DOI: 10.5061/dryad.hqbzkh1n9
title: Explaining the divergence of population trajectories for two interacting waterfowl species

description: Identifying the specific environmental features and associated
 density-dependent processes that limit population growth is central to
 both ecology and conservation. Comparative assessments of sympatric
 species allow for inference into how ecologically similar species
 differentially respond to their shared environment, which can be used to
 inform community-level conservation strategies. Comparative assessments
 can nevertheless be complicated by interactions and feedback loops among
 the species in question. We developed an integrated population model based
 on sixty-one years of ecological data describing the demographic histories
 of Canvasbacks (Aythya valisineria) and Redheads (Aythya americana), two
 species of migratory diving ducks that utilize similar breeding habitats
 and affect each other’s demography through interspecific nest parasitism.
 We combined this model with a transient life table response experiment to
 determine the extent that demographic rates, and their contributions to
 population growth, were similar between these two species. We found that
 demographic rates and, to a lesser extent, their contributions to
 population growth covaried between Canvasbacks and Redheads, but the
 trajectories of population abundances widely diverged between the two
 species during the end of the 20th century due to inherent differences
 between the species life-histories and sensitivities to both environmental
 variation and harvest pressure. We found that annual survival of both
 species increased during years of restrictive harvest regulations;
 however, recent harvest pressure on female Canvasbacks may be contributing
 to population declines. Despite periodic, and often dramatic, increases in
 breeding abundance during wet years, the number of breeding Canvasbacks
 declined by 13% whereas the number of breeding Redheads has increased by
 37% since 1961. Reductions in harvest pressure and improvements in
 submerged aquatic vegetation throughout the wintering grounds have
 mediated the extent to which populations of both species contracted during
 dry years in the Prairie Pothole Region. However, continued degradation of
 breeding habitats through climate-related shifts in wetland hydrology and
 agricultural conversion of surrounding grassland habitats may have
 exceeded the capacity for demographic compensation during the non-breeding
 season.
description: DATA COLLECTION We combined a series of
 long-term data sets into a single integrated population model that
 provided insights into how variation in seasonal survival (band releases
 and recoveries) and offspring production (harvest age-ratios) contributed
 to fluctuations in population growth (breeding survey, harvest estimates)
 for Canvasbacks and Redheads from 1961–2021.
 <em>Banding Data </em>–<em>
 </em>Information regarding the banding and subsequent harvest of
 ducks was acquired from the GameBirds Database CD (Bird Banding Lab, USGS
 Patuxent Wildlife Research Center, Laurel MD, USA,
 <em>version</em> August 2022). Male and female Canvasbacks and
 Redheads were captured following breeding but prior to the hunting season
 (Pre-Hunting) as ducklings (Local) or hatch year (HY; fledged juvenile)
 individuals as well as after hatch year (AHY; adult) individuals or
 following the hunting season (Post-Hunting) as an undifferentiated mixture
 of second year (SY) and after second year (ASY) individuals captured and
 released across North America from 1961–2022. We limited the pre-harvest
 banding data for both species to include all individuals banded and
 released alive in areas within the Canadian provinces of Alberta,
 Manitoba, Saskatchewan, as well as the states of Minnesota, Montana, North
 Dakota, and South Dakota within the USA (Fig. 1). For the pre-hunting
 banding group, we retained individuals captured between 1961–2021 during
 the late summer (Jul 15<sup>th</sup><em> </em>–
 Sep 15<sup>th</sup>) with a known sex (M or F) and age-class
 (local, HY or AHY) that were released without any additional markers
 considered to meaningfully affect survival of an individual (e.g., nasal
 saddles or dual banding were permissible but telemetered individuals were
 excluded; Lameris & Kleyheeg, 2017). For post-hunting
 banding<em>, </em>we limited the spatial boundary of banding
 efforts to only consider individuals released from the Atlantic, Central,
 or Mississippi Flyways (Fig. 1). We followed the same data selection
 procedures, but limited releases to occur between Jan.
