Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (*Milvus milvus*)use asterix (*) to get italics

Sollmann Rahel, Adenot Nathalie, Spakovszky Péter, Windt Jendrik, Brady J. MattssonPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2024

<p style="text-align: justify;">Counting the number of young in a brood from a distance is common practice, for example in tree-nesting birds. These counts can, however, suffer from over and undercounting, which can lead to biased estimates of fecundity (average number of nestlings per brood). Statistical model development to account for observation bias has focused on false negatives (undercounts), yet it has been shown that these models are sensitive to the presence of false positives (overcounts) when they are not accounted for. Here, we develop a model that estimates fecundity while accounting for both false positives and false negatives in brood counts. Its parameters can be estimated using a calibration approach that combines uncertain counts with certain ones, which can be obtained by accessing the brood, for example during ringing. The model uses multinomial distributions to estimate the probabilities of observing &nbsp;y young conditional on the true state of a brood z (i.e., true number of young) from paired uncertain and certain counts. These classification probabilities are then used to estimate the true state of broods for which only uncertain counts are available. We use a simulation study to investigate bias and precision of the model and parameterize the simulation with empirical data from 26 red kite nests visited with ground and nest-based counts during 2021 and 2022 in central Europe. In these data, bias in counts was at most 1 in either direction, more common in larger broods, and undercounting was more common than overcounting. This led to an overall 5% negative bias in fecundity in uncertain counts. The model produced essentially unbiased estimates (relative bias &lt; 2%) of fecundity across a range of sample sizes. This held true whether or not fecundity was the same &nbsp;for nests with paired counts and those with uncertain-only counts. But the model could not estimate parameters when true states were missing from the paired data, which happened frequently in small sample sizes (n = 10 or 25). Further, we projected populations 50 years into the future using fecundity estimates corrected for observation biases from the multinomial model, and based on “raw” uncertain observations. We found that ignoring observation bias led to strong negative bias in projected population size for growing populations, but only minor negative bias in declining populations. Accounting for apparently minor biases associated with ground counts is important for ensuring accurate estimates of abundance and population dynamics especially for increasing populations. This could be particularly important for informing conservation decisions in projects aimed at recovering depleted populations.</p>

https://doi.org/10.5281/zenodo.11468630You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

https://doi.org/10.5281/zenodo.11468630You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

false positives, false negatives, multinomial model, classification, calibration, population dynamics

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Demography, Statistical ecology

David Miller, dxm84@psu.edu, Jim Nichols, jnichols@usgs.gov, H. Resit Akcakaya, Resit.Akcakaya@stonybrook.edu, Patrick Scherler, patrick.scherler@vogelwarte.ch, Michael Schaub, michael.schaub@vogelwarte.ch, Mark Kery, marc.kery@vogelwarte.ch, Patrick Scherler [patrick.scherler@vogelwarte.ch] suggested: Eckhard Gottschalk egottsc1@uni-goettingen.de, Patrick Scherler [patrick.scherler@vogelwarte.ch] suggested: Steffen Oppel steffen.oppel@vogelwarte.ch, Steffen Oppel suggested: Hi, I recently joined the Red Kite team at the Swiss Ornithological Institute, and we have links with Brady Matsson and Jendrik Windt via an EU-funded LIFE project (however, we are not beneficiaries, and therefore have not received joint funding or published anything together - hence I saw no conflict of interest). We could evaluate this manuscript jointly within our research group (2-3 post-docs, 3 full-time staff members) and provide either a joint or separate recommendations if this is helpful. Please let me know what you prefer. I requested an extension because nobody will be working for the next 2 weeks!, Giacomo Tavecchia suggested: Olivier Gimenez olivier.gimenez@cefe.cnrs.fr, Giacomo Tavecchia suggested: Andrew Royle aroyle@usgs.gov

e.g. John Doe john@doe.com

No need for them to be recommenders of PCIEcology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

2023-12-11 08:52:22

Nigel Yoccoz