\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Low-coverage whole-genome sequencing for highly accurate population
assignment: Mapping migratory connectivity in the American redstart
(Setophaga ruticilla)}



\author[1]{Matthew DeSaix}%
\author[2]{Eric Anderson}%
\author[1]{Christen Bossu}%
\author[3]{Christine Rayne}%
\author[1]{Teia Schweizer}%
\author[4]{Nicholas Bayly}%
\author[5]{Darshan Narang}%
\author[6]{Julie Hagelin}%
\author[7]{H. Lisle Gibbs}%
\author[8]{James Saracco}%
\author[9]{Thomas W Sherry}%
\author[10]{Michael Webster}%
\author[11]{Thomas Smith}%
\author[12]{Peter Marra}%
\author[1]{Kristen  Ruegg}%
\affil[1]{Colorado State University}%
\affil[2]{NOAA Fisheries Southwest Fisheries Science Center Fisheries Ecology Division}%
\affil[3]{Colorado State University Department of Biology}%
\affil[4]{SELVA Investigación para la conservación en el Neotropico}%
\affil[5]{Trinidad and Tobago Field Naturalists’ Club}%
\affil[6]{State of Alaska Department of Fish and Game}%
\affil[7]{The Ohio State University}%
\affil[8]{The Institute for Bird Populations}%
\affil[9]{Tulane University}%
\affil[10]{Cornell Lab of Ornithology}%
\affil[11]{UCLA Center for Tropical Research}%
\affil[12]{Georgetown University}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Understanding the geographic linkages among populations across the
annual cycle is an essential component for understanding the ecology and
evolution of migratory species and for facilitating their effective
conservation. While genetic markers have been widely applied to describe
migratory connections, the rapid development of new sequencing methods,
such as low-coverage whole genome sequencing (lcWGS), provides new
opportunities for improved estimates of migratory connectivity. Here, we
use lcWGS to identify fine-scale population structure in a widespread
songbird, the American Redstart (Setophaga ruticilla), and accurately
assign individuals to genetically distinct breeding populations.
Assignment of individuals from the nonbreeding range reveals
population-specific patterns of varying migratory connectivity. By
combining migratory connectivity results with demographic analysis of
population abundance and trends, we consider full annual cycle
conservation strategies for preserving numbers of individuals and
genetic diversity. Notably, we highlight the importance of the Northern
Temperate-Greater Antilles migratory population as containing the
largest proportion of individuals in the species. Finally, we highlight
valuable considerations for other population assignment studies aimed at
using lcWGS. Our results have broad implications for improving our
understanding of the ecology and evolution of migratory species through
conservation genomics approaches.%
\end{abstract}%



\sloppy


\textbf{Low-coverage whole genome sequencing for highly accurate
population assignment: Mapping migratory connectivity in the American
Redstart (\emph{Setophaga ruticilla})}

\textbf{Running title:} American Redstart migratory connectivity

\textbf{Authors} : Matthew G. DeSaix\textsuperscript{1}, Eric C.
Anderson\textsuperscript{2,1,3}, Christen M. Bossu\textsuperscript{1},
Christine E. Rayne\textsuperscript{1}, Teia M.
Schweizer\textsuperscript{3}, Nicholas J. Bayly\textsuperscript{4},
Darshan S. Narang\textsuperscript{5}, Julie C.
Hagelin\textsuperscript{6}, H. Lisle Gibbs\textsuperscript{7,8}, James
F. Saracco\textsuperscript{9}, Thomas W. Sherry\textsuperscript{10,11},
Michael S. Webster\textsuperscript{12,13}, Thomas B.
Smith\textsuperscript{14,15}, Peter P. Marra\textsuperscript{16,17},
Kristen C. Ruegg\textsuperscript{1}

\textbf{Affiliations} :

\textsuperscript{1} Department of Biology, Colorado State University,
Fort Collins, Colorado, USA

\textsuperscript{2} Fisheries Ecology Division, Southwest Fisheries
Science Center, National Marine Fisheries Service, NOAA, Santa Cruz,
California, USA

\textsuperscript{3} Department of Fisheries, Wildlife, and Conservation
Biology, Colorado State University, Fort Collins, Colorado, USA

\textsuperscript{4} SELVA Investigaci\selectlanguage{ngerman}ón para la conservación en el
Neotropico, DG42A \#20-37, Bogotá, Colombia

\textsuperscript{5} Trinidad and Tobago Field Naturalists' Club, Port of
Spain, Trinidad and Tobago

\textsuperscript{6} Threatened, Endangered and Diversity Program, Alaska
Department of Fish and Game, Fairbanks, Alaska, USA

\textsuperscript{7} Department of Evolution, Ecology, and Organismal
Biology, The Ohio State University, Columbus, Ohio, USA

\textsuperscript{8} Ohio Biodiversity Conservation Partnership, The Ohio
State University, Columbus, Ohio, USA

\textsuperscript{9} The Institute for Bird Populations, Petaluma,
California, USA

\textsuperscript{10} Department of Ecology and Evolutionary Biology,
Tulane University, New Orleans, Louisiana, USA

\textsuperscript{11} Department of Biological Sciences, Dartmouth
College, Hanover, New Hampshire, USA

\textsuperscript{12} Cornell Lab of Ornithology, Ithaca, New York, USA

\textsuperscript{13} Department of Neurobiology and Behavior, Cornell
University, Ithaca, New York, USA

\textsuperscript{14} Center for Tropical Research, Institute of the
Environment \& Sustainability, University of California Los Angeles, Los
Angeles, California, USA

\textsuperscript{15} Department of Ecology and Evolutionary Biology,
University of California Los Angeles, Los Angeles, California, USA

\textsuperscript{16} Department of Biology,~Georgetown University,
Washington, DC, USA

\textsuperscript{17} McCourt School of Public Policy,~Georgetown
University, Washington, DC, USA

Corresponding author: mgdesaix@gmail.com

\textbf{Abstract}

Understanding the geographic linkages among populations across the
annual cycle is an essential component for understanding the ecology and
evolution of migratory species and for facilitating their effective
conservation. While genetic markers have been widely applied to describe
migratory connections, the rapid development of new sequencing methods,
such as low-coverage whole genome sequencing (lcWGS), provides new
opportunities for improved estimates of migratory connectivity. Here, we
use lcWGS to identify fine-scale population structure in a widespread
songbird, the American Redstart (\emph{Setophaga ruticilla} ), and
accurately assign individuals to genetically distinct breeding
populations. Assignment of individuals from the nonbreeding range
reveals population-specific patterns of varying migratory connectivity.
By combining migratory connectivity results with demographic analysis of
population abundance and trends, we consider full annual cycle
conservation strategies for preserving numbers of individuals and
genetic diversity. Notably, we highlight the importance of the Northern
Temperate-Greater Antilles migratory population as containing the
largest proportion of individuals in the species. Finally, we highlight
valuable considerations for other population assignment studies aimed at
using lcWGS. Our results have broad implications for improving our
understanding of the ecology and evolution of migratory species through
conservation genomics approaches.

