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\begin{document}

\title{Effect of spatial and temporal urban isolation on the genetic diversity,
acoustic variation, and morphological characteristics of an urban
survivor bird species}



\author[1]{Luis Cueva}%
\author[2]{Eric Fuchs}%
\author[2]{Gilbert Barrantes}%
\author[2]{Ruth Madrigal-Brenes}%
\author[1]{Luis Sandoval}%
\affil[1]{Universidad de Costa Rica Escuela de Biología}%
\affil[2]{Universidad de Costa Rica Escuela de Biologia}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
Urbanization alters ecosystems, fragmenting natural habitats, and hence,
increasing isolation between populations. Therefore, a reduction in gene
flow among isolated populations is expected with greater distance and
time since fragmentation. Changes in the structure, density, or
community composition in the remaining habitats often result in species`
modifications of vocalizations and morphological traits. However, the
relationship between genetic, vocalizations and morphological divergence
in urban areas over time remains poorly understood. We analyzed ten
years of genetic, acoustic, and morphological data from isolated
populations of the white-eared ground-sparrow. We used seven
microsatellites (SSRs), recorded, and measured five acoustic traits, and
measured six morphological traits, and compared them over a period of
ten years. We found an increase in inbreeding, increase in song
duration, number of elements, and frequency of maximum amplitude, but a
reduction in female body size and changes in male beak. However, we only
identified a significant correlation between genetic diversity and the
acoustic characteristics of song.%
\end{abstract}%



\sloppy


Effect of spatial and temporal urban isolation on the genetic diversity,
acoustic variation, and morphological characteristics of an urban
survivor bird species

Luis Cueva\textsuperscript{1,2*}, Eric J. Fuchs\textsuperscript{1},
Gilbert Barrantes\textsuperscript{1}, Ruth
Madrigal-Brenes\textsuperscript{1} \& Luis Sandoval\textsuperscript{1}

\textsuperscript{1}Universidad de Costa Rica, Escuela de Biolog\selectlanguage{ngerman}ía,
Ciudad Universitaria, Rodrigo Facio, San Pedro de Montes de Oca, San
José, 11501-2060

\textsuperscript{2}Programa de Posgrado en Biología, Universidad de
Costa Rica, Escuela de Biología, Sistema de Estudios de Posgrado, Ciudad
Universitaria, Rodrigo Facio, San Pedro de Montes de Oca, San José,
11501-2060

*Contact information: Luis Cueva, San Pedro, San José 11501-2060, +506
62784357,\emph{luisfer\_cueva@hotmail.com}

Abstract

Urbanization alters ecosystems, fragmenting natural habitats, and hence,
increasing isolation between populations. Therefore, a reduction in gene
flow among isolated populations is expected with greater distance and
time since fragmentation. Changes in the structure, density, or
community composition in the remaining habitats often result in species`
modifications of vocalizations and morphological traits. However, the
relationship between genetic, vocalizations and morphological divergence
in urban areas over time remains poorly understood. We analyzed ten
years of genetic, acoustic, and morphological data from isolated
populations of the white-eared ground-sparrow. We used seven
microsatellites (SSRs), recorded, and measured five acoustic traits, and
measured six morphological traits, and compared them over a period of
ten years. We found an increase in inbreeding, increase in song
duration, number of elements, and frequency of maximum amplitude, but a
reduction in female body size and changes in male beak. However, we only
identified a significant correlation between genetic diversity and the
acoustic characteristics of song.

Keywords: \emph{Melozone leucotis,} Costa Rica, genetic structure,
Bioacoustic

Introduction

Urban development results in rapid environmental changes (Grimm et al.,
2008; Johnson \& Munshi-South, 2017) which transform natural habitats
into small patches of vegetation surrounded by a matrix of man built
constructions such as buildings and roads. These fragments with
depauperate resources are also frequently highly contaminated by noise,
lights, or solid debris (Fahrig \& Rytwinski, 2009; Biamonte et al.,
2011). This reduction in natural habitats isolates populations, as urban
development restricts or eliminates the movement of individuals across
populations (Lynch \& Baker, 1994; Crooks et al., 2004), lowering gene
flow and, consequently, the genetic diversity of populations (Lynch \&
Baker, 1994; Johnson \& Munshi-South, 2017). In birds, changes in
behavior (e.g., vocalizations, nest construction, predator responses)
and morphology (e.g., bill and body size) have also frequently been
reported as a consequence of different selective pressures acting on
isolated populations (e.g., sexual selection, predation, environment)
(Slabbekoorn \& Smith, 2002; Brumm, 2004; Warren et al., 2006; Foster et
al., 2008; Laiolo \& Arroyo, 2011; Luther \& Derryberry, 2012; Sandoval
et al., 2014; Geffroy et al., 2020; Corrales-Moya et al., 2021; Méndez
et al., 2021)