 1<sup>st</sup><em> </em>– March
 15<sup>th</sup> from 1962–2022. Because too few banders
 differentiated SY from ASY at time of banding, we treated all post-hunting
 samples as AHY adults. Individuals banded during this period that were
 reported to be harvested during the winter they were originally banded
 were censored from the analysis, as the underlying model assumption was
 that this cohort of individuals had already survived the current hunting
 season. For both seasonal banding efforts, we only
 included recoveries of hunter-shot individuals harvested between September
 and February in which a known year-of-death could be ascertained. In
 addition to self-reported recoveries (i.e., reported by the hunter), we
 included hunter-harvested individuals that were instead reported by
 federal, state, or provincial entities (e.g., outcomes of hunter check
 stations or other forms of solicitation). We limited the dataset to only
 include recoveries of hunter-harvested individuals killed within 15 years
 of initial banding, which represented &gt; 99% of pre-hunting and
 post-hunting recoveries. This cut-off was arbitrarily selected but did not
 meaningfully bias parameter estimation while vastly improving
 computational efficiency by bypassing the estimation of hundreds of
 zero-equivalent cell probabilities (<em>personal communication
 </em>S. Bonner). <em>Harvest
 Intensity</em> – We used the average number of Canvasbacks or
 Redheads allowed to be harvested per day (i.e., bag limit; (Appendix S1:
 Tables S1a-b) across the U.S. portions of the Atlantic, Mississippi,
 Central, and Pacific flyways during each year of the study as an index of
 harvest regulatory pressure. Annual harvest restrictions were acquired
 from the published literature (Péron et al., 2012), the annual release of
 the Late-Season Migratory Bird Hunting Regulations (e.g., USFWS 2022), and
 direct requests to the U.S. Fish and Wildlife Service. For these species,
 liberal harvest regulations were bag limits of two (Canvasbacks) and two
 to four (Redheads) allowable harvest per day, whereas conservative harvest
 regulations were either a bag limit of one individual per day or total
 closure. <em>Harvest Composition </em>–
 Data describing the age and sex structure of the harvested Canvasback and
 Redhead populations were derived from the annual Parts Collection surveys
 conducted by the U.S. Fish and Wildlife Service (USFWS) where a subset of
 hunters submit a wing from every duck they harvested (Pearse et al. 2014).
 These data were acquired through a direct request to the U.S. Fish and
 Wildlife Service. Additionally, estimates of the total number of
 Canvasbacks and Redheads harvested in the United States and Canada were
 derived from the Harvest Information Program (Steeg et al., 2002) and
 Canadian National Harvest Survey (Smith et al., 2022),
 respectively. <em>Breeding Duck and Pond
 Densities </em>– The relative number of breeding Canvasbacks and
 Redheads, as well as the relative amount of their breeding habitat (i.e.,
 flooded ponds) within the Prairies were calculated using count data from
 the USFWS Waterfowl Breeding Population and Habitat Survey (hereafter
 BPOP; Smith, 1995), which has conducted an annual survey of breeding
 waterfowl and their habitats throughout the core part of these species’
 breeding ranges (i.e., central Canada and the north-central United States)
 during the spring from 1961 through 2022 (U.S. Fish and Wildlife Service,
 2022). However, BPOP surveys did not occur during 2020 and 2021. For the
 purposes of this study, we limited the spatial extent of BPOP survey to
 only include transects flown within Alberta, Manitoba, Saskatchewan,
 Montana, North Dakota, and South Dakota.
 <em>Agriculture Development </em>– The amounts of
 active cropland in the Prairies during each year of the study were
 estimated from Canada and United States Agriculture Census data (see
 Buderman <em>et al.</em>, 2020). Annual estimates of active
 cropland acreages were summarized to represent an index of agricultural
 development during 1961–2021. Although agricultural development is
 predicted to have greater impact on upland-nesting dabbling ducks (Duncan
 and Devries 2018), it also impacts the wetland habitats in which
 Canvasbacks and Redheads forage and nest, as well as the predator
 communities that can access overwater nesting pochards (Sargeant et al.