\textbf{Keywords} : assignment bias, conservation genomics, effective
sample size, migration, population genetics, songbird



\textbf{Introduction}

Long-distance migratory species pose distinct challenges to studies of
ecology, evolution, and conservation because they occupy different
geographical regions throughout the year that can be separated by
thousands of kilometers. At each stage in the migratory annual cycle,
migrant populations are subject to various stressors that can influence
their fitness . As a result, effective conservation efforts require
understanding migratory connectivity, defined as the links between
different geographic regions used across the annual cycle . In the past
20 years, population genetics has become a well-established means for
tracking migratory populations, especially for studies involving large
sample sizes or small-bodied individuals . However, the value of genetic
markers is often limited by the amount of genetic differentiation in a
species and the availability of genetic data from individuals across the
annual cycle .

Population assignment methods originated in the early 1980s and 1990s as
a means of identifying breeding origins of migratory individuals back to
distinct tributaries (in the case of fish) or geographic regions (in the
case of bears) . Early methods relied on genetic markers that were
limited to identifying only deep phylogeographic breaks within species .
In recent years, next generation sequencing has facilitated the
screening of a significantly larger number of genetic markers allowing
for the delineation of breeding populations at finer spatial scales .
Cost-effective delineation of patterns of migratory connectivity was
made possible by designing single nucleotide polymorphisms (SNP) assays
for a subset of these markers that were particularly useful for
population assignment . While recent reductions in the cost of whole
genome sequencing have made it possible to directly use low-coverage
whole genome sequencing (lcWGS) data to screen migrant samples, the lack
of software capable of dealing with the increase in marker number has
prevented this method from being used for population assignment (DeSaix
et al. in review).

Low-coverage WGS has made sequencing more affordable for non-model
organisms by reducing the sequencing effort per individual, however it
has distinct challenges. One of these challenges is dealing with low
sequencing read depths per individual, which necessitates the use of
probabilistic frameworks for genotype calling to account for the
uncertainty inherent in the data . Accurate estimates of parameters such
as allele frequency can be obtained by prioritizing larger sample sizes
of individuals with lower sequencing depth . Guidelines for achieving
accurate allele frequency estimation with lcWGS include sequencing
individuals at a minimum of 1X coverage or having at least 10
individuals sequenced with a total sequencing depth of at least 10X . To
take advantage of lcWGS data for population assignment, DeSaix et al.
(\emph{in review} ) recently developed a software package, WGSassign,
that accounts for uncertainty inherent to lcWGS data in population
assignment tests. Here, for the first time, we use lcWGS data to assign
migrants to their population of origin.

The American Redstart (\emph{Setophaga ruticilla} ) is an ideal system
for evaluating the potential gains in effectiveness achievable by using
lcWGS data for population assignment because previous studies using a
variety of methods provide a strong foundation for comparisons. The
American Redstart is a widely distributed migratory songbird with a
breeding distribution across North America and stationary nonbreeding
distribution throughout the Caribbean, northern South America, Central
America, and Mexico . For several decades, the American Redstart has
been a model species for understanding migratory ecology and has been
used to elucidate territoriality on the wintering grounds , foraging
behavior , habitat selection , and carry-over effects of stressors
across the annual cycle . Phylogeographic structure has previously been
detected between a small region in the Maritime Provinces, specifically
in Newfoundland and New Brunswick in the northeastern portion of the
range, and the rest of the continental breeding range using mtDNA .
Subsequent analysis of migratory connectivity using mtDNA revealed that
Newfoundland breeders overwintered on the islands of Puerto Rico and the
Dominican Republic, while continental breeding birds overwintered across
the entire nonbreeding range . Stable isotope studies have shown strong
migratory connectivity, with eastern breeding birds overwintering in the
Caribbean and western breeding birds overwintering in Central America
and Mexico , but whether these migratory differences correspond to
genetic differentiation has not been tested.

Here we aim to demonstrate the effectiveness of using lcWGS data for
population assignment of nonbreeding individual using the American
Redstart as a model species. Our main objectives were: 1) Identify
population-specific migratory connectivity in the American Redstart
using lcWGS data, 2) Assess conservation implications of migratory
connectivity by identifying relative abundance and trends in population
size, and 3) Provide study design recommendations to facilitate the use
of lcWGS data in other population assignment studies. Our results have
broad implications for improving our understanding of the ecology and
evolution of migratory species through conservation genomics approaches.

\textbf{Methods}

\emph{Genetic sampling and library preparation}

Sample site locations were chosen to maximize sampling coverage across
the breeding and nonbreeding ranges of the American Redstart. We used
genetic samples from a total of 330 individuals: 182 individuals from 16
locations across the breeding range and 148 individuals from 15
locations in the nonbreeding range (Table S1). Sample collection
occurred between 1993 and 2022 and consisted of either blood from
brachial venipuncture or feathers. We extracted DNA from blood samples
using the standard protocol for Qiagen DNEasy Blood and Tissue Kits and
we modified the protocol to maximize DNA yield from feathers. Whole
genome sequencing libraries were prepared following modifications of
Illumina's Nextera Library Preparation protocol . Pooled libraries were
sequenced on eight HiSeq 4000 lanes at Novogene Corporation Inc with a
target sequencing depth of 2X per individual.

