scholarly journals bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Brandon P. M. Edwards ◽  
Adam C. Smith
Keyword(s):  
Bayesian Analysis ◽  
Survey Data ◽  
North American ◽  
R Package ◽  
Hierarchical Bayesian ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Hierarchical Bayesian Analysis ◽  
Bird Survey

scholarly journals bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data

2020 ◽  
Author(s):  
Brandon P.M. Edwards ◽  
Adam C. Smith
Keyword(s):  
North American ◽  
Monitoring Program ◽  
Bird Species ◽  
R Package ◽  
The United States ◽  
United States Geological Survey ◽  
Hierarchical Bayesian ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Bird Survey

AbstractThe North American Breeding Bird Survey (BBS) is the primary ecological monitoring program used to assess the population, status, and trend of North American birds. As such, accessible analysis of BBS data is crucial to wildlife conservation/management and ecological science in North America. The R package bbsBayes was developed as a wrapper for the analysis of BBS data using hierarchical Bayesian models, including the models currently used by the Canadian Wildlife Service and the United States Geological Survey. The goal of bbsBayes is to provide an accessible package for anyone in the conservation community to estimate population trajectories (time-series) and trends (rates of change) for any of the 400+ bird species monitored by the BBS, and to allow more advance users to easily access the data and model-templates necessary to customize an analysis for their research.

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 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 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

scholarly journals Predicting Species' Geographic Distributions Based on Ecological Niche Modeling

The Condor ◽  
2001 ◽  
Vol 103 (3) ◽  
pp. 599-605 ◽  
Author(s):  
A. Townsend Peterson
Keyword(s):  
North American ◽  
Model Building ◽  
Ecological Niche Modeling ◽  
Predictive Ability ◽  
Biased Sampling ◽  
Ecological Niches ◽  
Breeding Bird Survey ◽  
Breeding Bird ◽  
Bird Survey ◽  
Geographic Distributions

Abstract Recent developments in geographic information systems and their application to conservation biology open doors to exciting new synthetic analyses. Exploration of these possibilities, however, is limited by the quality of information available: most biodiversity data are incomplete and characterized by biased sampling. Inferential procedures that provide robust and reliable predictions of species' geographic distributions thus become critical to biodiversity analyses. In this contribution, models of species' ecological niches are developed using an artificial-intelligence algorithm, and projected onto geography to predict species' distributions. To test the validity of this approach, I used North American Breeding Bird Survey data, with large sample sizes for many species. I omitted randomly selected states from model building, and tested models using the omitted states. For the 34 species tested, all predictions were highly statistically significant (all P < 0.001), indicating excellent predictive ability. This inferential capacity opens doors to many synthetic analyses based on primary point occurrence data. Predicción de Áreas de Distribución de Especies con Pase en Modelaje de Nichos Ecológicos Resumen. Avances recientes en los sistemas de información geográfica y su aplicación en la biología de conservación presentan la posibilidad de analisis nuevos y sintéticos. La exploración de estas posibilidades, de todas formas, se limita por la calidad de información disponible: la gran mayoria de datos respecto a la diversidad biológica son incompletos y sesgados. Por eso, procedimientos de inferencia que proveen predicciones robustas y confiables de distribuciones de especies se hacen importantes para los análisis de la biodiversidad. En esta contribución, se desarrollan modelos de los nichos ecológicos por medio de un algoritmo de inteligencia artificial, y los proyeccionamos en la geografía para predecir las distribuciones geográficas de especies. Para probar el método, se usan los datos del North American Breeding Bird Survey, con tamaños de muestra grande. Se construyeron modelos con base en 30 estados unidenses seleccionados al azar, y se probaron los modelos con base en los 20 estados restantes. De las 34 especies que se analizaron, todos mostraron un alto grado de significanza estadística (todos P < 0.001), lo cual indica un alto grado de predictividad. Esta capacidad de inferencia abre la puerta a varios analisis sintéticos con base en puntos conocidos de ocurrencia de especies.

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