Provides functions and data sets needed to replicate the analyses shown in the two-volume publication, Applied Hierarchical Modeling in Ecology: analysis of distribution, abundance and species richness in R and BUGS by Marc K<U+00E9>ry and Andy Royle, Academic Press (Vol 1, 2016; Vol 2, 2021).
sim.fnSimulate a homogeneous Poisson point process and illustrate the fundamental relationships between intensity, abundance and occurrence (AHM1 - section 1.1)
data.fnSimulate count data that are replicated in space and in time according to the binomial N-mixture model of Royle (2004) (this is for much simpler cases than is possible with function simNmix in Chapter 6 below) (AHM1 - 4.3)
ppc.plotPlot results from posterior predictive checks in section AHM1 - 6.8, for a fitted binomial N-mixture model object with JAGS
simNmixSimulate count data and individual detection histories for binomial and multinomial mixture models respectively under a wide range of conditions (AHM1 - 6.9.3)
plot_Nmix_resiDo diagnostic plots for one binomial N-mixture model fitted with all three mixture distributions currently available in unmarked: Poisson, negative binomial and zero-inflated Poisson (AHM1 - 6.9.3)
map.Nmix.resiProduce a map of the residuals from a binomial N-mixture model (see Section AHM1 - 6.9.3)
simpleNmixSimulate count data under a very simple version of the binomial mixture model, with time for space substitution (AHM1 - 6.12)
playRNPlay Royle-Nichols (RN) model: generate count data under the binomial N-mixture model of Royle (2004), then 'degrade' the data to detection/nondetection and fit the RN model using unmarked and estimate site-specific abundance (AHM1 - 6.13.1)
instRemPiFun, crPiFun, crPiFun.Mb, MhPiFunDefine the relationship between the multinomial cell probabilities and the underlying detection probability parameters (i.e., a pi function) in various designs (AHM1 - 7.8 and AHM2 - Chapter 2)
sim.ldataSimulate data under a non-hierarchical line transect distance sampling model (AHM1 - 8.2.3)
sim.pdataSimulate data under a non-hierarchical point transect (= point count) distance sampling model (AHM1 - 8.2.5.1)
simHDSSimulate data under a hierarchical distance sampling protocol (line or point) (AHM1 - 8.5.1)
simHDSgSimulate data under a hierarchical distance sampling (HDS) protocol with groups (AHM1 - 9.2.1)
simHDStrSimulate data under a time-removal/distance sampling design (AHM1 - 9.3.2)
simHDSopenSimulate open hierarchical distance sampling data (AHM1 - 9.5.4)
issj.simSimulate data under the open distance sampling protocol for the Island Scrub Jays (AHM1 - 9.7.1)
sim.spatialDSSimulate data under a basic spatial distance sampling model (AHM1 - 9.8.3)
sim.spatialHDSSimulate data under a spatial hierarchical distance sampling model (AHM1 - 9.8.5)
simOccSimulate detection/nondetection data under static occupancy models under a wide range of conditions (AHM1 - 10.5)
sim3OccSimulate detection/nondetection data under a static 3-level occupancy model (AHM1 - 10.10)
simOccttdSimulate 'timing data' under a static time-to-detection occupancy design (AHM1 - 10.12.1)
wigglyOccSimulate detection/nondetection data under a static occupancy model with really wiggly covariate relationships in occupancy and detection probability (AHM1 - 10.14)
spline.prepPrepare input for BUGS model when fitting a spline for a covariate (AHM1 - 10.14)
simCommSimulate detection/nondetection or count data under a community occupancy or abundance model respectively (AHM1 - 11.2)
simNpCSimulate data on abundance (N), detection probability (p) and resulting counts (C) under a counting process with imperfect detection (AHM2 - 1.2)
simPOPSimulate count data under a demographic state-space, or Dail-Madsen, model (no robust design) (AHM2 - 1.7.1)
simPHSimulate count data with phenological curves within a year (AHM2 - 1.8.1)
graphSSMPlot trajectories of counts and latent abundance from a fitted Gaussian state-space model (AHM2 - 1.6.1)
simDM0Simulate count data from a Dail-Madsen model under a robust design, no covariates (AHM2 - 2.5.1)
simDMSimulate count data from a Dail-Madsen model under a robust design, with covariates (AHM2 - 2.5.5)
simMultMixSimulate “removal” count data from a multinomial-mixture model (AHM2 - 2.7.1)
simFrogDiseaseSimulate detection data for diseased frogs (AHM2 - 2.9.1)
simDynoccSimulate detection/nondetection data under a dynamic occupancy model under a wide range of conditions (AHM2 - 4.4)
simDemoDynoccSimulate detection/nondetection data under a demographic dynamic occupancy model (AHM2 - 4.12)
