Determinants of natal dispersal distances in North American birds

Abstract

Natal dispersal—the movement from birth site to first breeding site—determines demographic and population genetic dynamics and has important consequences for ecological and evolutionary processes. Recent work suggested that one of the main factors determining natal dispersal distances is the cost of locomotion. We evaluated this hypothesis using band recovery data to estimate natal dispersal distances for 50 North American bird species. We then analyzed the relationships between dispersal distances and a suite of morphological and ecological predictors, including proxies for the cost of locomotion (flight efficiency), using phylogenetic regression models. We found that flight efficiency, population size, and habitat influence natal dispersal distances. We discuss how the effects of population size and habitat can also be related to mobility and locomotion. Our findings are consistent with a predominant effect of adaptations for mobility on dispersal distances. Dispersal is fundamental to many processes in ecology and evolution. We evaluated the hypothesis that the cost of movement is most influential in dispersal process in North American birds. We found that flight efficiency, population size, and habitat influence natal dispersal distance, although the individual effects are difficult to disentangle.

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Ecology and Evolution. 2023;13:e9789.  
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https://doi.org/10.1002/ece3.9789
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1 | INTRODUCTION
Dispersal is an important aspec t of ecology and evolution that influ-
ences many processes such as population spatial dynamics, species
distributions, gene flow, and speciation (Bohonak, 1999; Bowler &
Benton, 2005; Claramunt et al., 2012; Gaston, 2003; Hanski, 1998;
Greenwood, 1980; Pigot & Tobias, 2015). Despite its importance,
the factors that influence dispersal distances are poorly understood.
Most theories have framed dispersal as an adaptive strategy for
individuals facing competition, inbreeding, or variation in resource
abundance (Bowler & Benton, 2005; Clobert et al., 2009; Matthys en,
2005; McCaslin et al., 2020; Moore & Aki, 198 4; Nelson- Flower
et al., 2012). In those cases, dispersal distances are predicted to be
longer in species that live in dense populations with high rates of
inbreeding. But empirical evidence supporting these predictions has
been ambiguous (Duputié & Massol, 2013; Matthysen, 2012; Paradis
et al., 1998; Ronce, 2007). On the other hand, dispersal distances
might not reflect an adaptive strategy but instead, a by- product of
movements intended for other activities such as foraging and com-
muting (Burgess et al., 2016; Claramunt, 2021; Matthysen, 2012).
The lack of empirical evidence supporting theoretical predictions
can be attributed in part to the difficulties in collecting dispersal dis-
tance data. At the most basic level, measuring dispersal distance re-
quires an organism to be marked and later recaptured. But recapture
rates are t ypically low and local studies can miss the recapture of indi-
viduals t hat have disperse d out of the study a rea, leading to a n underes-
timation of dispersal distances (Koenig et al., 1996 ; Tittler et al., 2009).
This, plus varying sampling methods and geographical scales have
Received:25May2022
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Revised:2December2022
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Accepted:10January2023
DOI: 10.1002 /ece3.9789
RESEARCH ARTICLE
Determinants of natal dispersal distances in North American
birds
Jonathan J. Chu1 | Santiago Claramunt1,2
This is an op en access ar ticle under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provide d the original work is properly cited.
© 2023 The Authors. Ecol ogy and Evoluti onpublishedbyJohnWiley&SonsLtd.
1Department of Ecology and Evolutionary
Biology, University of Toronto, Toronto,
Ontario, Canada
2Depar tment of Natu ral Histor y, Royal
Ontario Museum, Toronto, Ontario,
Canada
Correspondence
JonathanJ.Chu,DepartmentofEcology
and Evolutionary Biology, Univer sity of
Toronto,25WillcocksStreet,Toronto,ON
M5S3B2,Canada.
Email: jonathanjojo.chu@mail.utoronto.ca
Funding information
NaturalSciencesandEngineering
Research Council of Canada, Grant/Award
Number: RGPIN- 2018- 06747
Abstract
Natal dispersal— the movement from birth site to first breeding site— determines de-
mographic and population genetic dynamics and has important consequences for
ecological and evolutionary processes. Recent work suggested that one of the main
factorsdeterminingnataldispersaldistancesisthecostoflocomotion.Weevaluated
this hypothesis using band recovery data to estimate natal dispersal distances for 50
NorthAmericanbirdspecies.Wethenanalyzedtherelationshipsbetweendispersal
distances and a suite of morphological and ecological predictors, including proxies for
thecostof locomotion(flight efficiency),usingphylogeneticregressionmodels. We
foundthatflightefficiency,populationsize,andhabitatinfluencenataldispersaldis-
tances.Wediscusshowtheeffectsofpopulationsizeandhabitatcanalsoberelated
to mobility and locomotion. Our findings are consistent with a predominant effect of
adaptations for mobility on dispersal distances.
