DOI: 10.5061/dryad.j6q573nh4
title: Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species.

description: Non-additive effects from multiple interacting stressors can have
 unpredictable outcomes on wildlife. Stressors that initially have
 negligible impacts may become significant if they act in synergy with
 novel stressors. Wildlife markers can be a source of physiological stress
 for animals and are ubiquitous in ecological studies. Their potential
 impacts on vital rates may vary over time, particularly when changing
 environments impose new stressors. In this study, we evaluated the
 temporal changes in the combined impact of two stressors, one constant
 (collar-marking) and another one variable over time (hunting intensity),
 in greater snow geese (Anser caerulescens atlantica). Over a 30-year
 period (1990-2019), hunting regulations were liberalized twice, in 1999
 and 2009, with the instauration of special spring and winter hunting
 seasons, respectively. We evaluated the effect of collars on goose
 survival through this period of changing hunting regulations. We compared
 annual survival of &gt;20,000 adult females marked with and without
 neck collars using multievent capture-recapture models, and partitioned
 hunting from non-hunting mortality. Survival of geese marked with or
 without collars was similar in 1990-1998, before hunting regulations were
 liberalized (average survival[95% C.I.]: 0.87[0.84, 0.89]). However,
 absolute survival of collared geese was 0.05[0.03, 0.07] lower than that
 of non-collared geese between 1999 and 2009, and 0.12[0.09, 0.15] lower
 after hunting regulations were liberalized further in 2009. Hunting and
 non-hunting mortality were both higher in collared birds compared to those
 without collars. The interaction between the effects of collars and
 hunting was synergistic because collars affected survival only after the
 hunting pressure increased significantly. These cumulated stresses
 probably reduced goose body condition sufficiently to increase their
 vulnerability to multiple sources of mortality. Synthesis and
 applications: Researchers relying on long-term marking programs should
 evaluate the effect of markers periodically rather than solely in the
 beginning, as interactions with changing environmental conditions may
 eventually affect conclusions of studies based on marked animals. Here, we
 provide a rare demonstration in a natural setting that a combination of
 stressors can push animals beyond a threshold where vital rates are
 affected, even when one stressor applied alone initially had no detectable
 impact.
description: Dataset A: Individual Capture histories The
 capture histories were derived from the capture-mark-recapture (and
 recovery) data presented in the main text of the manuscript linked to this
 dataset. The dataset was thoroughly inspected and checked for errors (e.g.
 birds observed before they were marked or observations made after the ring
 of a dead bird was reported), and histories containing errors (~200
 histories) were removed. The data file is formatted to
 be read by program E-SURGE  [Data -&gt; Load Data(Biomeco); make sure
 the 'All files (*.*)' option is selected in the browser window].
 The first line contains 2 numbers. The first is the number of unique
 capture histories and the second the number of columns in the main data
 block. Lines 4 to 2400 contain the unique capture histories. Each line
 represents a unique capture history and each of the first 30 columns
 represent observations for each yearly capture occasion from 1990 to 2019.
 A '1' represents a physical recapture of a leg-ringed only bird,
 a '2' the physical recapture or distant observation of a bird
 wearing a leg ring and a collar, a '3' represents the recovery
 of a dead bird during the interval preceding the occasion, and a
 '0' the absence of any observation at that particular capture
 occasion. The last column of capture histories is the number of birds
 sharing that unique history. Dataset B: Annual Greater
 Snow Goose Harvest Rate This file contains the
 standardized yearly harvest rate of greater snow geese in Canada and the
 USA between 1990 and 2018. This data was processed following the
 procedures described in Appendix 2 of the manuscript linked to this
 dataset. The data file is formatted to be read by
 program E-SURGE [Gemaco -&gt; File with external variables]. The first
 line contains one number indicating the number of covariates present in
 the file. The second line contains the number of occasions for which a
 temporal covariate is used (must match the number of capture occasions -
 1). The third line is the standardized yearly harvest rate used in the
 analyses calculating hunting and non-hunting mortality.
description: All data files can be read using program E-SURGE (Choquet, Rouan
 et al. 2009). This software is freely accessible from: https://www.cefe.cnrs.fr/fr/recherche/bc/bbp/264-logiciels Ref. : Choquet, R., Rouan, L., & Pradel, R. (2009). Program E-Surge: A Software Application for Fitting Multievent Models. In D. L. Thomson, E. G. Cooch, & M. J. Conroy (Eds.), <em>Modeling Demographic Processes In Marked Populations</em> (pp. 845–865). Springer US. https://doi.org/10.1007/978-0-387-78151-8_39