\emph{Bioinformatics}

We trimmed the sequence data to remove potential PCR artifacts using the
program TrimGalore version 0.6.5
(https://github.com/FelixKrueger/TrimGalore), a wrapper for Cutadapt .
We used the Burrows-Wheeler Aligner software version 0.7.17 to map reads
to a reference genome from the closely related Yellow Warbler
(\emph{Setophaga petechia} ; Bay et al. 2018). After mapping, the
resulting SAM files were sorted, converted to BAM files, and indexed
using Samtools version 1.9 . We marked read duplicates with
MarkDuplicates from GATK version 4.1.4.0 and clipped overlapping reads
with the clipOverlap function from bamUtil
(https://genome.sph.umich.edu/wiki/BamUtil:\_clipOverlap). Sequencing
depth for individuals was calculated using Samtools. Initial population
genetics analyses revealed a large effect in the data due to high
variation in sequencing depth among individuals. To reduce sequencing
depth variation, we followed the recommendations of and used the
DownsampleSam function from GATK to randomly down sample reads from BAM
files with greater than 2X coverage, to 2X coverage.

To identify genetic markers from low-coverage WGS data, we used
stringent filtering options in ANGSD version 0.9.40 (). We retained
reads with a mapping quality of at least 30 and base quality of at least
33. SNPs were identified based on a p-value of less than 1e-6. We
retained SNPs that had read data in at least 50\% of individuals
(\emph{n} = 165), a minor allele frequency greater than 0.05, and
minimum and maximum total depths of 231 and 924, respectively. The
minimum total depth threshold was chosen by the minimum number of
individuals required to call a variant (\emph{n} = 165) multiplied by
the mean sequencing depth of all individuals (1.4X). The maximum total
depth threshold was determined by 2 * total number of individuals * mean
sequencing depth. The filtered variants were output as genotype
likelihoods and used in subsequent analyses.

\emph{Genetically distinct breeding populations}

Given that signatures of population structure can be skewed by closely
related individuals, we used NGSrelate version 2 to identify and remove
individuals with with up to second-degree relationships (kinship
\textgreater{} 0.0884). We implemented principal components analysis
(PCA) and estimated individual admixture proportions using Pcangsd ,
which estimates individual allele frequencies to minimize bias from low
and variable sequencing depth. We determined the number of genetically
distinct breeding populations of American Redstarts by identifying
congruent geographic signatures of clustering in the PCA with groupings
of individuals based on admixture proportions. Posterior probabilities
of group membership from the admixture proportions were visualized on a
base map from Natural Earth (naturalearthdata.com) with each group
specified by a different colour, and clipped to the breeding range of
the American Redstart (Strimas-Mackey et al., 2021). Colour transparency
was scaled such that the highest posterior probability of group
membership is opaque while the smallest posterior probability is
transparent. Visualization was performed in R (R Core Team, 2021).
Genetic differentiation among the breeding populations was calculated by
creating site allele frequency files for each breeding population and
calculating F\textsubscript{ST} in ANGSD ().

\emph{Effective sample size and population assignment}

To assess our ability to accurately assign individuals of unknown origin
to breeding populations, we first determined the accuracy of assignment
of the known breeding origin individuals using a leave-one-out approach
implemented in WGSassign (DeSaix et al. \emph{in review} ).
Leave-one-out avoids assignment bias by iteratively removing an
individual from their given source population, re-estimating the allele
frequency of the source population, and then calculating the likelihood
of the individual's assignment to each population. Another source of
bias in assignment tests is variation in the precision of allele
frequency estimation, which arises from populations having different
numbers of samples and/or having differences in sequencing depth of
their individuals. To mitigate this bias, we tested two other approaches
for source population sampling design: 1) we reduced the number of
samples per breeding population to be the same as the population with
the fewest samples (size-standardized breeding populations; SSBPs) and
2) we followed the guidelines in DeSaix et al. (\emph{in review} ) to
standardize the effective sample sizes of the breeding populations
(effective-size-standardized breeding populations;
ESSBPs).\emph{Effective sample size} is a Fisher information metric that
determines the comparable number of individuals with known genotypes
that would reflect the same variance in estimated allele frequency as
the sampled low-coverage individuals (DeSaix et al., \emph{in review} ).
The purpose of ESSBPs is to equalize the effective sample size among
populations by removing individuals from the populations with the
highest effect sample sizes, thereby making the precision of allele
frequency estimation similar among the different populations. We used
WGSassign to calculate each breeding population's effective sample size
for the SSBPs and ESSBPs and performed leave-one-out assignment. We also
performed standard assignment with all breeding individuals not in the
standardized sets. Leave-one-out assignment for the full data set and
the combined leave-one-out assignment and standard assignment accuracy
were compared across all three source population sampling designs.
Posterior probabilities of assignment to a population were determined by
dividing the maximum likelihood of assignment over the sum of all
likelihoods. A cut-off of 0.8 was used for the posterior probability to
determine if an individual was confidently assigned to a population.

\emph{Low-coverage and population assignment}

Since the majority of our nonbreeding and breeding samples were feathers
and blood, respectively, we expected the nonbreeding samples to have
lower sequencing depth than breeding samples. Therefore, to ensure that
we could still achieve high assignment accuracy at lower depths for the
nonbreeding samples, which have unknown breeding origin, we first tested
assignment accuracy with low coverage breeding samples of known origin.
We used the set of individuals from our ESSBPs to estimate population
allele frequencies (our training set) and used the remaining breeding
samples as a test set. We created two data sets from the test set
individuals by down sampling to 0.1X and 0.01X. These two thresholds
were based on the majority of the nonbreeding samples being greater than
0.1X and the lowest coverage sample being 0.02X. To determine the
accuracy of assignment of individuals with low sequencing depths, we
assigned the test sets back to the standardized breeding populations and
compared the population assigned with the known population of origin.

