DOI: 10.5061/dryad.tdz08kq61
title: An integrated population model reveals source-sink dynamics for competitively subordinate African wild dogs linked to anthropogenic prey depletion

description: Many African large carnivore populations are declining due to decline of
 the herbivore populations on which they depend. The densities of apex
 carnivores like the lion and spotted hyena correlate strongly with prey
 density, but competitive subordinates like the African wild dog benefit
 from competitive release when the density of apex carnivores is low, so
 the expected effect of a simultaneous decrease in resources and dominant
 competitors is not obvious.  Wild dogs in Zambia’s Luangwa Valley
 Ecosystem occupy four ecologically similar areas with well-described
 differences in the densities of prey and dominant competitors, due to
 spatial variation in illegal offtake. We used long-term data to fit a
 Bayesian integrated population model (IPM) of the demography and dynamics
 of wild dogs in these four regions. The IPM used Leslie projection to link
 a Cormack-Jolly-Seber model of area-specific survival (allowing for
 individual heterogeneity in detection), a zero-inflated Poisson model of
 area-specific fecundity, and a state-space model of population size that
 used estimates from a closed mark-capture model as the counts from which
 (latent) population size was estimated. The IPM showed that both survival
 and reproduction were lowest in the region with the lowest density of
 preferred prey (puku, Kobus vardonii, and impala, Aepyceros melampus),
 despite little use of this area by lions. Survival and reproduction were
 highest in the region with the highest prey density, and intermediate in
 the two regions with intermediate prey density. The population growth rate
 (λ) was positive for the population as a whole, strongly positive in the
 region with the highest prey density, and strongly negative in the region
 with the lowest prey density. It has long been thought that the benefits
 of competitive release protect African wild dogs from the costs of low
 prey density. Our results show that the costs of prey depletion overwhelm
 the benefits of competitive release and cause local population decline
 where anthropogenic prey depletion is strong. Because competition is
 important in many guilds and humans are affecting resources of many types,
 it is likely that similarly fundamental shifts in population limitation
 are arising in many systems.
description: Data on the survival and reproduction of African wild dogs
 (<em>Lycaon</em> <em>pictus</em>) were collected
 by radiocollaring and direct observation in the Luangwa Valley ecosystem.
 The raw data were entered into a relational database (MS Access) and
 exported to CSV files using SQL queries. 
description: # An integrated population model reveals source-sink dynamics for
 competitively subordinate African wild dogs linked to anthropogenic prey
 depletion ## Description
 of the data and file structure Scott Creel, 4 January 2024 Data and JAGS
 code for the Bayesian Integrated Population Model of Creel et al. 2014. An
 integrated population model reveals source-sink dynamics for competitively
 subordinate African wild dogs linked to anthropogenic prey depletion.
 *Journal of Animal Ecology.* The data are in two comma-delimited files:
 **bayesian_cjs_Luangwa_WD_data_object.csv** This file contains capture
 histories and associated covariates for the survival component model. All
 of the data codes and columns are described below under code/software, as
 comments in R code that will read the data in the correct format for the
 integrated population model code. That code will create four R objects
 used in the model: * CH is a dataframe that holds capture histories, with
 one row per individual (463 individuals) and 63 columns. Each column
 represents one time interval, with nine intervals per year over a period
 of 7 years as described in the paper. In each cell, a zero indicates that
 an individual was not detected in that interval, and a 1 indicates that it
 was detected. * sex is a vector with 463 values, one for each individual
 in CH. It holds character data with values of 'Female' and
 'Male'. * x is a matrix that holds data on each
 individual's age at each time period considered in the analysis, with
 463 rows, one for each individual in CH, and 63 columns, each holding data
 for the same time bins as in CH. It holds numeric data with codes as
 follows: 1 = pup (less than 1 year old), 2 = yearling (one year old), 3 =
 adult (two years old or older). * g is a matrix that holds data on each
 individual's location at each time period considered in the analysis,
 with 463 rows, one for each individual in CH, and 63 columns, each holding
 data for the same time bins as in CH. It holds numeric data with codes as
 follows: 1=Lower Lupande, 2 = Main Game, 3 = North, 4 = Nsefu, 5 = other
 regions. Individuals with code 5 are not included the the final analysis
 of the paper. **lve_awd_fecundity_by_area.csv** This file contains data on
 annual fecundity and associated covariates for the reproduction component
 model. All of the data codes and columns are described below under
 code/software, as comments in R code that will read the data in the
 correct format for the integrated population model code. There is one row
 per individual, with two variables in each row. * X.Recruit1yr holds
 numeric data, giving the number of offspring that a female raised to one
 year of age. * area holds character data, giving the area in which the
 female lived in that year. These are the same 4 areas described above. ##
 Code/Software **awd_ipm.txt** is the JAGS code to fit the IPM to data from
 African wild dogs in the Luangwa Valley Ecosystem using the jags()
 function of the R package jagsUI.  Prior to running the jags() function,
 the data must be read into R objects and bundled into a list, for example
 with: \# Read capture histories and associated covariates to fit CJS model
 \# CH = 0/1, detection matrix of 463 rows (animals) and 63 columns
 (occasions) \# sex = 'Female', 'Male', vector with 463
 animals \# x 1=pup, 2=yearling, 3=adult, age matrix of 463 rows (animals)
 and 63 columns (occasions) \# g 1=Lower Lupande, 2 = Main Game, 3 = North,
 4 = Nsefu, region matrix, 463 x 63 data &lt;-
 read.csv("bayesian_cjs_Luangwa_WD_data_object.csv", header =
 TRUE) CH &lt;- data.matrix(data[,2:64]) sex &lt;- data.matrix(data[,65]) x
 &lt;- data.matrix(data[,66:128]) g &lt;- data.matrix(data[, 129:191])   \#
 Read fecundity and associated covariate to fit CJS model fecundity &lt;-
 read.csv("lve_awd_fecundity_by_area.csv", header = TRUE)   \#
 Bundle data and inspect \# first line is for CJS model of apparent
 survival \# second line is for ZIP model of fecundity \# third line is for
 the SSM model of population size jags.data &lt;- list(y=CH, f=f,
 nind=dim(CH)[1], n.occasions=dim(CH)[2], z=known.state.cjs(CH), area=g,
 nareaclass=4,                   C = fecundity$X.Recruit1yr, n =
 length(fecundity$X.Recruit1yr), region=area,                   Pop=Pop,
 n.occ= length(Pop)) str(jags.data) In the code above, the
 known.state.cjs() function is provided on page 182 of Kery, M &
 Schaub, M. 2012. Bayesian Population Analysis using WinBUGS. Academic
 Press, and the object Pop is a vector of integers = [115, 109, 112, 102,
 110, 88, 61].