The increase in distance between populations and the effect of drift and
inbreeding in populations of small size are the primary causes of the
loss of genetic diversity within populations and the increase in spatial
genetic structure in urban isolated bird populations (Keller \& Waller,
2002; Whitlock, 2004; Johnson \& Munshi-South, 2017; Miles et al.,
2019). In wren-tits \emph{Chamaea fasciata} , significant genetic
structure was found when populations were separated by a highway and a
strip of residential development, in southern California (Delaney et
al., 2010b). In the song sparrow \emph{Melospiza melodia,} researchers
found lower genetic diversity in populations where urban development had
been extensive and had occurred over long periods of time (Unfried et
al., 2013). These results indicate that urbanization has the potential
to lower genetic diversity within fragmented populations inside
urbanized matrices and increase the geographic genetic structure of bird
populations.

The observed patterns of genetic structure caused by urbanization are
analogous to differences in songs reported for several bird species:
white-bellied short wing\emph{Brachypteryx major} , rufous-and-white
wrens \emph{Thryophilus rufalbus} , and wedge-tailed sabrewing
\emph{Campylopterus pampa} . (Shaw \& Lesnick, 2009; Gonzalez et al.,
2011; Purushotham \& Robin, 2016; Graham et al., 2017). Variation or
polymorphism in the acoustic traits of songs among populations occurs in
response to different habitat characteristics (e.g., vegetation density
or noise levels), and they are aimed at improving sound transmission and
communication across different environments (Morton, 1975; Boncoraglio
\& Saino, 2007; Ey \& Fischer, 2009; Hardt \& Benedict, 2021). Acoustic
variation may also occur via female selection, differences in female
choice among populations may lead to selective changes in song
characteristics by males (Searcy \& Andersson, 1986; Laiolo \& Arroyo,
2011). Acoustic variations may also be the result of acoustic drift,
which occurs when a small group of birds randomly develops distinct
repertoires. (Baker \& Cunningham, 1985; Wilkins et al., 2013). For
example, in bird species that inhabit both urban and rural environments,
songs from urban populations had higher frequencies than populations
from birds inhabiting rural environments to cope with anthropogenic
noise (Hu \& Cardoso, 2009; Luther \& Derryberry, 2012; Méndez et al.,
2021). Differences in song traits may have evolved through acoustic
drift as in populations of Darwin's finches (B. R. Grant \& Grant,
1996). These changes in songs can be maintained or increase over time,
because songs are either learned or transmitted over generations (Boyd
\& Richerson, 1985; Warren et al., 2006). Hence, the correlation between
genetic and vocal polymorphism may arise due to the relationship between
song pattern diversity and the survival and reproductive success of
individuals, as well as the presence of distinct genotypes within each
population (Danielson-François et al., 2006; Irwin et al., 2008).

Variation in song acoustic characteristics (e.g., frequency and
duration), have also been related with the body and beak size of
individuals (Podos, 2001, 2010), because larger birds and larger beaks
tend to produce lower frequency songs (Wallschläger, 1980; Bradbury \&
Vehrencamp, 1998; Fletcher \& Tarnopolsky, 1999; Slabbekoorn \& Smith,
2002a; Brumm, 2009 ). For ex ample, in purple-crowned
fairy-wrens\emph{Malurus coronatus} , barn swallows \emph{Hirundo
rustica} , and black swans \emph{Cygnus atratus;} individuals with large
body sizes had lower song frequencies (Galeotti et al., 1997; Patel et
al., 2010; Hall et al., 2013). In House finches \emph{Haemorhous
mexicanus} from Arizona, urban individuals have longer and deeper beaks
than rural birds and emit songs with slower trills and wider frequency
range (Badyaev et al., 2008).

White-eared ground-sparrows, \emph{Melozone leucotis,} inhabit the
central valley of Costa Rica in isolated populations embedded inside the
greater urban area (Sandoval et al., 2015; Juárez et al., 2020;
Rodríguez-Bardía et al., 2022). Previous work on this species reported
that urbanization limits the local movement of individuals and gene flow
between populations, which resulted in significant genetic structuring
among Costa Rican populations (Rodríguez-Bardía et al., 2022). Previous
research has also identified changes in song dialects among different
populations (Sandoval et al., 2014, 2016; Bonilla-Badilla 2021).
Additionally, it has been observed that differences in the frequency and
duration of songs among populations are associated with levels of
anthropogenic noise. Specifically, males in populations exposed to
higher levels of noise tend to exhibit an increase in both the minimum
frequency and duration of their songs. (Sandoval et al., 2014, 2015,
2016; Juárez et al., 2020; Méndez et al., 2021; Rodríguez-Bardía et al.,
2022). These studies suggest that genetic and cultural traits are under
intense selective pressures imposed by urbanized environments. However,
it is still unknown how these traits change over time and how they track
population dynamics.