KEYWORDS
birds, ecomorphology, emigration, flight efficiency, mobility, wing morphology
TAXONOMY CLASSIFICATION
Functional ecology, Movement ecology

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made the comparative analysis of dispersal distances difficult. Paradis
et al. (1998 ) used the countrywide and long- term bird banding data
from the British Trust for Ornithology to estimate dispersal distances
for 75 British bird species allowing for direc t comparisons and com-
parative analysis (Claramunt, 2021; Dawideit et al., 2009; Garrard
et al., 2012). This dataset revealed that, contrary to theoretical expec-
tations,bod ys izeandlifehist or ychar ac teris ti cswe relargelyunre la te d
to dispersal distances (Claramunt, 2021; Paradis et al., 1998). Also, at
odds with theoretical expectations based on competition and inbreed-
ingavoidance,populationsizewasnegativelycorrelatedwithdispersal
distances (Claramunt, 2021; Paradis et al., 19 98). Migratory species
tend to disperse further (Dawideit et al., 2009; Paradis et al., 1998)
but this may be due to their high flight efficiency rather than an effect
of migrator y movements per se (Claramunt, 2021). Evidence for the
influence of habitat or diet on dispersal distances is still ambiguous
(Claramunt, 2021; Paradis et al., 1998). Overall, the factors emerg-
ing as most influential were proxies of long- distance flight efficiency,
suggesting a predominant role for the cost of movement on dispersal
patterns (Claramunt, 2021). However, all these findings are based on
an analysis of British birds. It is not known if avifaunas in other parts
of the world show similar patterns, but some evidence points to this
beingthecase(Weeksetal.,2022).
Inthisstudy,wesetouttofillthisgapbyanalyzingfactorsthat
influence natal dispersal distances in North American birds. We
estimated natal dispersal distances by using data from the North
American bird banding program following the approach of Paradis
et al. (1998 ). We conducted comparative analyses to assess the
influence of flight efficiency and behavioral and ecological traits
that have been theorized to influence natal dispersal distances
(Garrard et al., 2012; Paradis et al., 1998; Ronce, 20 07;Sutherland
et al., 2013).Byanalyzing theseresults andcomparing them with
those from British birds, we discuss the underlying factors that seem
to be controlling variation in natal dispersal distances in birds.
2 | METHODS
2.1 | Estimation of natal dispersal distances
Dispersal movements can be split into two categories, natal disper-
sal, defined as the movement of an animal from birth site to first
breeding site, and breeding dispersal, defined as the movement of
a breeding adult between breeding sites in subsequent years. This
study focuses on variation in natal dispersal distance as it is usually
the longest dispersal event among birds, and thus the most influ-
ential to gene flow, population dynamics, and metacommunity con-
nectivity (Greenwood & Harvey, 1982; Lester et al., 2007; Paradis
et al., 1998).WeusedrecordsfromtheNorthAmericanbirdbanding
program,conductedjointlybytheCanadianWildlifeServiceandthe
United States GeologicalSurvey (Buckleyet al.,1998), to estimate
natal dispersal dist ances following the methods outlined by Paradis
et al. (1998 ).
The records we used spanned bandings and recoveries from
1920to2019.Weobtainedallrecoveryrecordsofbirdsbandedas
nestlings or fledglings to ensure that the banding site reflects the
natal site. The distance between this point and the subsequent re-
covery location was taken as an estimate of the individual's natal dis-
persal distance, given the additional following conditions. Only birds
banded and recaptured during the species' breeding season and
within the species' breeding range were considered in order to avoid
the incorporation of migratory movements. Information on breeding
seasons was obtained from species accounts collated in Birds of the
World(Billerman etal.,2020). Given the variation in breeding sea-
sons within species, we took the earliest month and latest month
reported to be the bounds. To determine breeding range limits, the
most extreme points on shapefiles of the species' distribution were
set as latitude and longitude filters (BirdLife International, 2019).
Only birds recovered as mature adults were considered to ensure
thattherecapturelocationrepresentedapotentialbreedingsite.We
discardedrecordswithlocationuncertaintygreaterthan1′block.To
limit spatial biases in the location of recoveries due to human ac-
tivities, only birds found dead were used, thus excluding recover-
ies at banding stations or hunting sites. The resultant banding and
recovery data were plotted on maps of Nor th America to inspect
if filters were successful in limiting spurious effects and recoveries
along migratory routes.
Distances between banding and recovery sites, in kilome-
ters, were measured using the R package “geosphere” using the
“Vincenty” (ellipsoid) method (Hijmans, 2019). This method mea-
sures the shortest distance between two points on an ellipsoid ap-
proximating Earth's actual shape and should be the most accurate
estimation for the shortest distance between two geographic lo-
cations ( Vincenty, 1975). We then cal culated the geom etric mean
of dispersal distances for each species. Because standard errors in
natal dispersal dist ance estimates increased exponentially for spe-
cieswithfewerthanfiverecoveries,onlyspecieswithasamplesize
greater than four were retained for further analyses.
This method uses the distance between the fledgling site and
any recovery site after maturity as opposed to only individuals re-
covered in their first breeding site, which may be difficult to infer
from banding data alone. This may result in some breeding disper-
sal events being added to the estimated natal dispersal distances.
However, because breeding dispersal is usually less frequent and
shorter than natal dispersal, we expect this bias to be minor. In any
case, to account for this potential bias, we calculated the mean num-
ber of years between banding and recover y for each species and
included it as a covariate in our analyses.
