These data represent avian point count surveys conducted at 453 point sample survey locations on the 24,000 (approx) live-fire region of Fort Hood in central Texas. Surveys were conducted by independent double observers (2 per survey occasion) and as such we had a maximum of 3 paired survey histories, giving a maximum of 6 sample occasions (see MacKenzie et al. 2006, MacKenzie and Royle 2005, and Laake et al. 2011 for various sample survey design details). At each point, we surveyed for 5 minutes (technically broken into 3 time intervals of 2, 2, and 1 minutes; not used here) and we noted detections by each observer and collected distance to each observation within a set of distance bins (0-50, 50-100m; Laake et al. 2011) of the target species (Golden-cheeked warblers in this case) for each surveyor. Our primary focus was to use mark-recapture distance sampling methods to estimate density of Golden-cheeked warblers, and to estimate detection rates for the mark-recapture, distance, and composite model.
The format is a data frame with the following covariate metrics.
Visit number to the point
Species designation, either Golden-cheeked warbler (GW) or Black-capped Vireo (BV)
Distance measure, which is either NA (representing no detection), or the median of the binned detection distances
ID value indicating which observers were paired for that sampling occasion
Observer ID, either primary(1), or secondary (2)
Detection of a bird, either 1 = detected, or 0 = not detected
Date of survey since 15 March 2011, numeric value
Predicted occupancy value for that survey hexagon based on Farrell et al. (2013)
Region.Label categorization, see R package mrds help
file for details on data structure
Amount of survey effort at the point
Number of days since 15 March 2011, numeric value
Unique ID for each paired observations
Bret Collier and Jeff Laake
In addition to detailing the analysis used by Collier et al.
(2013, In Review), this example documents the use of mrds for avian
point count surveys and shows how density models can be incorporated with
occupancy models to develop spatially explicit density surface maps. For
those that are interested, for the distance sampling portion of our
analysis, we used both conventional distance sampling (cds) and
multiple covariate distance sampling (mcds) with uniform and
half-normal key functions. For the mark-recapture portion of our analysis,
we tended to use covariates for distance (median bin width), observer, and
date of survey (days since 15 March 2011).
We combined our mrds density estimates via a Horvitz-Thompson styled
estimator with the resource selection function gradient developed in Farrell
et al. (2013) and estimated density on an ~3.14ha hexagonal grid across our
study area, which provided a density gradient for Fort Hood. Because there
was considerable data manipulation needed for each analysis to structure the
data appropriately for use in mrds, rather than wrap each analysis in
a single function, we have provided both the Golden-cheeked warbler and
Black-capped vireo analyses in their full detail. The primary differences
you will see will be changes to model structures and model outputs between
the two species.
Farrell, S.F., B.A. Collier, K.L. Skow, A.M. Long, A.J. Campomizzi, M.L. Morrison, B. Hays, and R.N. Wilkins. 2013. Using LiDAR-derived structural vegetation characteristics to develop high-resolution, small-scale, species distribution models for conservation planning. Ecosphere 43(3): 42. http://dx.doi.org/10.1890/ES12-000352.1
Laake, J.L., B.A. Collier, M.L. Morrison, and R.N. Wilkins. 2011. Point-based mark recapture distance sampling. Journal of Agricultural, Biological and Environmental Statistics 16: 389-408.
Collier, B.A., S.L. Farrell, K.L. Skow, A.M. Long, A.J. Campomizzi, K.B. Hays, J.L. Laake, M.L. Morrison, and R.N. Wilkins. 2013. Spatially explicit density of endangered avian species in a disturbed landscape. Auk, In Review.