The main objective of this study is to describe the changes in genetic
diversity, vocalization, morphology, and their relationship, over a
10-year period in populations of white-eared ground-sparrow isolated by
urban development in Costa Rica. We hypothesize that differences in
genetic diversity, song acoustics, and morphology will correlate with
the length of time communities have been separated, as population
divergence increases with isolation.

Material and Methods

We conducted this study in four populations of white-eared
ground-sparrows in Costa Rica: (1) Estación Biológica Monteverde (MTV),
Puntarenas province (10° 18\selectlanguage{english}' N, 84\selectlanguage{ngerman}° 48\selectlanguage{english}' W, 1600~m), characterized by
coffee plantations and large forest patches that connect with more
extensive mature forests (Rodr\selectlanguage{ngerman}íguez-Bardía et al., 2022) without an
urban isolation effect yet. (2) Getsemani (HDA), Heredia province (10 °
01'N, 84 ° 05'W, 1350 m); it is a non-urbanized site, with secondary
forest patches and abandoned coffee plantations, and dense thickets with
little human presence (Juárez et al. 2020). Urbanization started to
expand from the edges of the study area since 2000. (3) Jardín Botánico
Lankester (JBL), Cartago province (9 ° 50'N, 83 ° 53'W, 1370 m); it
includes secondary forests, gardens, and buildings, with little
disturbance and human presence, but immerse in a urban matrix that had
rapidly extended since 1990 (Juárez et al. 2020). (4) The Universidad de
Costa Rica (UCR), San José province (09 ° 56'N, 84 ° 05'W, 1200 m); is a
highly urbanized site, with a secondary forest reserve surrounded by
buildings, open areas and gardens, and it is exposed to intense human
disturbance (Juárez et al., 2020). Urban development began before the
1900's but most fragmentation occurred after the 1970´s (Biamonte et
al., 2011).

Historical and current sampling

Historical acoustic and morphological data was collected in 2011 and
2012 and was deposited in the Laboratorio de Ecología Urbana y
Comunicación Animal (LEUCA), Universidad de Costa Rica. These data are
part of a long-term research focus on analyzing the effect of
urbanization on behavior, morphology, and genetic diversity of bird
populations. To obtain historic genetic diversity estimates, we used
blood samples collected in 2011 -- 2012. We will refer for the
historical 2011 -- 2012-year sampling as ``period 1''.

Current acoustic and morphological data was collected from 2018 to 2022.
We recorded each banded pair using a Marantz PMD661 digital recorder and
a Sennheiser ME66/K6 unidirectional microphone. The recordings were
stored in WAV format with a sample rate of 44.1 kHz and a precision of
24 bits. All recording were conducted between 4:55 - 6:00 h when this
species is most vocally active (Sandoval et al., 2016). We captured
individuals using mist nets (12 x 2.5m, 15 mm mesh eye) inside each pair
territory. We provided each captured individual with a unique numbered
metal ring and a color combination of plastic rings. From each bird, we
obtained 10 \selectlanguage{greek}µ\selectlanguage{english}L of blood from the brachial vein and stored it in 95\%
ethanol or Lysis buffer for molecular analyses (Seutin et al., 1991;
Rodr\selectlanguage{ngerman}íguez-Bardía et al., 2022). We will refer for the period 2018 --
2022-year sampling as ``period 2''.

All procedures were conducted in accordance with Costa Rican law. The
Research Committee of the Biology School and the Animal Care Committee
(ACC) of the University of Costa Rica granted to LS, the principal
investigator, research permits and animal care protocols.

