AHMbook-package: Functions and data for the Book "Applied Hierarchical...

In AHMbook: Functions and Data for the Book 'Applied Hierarchical Modeling in Ecology' Vols 1 and 2

Functions and data for the Book “Applied Hierarchical Modeling in Ecology” Volumes 1 and 2

Description

Provides functions to simulate data sets from hierarchical ecological models, including all the simulations described by Marc Kéry and Andy Royle in the two-volume publication, Applied Hierarchical Modeling in Ecology: analysis of distribution, abundance and species richness in R and BUGS, Academic Press (Vol 1, 2016; Vol 2, 2021), plus new models developed after publication of the books.

It also has all the utility functions and data sets needed to replicate the analyses shown in the books.

SIMULATION FUNCTIONS

Introduction

sim.fn

Simulate a homogeneous Poisson point process and illustrate the fundamental relationships between intensity, abundance and occurrence (AHM1 - section 1.1)

data.fn

Simulate 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)

Abundance from Counts: Binomial N-Mixture models

simNmix

Simulate count data and individual detection histories for binomial and multinomial mixture models respectively under a wide range of conditions (AHM1 - 6.9.3)

simpleNmix

Simulate count data under a very simple version of the binomial mixture model, with time for space substitution (AHM1 - 6.12)

playRN

Play 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)

Abundance from Hierarchical Distance Sampling

sim.ldata

Simulate data under a non-hierarchical line transect distance sampling model (AHM1 - 8.2.3)

sim.pdata

Simulate data under a non-hierarchical point transect (= point count) distance sampling model (AHM1 - 8.2.5.1)

simHDS

Simulate data under a hierarchical distance sampling protocol (line or point) (AHM1 - 8.5.1)

simHDSpoint

A simplified version of simHDS for point transects only.

simHDSg

Simulate data under a hierarchical distance sampling (HDS) protocol with groups (AHM1 - 9.2.1)

simHDStr

Simulate data under a time-removal/distance sampling design (AHM1 - 9.3.2)

simHDSopen

Simulate open hierarchical distance sampling data (AHM1 - 9.5.4)

issj.sim

Simulate data under the open distance sampling protocol for the Island Scrub Jays (AHM1 - 9.7.1)

sim.spatialDS

Simulate data under a basic spatial distance sampling model (AHM1 - 9.8.3)

sim.spatialHDS

Simulate data under a spatial hierarchical distance sampling model (AHM1 - 9.8.5)

simIDS

Simulate data for an integrated distance sampling, point count and occupancy study

Static Occurrence using Site-Occupancy Models

simOcc

Simulate detection/nondetection data under static occupancy models under a wide range of conditions (AHM1 - 10.5)

simOccCat

As above, but allows simulation of categorical covariates

sim3Occ

Simulate detection/nondetection data under a static 3-level occupancy model (AHM1 - 10.10)

simOccttd

Simulate 'timing data' under a static time-to-detection occupancy design (AHM1 - 10.12.1)

wigglyOcc

Simulate detection/nondetection data under a static occupancy model with really wiggly covariate relationships in occupancy and detection probability (AHM1 - 10.14)

Hierarchical Models for Communities

simComm

Simulate detection/nondetection or count data under a community occupancy or abundance model respectively (AHM1 - 11.2)

Relative Abundance Models for Population Dynamics

simNpC

Simulate data on abundance (N), detection probability (p) and resulting counts (C) under a counting process with imperfect detection (AHM2 - 1.2)

simPOP

Simulate count data under a demographic state-space, or Dail-Madsen, model (no robust design) (AHM2 - 1.7.1)

simPH

Simulate count data with phenological curves within a year (AHM2 - 1.8.1)

Modeling Population Dynamics with Count Data

simDM0

Simulate count data from a Dail-Madsen model under a robust design, no covariates (AHM2 - 2.5.1)

simDM

Simulate count data from a Dail-Madsen model under a robust design, with covariates (AHM2 - 2.5.5)

simMultMix

Simulate “removal” count data from a multinomial-mixture model (AHM2 - 2.7.1)

simFrogDisease

Simulate detection data for diseased frogs (AHM2 - 2.9.1)

Hierarchical Models of Survival

simCJS

Simulate individual capture history data under a Cormack-Jolly-Seber (CJS) survival model (AHM2 - 3.2.2)

Dynamic Occupancy Models

simDynocc

Simulate detection/nondetection data under a dynamic occupancy model under a wide range of conditions (AHM2 - 4.4)

simDemoDynocc

Simulate detection/nondetection data under a demographic dynamic occupancy model (AHM2 - 4.12)

Dynamic Community Models

simDCM

Simulate detection/nondetection data under a general dynamic community model (site-occupancy variant) (AHM2 - 5.2)

Spatial Models of Distribution and Abundance

simDynoccSpatial

Simulate detection/nondetection data under a dynamic occupancy model with spatial covariate and spatial autocorrelation (AHM2 - 9.6.1.1). See also simDynoccSpatialData

simExpCorrRF

Simulate data from a Gaussian random field with negative exponential correlation function (AHM2 - 9.2)

simOccSpatial

Simulate detection/nondetection data under a spatial, static occupancy model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)

simNmixSpatial

Simulate counts under a spatial, static binomial N-mixture model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)

Integrated Models for Multiple Types of Data

simPPe

Simulate a spatial point pattern in a heterogeneous landscape and show aggregation to abundance and occurrence ('e' for educational version) (AHM2 - 10.2)

simDataDK

Simulate data for an integrated species distribution model (SDM) of Dorazio-Koshkina (AHM2 - 10.6.1)

