Grzegorz Neubauer, Arkadiusz Sikora. 2023: Heterogeneity in song rates in the Collared Flycatcher (Ficedula albicollis) explained with the availability parameter in generalized N-mixture models: Its importance for abundance estimates in avian aural counts. Avian Research, 14(1): 100080. DOI: 10.1016/j.avrs.2023.100080
Citation: Grzegorz Neubauer, Arkadiusz Sikora. 2023: Heterogeneity in song rates in the Collared Flycatcher (Ficedula albicollis) explained with the availability parameter in generalized N-mixture models: Its importance for abundance estimates in avian aural counts. Avian Research, 14(1): 100080. DOI: 10.1016/j.avrs.2023.100080

Heterogeneity in song rates in the Collared Flycatcher (Ficedula albicollis) explained with the availability parameter in generalized N-mixture models: Its importance for abundance estimates in avian aural counts

Funds: Field work was funded by the General Directorate for Environmental Protection, Poland, within the inventory of birds in the SPA Natura 2000 Napiwodzko-Ramucka Forest
More Information
  • Corresponding author:

    E-mail address: grzegorz.neubauer@uwr.edu.pl (G. Neubauer)

  • Received Date: 30 Jun 2022
  • Rev Recd Date: 06 Dec 2022
  • Accepted Date: 12 Jan 2023
  • Available Online: 14 Apr 2023
  • Publish Date: 31 Jan 2023
  • Binomial N-mixture models are commonly applied to estimate abundance unaffected by imperfect detection, but are known to be sensitive to violations of assumptions. One of the model's assumptions, the independence of detections has rarely been tested. It requires that during a survey, detection of one individual does not affect detection of another individual. This assumption can be frequently violated in passerine birds, which exhibit territorial behaviour by singing, since neighbouring individuals are likely to motivate each other to vocalize, leading to non-independence in singing activity and in the following detection rate. Here, we explored this phenomenon with the generalized, binomial version of the N-mixture model, where detection probability is decomposed into availability probability φ – which can be interpreted as per capita song rate or the probability of vocalising – and actual detection probability p, given vocalisations take place. Using repeated counts of the Collared Flycatcher (Ficedula albicollis) as a case study, and treating the maximum observed counts C at a site i as an explanatory covariate for φ, we showed that per capita song rates increased with observed counts at a site. Hence, if song rates vary due to local abundance, including C as an explanatory variable for song rate addressed with φ, helps to explain this variation (which otherwise goes undetected) and improves inferences under the model. This had strong effects on the resulting abundance estimates: if this relationship was ignored in the models, total estimated population sizes were consequently lower by as much as 22–27%, compared to when this effect was included. Since it is likely that song rates may commonly be density-dependent in birds manifesting territorial behaviours by singing, further tests addressing violations of independence assumptions in these models are needed. As suggested by Kéry and Royle (2016), despite some form of circularity likely being involved, modelling heterogeneity in the detection process with the help of C in standard N-mixture models (which, given availability, conflate availability with detection in a single parameter) should be applicable as well.

  • In the original version of this article, we published a figure showing a gap in the confidence intervals for body and tail due to data paucity for mid stages of moult progress. Here, we amended this problem adding data from the 2023 moulting season, during which we obtained 139 moult records from 98 individuals. The final sample size used for plotting these results are shown in the caption below. This amendment corroborates the conclusion already stated: body moult does not seem to be under physiological constraints, although primary moult appears to be tightly controlled to reduce aerodynamic losses.

    Figure  8.  Moult speed in the House Sparrow, calculated as the average mass gain during the elapsed time between capture and recaptures. Local polynomial regression on moult progress calculated as the mean moult progress between consecutive captures (shaded ribbons depict 95% confidence intervals). Mass gain of primaries and rectrices have been calculated from one wing and tail side, respectively, and then multiplied by 2, i.e., assuming symmetry. Sample sizes for body and rectrices and primaries were n = 76 and 98 within-year recaptures from 56 to 40 individuals, respectively.

    The authors would like to apologise for any inconvenience caused.

    Santi Guallar: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Javier Quesada: Funding acquisition, Resources, Validation, Visualization, Writing – review & editing.

  • Barker, R.J., Schonfield, M.R., Link, W.A., Sauer, J.R., 2018. On the reliability of N-mixture models for count data. Biometrics 74, 369−377.
    Bötsch, Y., Jenni, L., Kéry, M., 2019. Field evaluation of abundance estimates under binomial and multinomial N-mixture models. Ibis 162, 902−910.
    Chandler, R.B., Royle, J.A., King, D.I., 2011. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429−1435.
    Costa, A., Oneto, F., Salvidio, S., 2019. Time-for-space substitution in N-mixture modeling and population monitoring. J. Wildl. Manag. 83, 737−741.
    Costa, A., Salvidio, S., Penner, J., Basile, M. 2021. Time-for-space substitution in N-mixture models for estimating population trends: A simulation-based evaluation. Sci. Rep. 11, 4581.
    Fiske, I., Chandler, R., 2011. Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1−23.
    Hunt, J.W., Weckerly, F.W., Ott, J.R., 2012. Reliability of occupancy and binomial mixture models for estimating abundance of Golden-cheeked Warblers Setophaga chrysoparia. The Auk 129, 105-114.
    Kéry, M., 2018. Identifiability in N-mixture models: A large-scale screening test with bird data. Ecology 99, 281−288.
    Kery, M., Royle, A.J., 2016. Applied Hierarchical Modeling in Ecology. Analysis of Distribution, Abundance and Species Richness in R and BUGS. Vol. 1. Prelude and Static Models. Academic Press, London, UK.
    Link, W.A., Schofield, M.R., Barker, R.J., Sauer, J.R., 2018. On the robustness on N-mixture models. Ecology 99, 1547−1551.
    MacKenzie, D.I., Nichols, J.D., Hines, J.E., Knutson, M.G., Franlin, A.B., 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200-2207.
    Neubauer, G., Sikora, A., 2013. Detection probability of the Collared Flycatcher Ficedula albicollis during quick, multiple surveys: a case study in an isolated population in northern Poland. Ornis Fennica 90, 211−221.
    Neubauer, G., Sikora, A., 2020. Abundance estimation from point counts when replication is spatially intensive but temporally limited: Comparing binomial N-mixture and hierarchical distance sampling models. Ornis Fennica 97, 131−148.
    Neubauer, G., Wolska, A., Rowiński, P., Wesołowski, T., 2022. N-mixture models estimate abundance reliably: A field test on Marsh Tit using time-for-space substitution. Ornithol. Appl. 124, 1−13.
    Nichols, J.D., Thomas, L., Conn, P.B., 2009. Inferences about landbird abundance from count data: Recent advances and future directions. In: Thomson, D.L., Cooch, E.G., Conroy, M.J. (Eds.), Modeling Demographic Processes in Marked Populations. Springer, New York, NY, pp. 201−235.
    Royle, J.A., 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108−115.
    R Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
    Warren, C.C., Veech, J.A., Weckerly, F.W., O'Donnell, L., Ott, J.R., 2013. Detection heterogeneity and abundance estimation in population of Golden-cheeked Warblers Setophaga chrysoparia. The Auk 130, 677−688.
  • Related Articles

Catalog

    Figures(3)  /  Tables(2)

    Article Metrics

    Article views (135) PDF downloads (97) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return