 1993, Bartzen et al. 2010). <em>Winter Habitat
 </em>–<strong><em> </em></strong>Winter
 habitat conditions were assumed to be related to submerged aquatic
 vegetation (SAV) within the Chesapeake for Canvasbacks and environmental
 salinity (TDS; total dissolved solids) in the Laguna Madre for Redheads.
 Although Redheads likely respond to variation in SAV, time series data
 describing SAV were not available for the Laguna Madre. Therefore, we
 assumed that annual fluctuations in salinity were an informative proxy of
 both SAV conditions and osmotic constraints (Quammen and Onuf 1993, Moore
 2009), which in turn was representative of winter habitat conditions that
 simultaneously influenced Redhead food availability and harvest risk
 (Ballard <em>et al</em>. 2021)..
 <em>Climate Data</em> – We used the average
 Pacific/North American (PNA; Leathers <em>et al.</em>, 1991)
 teleconnection pattern from April–July as an index of drought severity or
 environmental stress during the breeding season throughout the Prairies,
 and average sea-surface temperatures (SST) from September–March in the
 Chesapeake and Laguna Madre as an index of winter severity for Canvasbacks
 and Redheads, respectively (see Data Availability statement).
description: # Manuscript Details: ## Journal Name: Ecological Monographs (submitted)
 ### Title: Explaining the divergence of population trajectories for two
 interacting waterfowl species. Author(s): Gibson, D.(1,2a), T.W. Arnold
 (2), F.E. Buderman (3) D.N. Koons (1), 1 Department of Fisheries,
 Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN
 55455 2 Department of Ecosystem Science and Management, Pennsylvania State
 University, University Park, PA 16802 3 Department of Fish, Wildlife, and
 Conservation Biology & Graduate Degree Program in Ecology, Colorado
 State University, Fort Collins, Colorado 80523 USA a Corresponding Author:
 [gibso678@umn.edu](mailto:gibso678@umn.edu) # Decomposing the drivers of
 Canvasback and Redhead population change: Code and data to develop
 explantory variables, build a population model, and perform a transient
 life table response experiment --- We have provided the raw agricultural
 (**crop.rdata**), wetland abundance (**ponds.rdata**), and winter habitat
 (**winter.rdata**) data, and Bayesian model code, model constraints, and
 initial values needed to run the environmental covariate sub-models
 (**generate_covariates.R**) that, inconjunction with data describing
 variation in waterfowl survival, fecundity, and population sizes
 (**model_files.rdata**) were used to inform the integrated population
 model (**IPM_code.R**) for Canvasback and Redhead ducks in North America
 from 1961 - 2021. The environmental and population sub-models were
 parameterized using the Nimble library platform in R. The posterior
 distributions from these models (**estimated_vital_rates.rdata**) were
 used to perform a transient lifetable response experiment
 (**LTRE_Code.R**) ## Data Descriptions ### **crop.rdata** * *crop.dat* :
 The log-scaled amount of active cropland (in thousands of hectares) within
 the Prairie Pothole Region, which was calculated and provided by Canada
 and United States Agriculture Census data. Census data from each country
 occurred approximately every 5 years. Values during years in which a
 census was not completed are represented with NAs. * *crop.constants* :
 The model constraints required to perform the agricultural conditions
 sub-model in Nimble. See **generate_covariates.R** * *crop.inits* : The
 initial values for parameters of interest required to perform the
 agricultural conditions sub-model in Nimble. See **generate_covariates.R**
 ### **winter.rdata** * *winter.dat* : Data file containing informating
 describing the amount of sub-aquatic vegetation or salinity in the
 Cheasapeake Bay or Laguna Madre, as well as potential environmental
 correlates. * *sav* : Sub-aquatic Vegetation (log-scaled hectares) in the
 Cheasapeake Bay, USA * *salinity.lgm* : Measured salinity (log-scaled)
 throughout the Laguna Madre of Texas, USA * *salinity.chb* : Measured
 salinity (log-scaled) in the Cheasapeake Bay, USA * *covs.lgm* :
 Environmental covariates fit to describe salinity in the Laguna Madre of
 Texas, USA * ACE : Accumulated Cyclone Energy * RESIDS : Residual Sea
 Surface Temperaturesn the Laguna Madre * PREC6 : Precipitation in the
 Laguna Madre during the spring and summer * FLOW6 : Freshwater flow in the
 Laguna Madre during the spring and summer * *covs.chb* : * ACE :
 Accumulated Cyclone Energy * RESIDS : Residual Sea Surface Temperatures in
 the Cheasapeake Bay * PREC : Precipitation in the Laguna Madre during the
 spring and summer in the Cheasapeake Bay * *winter.constants* : The model
 constraints required to perform the winter habitat sub-model in Nimble.