DNA extraction and SSR amplification

We extracted DNA from blood samples using the DNeasy blood and tissue
Kit (Qiagen Inc., Valencia, CA, USA) following the manufacturer's
protocol. We used seven primers: Mme2, Mme7, Mme8, As\selectlanguage{greek}µ\selectlanguage{english}15, Esc\selectlanguage{greek}µ\selectlanguage{english}6, Gf01 y
Gf05 (Petren, 1998; Jeffery et al., 2001; Bulgin et al., 2003) which
have been previously shown to be polymorphic and amplified well for this
species (Rodr\selectlanguage{ngerman}íguez-Bardía et al., 2022). We followed the Qiagen
Multiplex master kit ((Qiagen Inc., Valencia, CA) procedure to amplify
the aforementioned SSRs. All markers were amplified through Polymerase
Chain Reactions (PCR) using 10 \selectlanguage{greek}μ\selectlanguage{english}L reaction, containing 2 \selectlanguage{greek}μ\selectlanguage{english}L of 0.4~\selectlanguage{greek}µ\selectlanguage{english}M
primer mix, 1.5 \selectlanguage{greek}μ\selectlanguage{english}L of 20~ng of template DNA, 5 \selectlanguage{greek}μ\selectlanguage{english}L of Multiplex Master
Kit (Qiagen) for mixed primers and Top Taq Master Kit (Qiagen) for Gf01
and 1.5 \selectlanguage{greek}μ\selectlanguage{english}L of water nuclease free (Qiagen). We followed the PCR thermal
profiles and mixes described in Rodriguez et al. (2022). The PCR was
performed in a Veriti thermocycler (Applied Biosystems, Foster City,
CA, USA). Capillary electrophoresis was performed on a 3500 genetic
analyzer (Applied Biosystems) using Hi-Di formamide and GeneScan 500
LIZ dye size standard (Applied Biosystems). Multilocus genotypes were
scored using GeneMarker 1.91 (SoftGenetics, State College, PA, USA).

Acoustic analyses

We analyzed all songs using Raven pro 1.6 software (Cornell Lab of
Ornithology, Ithaca, NY, USA). We classified each song according to its
structure in different types following the classification of the sound
library for white-eared ground-sparrow available in LEUCA and used in
previous studies (Sandoval et al., 2014, 2015, 2016; M\selectlanguage{ngerman}éndez et al.,
2020). In addition, we measured the following acoustic characteristics:
duration (s), minimum frequency (kHz), maximum frequency (kHz),
frequency of maximum amplitude (kHz), and the number of elements. We
used different windows to obtain the measurements: the spectrogram
window was used to identify and classify sounds, the power spectrum was
used to measure frequency characteristics with a threshold of -30dB
relative to vocalization peak, and the oscillogram to measure the
temporal characteristics of sounds. Spectrograms were constructed using
a Hann window with a size of 512 samples, 3 dB bandwidth of 124 Hz; a
Time grid with an overlap of 50\% and hop size of 5.80 ms; and a
frequency grid spacing with DFT size of 512 samples and grip spacing of
86.1 Hz.

Morphometric measurements

We measured six morphological traits: tarsus length (from the
intertarsal joint to the middle of the foot sole), tail length, wing
chord length (unflattened), exposed culmen length (from the tip of the
beak to base of skull), beak width (at the beak gape), and beak depth
(measured at right angles to the point on the lower mandible where the
feathers end) following the protocol described in Sandoval \& Mennill,
(2013). We used a dial caliper (model: SPI Plastic Caliper 150mm,
AVINET, NY, USA) to get the bill and tarsus measurements, a metal wing
ruler (model: WING15ECON, AVINET, NY, USA) to measure wing chord and
tail length.

Statistical analyses

\emph{Genetic analyses}

Mme7 is a sex-linked marker and it deviated from Hardy Weinberg
Equilibrium (HWE), therefore, we coded it as a missing allele for
females and juveniles in further analyses (Rasner et al., 2004;
Rodríguez-Bardía et al., 2022). We genotyped 76 individuals, 28 males
from `period 1', 25 males from `period 2', 14 females from `period 1',
and 9 females from `period 2'. We calculated expected heterozygosity
(\(H_{e}\)), observed heterozygosity (\(H_{o}\)),
allelic richness (\(A_{r}\)), and inbreeding coefficients
(\(F_{\text{IS}}\)) for each population and time period (e.g., MTV
period 1 and MTV period 2). We estimated confidence intervals for
\emph{F\textsubscript{IS}} values using 9999 bootstraps as implemented
in the ``hierfstat'' package in R (R Core Team, 2020). To compare
differences in heterozygosity for the same populations between time
periods we used z-scores from a Wilcoxon signed rank test as implemented
in the ``coin'' package in R (R Core Team, 2020). To estimate changes in
genetic structure among populations over a decade, we calculated Nei's
coefficient (\(G_{\text{st}}\)) among populations within time periods,
using 9999 bootstrap values to estimate confidence intervals. We assumed
that nonoverlapping confidence intervals indicated significant
differences in genetic structure between periods. We also tested for
differences in genetic diversity between time periods using an analysis
of molecular variance (AMOVA) with 9999 permutations using the ``poppr''
package in R (R Core Team, 2020). We clustered individuals in each time
period into the most likely configuration of clusters based on allele
frequency similarity using the Bayesian clustering algorithms
implemented in the program STRUCTURE V.2.3.4 (Pritchard et al., 2000).
To determine the most probable number K, we utilized a method
implemented in a study that used data from some of the same populations
(Rodríguez-Bardía et al., 2022). Thus, we used correlated allele
frequencies and the admixture model with 300 000 Markov chains, and a
burning of 30 000 chains. We grouped individuals in 1 to 6 clusters with
20 repetitions for each cluster (Rodríguez-Bardía et al., 2022). To
determine the most likely number of K clusters we used Structure
Harvester 0.6.94 (Evanno et al., 2005). After determining the most
likely number of K cluster, we conducted another structure analysis with
1~000 000 Markov chains and a burn-in of 100~000 chains to assure proper
chain mixture (Rodríguez-Bardía et al., 2022).