\emph{Determining breeding origin of individuals on the nonbreeding
range}

We assigned individuals sampled from the nonbreeding range to the ESSBPs
using WGSassign. Since these individuals are of unknown origin, we
assumed the accuracy of their assignment would be comparable to the
accuracy achieved with known breeding samples. Individuals at the
periphery or boundaries of genetically distinct populations may have
admixed genomes that are not truly representative of either population.
While posterior probabilities of assignment are typically used to detect
admixture and determine confidence of assignment, our preliminary
results showed that posterior probabilities of assignment were
unreliable with lcWGS data (see \emph{Results} and \emph{Discussion} ).
Therefore, we split up the genotype likelihood data into 10 subsets of
400,000 SNPs, in order of genomic region, for each individual and used a
consistency of assignment threshold of 0.8 (\emph{i.e.} , at least 8 of
the 10 datasets being assigned to the same population) to determine
confidence in assignment. We validated this approach on the 47 breeding
individuals used as the testing set for the ESSBPs and then used it for
the 148 individuals from the nonbreeding range.

\emph{Demographic analysis}

We estimated relative population size indices and population trends
(1968-2021) for each of the five breeding populations and across the
entire breeding range using Breeding Bird Survey (BBS) data (Pardieck et
al., 2020). We used a hierarchical over-dispersed Poisson model (Sauer
et al., 2011) to analyze the BBS data. All BBS routes within a 50-km
buffer of a polygon defining one of the breeding populations were
assigned to that breeding population. This breeding population
assignment was then included as the fixed stratum intercept and trend
effects of the log-linear model of the Poisson mean. We estimated
current (2017-2021) population size indices by summarizing posterior
distributions of estimated mean route-level counts that were weighted by
geographic area encompassed by the breeding population polygon and the
proportion of routes in the polygon with American Redstart detections
(Sauer \& Link, 2011). Long-term trends in population size were
estimated as the geometric mean of yearly changes from 1968-2021 (Sauer
\& Link, 2011). We implemented the hierarchical model in JAGS 4.3.1
(Plummer, 2003) using the jagsUI (Kellner \& Meredith, 2021) package in
R (R Core Team, 2022). We assigned vague prior distributions for all
model parameters and hyperparameters. Posterior distributions were
derived from 40,000 simulated values of four chains from the posterior
distribution after an adaptive phase of 20,000 iterations and burn-in of
10,000 samples of the Gibbs sampler and thinning by 3. Markov chains
were determined to have successfully converged based
on\(\hat{R}\)\textless{}1.1 for posterior estimates of all
parameters (Gelman \& Hill, 2007).

\textbf{Results}

\emph{Genetically distinct breeding populations}

Sequencing efforts resulted in sequences from 330 individuals with a
mean coverage of 1.6X (range: 0.02X -- 5.2X; Table S1). For the 182
breeding samples, the mean coverage was 1.7X (range: 0.6X -- 5.2X),
while the 148 nonbreeding samples had a mean coverage of 1.5X (range:
0.02 -- 3.4X). Down sampling individuals above 2X coverage to 2X
coverage, resulted in an overall mean coverage of 1.4X (1.5X for
breeding samples, 1.3X for nonbreeding samples). Our SNP filtering
produced genotype likelihood data for 4,722,390 variants. We removed 13
individuals from subsequent analyses due to high relatedness by removing
a single individual from each related pair. Principal components
analysis with the breeding samples revealed five genetic clusters that
aligned with geography: Western Boreal (Alaska to Saskatchewan), Basin
Rockies (South Dakota and Montana), Southern Temperate (from Missouri,
east to Maryland, south to Louisiana), Northern Temperate (from
Minnesota, east to Quebec, south to Pennsylvania), and Maritime
Provinces (New Brunswick and Newfoundland; Figure S1). Admixture results
for five groups revealed a similar delineation of individuals as in the
principal components analysis (Figure 1, Figure S1). Pairwise
F\textsubscript{ST} values among these breeding populations had a mean
of 0.009 and ranged from the weakest differentiation
(F\textsubscript{ST} = 0.004) between the Northern Temperate and
Southern Temperate groups and the strongest (F\textsubscript{ST} =
0.018) between Maritime Provinces and Basin Rockies (Table S2). Based on
our comprehensive sampling of the core regions of the American Redstart
breeding range and the population structure results, we expect there to
be no unsampled genetically distinct breeding populations. For example,
while we did not sample individuals from Alaska, we expect Alaska
breeding birds to be a part of the Western Boreal breeding population.

\emph{Effective sample size and assignment}

Breeding populations ranged in number of samples from 27 (Maritime
Provinces and Basin Rockies) to 47 (Southern Temperate) and ranged in
effective sample size from 12.3 -- 24.6. Mean accuracy of leave-one-out
assignment with these individuals was 89.3\% (151 out of 169
individuals), and accuracy by breeding population ranged from 63.0\%
(Maritime Provinces) to 100\% (Southern Temperate and Basin Rockies).
All 18 individuals that were inaccurately assigned were assigned to a
breeding population with higher effective sample size than the known
breeding population (Figure 2). All posterior probabilities of
assignment (accurate and inaccurate) were greater than 0.8 (Table S3).

The SSBPs all had 27 samples but ranged in effective sample size from
12.3 -- 16.1. The mean accuracy of assignment was 97.0\% (164 out of 169
individuals) for the leave-one-out assignment with the 135 individuals
in the SSBPs and standard assignment for the remaining 34 individuals.
Three individuals were incorrectly assigned in the leave-one-out
assignment test and two individuals were incorrectly assigned in the
standard assignment test. All five incorrectly assigned individuals were
assigned to a breeding population with higher effective sample size than
the known breeding population (Figure 2).

The ESSBPs ranged in number of samples from 21 (Basin Rockies) to 27
(Maritime Provinces) but had minimal variation in their effective sample
sizes (range: 12.0 -- 12.5). Mean assignment accuracy was 99.4\% (168
out of 169 individuals) for the leave-one-out-assignment with the 122
individuals in the ESSBPs and standard assignment for the remaining 47
individuals. Only one individual was incorrectly assigned from the
Northern Temperate population to the Southern Temperate population, and
this did not correspond to a breeding population with higher effective
sample size. Interestingly, this same individual (sampled in Minnesota
from the Northern Temperate population) also stands out in the PCA
results as clustering more closely with individuals from the Southern
Temperate population (Figure S1). Given the higher accuracy of
assignment with the ESSBPs, compared to the other sets of breeding
individuals, we continued subsequent assignment testing using only the
ESSBPs data set as the source populations.