simDCMSimulate detection/nondetection data under a general dynamic community model (site-occupancy variant) (AHM2 - 5.2)
valid_dataPartial validation of simulated data with false positives (AHM2 - 7.6.2)
modSelFPA stop-gap function for modSel for fitted false positive occupancy models (AHM2 - 7.2.2)
getLVcorrMatCompute the correlation matrix from an analysis of a latent variable occupancy or binomial N-mixture model (AHM2 - 8.4.2)
simDynoccSpatialSimulate detection/nondetection data under a dynamic occupancy model with spatial covariate and spatial autocorrelation (AHM2 - 9.6.1.1)
simExpCorrRFSimulate data from a Gaussian random field with negative exponential correlation function (AHM2 - 9.2)
simOccSpatialSimulate detection/nondetection data under a spatial, static occupancy model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)
simNmixSpatialSimulate counts under a spatial, static binomial N-mixture model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)
simSpatialDSlineSimulate line transect distance sampling data with spatial variation in density (AHM2 - 11.5)
simSpatialDSteSimulate data for replicate line transect distance sampling surveys with spatial variation in density and temporary emigration (AHM2 - 11.8.1)
simDSMSimulate line transect data for density surface modeling (AHM2 - 11.10.1)
BerneseOberlandLandscape data for the Bernese Oberland around Interlaken, Switzerland (AHM2 - 9.2)
crestedTitCrested Tit data from the Swiss Breeding Bird Survey MHB (Monitoring H<U+00E4>ufige Brutv<U+00F6>gel) for 1999 to 2015 (AHM2 - 1.3)
cswaChestnut-sided Warbler data for point counts and spot-mapping from White Mountain National Forest (AHM2 - 2.4.3)
crossbillAHMCrossbill data from the Swiss Breeding Bird Survey for 2001 to 2012 (AHM2 - 4.9)
dragonfliesToy data set used in AHM1 - 3.1
duskySalamandersCounts of juvenile vs adult salamanders over 7 years (AHM2 - 2.9.2)
EurasianLynxData for Eurasian Lynx in Italy and Switzerland (AHM2 - 7.3.2)
FinnmarkData from surveys of birds in Finnmark in NE Norway (AHM2 - 5.7)
FrenchPeregrinesDetection data for peregrines in the French Jura (AHM2 - 4.11)
greenWoodpeckerCount data for Green Woodpeckers in Switzerland from the MHB (AHM2 - 2.2)
HubbardBrookPoint count data for warblers from Hubbard Brook, New Hampshire (AHM2 - 8.2)
jayThe European Jay data set (from the MHB) is now included in unmarked (AHM1 - 7.9)
MesoCarnivoresCamera trap data for 3 species of meso-carnivores (AHM2 - 8.2)
MHB2014Complete data from the Swiss Breeding Bird Survey MHB (Monitoring H<U+00E4>ufige Brutv<U+00F6>gel) for the year 2014 (AHM1 - 11.3)
spottedWoodpeckerData for Middle Spotted Woodpeckers in Switzerland (AHM2 - 4.11.2)
SwissAtlasHaA 1ha-scale subset of the count data from the Swiss Breeding Bird Atlas (AHM2 - 8.4.2)
SwissEagleOwlsTerritory-level, multi-state detection/nondetection data for Eagle Owls in Switzerland (AHM2 - 6.4)
SwissMarbledWhiteData from the Biodiversity Monitoring Program (LANAG) in the Swiss Canton of Aargau for Marbled White butterfly (AHM2 - 1.8.2)
SwissSquirrelsCount data for Red Squirrels in Switzerland from the Swiss breeding bird survey MHB (AHM1 - 10.9)
SwissTitsData for 6 species of tits in Switzerland from from the Swiss breeding bird survey MHB during 2004 to 2013 (AHM1 - 6.13.1)
treeSparrowData for Tree Sparrows in Alaska (AHM2 - 11.8.4)
ttdPeregrineTime-to-detection data for Peregrines (AHM1 - 10.12.2)
UKmarbledWhiteData from the UK Butterfly Monitoring Scheme (UKBMS) for Marbled White butterfly (AHM2 - 1.8.2)
wagtailDistance sampling data for Yellow Wagtails in The Netherlands (AHM1 - 9.5.3)
waterVolesDetection/nondetection data for the Mighty Water Vole of Scotland (AHM2 - 7.2.2)
wigglyLineCoordinates for a wiggly transect line (AHM2 - 11.9)
willowWarblerCapture-history (survival) data for Willow Warblers in Britain (AHM2 - 3.4.1)
zinitGenerate starting values for fitting survival models (introduced in AHM2 - 3.2.3).
standardizeStandardize covariates to mean 0, SD 1.
fitstats, fitstats2Calculate fit-statistics used in parboot GOF tests throughout the book (eg, Sections AHM1 - 7.5.4, AHM1 - 7.9.3, AHM2 - 2.3.3)
e2distCompute a matrix of Euclidean distances
image_scaleDraw scale for image (introduced in chapter AHM1 - 9.8.3)
bigCrossCorrReport cross-correlations above a given threshold
Color_RampsColor ramps for use with image or raster plots
The functions are listed by chapter below, where AHM1 refers to volume 1 and AHM2 to volume 2.