Spatially Explicit Distance Sampling

simSpatialDSline

Simulate line transect distance sampling data with spatial variation in density (AHM2 - 11.5)

simSpatialDSte

Simulate data for replicate line transect distance sampling surveys with spatial variation in density and temporary emigration (AHM2 - 11.8.1)

simDSM

Simulate line transect data for density surface modeling (AHM2 - 11.10.1)

DATA SETS

BerneseOberland

Landscape data for the Bernese Oberland around Interlaken, Switzerland (AHM2 - 9.2)

crestedTit

Crested Tit data from the Swiss Breeding Bird Survey MHB (Monitoring Häufige Brutvögel) for 1999 to 2015 (AHM2 - 1.3)

cswa

Chestnut-sided Warbler data for point counts and spot-mapping from White Mountain National Forest (AHM2 - 2.4.3)

crossbillAHM

Crossbill data from the Swiss Breeding Bird Survey for 2001 to 2012 (AHM2 - 4.9)

dragonflies

Toy data set used in AHM1 - 3.1

duskySalamanders

Counts of juvenile vs adult salamanders over 7 years (AHM2 - 2.9.2)

EurasianLynx

Data for Eurasian Lynx in Italy and Switzerland (AHM2 - 7.3.2)

Finnmark

Data from surveys of birds in Finnmark in NE Norway (AHM2 - 5.7)

FrenchPeregrines

Detection data for peregrines in the French Jura (AHM2 - 4.11)

greenWoodpecker

Count data for Green Woodpeckers in Switzerland from the MHB (AHM2 - 2.2)

HubbardBrook

Point count data for warblers from Hubbard Brook, New Hampshire (AHM2 - 8.2)

jay

The European Jay data set (from the MHB) is now included in unmarked (AHM1 - 7.9)

MesoCarnivores

Camera trap data for 3 species of meso-carnivores (AHM2 - 8.2)

MHB2014

Complete data from the Swiss Breeding Bird Survey MHB (Monitoring Häufige Brutvögel) for the year 2014 (AHM1 - 11.3)

spottedWoodpecker

Data for Middle Spotted Woodpeckers in Switzerland (AHM2 - 4.11.2)

SwissAtlasHa

A 1ha-scale subset of the count data from the Swiss Breeding Bird Atlas (AHM2 - 8.4.2)

SwissEagleOwls

Territory-level, multi-state detection/nondetection data for Eagle Owls in Switzerland (AHM2 - 6.4)

SwissMarbledWhite

Data from the Biodiversity Monitoring Program (LANAG) in the Swiss Canton of Aargau for Marbled White butterfly (AHM2 - 1.8.2)

SwissSquirrels

Count data for Red Squirrels in Switzerland from the Swiss breeding bird survey MHB (AHM1 - 10.9)

SwissTits

Data for 6 species of tits in Switzerland from from the Swiss breeding bird survey MHB during 2004 to 2013 (AHM1 - 6.13.1)

treeSparrow

Data for Tree Sparrows in Alaska (AHM2 - 11.8.4)

ttdPeregrine

Time-to-detection data for Peregrines (AHM1 - 10.12.2)

UKmarbledWhite

Data from the UK Butterfly Monitoring Scheme (UKBMS) for Marbled White butterfly (AHM2 - 1.8.2)

wagtail

Distance sampling data for Yellow Wagtails in The Netherlands (AHM1 - 9.5.3)

waterVoles

Detection/nondetection data for the Mighty Water Vole of Scotland (AHM2 - 7.2.2)

wigglyLine

Coordinates for a wiggly transect line (AHM2 - 11.9)

willowWarbler

Capture-history (survival) data for Willow Warblers in Britain (AHM2 - 3.4.1)

UTILITY FUNCTIONS

ppc.plot

Plot results from posterior predictive checks in section AHM1 - 6.8, for a fitted binomial N-mixture model object with JAGS

plot_Nmix_resi

Do 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.resi

Produce a map of the residuals from a binomial N-mixture model (see Section AHM1 - 6.9.3)

instRemPiFun, crPiFun, crPiFun.Mb, MhPiFun

Define 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)

spline.prep

Prepare input for BUGS model when fitting a spline for a covariate (AHM1 - 10.14)

graphSSM

Plot trajectories of counts and latent abundance from a fitted Gaussian state-space model (AHM2 - 1.6.1)

ch2marray

Convert capture history data to the m-array aggregation (AHM2 - 3.4.1)

valid_data

Partial validation of simulated data with false positives (AHM2 - 7.6.2)

getLVcorrMat

Compute the correlation matrix from an analysis of a latent variable occupancy or binomial N-mixture model (AHM2 - 8.4.2)

zinit

Generate starting values for fitting survival models (introduced in AHM2 - 3.2.3).

standardize

Standardize covariates to mean 0, SD 1.

fitstats, fitstats2

Calculate fit-statistics used in parboot GOF tests throughout the book (eg, Sections AHM1 - 7.5.4, AHM1 - 7.9.3, AHM2 - 2.3.3)

e2dist

Compute a matrix of Euclidean distances

image_scale

Draw scale for image (introduced in chapter AHM1 - 9.8.3)

bigCrossCorr

Report cross-correlations above a given threshold

Color_Ramps

Color ramps for use with image or raster plots

Author(s)

Marc Kéry, Andy Royle, Mike Meredith

AHMbook documentation built on Sept. 12, 2024, 6:37 a.m.