◽  
Arvind O. Panjabi
Keyword(s):  
Survey Data ◽  
North American ◽  
Breeding Bird Survey ◽  
Avian Conservation ◽  
Breeding Bird ◽  
Bird Survey ◽  
Conservation Assessments

scholarly journals Estimating Population Trends With a Linear Model: Technical Comments

The Condor ◽  
2004 ◽  
Vol 106 (2) ◽  
pp. 435-440 ◽  
Author(s):  
John R. Sauer ◽  
William A. Link ◽  
J. Andrew Royle
Keyword(s):  
North American ◽  
Population Change ◽  
Survey Design ◽  
Population Trends ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
The North ◽  
Bird Survey ◽  
Substantial Bias ◽  
Modelo Lineal

Abstract Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider. Estimando Tendencias Poblacionales con un Modelo Lineal: Comentarios Técnicos Resumen. A veces ha surgido controversia sobre la necesidad de considerar las limitantes del diseño de muestreo al estimar cambios poblacionales a partir de datos de conteos de aves. Los análisis de muestreos como el Muestreo de Aves Reproductivas de América del Norte (North American Breeding Bird Survey [BBS]) pueden ser bastante complejos; es natural preguntarse si esta complejidad es necesaria, o si los aná lisis estadísticos son desmedidos. Bart et al. (2003) proponen un análisis muy simple que sólo involucra regresión lineal simple, y contrastan su enfoque con los procedimientos basados en modelos. Nosotros revisamos los supuestos implícitos en el método que ellos proponen y documentamos que estos supuestos no son probablemente válidos para muestreos tales como el BBS. Una limitante fundamental de un enfoque basado exclusivamente en el diseño es la ausencia de controles para factores que influencian la detección de aves en los sitios de muestreo. Mostramos que el hecho de no modelar los efectos del observador en los datos de muestreo lleva a sesgos substanciales en las estimaciones de las tendencias poblacionales de las 20 especies que Bart et al. (2003) usaron como la base de sus simulaciones a partir de datos del BBS. Finalmente, notamos que las simulaciones presentadas en Bart et al. (2003) no brindan una evaluación útil del método que proponen ni tampoco ofrecen una comparación vá lida para la alternativa de estimación de ecuaciones que ellos consideran.

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

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81867 ◽  
Author(s):  
Florent Bled ◽  
John Sauer ◽  
Keith Pardieck ◽  
Paul Doherty ◽  
J. Andrew Royle
Keyword(s):  
Survey Data ◽  
North American ◽  
Spatially Explicit ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Explicit Approach ◽  
Bird Survey

scholarly journals Estimation Of Annual Indexes of Abundance and Trend From The American Woodcock Singing-Ground Survey:  Comparison Of 4 Models

2021 ◽  
Author(s):  
John R Sauer ◽  
William A. Link ◽  
Mark E. Seamans ◽  
Rebecca D. Rau
Keyword(s):  
Population Change ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Alternative Models ◽  
American Woodcock ◽  
Ground Survey ◽  
Bird Survey ◽  
Leave One Out ◽  
S Model ◽  
D Model

Status and trends of American Woodcock Scolopax minor populations in the eastern and central US and Canada are monitored via a Singing-ground Survey , conducted just after sunset along roadsides in spring.  Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, eastern and central management regions, and survey-wide.  In recent years, a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S Model) has been used to model population change. Recently, alternative models have been proposed for analysis of Singing-ground Survey data.  Analysis of a similar roadside survey, the North American Breeding Bird Survey , has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey.  Here, we use leave-one-out cross validation to compare model fit for the present Singing-ground Survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t -distributed extra-Poisson overdispersion effects (H models) as opposed to normally-distributed extra-Poisson overdispersion.   Leave-one-out cross validation results indicate that the D model was favored by the Bayesian predictive information criterion but a pairwise t -test indicated that model D was not significantly better-fitting to Singing-ground Survey data than the S model.  The H models are not preferable to the alternatives with normally-distributed overdispersion.   All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change.  However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models.   We recommend use of the D model for future Singing-ground Survey analyses.

scholarly journals North American Breeding Bird Survey status and trend estimates to inform a wide-range of conservation needs, using a flexible Bayesian hierarchical generalized additive model

2020 ◽  
Author(s):  
Adam C. Smith ◽  
Brandon P.M. Edwards
Keyword(s):  
Statistical Model ◽  
North American ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Bayesian Hierarchical ◽  
The North ◽  
Smooth Component ◽  
Bird Survey ◽  
Wide Range ◽  
Predictive Fit

ABSTRACTThe status and trend estimates derived from the North American Breeding Bird Survey (BBS), are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g. inferences about long-term change, annual fluctuations, population cycles, recovery of once declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAM) for the BBS, which share information on the pattern of population change across a species’ range. We demonstrate the models and their benefits using data a selection of species; and we run a full cross-validation of the GAMs against two other models to compare predictive fit. The GAMs have better predictive fit than the standard model for all species studied here, and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the IUCN. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.LAY SUMMARYThe status and trend estimates derived from the North American Breeding Bird Survey are critical sources of information for bird conservation, but they are partly dependent on the statistical model used.We describe a model to estimate population status and trends from the North American Breeding Bird Survey data, using a Bayesian hierarchical generalized additive mixed-model that allows for flexible population trajectories and shares information on population change across a species’ range.The model generates estimates that are broadly useful for a wide range of common conservation applications, such as IUCN status assessments based on trends or changes in the rates of decline for species of concern; and the estimates have better or similar predictive accuracy to other models., and

scholarly journals Using the North American Breeding Bird Survey to assess broad-scale response of the continent's most imperiled avian community, grassland birds, to weather variability

The Condor ◽  
2016 ◽  
Vol 118 (3) ◽  
pp. 502-512 ◽  
Author(s):  
Jessica M. Gorzo ◽  
Anna M. Pidgeon ◽  
Wayne E. Thogmartin ◽  
Andrew J. Allstadt ◽  
Volker C. Radeloff ◽  
...  
Keyword(s):  
North American ◽  
Grassland Birds ◽  
Breeding Bird Survey ◽  
Weather Variability ◽  
Avian Community ◽  
Breeding Bird ◽  
The North ◽  
Scale Response ◽  
Bird Survey ◽  
Broad Scale
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