2.2 | Predictors
Weusedtheaspectratioofthewingsasaproxyforflightefficiency.
The aspect ratio is the morphological characteristic of the wing that
is most influential in determining long- distance flight efficiency
(Pennycuick, 2008) and is calculated as:

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where B is the wingspan and Atot is the total wing area (the area of both
wings plus the area of the body between the wings) estimated as:
in which Aw is the area of a single wing, Cr is the root chord, the width
of the wing at it s proximal border, and E is the wing extent, mea-
sured as the distance between the root chord (perpendicular) and the
most distant feather tip (see Pennycuick [2008] and Claramunt and
Wright[2017 ] for illustrations and further details).
Estimates of single- wing areas were obtained from spread
wings photographed at the Royal Ontario Museum and from the
digital co llection of spre ad-win g images of the Slater Mu seum of
Natural History (https://digit alcol lecti ons.puget sound.edu/digit al/
colle ction/ slate rwing). Wingspan data were taken from museum
specimen records in VertNet (vertn et.org). Additional data on
wing area and wingspan were obtainedfrom the Wings Database
(Pennycuick, 2008) included in the Flight 1.25 software (h t t p s ://
booksite.elsevier.com/9780123742995/?ISBN=978 0 1 2 3 7429 95).
These dat a were used to estimate species' averages and do not
correspond to the individual birds used to estimate nat al dispersal
distances.
WeusedImageJ v.1.52(Schneider et al., 2012) to process and
measurewingimages.Wesetthescaleusingthescalebarsincluded
in the images and transformed the image into a binary image using
thethresholdingtool. Wethenmeasuredthe areaofthewing(
Aw
)
using the AnalyzeParticlestool. The root chord(
Cr
) and the wing
extent (
E
) were measured using the straight- line tool.
We also estim ated and teste d two alternat ive flight eff iciency
proxies— the hand- wing index and the lif t- to- drag ratio— for which
we describe methods and present results in Appendix A.
Two geographical predictors— migratory distance and breed-
ing range area— were obtained from geographic range shapefiles
(BirdLife International, 2019).Shapefileswereanalyzed usingQGIS
v.3.14(QGIS DevelopmentTeam, 2020).Weusedthedistancebe-
tween the centroids of the breeding and the wintering range as an
estimate of the species migration dist ance (using the “ Vincenty”
method).Weestimatedthebreedingrangearea(insquaredkilome-
ters)usingthe$areacommandinQGIS.
Ecological predictors were habitat, diet, foraging behavior,
and popu lation size. Habit at, diet, an d foraging beh avior informa-
tion were obtained from species accounts collated in Birds of the
World (Billermanetal.,2020). Habitat was divided into four cate-
gories: woodlands, open habitats, wetlands, and coasts. Species
that inhabit aquatic ecosystems were split between wetlands and
coasts, depending on whether their habitat consists of interior lakes,
wetlands, and rivers (wetlands) or beaches, marine coastlines, and
open ocea ns (coasts). Woodlands i nclude forest s and woodlands,
and open habitats include grasslands, deser ts, and steppes. Diet was
divided into four categories: herbivores, species that primarily feed
on plants; carnivores, species that primarily feed on vertebrates; in-
sectivores, species that primarily feed on insects and other inverte-
brates; and omnivores, species that feed on both plant s and animals.
Foraging behaviorwascategorized into five groups,effectivelyan
ordinal variable that corresponded to a continuum of the amount of
flight required to forage. The first group, surface foraging, includes
species that primarily forage while walking, wading, or floating so
flight is not needed for prey detection and capture (e.g., Mourning
Dove, Zenaida macroura, Otis et al., 2020). The second level, tree for-
aging, refers to species that forage in elevated vegetation like arbo-
real insect gleaners and tree climbers, that need to fly from branch
to branch or from tree to tree during foraging (e.g., Red- cockaded
Woodpecker,Dryobates borealis,Jackson,2020). The third level, sal-
lying, refers to species that take off from perches to pursue prey
after which they return to a perch (e.g., Least Flycatcher, Empidonax
minimus, Tarof & Briskie, 2020). The fourth level, aerial search, refers
to species that search for prey in flight but dive down to capture prey
on the ground or in the water (e.g., Red- tailed Hawk, Buteo jamaicen-
sis, Preston & Beane, 2020). The fifth level, aerial capture, refers to
species that search for, capture, and ingest prey on the wing (e.g.,
Purple Martin, Progne subis, Brown et al., 2021). Population sizes
were taken from Rosenberg et al. (2019) with data originally pub-
lished by Partners in Flight (Stantonetal.,2019). Populationsizes
represe nt the breeding population size across the species' e ntire
range in the UnitedStates and Canada estimatedfrom the North
AmericanBreedingBirdSurvey(Saueretal.,2 017). The population
sizeoftheAmericanOystercatcher,Haematopus palliatus, was ob-
tained from Andres et al. (2012).