\emph{Testing sequence depth thresholds for assignment}

To test whether lower coverage data would affect our ability to
accurately assign breeding individuals, we used the 47 individuals from
the breeding populations, that were not used in the ESSBPs, as a testing
set for further down sampling. We did not down sample individuals from
the ESSBPs because we did not want to lower the effective sample size of
the source populations, but rather test how well we could assign
individuals with lower coverage given the effective sample sizes of our
source populations (and the amount of genetic differentiation among
them). The testing set consisted of 22 individuals from the Southern
Temperate population, 14 individuals from the Northern Temperate
population, 6 individuals from Basin Rockies population, and 5
individuals from the Western Boreal population. The sequencing depth of
these 47 individuals ranged from 0.6X -- 2.0X. Down sampling these
individuals to 0.1X resulted in 100\% assignment accuracy, and further
down sampling to 0.01X resulted in 97.9\% accuracy (one individual from
the Southern Temperate population assigned to the Northern Temperate
population; Table 1, Table S4). The individual incorrectly assigned from
the Southern Temperate population was from a sampling site in
Pennsylvania which is on the border of our boundary for the Southern
Temperate and Northern Temperate populations.

\emph{Nonbreeding assignment}

Implicit in the assignment of the nonbreeding individuals to breeding
populations is that the breeding origin of these individuals is unknown.
Given that assignment to the ESSBPs had an accuracy of 99.4\% (168 out
of 169 samples) for individuals with sequencing coverage of 0.6X --
2.0X, and accuracy of 97.9\% (46 out of 47 individuals) for individuals
down sampled to sequencing coverage of 0.01X, we assumed that we could
correctly assign nonbreeding individuals (sequencing coverage range:
0.02X -- 2.0X, mean 1.3X) with high confidence. Assignment of the 148
nonbreeding individuals resulted in the largest number of individuals
being assigned to the Northern Temperate population (n = 64) and the
least number of individuals being assigned to the Basin Rockies
population (n = 2; Table S5). Of the 148 individuals, 139 individuals
had assignment consistency of at least 0.8 for the 10 subsets of data,
and these individuals were used to infer migratory connectivity. Testing
consistency of assignment on the 47 breeding individuals identified
three individuals with assignment consistency of \textless{} 0.8. One of
these individuals from Minnesota was previously identified as an outlier
in the PCA, and the other two individuals were from Pennsylvania, which
is on the boundary of the Southern Temperate and Northern Temperate
populations.

Mapping of the nonbreeding assignment results revealed patterns of
strong migratory connectivity across the breeding range. Notably, the
Maritime Provinces breeding population has strong connectivity with
eastern Colombia, the Northern Temperate breeding population with the
Greater Antilles, the Southern Temperate breeding population with the
Lesser Antilles, and the Western Boreal breeding population with Central
America and Mexico.

\emph{Demographic analysis}

Using 1,766 BBS routes from 1968-2021, we estimated the range-wide trend
in population size to be -0.29\% per year (95\% CI: -0.57, -0.02).
Trends among the breeding populations were variable (Table 2). The
Northern Temperate breeding population was estimated to be increasing by
0.67\% per year (95\% CI: 0.33, 1.01). The Southern Temperate breeding
population was estimated to be declining by 0.34\% per year (95\% CI:
-0.75, 0.06), but the credible intervals were overlapping 0, thus
indicating potential stability in that population. The remaining three
populations (Basin Rockies, Maritime Provinces, and Western Boreal) were
all estimated to be declining and had negative values for the upper
bounds of the 95\% credible intervals. The Northern Temperate population
had the highest relative abundance of 3.70 (95\% CI: 3.09, 4.52),
followed by Maritime Provinces (1.96; 95\% CI: 1.57, 2.49), Western
Boreal (0.66; 95\% CI: 0.52, 0.84), Southern Temperate (0.15; 95\% CI:
0.13, 0.17), and Basin Rockies (0.01; 95\% CI: 0.01, 0.02). The density
of the number of birds per BBS route was highest in the Maritime
Provinces (30.56; 95\% CI: 24.33, 38.60), followed by Northern Temperate
(15.16; 95\% CI: 12.68, 18.54), Western Boreal (1.66; 95\% CI: 1.31,
2.10), Southern Temperate (0.67; 95\% CI: 0.58, 0.78), and Basin Rockies
(0.16; 95\% CI: 0.10, 0.25).

\textbf{Discussion}

The results of this study demonstrate that lcWGS data is well-suited for
highly accurate population assignment, even with weakly differentiated
population structure. In the American Redstart, lcWGS data provided an
improvement over previous migratory connectivity studies using genetic
and stable isotope data by allowing us to identify five genetically
distinct breeding populations and clearly delineate population-specific
nonbreeding ranges. Identifying migratory connectivity of genetically
distinct populations is an essential step toward full annual cycle
conservation aimed at preserving unique genetic variation. To this end,
we integrate the migratory connectivity results with analysis of
population abundance and trends to demonstrate the conservation
implications of the observed population-specific migratory patterns.
More broadly, we also show that when using lcWGS data for population
assignment it is essential to implement a sampling design that
balances\emph{effective sample size} across source populations to avoid
assignment bias that arises from variation in sequencing depth and
population sample size.

\emph{Mapping migratory connectivity}

Population structure analyses identified five genetically distinct
breeding populations with weak genetic differentiation, in contrast to a
previous mtDNA analysis that identified only two populations split by a
phylogeographic break (Colbeck et al., 2008). Our delineation of the
Maritime Provinces breeding population in the far northeast portion of
the range corresponds with the Newfoundland population from the mtDNA
analysis. Colbeck et al. (2008) hypothesized that the phylogeographic
separation of Newfoundland and mainland American Redstart populations
was the result of two refugia during Pleistocene glaciations. Our
findings of weak genetic differentiation between the Maritime Provinces
and other breeding populations suggest that there is ongoing gene flow
among these populations. However, the limited admixture of individuals
sampled in Newfoundland supports the notion that geographic separation
of the island provides some barrier to gene flow, which has been
demonstrated in several other avian species . The weakest genetic
differentiation was found among the Western Boreal, Northern Temperate,
and Southern Temperate breeding populations (F\textsubscript{ST}:
0.004-0.006), suggesting limited barriers to gene flow. The Basin
Rockies breeding population had higher genetic differentiation with the
eastern breeding populations than the more northern Western Boreal
population, which corresponds to the Great Plains functioning as a
barrier to gene flow.