\emph{Acoustic analyses}

To analyze the effect of time on acoustic measurements, we analyzed
10,707 solo songs. We used a principal component analysis (PCA) using
the ``FactoMineR'' R package (Lê et al., 2008) to condense the five song
measurements into three variables with eigenvalues \textgreater{} 1,
that explained 76\% of total variance. The first principal component
(PC1) correlated with duration and number of elements, the second
principal component (PC2) correlated with minimum and maximum frequency,
and the third principal component (PC3) correlated with the frequency of
maximum amplitude (Table 1). We performed linear mixed models (GLMMs)
using the ``lme4'' package (Bates et al., 2015) to determine the effect
of populations and time periods on the variation of acoustic traits.
Specifically, we included in the model two independent variables:
Populations (four levels: MTV, HDA, UCR, and JBL), time periods (two
levels: period 1 and period 2), and the interactions between both
variables. Each of the three principal components (PC1, PC2, and PC3) of
the song was included as a response variable in the model, and the
territory inside each population as random factor. We additionally carry
out \emph{post hoc} tests when the model showed us significant
differences on pairwise comparisons between the main effects and the
two-factor interactions.

\emph{Morphological analyses}

To analyze the effect of time on body measurements, we measured 87
individuals, 55 males and 32 females. We used principal component
analysis (PCA) using ``FactoMineR'' to condense the six body
measurements into two variables with eigenvalues \textgreater{} 1, which
together explained 60\% of the total variance. The first principal
component (PC1) correlated with tarsus length, tail length, wing chord
length, and beak depth; the second principal component (PC2) correlated
with exposed culmen length and beak width (Table 2). We performed a
generalized linear mixed models (GLMMs) with a gamma error distribution,
using ``lme4''. In these analyses we included populations (MTV, HDA,
UCR, and JBL), time periods (period 1 and period 2) and the interactions
between both variables, as independent variables; each of the two
principal components (PC1 and PC2) as the response variable, and the
territory inside each population in which individuals were captured as
random factor. We also performed models for females and males because
males are larger than females (Sandoval \& Mennill, 2013). We carried
out post hoc tests when the model showed significant differences on
pairwise comparisons between the main effects and the two-factor
interactions.

Finally, to evaluate if divergence between different traits (i.e.,
genetic, acoustic, and morphology) is related, we conducted a partial
Mantel test to determine the strength of the correlation between
variables using 10,000 permutations implemented in the ``vegan'' package
in R (Oksanen et al., 2012). Females in this analysis were not
considered because they do not sing (Sandoval and Mennill 2014; Sandoval
et al. 2016). We used 46 males with data of the three variables. First,
we performed a PCA to condense the six body measurements and another PCA
to condense the five song measurements of the males. With the PCAs we
then obtained acoustic and morphological distance matrices created from
Euclidean distances of averaged population traits in time periods, and
the genetic distance matrix from Gst values.

Results

Genetic diversity

At UCR, observed heterozygosity (\(H_{o}\)) was greater in
'period 1' than in 'period 2' (z = 1.95, p = 0.05), whereas the genetic
diversity of the other populations did not differ between time periods
(all comparisons: z 1.01, p \textgreater{} 0.05) (Table 3). In Heredia,
period 1 had a lower expected heterozygosity (\(H_{e}\)) than
period 2 (z = -2.37, p \textless{} 0.05). In contrast, heterozygosity
was lower in 'period 1' for the JBL population than in 'period 2' (z =
-2.37, p \textless{} 0.05), whereas the other populations did not differ
(all comparisons: z = 0.68, p \textgreater{} 0.05) (Table 3). HDA, JBL,
and UCR showed significant inbreeding in 'period 2' (Table 3).