2.3 | Statistical analysis
Relationships between predictors and natal dispersal distance
wereassessedusingphylogeneticgeneralizedleastsquaresmodels
(PGLS)toaccountforphylogeneticnon-independenceamongspe-
cies (Freckleton et al., 2002). P GLS models we re fit by maximum
likelihood with the pgls func tion in the R package “caper” (Orme
et al., 2018). Phylogenetic non- independence (phylogenetic iner-
tiaor signal)isincorporatedintotheerrortermofPGLSmodelsby
specifying an error– covariance matrix representing the shared phy-
logenetic history between species pairs (shared branches in time
units), transformed by a parameter (λ) that moderates the intensit y
of phylogenetic inertia in relation to the strict Brownian motion ex-
pectation (Freckleton et al., 2002; Pagel, 1999). The phylogenetic
tree used was a maximum clade credibility tree computed using
TreeAnnotator (Bouckaer t et al., 20 14) from a sample of 1000 phy-
logenetic trees of the study species obtained from Birdt ree.org(Jetz
et al., 2012) using the Hackett et al. (2008)backbonetopology.We
also used Pagel's λ model (Pagel, 1999) to assess levels of phylo-
genetic signal in dispersal distances per se using function phylosig
in the phytools package (Revell, 2012). Continuous variables were
transformed by the natural logarithm to increase homoscedastic-
ity, in the case of natal dispersal distances (Faraway, 2005), and to
B
2
Atot
Atot =2Aw+Cr∙(B−2E)

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improve model likelihoods and the distribution of residuals for flight
efficiency, migration distance, mean number of year s between band-
ingandrecovery,andpopulationsizepredictors.
Weexploredallpredictorsindividuallyinsingle-predictormodels
and then constructed multi- predictor models with main effects and
second- order effects (interactions) between continuous predictors
andbinary predictors. Wedidnotconsidermodels with more than
five variables and greater than second- order effects. Models were as-
sessed using model selection and multi- model inferences techniques
using the Akaike information criterion ( AICc) and relative model prob-
abilities (Burnham & Anderson, 2002).Predictorswerestandardized
by subtracting the mean and dividing by 1 standard deviation before
analyses. Model fit and proportion of variance explained by the mod-
els were assessed by calculating coefficients of determination:
inwhichRSSmodel is the residual sum of squares of the full model and
SSnull is the sum of squares for the response in the nu ll model, as calcu-
lated by the pgls function (Orme et al., 2018). The null model included
only the intercept but used the same correlation structure as the full
model. Additionally, to assess variable importance, we calculated
the sum of model probabilities containing each variable (Burnham &
Anderson, 2002). Finally, we estimated model- averaged coefficients
and confidence intervals and built 95% confidence model sets by re-
taining models with a cumulative probability up to 0.95. Models were
built using the R package “MumIn” with func tion dredge, and mod-
el.avg for computing model- averaged estimates (Bartón, 2018).
Predictorswereassessedformulticollinearityusinggeneralized
variance inflation factors (GVIF). To allow for comparisons between
continuous and categorical variables with multiple levels, we cal-
culated a dimensionality correction for categorical variables (Fox &
Monette, 1992):
whereGVIFisthegeneralizedvarianceinflation factorsand df is the
degrees of freedo m of the predic tor (number of cate gories − 1). The
square of this result can be evaluated using VIF thresholds (<2: no col-
linearity, >5: high collinearity).
3 | RESULTS
Natal dispersal distances were estimated for 103 species, including
51withsamplesize>5 that were retained for analyses (Table S1). Of
these 51 species, we were able to estimate the wing aspect ratio of
45 (Table S2). Aspect ratio estimates were generated from an aver-
age of 3.6 specimens per species. Finally, one species was dropped
duetoinsufficient populationsizeinformation.Intotal,44species
were used to generate comparative models.
In single- predictor models, the aspect ratio was clearly the best-
performing model based on log- likelihoods and AIC values ( Table 1,
Figure 1b). Habitat was the best predictor based on variance ex-
plained (R2- values, Table 1). Mean number of years between banding
and recover y (recovery year) was positively correlated with dispersal
distances (Table 1, Figure 1j). It is the second- best single- predictor
model, but it achieved low model probability (Table 1). For other
predictors, model probability was low. Breeding range, migratory
behavior, migration distance, and diet, in particular, were poor pre-
dictors of natal dispersal distance (Table 1).
Among multi- predictor models, the top model contained aspect
ratio,populationsize,andhabitat,andexplained40%ofthevariation
(Figure 2, Table S3). Mos t habitat categorie s were significantly differ-
ent from the reference level (coast) in the best model (Table S4) and
the overall effect of habitat was statistically significant (Likelihood
ratio test: p- value = .010). The residual phylogenetic inertia was
λ = 0.50 for this model, indicating that phylogenetically structured
R
2=1−
RSS
model
SS
null
GVIF
1
(2×df
)
TABLE 1 Single-predictorPGLSmodelsofnataldispersaldistancefor44speciesofNorthAmericanbirds.
