10.5061/DRYAD.J9KD51CF0
Weegman, Mitch
0000-0003-1633-0920
University of Saskatchewan
Alisauskas, Ray
Environment and Climate Change Canada
Kellett, Dana
Environment and Climate Change Canada
Zhao, Qing
University of Missouri
Wilson, Scott
Environment and Climate Change Canada
Telensky, Tomas
Charles University
Data from: Local population collapse of Ross's and lesser snow geese
driven by failing recruitment and diminished philopatry
Dryad
dataset
2022
FOS: Natural sciences
2022-03-30T00:00:00Z
2022-03-30T00:00:00Z
en
https://doi.org/10.1111/oik.09184
https://doi.org/10.5281/zenodo.5816262
147962 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
We estimated survival and per capita production of young, as well as
emigration and immigration, from 1997 to 2017 in Ross's goose Anser
rossii and lesser snow goose Anser caerulescens caerulescens, which are
sympatric species of migratory birds that nest in the central Canadian
Arctic at one of the largest breeding colonies in North America. We formed
age-structured integrated population models for each species that jointly
analyzed live and dead encounter data as well as breeding adult population
size and fecundity data to understand drivers of population dynamics. We
compared the demography between species because both species increased
during the 1990s and early 2000s yet thereafter snow geese declined, while
Ross's geese continued to increase, then stabilized and similarly
declined. Declines in Ross's and snow goose populations were caused
by reduced per capita production of young, and juvenile survival, as well
as increased adult and juvenile emigration. Stronger declines in juvenile
survival in snow geese explain their earlier population decline compared
to Ross's geese. Despite the divergence in population trends in
Ross's and snow geese, we found strong synchrony in demographic rates
which suggested substantial emigration from this colony and similar
responses to environmental conditions. We provide a novel m-array
implementation specific to a multi-state Burnham model which greatly
improved computational efficiency and convergence of posterior estimates.
Please refer to ReadMe file.