10.5061/DRYAD.FTTDZ08NX
Bötsch, Yves
0000-0001-9171-8752
Swiss Ornithological Institute
Jenni, Lukas
Swiss Ornithological Institute
Kéry, Marc
Swiss Ornithological Institute
Field evaluation of abundance estimates under binomial and multinomial
N‐mixture models
Dryad
dataset
2019
abundance estimation
cavity nesters
detection probability
Forest birds
territory assignment
2019-12-02T00:00:00Z
2019-12-02T00:00:00Z
en
https://doi.org/10.1111/ibi.12802
190546 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Assessing and modelling abundance from animal count data is a very common
task in ecology and management. Detection is arguably never perfect, but
modern hierarchical models can incorporate detection probability and yield
abundance estimates that are corrected for imperfect detection. Two
variants of these models rely on counts of unmarked individuals, or
territories, (binomial N‐mixture models, or binmix) and on detection
histories based on territory mapping data (multinomial N‐mixture models
or multimix). However, calibration studies which evaluate these two
N‐mixture model approaches are needed. We analysed conventional territory
mapping data (three surveys in 2014 and four in 2015) using both binmix
and multimix models to estimate abundance for two common avian
cavity‐nesting forest species (Great Tit Parus major and Eurasian Blue
Tit Cyanistes caeruleus). In the same study area, we used two benchmarks:
(i) occupancy data from a dense nest box scheme; (ii) total number of
detected territories. To investigate variance in estimates due to the
territory assignment, three independent ornithologists conducted territory
assignments. Nest box occupancy yields a minimum number of territories,
since some natural cavities may have been used, and binmix model estimates
were generally higher than this benchmark. Estimates under the multimix
model were slightly more precise than binmix model estimates. Depending on
the person assigning the territories, the multimix model estimates became
quite different, either overestimating or underestimating the “truth”. We
conclude that N‐mixture‐models estimated abundance reliably, even for our
very small sample sizes. Territory‐mapping counts depended on territory
assignment and this carried over to estimates under the multimix model.
This limitation has to be taken into account when abundance estimates are
compared between sites or years. Whenever possible, accounting for such
hidden heterogeneity in the raw data of bird surveys, via including a
“territory editor” factor, is recommended. Distributing the surveys
randomly (in time and space) to editors may also alleviate this problem.