Model Intercept Coefficient p- Value df λLog(Lik) ΔAICc pmodel R2
Aspec t ratio −0.73 2.12 ± 1.56 .0 087 20.33 −62.7 0.94 .15
Recovery years 2.37 0.78 ± 0.43 .0007 20.42 −66.2 7.0 4 .03 .21
Populationsize 4.98 −0.11± 0.13 .0871 20. 31 −66.9 8 .47 .01 .06
Habitat 4.23 . 0027 40.40 −64.7 8.61 .01 .26
Mass 1.81 0. 28 ± 0.17 .0 016 20.00 −68.3 11 .23 .00 .19
Foraging behavior 3.84 .1171 50.36 −68.3 18.36 .00 .15
Migration behavior 3.56 .4 351 20.39 −71.9 18.53 .00 .01
Migration distance 3.23 0.04 ± 0.12 .4895 20 .41 −72. 0 18.6 3 .00 .01
Range 3.16 0.02 ± 0.16 .7778 20.47 −72 . 2 19. 02 .00 <.00
Diet 3.85 .26 49 40.42 −70. 1 19.4 9 .00 .08
Note:Aspectratio,populationsize,bodymass,migrationdistance,geographicrangesize,andmeanyearsbetweenbandingandrecoverywere
log transformed. For continuous predictors, p- values correspond to regression coefficients; for categorical predictors, p- values correspond to a
comparison with a null (for details on coefficients for each level, see Tables S8– S11). λ estimates the degree of phylogenetic non- independence
in residuals on a scale from 0 to 1. Log(Lik) is the log likelihood, ΔAICc is the difference between the AICc of t he best model and the given model,
pmodel is the model probability, and R2 is the coefficient of determination.

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variation is still present in model residuals. However, even the best
model attained low model probability, it was followed by multiple
models with similar fit (ΔAIC < 2),andthe95%confidencemodelset
contained 109 models revealing considerable model uncertainty.
All models within 5 AIC units of the best model contained aspect
ratio.Population sizealso occurredfrequentlyamong these mod-
els, whereas other ecological variables occurred in the best models
inconsistently. This fact is reflected in the estimated variable impor-
tance, which indicated that the aspect ratio was the most important
variable(0.99) followed by populationsize(0.89,Figure 3). In con-
trast, although appearing in the best model, habitat had lower im-
portance (0.46, Figure 3) and all other predictors were unimportant.
(Figure 3). Most model- averaged estimates had wide confidence in-
tervalsthatincludezero(Table S5).
Inspection of bivariate plots showed no strong colinear relation-
ships among predictors (Figures S1 and S2). However, generalized
variance i nflation facto rs (GVIF) sugge sted that there is mo derate mul-
ticollinearity involving the aspect ratio and recovery years ( Table S6).
4 | DISCUSSION
We successfully estimated natal dispersal distances for 51
North American bird species. Our method, adapted from Paradis
FIGURE 1 Therelationshipbetweennataldispersaldistanceand10morphological,behavioral,andecologicalfactorsin44speciesof
NorthAmericanbirds.Linescorrespondedtosingle-predictorphylogeneticgeneralizedleast-squaresmodels.Solidlinesaremodelswhose
slopesweresignificantlydifferentfromzero.HabitatcategoriesinGareasfollows:coasts(C),open(O),wetlands(WE),andwoodlands
(WO).DietcategoriesinHareasfollows:carnivores(c),herbivores(h),insectivores(i),andomnivores(o).ForagingbehaviorcategoriesinI
are ranked in increasing flight requirements: surface foraging (1), tree foraging (2), sallying (3), aerial search (4), and aerial capture (5).
FIGURE 2 Relationshipamongflight
efficiency (wing aspect ratio), population
size,habitat,andnataldispersaldistance
for 44 species of North American birds.
Dotsizeisproportionaltopopulationsize
up to 100 million individuals (species with
greater populations are depicted with
thesamesizedpoint).Dotcolorindicates
habitat: coasts (gray), open (yellow),
wetlands (blue), and woodlands (green).

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CHU and CLARAMUNT
et al. (1998 ), produced dispersal es timates that are st andardized,
comparable, and easily replicated. These data add to the growing
body of dispersal distances estimated from banding data (Martin &
Fahrig, 2018; Paradis et al., 1998;Weeksetal.,2022) and will fa-
cilitate future comparative analysis. Because of the number of years
elapsed between banding and recovery, some breeding dispersal
events may have been added, producing an overestimation of natal
dispersal distances. This may limit the use of these estimates for
applications in conser vation or management that require accurate
natal dispersal dist ances. However, this bias did not obscure the ef-
fect of biologically relevant factors on the dispersal process in our
analysis.
Ourresultssuggestthatflightefficiency,populationsize,andtoa
lesser ex tent, habit at influence natal dispersal distance in this group
ofNorthAmerican birds.Onlyflightefficiencyand populationsize
consistently appear in the models that better predicted natal dis-
persal distances, and thus, only these two factors had high variable
importance. Model- averaged parameter estimates had wide confi-
dence intervals, but this uncertainty could be explained by moderate
levels of multicollinearity increasing variance in parameter estimates.
Additionally, residual phylogenetic inertia was moderate in our natal
dispersal models suggesting that there are additional phylogeneti-
cally structured factors influencing dispersal distances. These may
include taxon- dependent biases in dispersal estimates; for example,
gulls and terns showed shor ter dispersal distances than what their
high aspect ratio wings would predict, perhaps as a result of strong
philopatric tendencies in these species (Figure S3). Moderate levels
of multicollinearity suggest that natal dispersal distances may be in-
fluenced by an aspect of a bird's biology that is correlated strongly
withflightefficiency,populationsize,andhabitat.Weproposethat
this aspect is the degree of mobility of the species.
