DOI: 10.5061/dryad.3bk3j9kkv
title: Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution

description: Imperfect detection in field studies on animal abundance, including birds,
 is common and can be corrected for in various ways. The binomial N-mixture
 (hereafter binmix) model developed for this task is widely used in
 ecological studies owing to its simplicity: it requires replicated count
 results as the input. However, it may overestimate abundance and be
 sensitive to even small violations of its assumptions. We used a 33-year
 dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine,
 from Białowieża Forest, Poland to validate inference from binmix models by
 comparing model-estimated abundances to the true number of breeding pairs
 within the plots, determined by exhaustive population study. The abundance
 estimates, derived from six springtime (April-May) counts of males on each
 plot in each year, were highly reliable: 116 out of 132 year-plot
 estimates (88%) included the true number of pairs within the 95%
 confidence intervals. Over- and underestimations were thus rare and
 similarly frequent (9 and 12 cases, respectively), with a tendency to
 overestimate at low densities and underestimate at high densities. Marsh
 Tits sing rarely but the frequency of countersinging increases with
 abundance, leading to non-independence in detections. When accounted for
 in a submodel for detection, the per-survey number of countersinging
 events positively affected detection probability but only weakly affected
 abundance estimates. Simulations further demonstrate that this property,
 overestimation at low densities and underestimation at high densities, may
 be a systematic bias of binmix model even if density-dependent detection
 is absent. While the behaviour of binmix models in specific situations
 requires more study, we conclude that these models are a valid tool to
 estimate abundance reliably when intensive population monitoring is not
 feasible.
description: The readme file contains an explanation of each of the variables
 in the datasets. Detailed information on field methods used to collect
 these data can be found in the associated manuscript referenced
 above.
description: The dataset contains three data files which are read when
 executing the R codes and three code files. The data
 files include: 1. results of counts of singing Marsh
 Tit Poecile palustris males in Białowieża National Park, Poland, 1987-2019
 (file 'pl_nc_v5_1987-2019.txt'); 2. the file
 with the number of countersinging males and cases recorded during surveys
 (file 'countersinging.txt'); 3. the file with
 the true number of breeding pairs on the plots where counts have been
 performed (file 'True_state.txt'). R code
 files needed to repeat the analyses presented in the paper
 include: 1.
 'Rcode_model_selection_P_NB_ZIP.R' to perform model selection
 (separated from the main code in pt 2, due to long computing
 time) 2. 'Rcode_binmix.R' containing code to
 read relevant data and perform N-mixture models and generalized linear
 mixed models fitting, along with drawing the main figures in the
 paper. 3. 'Rcode_Simulations.r' to perform
 simulations reported in the paper and draw figures. All
 the codes are richly commented and have a simple structure to ease
 understanding. Note that executing the entire code 2 (w/o bootstrapped GOF
 test) takes c 20 minutes on a 2.6 GHz i5 processor and executing model
 selection code could even be longer.