Using the five genetically distinct breeding populations allowed us to
document at a fine scale more complex migratory patterns than previously
identified. At the continental scale, our results broadly correspond to
previous stable isotope studies that found eastern breeding American
Redstarts overwintered in the eastern nonbreeding range and western
breeders overwintered in the west . In several other species of
Nearctic--Neotropical migrants, similar patterns of parallel migration
have also been observed . However, in contrast to previous isotope
analyses in American Redstart , our use of genomic data allowed us to
clearly differentiate Maritime Provinces and Northern Temperate breeding
birds and revealed that individuals breeding in the Maritime Provinces
do not follow the parallel migration pattern. Parallel migration would
result in these breeders being found in the far eastern portion of the
nonbreeding range (e.g., Lesser Antilles and Trinidad and Tobago).
Instead, individuals from the Maritime provinces bypass the Caribbean
portion of the nonbreeding range and have a ``leap-frog'' migratory
pattern to eastern Colombia. One explanation for the discordance of the
Maritime Provinces migratory connectivity patterns from the rest of the
breeding populations is the phylogeographic separation of these regions
documented by Colbeck et al. (2008). Migration routes are influenced by
the historical separation of Pleistocene glacial refugia and in the
American Redstart, an Atlantic Shelf (near the Maritime Provinces) and
eastern continental refugia are hypothesized to have caused the observed
phylogeographic separation of these regions . A previous mtDNA analysis
of American Redstart migratory connectivity only detected several
individuals from the Maritime Provinces population in the Caribbean
islands of the Dominican Republic and Puerto Rico but lacked samples
from South America . Our results suggest that the Maritime Provinces
breeding population has the strongest connectivity with eastern
Colombia, though the full extent of the population's nonbreeding
distribution throughout South America is unknown.

Our use of genomic data allowed us to characterize migratory
connectivity at a fine scale and identify distinct regions on the
wintering range that separate breeding populations. In southern Central
America, a clear split between the two sampling sites in Costa Rica, a
separation of 360 km, occurs where the northern site predominantly has
individuals from the Western Boreal breeding population and the southern
site has individuals from the Southern Temperate population. This split
corresponds with a biogeographic separation of drier broadleaf forest in
the northern Pacific side of Costa Rica and moist broadleaf forest in
the southern Pacific side (Corrales, Bouroncle, \& Zamora 2015). Another
geographic split in breeding origin occurs between the Lesser Antilles
(Southern Temperate) and the Greater Antilles (Northern Temperate) in
the Caribbean. Sampling of American Redstarts in Colombia was limited to
the eastern slopes of the East Andes, and may not represent the wider
Andes, given that the three chains of the Andes that run through
Colombia influence connectivity patterns in the Canada
Warbler,\emph{Cardellina canadensis} Further population assignment
studies that include sampling of American Redstarts from the Central and
Western Andes, and the Caribbean region of Colombia, may identify the
Andes Mountains as another barrier in the nonbreeding region, creating
geographic splits in breeding origin for this species.

\emph{Conservation implications}

Describing migratory connectivity is essential for informing effective
wildlife conservation and management decisions involving migratory
species Our delineation of five breeding populations, and their linkages
to wintering regions, provides the necessary information to prioritize
regions for conservation and improve our understanding of the underlying
drivers of abundance. While our demographic analysis highlights that the
species is declining overall from 1966 -- 2021, there is wide variation
in trends among the breeding populations. The Northern Temperate
population has the largest population of American Redstarts on the
breeding grounds and is increasing in abundance. One potential
explanation for the increase in abundance is that birds in the southern
portion of the breeding range have shifted their breeding latitude
northward in response to climate change, as has been documented in other
Nearctic-Neotropical migrants . However, our results do not depict a
correspondingly large decline in the Southern Temperate breeding
population which would be the source population of northward movement.
Given that the Northern Temperate breeding population has strong
connectivity with the Greater Antilles archipelago in the Caribbean,
efforts aimed at conserving the greatest proportion of the global
distribution of American Redstarts could focus on the Northern
Temperate-Greater Antilles migratory population.

The Maritime Provinces population had the second highest abundance of
American Redstarts and the highest density of individuals. Despite being
geographically adjacent to the Northern Temperate breeding population,
Maritime Provinces individuals were detected almost exclusively outside
the Caribbean, along the eastern slopes of the Andes of Colombia. Our
demographic analysis highlighted the Maritime Provinces to be the second
fastest declining population. Thus, future research into the stressors
driving this decline could focus on the breeding region as well as
stationary nonbreeding region of eastern Colombia. Notably, other
populations of long-distance migratory birds connected to the Eastern
Andes are also experiencing declines, including populations of Canada
Warbler (Wilson et al., 2018) and Cerulean Warbler, \emph{Setophaga
cerulea} (Raybuck et al., 2022). Additionally, in species such as the
Canada Warbler, migration routes between North and South America can
concentrate in small regions of Central America which can also affect
population trends . Given the phylogeographic split of the Maritime
Provinces breeding population with the mainland (Colbeck et al., 2008),
conservation of this migratory population may also be important for
preserving genetic diversity within the species. The Western Boreal
population, ranging from Alaska to Saskatchewan, was characterized by
the demographic analysis as having the third highest abundance and
density, with population declines larger than the range-wide decline.
Strong migratory connectivity with Mexico and Central America highlights
the need for conservation efforts to focus on the most western portion
of the range for this migratory population.

The Southern Temperate breeding population is unique in American
Redstarts, in that nonbreeding individuals were sampled in both the far
eastern Caribbean as well as in Central America. Our lack of sampling
between these regions in northern South America precludes our ability to
describe whether there is a migratory divide within the Southern
Temperate population, or individuals are spread across this portion of
the nonbreeding range. While weak connectivity across a large
nonbreeding distribution could promote resilience from stressors on any
single portion of the nonbreeding distribution , this makes targeting
regions for conservation difficult.