The energetic cost of locomotion is expected to influence dis-
persal distances, but empirical evidence supporting this prediction
is scarce (Bonte et al., 2012; Mat thysen, 2012). In birds, because
flight is a prominent mode of locomotion and is energetically intense
(Norberg, 19 90; Rayner, 1988), flight efficiency may have a strong
influence on dispersal distances (Claramunt, 2021). A comparative
analysis of British birds found that flight efficiency, as estimated
from wing morphology, was the main factor explaining variation in
natal dispersal distances (Claramunt, 2021).Wefoundthatflightef-
ficienc y is also the main factor influencing dispersal distances among
this group of North American birds, independently confirming this
prediction in a different continent, and suggesting that this pattern
may be general across all birds.
The importance of flight efficiency, as estimated from wing mor-
phology, in determining dispersal distances in birds supports the
already prominent use of wing morphology as a proxy for disper-
sal ability in ecology and evolution (Claramunt et al., 2012;Sheard
et al., 2020; Tobias et al., 2020;Weeksetal.,2022). Like in British
birds (Claramunt, 2021), we found that more precise estimates
of flight efficiency, such as the aspect ratio and the lif t- to- drag
ratio, resulted in better predictions of natal dispersal distance (see
Appendix, Table S8). The lower performance of the hand- wing index
can be explained by the fact that it only estimates the elongation of
the manual portion of the wing, ignoring variation in arm length, thus
underestimating the wing elongation of birds with long arms such as
albatrosses, and overestimating wing elongation of birds with shor t
armssuchasswiftsandhummingbirds(Claramunt&Wright,2017 ).
Because of its role in determining the efficiency of movements
through space, flight efficiency can also influence other aspects of
birds' biology. For example, flight efficiency has a strong influence
on the species' ability to cross habitat gaps and move across frag-
mented landscapes (Claramunt et al., 2012, 2022; Hartfelder et al.,
2020; Ibarra- Macias et al., 2011; Naka et al., 2022). Flight efficiency
may also be linked to foraging behavior and, by ex tension, to diet and
habitat (Norberg, 1990; Rayner, 1988;Sherry,2016). For example,
forest- dwelling birds tend to have low aspec t ratio wings and move
short distances from tree to tree, whereas coastal birds have high as-
pect ratio wings and high flight efficiency to endure long flight s over
water. Therefore, it is likely that the covariation between wing shape
and these ecological characteristics is driven by adaptations to dif-
ferent levels of mobility and the degree of development of an aerial
lifestyle(Weeks et al., 2022). Because the aspect ratio is a strong
predictor of long- distance locomotor efficiency in birds (Claramunt
&Wright,2017; Norberg, 19 90; Pennycuick, 2008), it should closely
reflect adaptations for mobility, resulting in a strong signal of multi-
collinearity with ecological and behavioral factors related to the use
of space.
Adaptations for mobility and the distribution of resources may
explain the correlation between habitat and dispersal distances.
Speciesinhighlyproductivehabitatssuchasforestsdisperseshorter
distances compared to species in habitats of lower productivity such
FIGURE 3 Variableimportancefrommulti-predictorPGLS
models explaining natal dispersal distance among 44 species of
North American birds. Variable importance was calculated as the
sum of the probabilities of the models containing the given variable.

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CHU and CLARAMUNT
as grasslands and deserts (open habitats). Freshwater wetlands are
highly productive but are sparsely distributed, or linear (rivers);
therefore, many wetland species may still require moderate- to- high
levels of mobility. Coastal habitats are exploited by highly mobile
species because of the mobility and limited accessibility of oceanic
prey or for commuting to appropriate roosting and breeding sites
along the coast (Davoren et al., 2003; Preston, 1990). Therefore,
the effect of habitat on dispersal distances might also be mediated
by the movement needs of the species. For that reason, once flight
efficiency is taken into account, habitat ceases to be an important
variable in explaining dispersal distances.
Adaptations for mobility can also explain the negative correla-
tion betweennataldispersaldistance and population size:species
with larger populations exhibited shorter natal dispersal distances.
This result reveals that this pattern is not unique to the British avi-
fauna (Claramunt, 2021; Paradis et al., 19 98) but may be more gen-
eral.Populationsizemaybenegativelyrelatedtodispersaldistance
because species that use widespread and rich resources or habitats
are abundant and do not need to disperse much to find resources
(Paradis et al., 1998). On the other hand, species such as raptors or
oceanic birds that exploit resources that are sparsely distributed
show both low population densities and adaptations for mobility and
flight efficiency, which indirectly result in long dispersal distances.
