DOI: 10.5061/dryad.0rxwdbsbv
title: Data from: A hierarchical population model for the estimation of latent prey abundance and demographic rates of a nomadic predator

description: Linking the demographic parameters underlying population change to
 environmental conditions is a central goal of population demography.
 However, multicollinearity among processes in ecological studies can
 complicate parameter estimation and inference. We sought to demonstrate
 the use of structural equation modelling, a technique for estimating
 hypothesized causal pathways among collinear observed and unobserved
 variables, in the context of integrated population models. We monitored a
 population of Tengmalm's owls (Aegolius funereus) breeding in the
 Jura Mountains of northwestern Switzerland and eastern France for 31 years
 (1990-2020) and collected data on captured prey items. We use concepts
 central to structural equation models (i.e., latent variables) and
 integrated population models to estimate the effects of latent prey
 abundance on Tengmalm's owl demographic parameters. We observed
 strong positive effects of latent prey abundance during time t on clutch
 size, fledging probability, and immigration rates into the breeding
 population, and strong effects of positive changes in latent prey
 abundance from time t to t+1 on first-year and adult survival. We also
 observed long-term declines in immigration into the study area. Our work
 provides a straightforward example of incorporating concepts central to
 structural equation models (e.g., latent variables) to model environmental
 processes underlying demographic rates in integrated population models,
 and has interesting implications for metapopulation ecology of
 Tengmalm's owl populations in Europe.
description: See the manuscript (Riecke et al. 2025, Ecological Modelling) and
 other cited research within for full details of data collection.
description: # Data from: A hierarchical population model for the estimation of latent
 prey abundance and demographic rates of a nomadic predator Dataset DOI:
 [https://doi.org/10.5061/dryad.0rxwdbsbv](https://doi.org/10.5061/dryad.0rxwdbsbv) ## Description of the data and file structure ### Files and variables #### **File: input.RData** **Description:** These are the input data used to run the model that estimates Tengmalm's owl demographic parameters and abundance. Brief descriptions of  ch: capture-history marr.a: a multinomial array (marr) used to summarize adult (a) capture-recapture data marr.j: a multinomial array used to summarize juvenile (j) capture-recapture data  band: list of unique marks (rings) applied to birds) brood.age: a covariate for reproductive success (and clutch size) denoting whether each brood was tended by an SY (first-time breeder) or ASY (adult) female brood.year: denotes which year each brood was hatched in (brood year == 1 denotes the year 1990) c: clutch size of each observed nest date: initiation or laying date of each observed nest f: number of fledglings (must be &lt;= c) known.age: a vector denoting which broods had a known age female known.c: a vector denoting which nests had a known clutch size known.d: a vector denoting which nests had a known initiation or laying date mouse: number of mouse remains observed in nest bowls of sampled nests n: number of marked adult owls n.years: number of years prey.year: denotes which years nests sampled for prey remains were sampled in (prey.year == 1 denotes the year 1990) rel.a: a vector of releases (rel) of adults (a) during each year of the study rel.j: a vector of releases (rel) of juveniles (j) during each year of the study start: first year of the study (1990) territory: territory each nest sampled for prey remains was located in vole: number of vole remains observed in nest bowls of sampled nests x = vole + mouse y.nests = number of nests discovered per year with equivalent effort zB: z-standardized nests zT: z-standardized year #### **File: sim\_results\_250sims.RData** **Description:** Summarized output from the simulation ch: a simulated capture history from the last (nSims) simulation jags.data: data supplied to JAGS kappa.est: a storage array used to store model output across simulations m: model output marr: a summarized multistate multinomial array phi: simulated survival parameters r: number of releases of each age class z: true latent states of simulated owls from the last (nSims) simulation zI/zJ: used to simulate data  A: number of adults alpha: parameter values used to simulate the data alpha.est: a storage array used to store model output across simulations beta: parameter values used to simulate the data beta.est: a storage array used to store model output across simulations c: clutches of simulated observed nests d: initiation dates of simulated observed nests delta: simulated mean initiation dates eta: simulated random variation in rodent abundance i,I,j,J: values used in data simulation, see script for more details kappa: decrease in immigration over time loop/n: vectors used to loop when simulating data N: abundance of simulated population nb,nc,ni,nt: MCMC settings for JAGS ns: number of states (2; 1 = juvenile, 2 = adult) nSims: number of simulations to run nu: simulated mean prey capture rates omega: simulated expected number of immigrants p: age-specific detection, set equivalent for both age-classes at 0.4 parameters: parameter names pi: juvenile sex ratio psi: egg-to-fledging probability for last simulation q: 1 - p rel: number of releases (corresponds to m-array; see Kery and Schaub 2021 for more details) S/t/tmp: values used during data simulation, see script for more details x: observed rodent remains at simulated nests xi: expected clutch sizes during each year y: observed counts zeta: factor loadings for SEM zeta.est: a storage array used to store model output across simulations zT: z-standardized time ## Code/software Program R is required to view the data and run the associated code. model.R is the model file used to estimate Tengmalm owl demographic rates and abundance via the IPM described in the manuscript simulation.R is the script used to simulate data, recover simulated parameter estimates, and summarize model results across multiple simulations.