Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation

The analysis of large heterogeneous data sets of avian point‐count surveys compiled across studies is hindered by a lack of analytical approaches that can deal with detectability and variation in survey protocols. We reformulated removal models of avian singing rates and distance sampling models of the effective detection radius (EDR) to control for the effects of survey protocol and temporal and environmental covariates on detection probabilities. We estimated singing rates and EDR for 75 boreal forest songbird species and found that survey protocol, especially point‐count radius, explained most of the variation in detectability. However, environmental and temporal covariates (date, time, vegetation) affected singing rates and EDR for 73% and 59% of species, respectively. Unadjusted survey counts increased by an average of 201% from a 5‐min, 50‐m radius survey to a 10‐min, 100‐m radius survey (n = 75 species). This variability was decreased to 8·5% using detection probabilities estimated from a combination of removal and distance sampling models. Our modelling approach reduced computation when fitting complex models to large data sets and can be used with a wide range of statistical techniques for inference and prediction of avian densities.

[1]  S. Buckland.  Introduction to distance sampling : estimating abundance of biological populations , 2001 .

[2]  J. Andrew Royle,et al.  MODELING ABUNDANCE EFFECTS IN DISTANCE SAMPLING , 2004 .

[3]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[4]  Jonathan Bart,et al.  MONITORING THE ABUNDANCE OF BIRD POPULATIONS , 2005 .

[5]  F. Ramsey,et al.  Length of count period as a possible source of bias in estimating bird densities , 1981 .

[6]  J. Nichols,et al.  Inferences About Landbird Abundance from Count Data: Recent Advances and Future Directions , 2009 .

[7]  A. Zeileis,et al.  Regression Models for Count Data in R , 2008 .

[8]  Jean H. Meeus,et al.  Astronomical Algorithms , 1991 .

[9]  Stephen T. Buckland,et al.  Spatial models for line transect sampling , 2004 .

[10]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[11]  Brian Dennis,et al.  Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. , 2007, Ecology letters.

[12]  Steve Kelling,et al.  The Avian Knowledge Network : A partnership to organize, analyze, and visualize bird observation data for education, conservation, research, and land management , 2009 .

[13]  Kenneth H. Pollock,et al.  Statistical approaches to the analysis of point count data: a little extra information can go a long way , 2005 .

[14]  Péter Sólymos,et al.  Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error , 2012 .

[15]  Brett G. Dickson,et al.  Analyzing designed experiments in distance sampling , 2009 .

[16]  Péter Sólymos,et al.  Using Binomial Distance-Sampling Models to Estimate the Effective Detection Radius of Point-Count Surveys Across Boreal Canada , 2012 .

[17]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[18]  Douglas H. Johnson.  In Defense of Indices: The Case of Bird Surveys , 2008 .

[19]  Kenneth H. Pollock,et al.  A REMOVAL MODEL FOR ESTIMATING DETECTION PROBABILITIES FROM POINT-COUNT SURVEYS , 2002 .

[20]  S. Matsuoka,et al.  Estimation of Avian Population Sizes and Species Richness Across a Boreal Landscape in Alaska , 2009 .

[21]  Duane R. Diefenbach,et al.  INCORPORATING AVAILABILITY FOR DETECTION IN ESTIMATES OF BIRD ABUNDANCE , 2007 .

[22]  D. Bates,et al.  Linear Mixed-Effects Models using 'Eigen' and S4 , 2015 .

[23]  J. Andrew Royle.  N‐Mixture Models for Estimating Population Size from Spatially Replicated Counts , 2004, Biometrics.

[24]  Péter Sólymos,et al.  dclone: Data Cloning in R , 2010, R J..

[25]  David R. Anderson,et al.  LANDBIRD COUNTING TECHNIQUES: CURRENT PRACTICES AND AN ALTERNATIVE , 2002 .

[26]  Scott K. Robinson,et al.  Exploring the role of physiology and biotic interactions in determining elevational ranges of tropical animals , 2013 .

[27]  Erin M. Bayne,et al.  Dealing with detection error in site occupancy surveys: what can we do with a single survey? , 2012 .

[28]  E. Bayne,et al.  Toward conservation of Canada's boreal forest avifauna: design and application of ecological models at continental extents. , 2010 .

[29]  F. Thompson,et al.  Comparison of Methods for Estimating Density of Forest Songbirds from Point Counts , 2011 .

[30]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[31]  C. S. Robbins,et al.  Managing and Monitoring Birds Using Point Counts: Standards and Applications , 1995 .

[32]  Richard J. Barker,et al.  Statistical Aspects of Point Count Sampling , 1995 .

[33]  Kenneth H. Pollock,et al.  A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts , 2007 .

[34]  D. Stralberg,et al.  Climate and vegetation hierarchically structure patterns of songbird distribution in the Canadian boreal region , 2014 .

[35]  Kenneth H. Pollock,et al.  TIME-OF-DETECTION METHOD FOR ESTIMATING ABUNDANCE FROM POINT-COUNT SURVEYS , 2007 .

[36]  Len Thomas,et al.  IMPROVING ESTIMATES OF BIRD DENSITY USING MULTIPLE- COVARIATE DISTANCE SAMPLING , 2007 .

[37]  R. Huggins.  Some practical aspects of a conditional likelihood approach to capture experiments , 1991 .

[38]  T. Schweder.  Advanced Distance Sampling: Estimating Abundance of Biological Populations , 2007 .

[39]  Subhash R. Lele,et al.  Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning , 2010 .

[40]  Jim Schieck,et al.  Biased detection of bird vocalizations affects comparisons of bird abundance among forested habitats , 1997 .

[41]  Jonathan Bart,et al.  DOUBLE SAMPLING TO ESTIMATE DENSITY AND POPULATION TRENDS IN BIRDS , 2002 .

[42]  D. Dawson,et al.  Effect of Distance-Related Heterogeneity on Population Size Estimates from Point Counts , 2009 .

[43]  Kenneth H. Pollock,et al.  Large scale wildlife monitoring studies: statistical methods for design and analysis , 2002 .