Intraspecific attraction and mating oppor tunities may also
explain the negative relationships between population sizes and
dispersal distances independently from the distribution of other
resources. Higher conspecific density increases mating opportu-
nities and may decrease the need to move far ther for mating and
breeding(Doligezetal., 2003; Stamps,19 94;Wagner,19 93). But
density itself should be controlled by resource availability, and the
fact that this correlation is also inter twined with flight efficiency
suggests that species adaptations to mobility are also involved
(Bowman, 2003; Claramunt, 2021;Stephensetal.,2019). A caveat
to these explanations is the fact that the population estimates used
in this analysis represent entire continental breeding populations
and may not reflect local population densities. More research is
warrantedtounpackthispatternbetweenpopulationsizeanddis-
persal distance.
Given that levels of mobility required by the distribution of re-
sources may explain variation in dispersal distances, it is surprising
that foraging behavior did not exhibit the expected pattern of in-
creased dispersal distances with increased amount of flight required
for foraging (Figure 1j).Surfaceforagers,thebirdsthatintheoryre-
quire the least amount of flight on the foraging spectrum, showed
unexpectedly high dispersal distances. Although some surface for-
agers such as ducks and shorebirds do not fly during foraging, they
are highly mobile in order to reach appropriate foraging grounds.
Surf ace foraging is als o often associate d with unprodu ctive habi-
tats that lack dense and tall vegetation. In these habitats, species
need larger foraging areas and thus enhanced mobility in order to
obtain sufficient resources. Finally, even in productive tropical
forests, ground- foraging species may show higher levels of mobil-
ity than species that forage on understory vegetation (Claramunt
et al., 2022; Naka et al., 2022). These factors may complicate the
relationship between foraging behavior and dispersal distances.
Wefoundthatmigratorybehavior wasapoorpredictorofdis-
persal distance (Table 1). British birds showed a strong effect of mi-
grator y behavior on dispersal distance (Dawideit et al., 2009; Paradis
et al., 1998), and both theory and empirical evidence suggest mi-
gratory behavior and migration distances depend on long- distance
flight efficiency (Chu et al., 2022; Minias et al., 2015; Norberg, 1990 ;
Nowakowski et al., 2014; Vágási et al., 2016; Vincze et al ., 2019).
However, other lines of evidence suggest that migration and dis-
persal may be completely decoupled. Despite the apparently strong
effect of migration on dispersal among British birds, most migratory
species showed a trend of increasing dispersal distances with in-
creasing flight efficiency that was very similar to the one shown by
non- migratory species (Claramunt, 2021). Long- distance migration
is made possible by unique physiological and behavioral adaptations
such as fat accumulation and stopover strategies that may increase
flight capabilities beyond the normal capabilities of species during
non- migrator y periods (Butler, 2016; Pennycuick, 2008; Winkler
et al., 2016). In addition, strong philopatric tendencies among mi-
grants(Wingeretal.,2019;Winkleretal.,2016)mayminimizeand
practically nullify any potential effect of migratory movements
on dispersal distance. In sum, migratory movements and dispersal
movements in birds may be more dissociated than usually assumed
and more research is needed on this topic.
We did not find an association between dispersal distances
and geogr aphic range size. De spite clear the oretical expe ctations
and some empirical evidence (Alzate & Onstein, 2022; Arango
et al., 2022; Capurucho et al., 2020; Laube et al., 2012; Lester
et al., 20 07), we found that natal dispersal distances of widespread
species were similar to those of range- restricted species. A potential
correlationbetween geographicrangesizeanddispersal distances
may be dampened by the negative correlation between dispersal
distances and population size, as widespread species tend to be
more abundant (Bock & Ricklefs, 198 3; Brown & Munger, 1985) and
less dispersive (Claramunt, 2021, this study). Further research on
the interrelationships among dispersal, abundance, and geographic
rangesizeiswarranted.
5 | CONCLUSION
Wefoundthatnataldispersaldistancesdependonthespecies'flight
efficiencyandarealsocorrelatedwithpopulationsize.Wepropose
that the int errelationshi p among flight ef ficiency, populat ion size,
and dispersal can be explained by adaptations to dif ferent levels of
mobilityrelatedtoresourcedensity and distribution. We highlight
the importance of flight efficiency as a determinant of dispersal dis-
tance and find suppor t for the use of wing morphology to infer dis-
persal ability in birds. The relationship between flight efficiency and
dispersal ability in birds provides a framework to explore broader
ecological and evolutionary phenomena associated with dispersals
such as community connectivity and the interrelationships among

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geographic distribution, dispersal, and abundance. It may also be
helpful in assessing targets for conservation because how birds re-
spond to climate change and habitat fragmentation may depend on
their dispersal capabilities (Claramunt et al., 2022; Desrochers, 2010;
Martin et al., 2017; Tingley et al., 2009). Despite dispersal's far-
reaching effects on ecology and evolution, there is still much to be
learned about its causal factors and macroecological consequences.
AUTHOR CONTRIBUTIONS
Jonathan J. Chu:Conceptualization(equal);datacuration(lead);formal
analysis (lead); investigation (equal); methodolog y (equal); project ad-
ministration (equal); writing – original draft (lead); writing – review and
editing (equal). Santiago Claramunt: Conceptualization(equal); data
curation (supporting); formal analysis (supporting); funding acquisition
(lead); investigation (equal); methodology (equal); project administra-
tion (equal); supervision (lead); writing – review and editing (equal).