 See **generate_covariates.R** * *winter.inits* : The initial values for
 parameters of interest required to perform the winter habitat sub-model in
 Nimble. See **generate_covariates.R** ### **pond.rdata** * *pond.dat* :
 The estimated number of ponds (in thousands) in the prairies
 (**$prairies**) and the associated standard error (**$sig_ponds**) that
 were generated from the annual Breeding Population Survey for Waterfowl
 and their Habitats. The breeding population survey was not conducted in
 2020 and 2021 due to COVID restrictions and values during this time frame
 are represented by NAs * *pond.constants* : The model constraints required
 to perform the breeding pond abundance sub-model in Nimble. See
 **generate_covariates.R** * *pond.inits* : The initial values for
 parameters of interest required to perform the breeding pond abundance
 sub-model in Nimble. See **generate_covariates.R** ###
 **model\_files.rdata** * dat : The data file necessary to run the Redhead
 and Canvasback Integrated Population Model in Nimble. This file contains
 the annual estimates in each modeled covariate and their associated
 uncertainity. Likewise, this file also includes the demographic data
 necessary estimate waterfowl fecundity (age ratios at harvest),
 cause-specific mortality (band recoveries), and abundance (bpop surveys
 and total harvest). Due to the complexity of these data, please contact
 the lead author for questions regarding these data, their use, and the
 modeling procedure. * constants : The model constraints required to
 perform the integrated population model for Canvasbacks and Redheads in
 Nimble. See **IPM_code.R** * inits : The initial values for parameters of
 interest required to perform the integrated population model for
 Canvasbacks and Redheads in Nimble. See **IPM_code.R** ###
 **estimated\_vital\_rates.rdata** **File components correspond with the
 estimates generated from the Integrated Population Model (IPM_code.R**)
 that were required to perform the Life Table Response Experiment
 (**LTRE_Code.R**) * *Reproductive Success* * *fecundity*: individual draws
 (dim 3 from) posterior distributions for annual per-capita fecundity rates
 for Canvasbacks (dim 1: level: 1) and Redheads (dim 1: level: 2) from
 1961:2020 (dim 2) * *bp* individual draws (dim 2 from) posterior
 distributions for the discrepancy between HY and AHY female reproducive
 success in Canvasbacks (dim 1: level: 1) and Redheads (dim 1: level: 2) *
 *Abundance*: * *naf*: individual draws (dim 3 from) posterior
 distributions for the breeding abudances of AHY female Canvasbacks (dim 1:
 level: 1) and Redheads (dim 1: level: 2) from 1961:2020 (dim 2) * *nam*:
 individual draws (dim 3 from) posterior distributions for the breeding
 abudances of AHY male Canvasbacks (dim 1: level: 1) and Redheads (dim 1:
 level: 2) from 1961:2020 (dim 2) * *njf*: individual draws (dim 3 from)
 posterior distributions for the breeding abudances of HY female
 Canvasbacks (dim 1: level: 1) and Redheads (dim 1: level: 2) from
 1961:2020 (dim 2) * *njm*: individual draws (dim 3 from) posterior
 distributions for the breeding abudances of HY male Canvasbacks (dim 1:
 level: 1) and Redheads (dim 1: level: 2) from 1961:2020 (dim 2) * *Harvest
 Mortality* * *kappa_ahy_c_f*: individual draws (dim 2 from) posterior
 distributions for the harvest probabilities AHY female Canvasbacks from
 1961:2020 (dim 1) * *kappa_ahy_c_m*: individual draws (dim 2 from)
 posterior distributions for the harvest probabilities AHY male Canvasbacks
 from 1961:2020 (dim 1) * *kappa_ahy_r_f*: individual draws (dim 2 from)
 posterior distributions for the harvest probabilities AHY female Redheads