Genetic diversity was significantly structured in the metapopulation;
however, structure did not differ between time periods: period 1
(\(G_{\text{st}}\) = 0.07, 95\% CI = 0.06 and 0.09) and period 2
(\(G_{\text{st}}\) = 0.07, 95\% CI = 0.06 and 0.09). AMOVA results
showed that 12\% of total genetic variation was partition among
individuals in different time periods. There were significant
differences in allele frequencies between periods (i.e., period 1 vs.
period 2; \(F_{\text{ct}}\) = 0.120, p \textless{} 0.05), as well as
among populations within time periods (\(F_{\text{sc}}\) = 0.15, p
\textless{} 0.001). In both time periods, the Evanno method suggested
that populations were likely grouped into two clusters (i.e., K=2). In
`period 1', all MTV individuals were assigned to cluster 1, while nearly
all HDA, UCR, and JBL individuals were assigned to cluster 2 (Figure 1).
In `period 2' individuals were also grouped into K = 2 clusters, with
individuals from MTV grouped in cluster 1, and the individuals from the
other populations were almost equally distributed into clusters 1 and 2
(Figure 1).

Acoustic characteristics of songs

We found that the duration and number of elements (PC1) were affected by
the time period and by the interaction between population and time
period (Table 4). In MTV, songs increased in duration and number of
elements in `period 2' (Figure 2). The minimum and maximum frequency
(PC2) were not affected by time, but it differed significantly among
populations (Table 4). UCR had higher minimum frequency and lower
maximum frequency than MTV and HDA; while JBL had lower minimum
frequency and higher maximum frequency compared to other populations
(Figure 2). The frequency of maximum amplitude (PC3) showed significant
differences in the interaction population × time period (Table 4). The
songs increased the frequency of maximum amplitude in HDA period 2,
relative to 'period 1', whereas UCR and JBL decreased in 'period 2'
compared to 'period 1'.

Morphology

We found a significant interaction between population and time period in
females for tarsus length, tail length, wing chord length, and beak
depth (PC1) (Table 5). These morphological variables represented by the
PC1 decreased between periods 1 and 2 for MTV and JBL (Figure 3). The
exposed culmen length and beak width (PC2) did not show significant
differences (Table 5) among sites or time periods. For males we also
found a significant interaction between population and time period for
PC1 (Table 5). Tarsus length, tail length, wing chord length, and beak
depth were smaller in JBL period 2 compared to period 1 (Figure 3). The
exposed culmen length and beak width (PC2) were affected by the
interaction between population and time period (Table 5). Birds in MTV
period 2 had shorter exposed culmen and bigger beak width than in period
1, and birds in JBL period 2 had bigger exposed culmen length and
smaller beak width than in period 1 (Figure 3).

Relationship between variables

When acoustic differences were controlled, the partial Mantel test
revealed a non-significant association between genetic and morphological
distances (r = -0.06, p = 0.40; Figure 4). Similarly, we found no
significant correlation between morphology and acoustic distances, while
accounting for genetic differences (r = 0.24, p = 0.10; Figure 4).
However, we found a significant correlation between genetic distances
and acoustic distances when morphological distances were accounted for
(r = 0.49, p \textless{} 0.05; Figure 4).

Discussion

Our findings showed an increase in inbreeding, as well as changes in
acoustic and morphological traits over a ten-year period, however only
genetic distances and song divergence correlated. These results are
likely explained by the interaction between changes in habitat quality
and the barriers to gene flow produced by rapid urban development in the
sampling area (Biamonte et al., 2011; Rodríguez-Bardía et al., 2022).
Particularly considering the low mobility, isolation and specific
habitat requirements of white-eared ground-sparrow (Delaney et al.,
2010a; Rodríguez-Bardía et al., 2022; Soulé et al., 1988).

Genetics

We found an increase in inbreeding in three populations exposed to rapid
urbanization in the Costa Rican Central Valley (HDA, JBL, and UCR).
Inbreeding is caused by consanguineous mattings, and its extent is
proportional to a reduction in effective population sizes, or a
reduction in the dispersal of individuals among populations (Frankham et
al., 2002). Therefore, the increase in inbreeding could be explained by
urban expansion which rapidly transforms natural ecosystems into
homogenized environment that limits resources to small suboptimal
patches of vegetation (Biamonte et al., 2011; Fahrig \& Rytwinski, 2009;
Miles et al., 2019). These drastic changes brought about by urbanization
restrict the movement of birds between populations (Rodríguez-Bardía et
al., 2022). Consequently, small and isolated populations increase the
likelihood of mating with relatives (Wright et al., 2008). A similar
increase in inbreeding associated with isolation and urban development
was reported for the european treefrogs \emph{Hyla arborea} (Andersen et
al., 2004) and was also found in birds, the taita thrush \emph{Turdus
helleri} (Lens et al., 2000).