ACKNOWLEDGEMENT
We would like to ack nowledge that th e data used in thi s study
were collected by the efforts of many volunteer bird banders
for the Nor th American Bird Banding Program administered by
Canada'sBirdBandingOfficeandtheUnitedStatesBirdBanding
Laboratory. We thank Danny Bystrak and Véronique Drolet-
Gratton of the BBL and BBO respectively for handling data re-
quests and providing early advice on banding data usage. We
thankMarie-JoséeFortin,JasonWeir,LukeMahler,NjalRollinson,
Mark Peck, Oliver Haddrath, Talia Lowi- Merri and Hellen Fu
for the helpful discussions and advice on early versions of this
study.WeacknowledgethesupportoftheNaturalSciencesand
Engineering Research Council of Canada (NSERC), Discovery
Grant RGPIN- 2018- 06747.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAIL ABILI TY STATEMENT
Data are available in the Dr yad Digital Repository ht t p s :// d oi .
org/10.5061/dryad.15dv4 1p1x (Chu & Claramunt, 2023).
ORCID
Jonathan J. Chu https://orcid.org/0000-0003-0765-494X
Santiago Claramunt https://orcid.org/0000-0002-8926-5974
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CHU and CLARAMUNT
SUPPORTING INFORMATION
Additional supporting information can be found online in the
SupportingInformationsectionattheendofthisarticle.
How to cite this article: Chu,J.J.,&Claramunt,S.(2023).
Determinants of natal dispersal distances in North American
birds. Ecology and Evolution, 13, e9789. ht t p s :// do i .
org /10.1002/ece3.9789
APPENDIX A
Evaluation of alternative flight efficiency proxies
Wecalculated three proxiesfor flightefficiency:hand-wingindex,
aspectratio, andlift-to-dragratio (Claramunt&Wright,2017). The
hand- wing index is a proxy for the aspect ratio of the hand portion
of the wing that can be measured on traditional museum specimens
(Claramunt et al., 2012). The hand- wing index is defined as:
where Lw is the distance between the carpal joint and the tip of the
longest primary feather, and S1 is the distance between the carpal joint
and the tip of the first secondary feather. Hand- wing indices for all
specieswereobtainedfromSheardetal.(2020).
The aspect ratio is the morphological characteristic of the wing
that is most influential in determining long- distance flight efficiency
(Norberg, 1990 ; Pennycuick, 2008). It is calculated as:
where B is the wingspan and Atot is the total wing area (the area of both
wings plus the area of the body between the wings) estimated as:
in which Aw is the area of a single wing, Cr is the width of wing at the
base (known as the root chord), and E is the wing extent, measured as
the distance from the base to the tip of the longest primary feather
perpendicular to Cr(seeClaramunt&Wright,2017; Pennycuick, 2008
for illustrations and further details).
Lift- to- drag ratio is the ratio of the lift force that counteracts the
bodyweighttothedragforcesduringhorizontalsteadyflight,andit
is given by the formula:
where m is body mass, g is the gravitational acceleration, V is the for-
ward velocity, and P is the power required for flight. P can be estimated
from aerodynamic models of avian flight and morphological variables
(Claramunt&Wright,2017; Pennycuick, 2008). The three main com-
ponents of the mechanical power required need three morphological
measurements: wingspan, wing area, and body mass.
Wingspanandwingareameasurementsweretakenfromspread
wing images and museum specimen records (further details out-
lined in main text). Body masses were obtained from Tobias and
Pigot (2019), which were based on Dunning (2007). As empirical
field measurements of flight velocit y are not available for most
species, we estimated the maximum lif t- to- drag ratio for each spe-
ciesbymaximizing itwithrespect tovelocityusingfunctionoptim
in R 4.0 (R Core Team, 2 019). This approach is based on evidence
suggestingthatbirds tend to fly at velocitiesthatmaximizes their
lift- to- drag ratio (known as maximum- range velocities) during mi-
gration or commuting (Bruderer & Boldt, 2001; Pennycuick, 1997;
Pennycuick et al., 2013). For the estimation of powers, we assumed
an induced power factor = 1, a body drag coefficient = 0.1, and es-
timatedthebody'sfrontalareafromthebodymassas0.01 m2/3 fol-
lowing Pennycuick (2008), Pennycuick et al. (2013), and Claramunt
andWright(2017).
Aspect ratio and lif t- to- drag and hand- wing indexes were used
as predictors of natal dispersal distances using phylogenetic least
squaresmodels(PGLS).
All three flight efficiency proxies were positively and significantly
correlated with dispersal distance. Aspect ratio was the best proxy
in terms of both models' fit, AIC, and explained variance (Table S7).
The lift- to- drag ratio was the second- best proxy, only slightly under-
performing in fit, AIC, and explained variance. The hand- wing index
had the lowest performance of the three proxies (Table S7).Similar
results were found among British birds, in which the wing's aspect
ratio and the lift- to- drag ratio outperformed the hand- wing index
(Claramunt, 2021).
100
∙
(
Lw−S1
)
Lw
B
2
Atot
Atot =2Aw+Cr∙(B−2E)
mgV
P