Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species.
LeTourneux, Frédéric et al. (2022), Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species., Dryad, Dataset, https://doi.org/10.5061/dryad.j6q573nh4
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 >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.
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 -> 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 -> 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.
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.), Modeling Demographic Processes In Marked Populations (pp. 845–865). Springer US. https://doi.org/10.1007/978-0-387-78151-8_39
Environment and Climate Change Canada
Natural Sciences and Engineering Research Council of Canada
U.S. Fish and Wildlife Service
Arctic Goose Joint Venture
Polar Continental Shelf Program (Natural Resources Canada)
Northern Scientific Training Program (Polar Knowledge Canada)
Atlantic Flyway Council
ArcticNet network of Center of Excellence
Fonds de recherche du Québec – Nature et technologies Strategic Cluster Program