Our analysis found that spatial genetic structure is more affected by
distance than by time. This result is consistent with the isolation by
distance and the urban resistance to gene flow previously reported for
this species (Rodríguez-Bardía et al., 2022). Additionally, this
condition is typical for species with low mobility and specific habitat
requirements, such as the white-eared ground-sparrow.(Delaney et al.,
2010b; Rodríguez-Bardía et al., 2022; Soulé et al., 1988). However, the
Bayesian clustering suggests that some movement of individuals may still
occur among populations. Therefore, our results suggest that we are
witnessing the progressive decline of gen flow and genetic diversity
over time.

The indirect evidence of limited gene flow found in our study, could be
also a result of the interaction between philopatry, territoriality and
the lack of suitable habitat for populations of ground-sparrows in an
urban setting. Philopatry and territoriality keep individuals within a
population, thereby decreasing gene flow and increasing genetic
differentiation between populations (Bounas et al., 2018;
Rodríguez-Bardía et al., 2022). In urbanized sites ground-sparrow males
may increase their territories to search for additional food sources
(Juárez et al., 2020). This foraging behavior could obligate males to
migrate when resources are scarce. Thus, this behavior could explain to
the movement of individuals showed in our Bayesian clustering.

On the contrary, individuals inhabiting isolated populations might
develop local adaptations, such as different dialects and variations in
the frequency and duration of songs, as shown for the white-eared
ground-sparrow (Sandoval et al., 2014, 2015, 2016; Juárez et al., 2020;
Méndez et al., 2021). These adaptations could further reduce individuals
dispersal between populations, thus contributing to increase genetic
differences among remaining populations (Morhina et al., 2017). For
example, in white-crowned sparrows \emph{Zonotrichia leucophrys} , the
genetic structure was correlated with song dialects, because song
dialects act as barriers for migrant males, increasing genetic structure
of populations (MacDougall-Shackleton \& MacDougall-Shackleton, 2001).

We only observed decline in genetic diversity in the population at the
most urbanized location (\(H_{\text{o\ }}\) UCR period 1: 0.62
vs\(H_{\text{o\ }}\) UCR period 2: 0.32). This result is consistent with
previous research indicating that urbanization reduces genetic diversity
in wren-tits \emph{Chamaea fasciata} , side-blotched lizards \emph{Uta
stansburiana} , western skinks \emph{Plestiodon skiltonianus} , and
western fence lizards \emph{Sceloporus occidentalis} inhabiting more
isolated habitat patches (Delaney et al., 2010b). Therefore, cities and
human-made structures are clearly acting as a barriers to gene flow
(Rodríguez-Bardía et al., 2022), that limits the movement of individuals
among vegetation remnants within cities. So, in the span of a decade we
were able to document a decrease in genetic diversity of this bird
species in the most urbanized populations. Due to the small number of
individuals captured at UCR during `period 2' our findings must be
interpreted with caution.

Acoustics

Differences in solo songs of white-eared ground-sparrows are concordant
with previous studies which reported acoustic differences as a possible
adaptation to urban noise (Méndez et al., 2020; Sandoval et al., 2016;
Sandoval \& Mennill, 2014; Bonilla-Badilla 2021). However, we
additionally discovered a correlation between acoustic and genetic
distances while controlling for morphology. These results reaffirmed the
urban development as an important barrier among populations, that
isolate and limit the movements of individuals (Rodríguez-Bardía et al.,
2022; Sosa-López et al., 2013). The same relationship between genetic
and acoustic distances has been reported in populations of rufous-naped
wren \emph{Campylorhynchus rufinucha} which lost connectivity by an
historically isolation due to a marine barrier during the formation of
the Isthmus of Tehuantepec in the late Pleistocene (Vázquez-Miranda et
al., 2009). The correlation between genetic and acoustic distances, and
the pattern of isolation by distance previously reported in the
white-eared ground-sparrow (Rodríguez-Bardía et al., 2022), suggest an
important role of cultural drift or sexual selection (Camacho-Alpízar et
al., 2018; Irwin et al., 2008; West-Eberhard, 1983). White-eared
ground-sparrow uses solo songs for female attraction, and males, after
learning songs are not able to learn new songs (Sandoval et al., 2016;
Bonilla-Badilla 2021). Thus, if females are choosing specific acoustic
traits, specific song types or young males are imitating specific songs
in each population, a specific phenotype would be favored over
generations (West-Eberhard, 1983). For example, female great
tits,\emph{Parus major,} prefer males that emit song types with higher
frequencies in noisy environments (Halfwerk et al., 2011). In this
context the higher frequencies in urbanized populations and the
different dialects among populations of white-eared ground-sparrows
(Méndez et al., 2020; Sandoval et al., 2016; Sandoval \& Mennill, 2014;
Bonilla-Badilla 2021), could be reflecting sexual selection acting over
populations.