\emph{Low-coverage WGS for population assignment}

In addition to elucidating fine-scale migratory connectivity patterns in
the American Redstart, our results provide important considerations for
other population assignment studies using lcWGS. We found that
balancing\emph{effective sample sizes} of the source populations to
within one effective individual of each other was essential for accurate
assignment. Even when the actual number of individuals used per
population was the same, variation in mean depth (1.3X -- 1.9X) between
populations skewed the effective sample sizes, resulting in decreased
assignment accuracy. Other studies with known genotypes from RADseq have
demonstrated the influence of actual sample size on overall assignment
accuracy but not how it affects assignment bias . The effective sample
sizes needed per population for accurate assignment and the degree of
standardizing these values will depend on the population structure of
the study system. For example, study systems with higher genetic
differentiation between populations may not need to finely standardize
effective sample size to achieve high assignment accuracy. We suggest
that other population assignment studies similarly evaluate the
influence of source population effective sample size on known source
individuals before assigning individuals of unknown origin. Reducing the
effective sample size of a sampled population can be achieved by either
removing individuals or down sampling the read depth. In this study, we
chose to remove individuals, and used the individuals' effective sample
sizes as a guide for how many individuals to remove from each population
(resulting in 21 -- 27 samples per population). For studies with smaller
sample sizes, it may be worthwhile to investigate if retaining all
individuals, but down sampling reads is a better alternative for
standardizing effective sample sizes to retain more variation from
individuals.

Importantly, here we demonstrate that individuals with very low whole
genome coverage (0.01X -- 0.1X) can still be accurately assigned to
source populations with sufficient effective sample sizes. These results
suggest that increasing the number of samples and decreasing individual
sequencing depth is an effective study design strategy for population
assignment. For migratory connectivity studies, increased sampling (both
number of individuals at each location and the number of locations
sampled) across nonbreeding stages of the annual cycle can drastically
improve our understanding of population-level connectivity at low cost.
Combined with cost-effective approaches for library preparation (e.g. ),
lcWGS is increasingly becoming economically feasible for a wide-range of
studies. However, a trade-off with lcWGS is that the sequence data
processing requires additional costs associated with time spent on the
bioinformatics analysis. For studies interested in population assignment
with a large number of samples, increasing the number of samples per
lane, thereby decreasing the mean average sequencing depth, may make
lcWGS economically feasible compared to other sequencing methods. For a
comprehensive review of coverage guidelines for different types of
analyses with low-coverage WGS data see Lou et al. (2021).

An interesting aspect of our results was that all posterior
probabilities of assignment were \textgreater{} 0.8, even for
potentially admixed individuals. A standard method to determine
assignment confidence in population assignment studies is to use a
cutoff value for posterior probabilities of assignment . Individuals
with low posterior probabilities of assignment (e.g., \textless{} 0.8)
can be highly admixed. Thus, it is inaccurate to classify them as from a
specific population. However, we suspect that with lcWGS data, the high
prevalence of loci with single read results in the likelihood being
highest for a homozygous genotype. Thus, admixed individuals may
``switch'' their population of maximum likelihood depending on the loci
used for assignment. Our use of an assignment consistency threshold
addressed this concern by creating subsets of genomic data for
population assignment to determine if individuals could reliably be
assigned to a single population when different loci were used. Testing
the assignment consistency threshold with known source individuals
revealed three individuals with inconsistent assignment (\textless{}
0.8, i.e., 8 out of 10 genomic datasets) and were likely admixed between
pure Northern Temperate and Southern Temperate populations. These
results highlight that the consistency of assignment may be more
reliable than posterior probabilities for confidently assigning
individuals of unknown origin. Further development of spatially explicit
assignment methods for genotype likelihood data would be helpful for
determining the likely origin of admixed individuals at the periphery of
source populations.

\emph{Conclusion}

Low-coverage WGS is a powerful and potentially cost-effective approach
for population assignment studies. We demonstrate that high assignment
accuracy can be obtained for weakly differentiated populations, even for
individuals with very low sequencing coverage (\textless{} 0.1X). We
further demonstrate the importance of balancing the effective sample
sizes of source populations to avoid assignment bias due to variation in
the precision of allele frequency estimation. By applying these methods
to the American Redstart, we reveal broad-scale parallel migration and
highlight unique population-specific patterns of connectivity. In
combination with our demographic analysis, we demonstrate the importance
of the Northern Temperate-Greater Antilles migratory population to the
total abundance of the species. Furthermore, our identification of
nonbreeding regions for the genetically distinct breeding populations
provides a foundation for a full annual cycle approach towards
preserving genetic diversity. Together, our results provide a valuable
framework for studies that aim to use lcWGS to understand the ecology
and evolution of migratory species.

\textbf{Acknowledgments}

This study was funded through a State Wildlife Grant (SWG T-36, Project
2) administered by the Alaska Department of Fish and Game's Threatened,
Endangered and Diversity Program, an NSF CAREER award (008933-00002), a
generous donation from an anonymous donor, and a National Geographic
grant (WW-202R-170) to KCR, as well as an NSF LTREB (No. 0649679,
1242584) grant to PPM and TWS, and funding from the Smithsonian
Institute's James Bond Endowment Fund to PPM. Writing of this paper was
initiated while MGD and ECA were scientists-in-residence in the Mobile
High Altitude Venue for Ecological Analysis, Genetics, and Statistics on
location in Moab, Utah for five days in March 2023. This is contribution
number mHAVEAGAS-002. We gratefully acknowledge the services and kind
staff at the Grand County Public Library in Moab. Field work was
conducted under permits from the USGS, the Jamaican National Environment
and Protection Agency, and Smithsonian National Zoo IACUC approval
14-03. Sample collection in Colombia was undertaken as part of a study
funded by Environment and Climate Change Canada and both sample
collection and export were conducted under permits (resolución 00874)
from the Autoridad Nacional de Licencias Ambientales (ANLA). Sample
collection and export in Trinidad and Tobago were conducted under the
Special Game License (Chapter 67:01 Section 10) and Special Export
License from the Wildlife Section Forestry Division, and we thank Carl
Fitzjames, Richard Smith, Richelle Smith, Shivam Mahadeo, and Laura
Baboolal for sample collection. We thank Keith Hobson, Anne-Marie
Barber, Lorie Collins, Robert Dawson, Lilie DeSousa, Jose Diaz, Emmanuel
Milot, David Okines, and John Woods for sample collection and
preparation and support from the Max Bell Foundation and Environment
Canada. We thank staff of the Institute for Bird Populations and MAPS
and MoSI program cooperators for providing samples or assisting with
sample collection. We thank Jeanie Woltz, Junior Tremblay, Jacques
Ibarzabal, Tim Kita, John Woodcock, Alexis Cerezo, Susan Koenig, Ingrid
Tello-Lopez, Rafael Rueda Hernandez, Fred Schaffner, Stacey Hayden, Lori
Walewski, Chantal Villeneuve, Chase Mendenhall, and David Curiel for
sample collection. We thank Diana Baetscher for providing feedback on a
draft of this manuscript. This work utilized the Alpine high performance
computing resource at the University of Colorado Boulder. Alpine is
jointly funded by the University of Colorado Boulder, the University of
Colorado Anschutz, Colorado State University, and the National Science
Foundation (award 2201538).