 from 1961:2020 (dim 1) * *kappa_ahy_r_m*: individual draws (dim 2 from)
 posterior distributions for the harvest probabilities AHY male Redheads
 from 1961:2020 (dim 1) * *kappa_hy_c_f*: individual draws (dim 2 from)
 posterior distributions for the harvest probabilities HY female
 Canvasbacks from 1961:2020 (dim 1) * *kappa_hy_c_m*: individual draws (dim
 2 from) posterior distributions for the harvest probabilities HY male
 Canvasbacks from 1961:2020 (dim 1) * *kappa_hy_r_f*: individual draws (dim
 2 from) posterior distributions for the harvest probabilities HY female
 Redheads from 1961:2020 (dim 1) * *kappa_hy_r_m*: individual draws (dim 2
 from) posterior distributions for the harvest probabilities HY male
 Redheads from 1961:2020 (dim 1) * *Winter Mortality* * *s.winter_ahy_c_f*:
 individual draws (dim 2 from) posterior distributions for the natural
 winter survival probabilities AHY female Canvasbacks from 1961:2020 (dim
 1) * *s.winter_ahy_c_m*: individual draws (dim 2 from) posterior
 distributions for the natural winter survival probabilities AHY male
 Canvasbacks from 1961:2020 (dim 1) * *s.winter_ahy_r_f*: individual draws
 (dim 2 from) posterior distributions for the natural winter survival
 probabilities AHY female Redheads from 1961:2020 (dim 1) *
 *s.winter_ahy_r_m*: individual draws (dim 2 from) posterior distributions
 for the natural winter survival probabilities AHY male Redheads from
 1961:2020 (dim 1) * *s.winter_hy_c_f*: individual draws (dim 2 from)
 posterior distributions for the natural winter survival HY female
 Canvasbacks from 1961:2020 (dim 1) * *s.winter_hy_c_m*: individual draws
 (dim 2 from) posterior distributions for the natural winter survival HY
 male Canvasbacks from 1961:2020 (dim 1) * *s.winter_hy_r_f*: individual
 draws (dim 2 from) posterior distributions for the natural winter survival
 HY female Redheads from 1961:2020 (dim 1) * *s.winter_hy_r_m*: individual
 draws (dim 2 from) posterior distributions for the natural winter survival
 HY male Redheads from 1961:2020 (dim 1) * *Summer Mortality* *
 *s.summer_c_f*: individual draws (dim 2 from) posterior distributions for
 the natural summer survival probabilities female Canvasbacks from
 1961:2020 (dim 1) * *s.summer_c_m*: individual draws (dim 2 from)
 posterior distributions for the natural summer survival probabilities male
 Canvasbacks from 1961:2020 (dim 1) * *s.summer_r_f*: individual draws (dim
 2 from) posterior distributions for the natural summer survival
 probabilities female Redheads from 1961:2020 (dim 1) * *s.summer_r_m*:
 individual draws (dim 2 from) posterior distributions for the natural
 summer survival probabilities male Redheads from 1961:2020 (dim 1) ##
 Model Code Descriptions * generate_covariates.R is an R object that
 contains the code to run each of the environmental sub-models. Results
 from these models are already embedded in the **model_files.rdata** file.
 * Data prerequisite: crops.rdata, ponds.rdata, winter.rdata * IPM_code.R
 is an R object that contains the Nimble code necessary to perform the
 multispecies intergrated population model. * Data prerequisite:
 model_files.rdata * LTRE_Code.R is an R object that contains the code
 necessary to perform a transient life table response experiment based on
 vital rates estimated from the multispecies IPM. * Data prerequisite:
 estimated_vital_rates.rdata ## Code/Software R is required to load each
 .rdata file and run each .R script; the script was created using version
 4.4.0. The Nimble library package in R in addition to its dependencies are
 required to perform the Bayesian analyses presented in
 generate_covariates.R and IPM_code.R