\textbf{Conflict of interest statement}

The authors declare no conflict of interest.

\textbf{Data availability statement}

Genomic data have been deposited in the Dryad repository {[}PUT DOI HERE
FROM MOLECULAR ECOLOGY{]}. All analysis scripts have been made available
at https://github.com/mgdesaix/amre-mc.

\textbf{Benefit-sharing statement}

An international research collaboration was developed with scientists
facilitating genetic sample collection and all collaborators are
included as co-authors. The contributions of all individuals to the
research are described in the acknowledgments. The results of the
research have been shared with the broader scientific community and
address a priority concern of the conservation of migratory species.

\textbf{Author Contributions}

MGD and KCR designed the study. Sample collection was facilitated and
performed by MGD, NJB, DSN, JCH, HLG, TWS, MSW, TBS, PPM, and KCR.
Laboratory work was performed by CER and TMS. MGD performed the
bioinformatics data generation. CMB provided bioinformatics support and
data management. ECA contributed to the population assignment study
design. MGD and JFS performed the data analysis. MGD and CMB generated
the figures of the manuscript. MGD and KCR wrote the paper and all
authors edited and provided feedback to manuscript drafts.

\textbf{References}



\textbf{Figures}

\textbf{Figure 1.} The population structure on the breeding range is
delineated by five genetically distinct clusters (colored polygons) from
the results of the admixture analysis (top panel). Population structure
was determined using 169 individuals from 16 sites (black points) on the
breeding range. Individuals sampled from the non-breeding range were
determined to originate from a given breeding population through
population assignment tests (n = 138; colored circles). The point colors
on the nonbreeding range represent the breeding population of maximum
likelihood of assignment and the extent of the nonbreeding range is
provided by the grey polygon. Strong migratory connectivity is evident
from the general separation of breeding population assignment across the
wintering range.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} Population assignment of known breeding individuals
revealed assignment bias from unequal effective sample sizes. Circles
represent the breeding populations (colored), with circle size
representing effective sample size, and arrows represent the assignment
of individuals from their known breeding population to their assigned
population. Arrows are scaled in size by the number of individuals
assigned. Colored arrows represent the correct individuals assigned to a
breeding population, whereas black arrows indicate incorrect assignment
to a different population. A) When using all samples to calculate allele
frequencies in breeding populations, all incorrectly assigned
individuals (n = 18) were assigned to a population with higher effective
sample size. B) Standardizing breeding populations by sample sizes (27
individuals per population) resulted in less incorrect assignment (n =
5), but all individuals were still assigned to another population with
higher effective sample size. C) Standardizing breeding populations to
approximately the same effective sample size (\textasciitilde{}12
effective individuals), resulted in only one individual being
incorrectly assigned. In all cases, incorrect assignment was typically
to a geographically neighboring population.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/624774/articles/649788-low-coverage-whole-genome-sequencing-for-highly-accurate-population-assignment-mapping-migratory-connectivity-in-the-american-redstart-setophaga-ruticilla}



\textbf{Tables}

\textbf{Table 1.} Assignment accuracy of the known breeding samples used
as the testing set (n = 47) for the effective size standardized breeding
populations. Down sampling to both 0.1X and 0.01X achieved high
assignment accuracy.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Depth} & \textbf{Western Boreal} & \textbf{Basin Rockies} &
\textbf{Southern Temperate} & \textbf{Northern Temperate} &
\textbf{Total}\tabularnewline
\midrule
\endhead
Full & 100\% (5/5) & 100\% (6/6) & 100\% (22/22) & 93\% (13/14) & 98\%
(46/47)\tabularnewline
0.1X & 100\% (5/5) & 100\% (6/6) & 100\% (22/22) & 100\% (14/14) & 100\%
(47/47)\tabularnewline
0.01X & 100\% (5/5) & 100\% (6/6) & 95\% (21/22) & 100\% (14/14) & 98\%
(46/47)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Demographic analysis of Breeding Bird Survey data for
breeding populations of American Redstart.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Population} & \textbf{No. of BBS routes} & \textbf{Average trend
(95\% CI)} & \textbf{Relative abundance index (95\% CI)} & \textbf{Route
density (95\% CI)}\tabularnewline
\midrule
\endhead
Western Boreal & 228 & -0.90 (-1.54, -0.27) & 0.66 (0.52, 0.84) & 1.67
(1.31, 2.11)\tabularnewline
Basin Rockies & 84 & -2.38 (-3.40, -1.30) & 0.01 (0.01, 0.02) & 0.16
(0.10, 0.25)\tabularnewline
Southern Temperate & 673 & -0.34 (-0.75, 0.06) & 0.15 (0.13, 0.17) &
0.67 (0.58, 0.78)\tabularnewline
Northern Temperate & 615 & 0.67 (0.33, 1.01) & 3.70 (3.10, 4.52) & 15.16
(12.68, 18.54)\tabularnewline
Maritime Provinces & 166 & -1.26 (-1.73, -0.77) & 1.97 (1.57, 2.49) &
30.56 (24.33, 38.60)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-01/Figure-01}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-02/Figure-02}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