Morphology

The female body size of white-eared ground-sparrow decreases in MTV and
JBL as well as males of JBL population along the sampling periods. Body
size often decreases in response to a decrease in food availability and
quality (Goodman et al., 2012; Salewski et al., 2014; Yom-Tov \& Geffen,
2011). Introduced species such as rats and mice may compete for food
resources of our study species. A similar reduction in body size was
observed in the lesser sheathbills \emph{Chionis minor,} in Marion
Island, due to the introduction of rats and cats that eat terrestrial
macro-invertebrates, the sheathbills primary food resource (Huyser et
al., 2000). In white-eared ground-sparrows reduction in body size may
also be caused by lower-quality food as a result of urbanization
(Mennechez \& Clergeau, 2006). Higher predation risks could also play an
important role in size reduction, because smaller individuals may be
better able to avoid cats and other predators (Seress et al., 2011).
Given that sparrows are a common prey of cats (Seress et al., 2011;
Yom-Tov \& Geffen, 2011), this would be an important adaptation (or
selection process) for ground-sparrows facing urbanization.

In JBL males increased the exposed culmen length and decreased beak
width between sampling periods. Due to the widespread use of bird
feeders, the trend toward longer beaks has been linked to the
consumption of larger prey or generalized feeding (Bosse et al., 2017;
Hüppi \& Geiger, 2022; Rolshausen et al., 2009). In great
tits,\emph{Parus major,} an increase in birds with longer beaks was
associated with an increase in fitness, because birds were able to feed
from bird feeders (Bosse et al., 2017). A decrease in beak width
reflects a decrease in bite force (Badyaev et al., 2008). For example,
the house finches \emph{Haemorhous mexicanus} that inhabit urban
populations in Arizona have a narrower beak, and less bite force,
because they consume softer human food.(Badyaev et al., 2008; de León et
al., 2011; Hüppi \& Geiger, 2022). In contrast, in MTV males decrease
their exposed culmen length and increase their beak width. The increase
in beak width reflect that birds are likely consuming larger and tougher
food, which would require an increase in bite force (de León et al.,
2011; Hüppi \& Geiger, 2022). Differences in beak lengths between
populations may also be the result of individuals consuming novel foods,
regardless of the morphology of the species; indicating adaptation
toward exploiting different food resources (de León et al., 2011; P. R.
Grant \& Grant, 2002).

Comparing four populations of white-eared ground-sparrows over a decade
revealed changes in levels of inbreeding, spectrotemporal song
characteristics, and morphological traits. Interestingly, we only found
a positive relationship between differences in genetic divergence among
populations and song divergence. We suggest that this correlation
corroborates the role of urbanization as a barrier for white-eared
ground-sparrows. The correlation may also be explained by cultural drift
or sexual selection, as it is possible to observe changes in acoustic
signals associated with genetic divergence if selection for song
preferences acts continuously in populations (Irwin et al., 2008;
Sathyan et al., 2017). The relationship between genetic and phenotypic
traits is often slow and complex, due to the different forces acting at
the same time (Irwin et al., 2008; Carnicer et al., 2009; Sathyan et
al., 2017). Only long-term studies in urban environments shed light on
the processes of adaptation of different species to the drastic changes
imposed by urbanization, since species with different life history
traits may response and adapt in different ways to the same
environmental changes (Rodríguez-Bardía et al., 2022).

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Table 1. Results of principal components analysis of solo songs of the
white-eared ground-sparrow \emph{Melozone leucotis} . The asterisk
indicates the variables that have a larger contribution in each
component.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Song measurements & Principal component & Principal component &
Principal component\tabularnewline
\midrule
\endhead
& PC1 & PC2 & PC3\tabularnewline
Duration (s) & 0.82* & 0.25 & -0.09\tabularnewline
Minimum frequency (kHz) & -0.26 & 0.78* & 0.24\tabularnewline
Maximum frequency (kHz) & 0.55 & -0.58* & 0.30\tabularnewline
Frequency of maximum amplitude (kHz) & 0.11 & 0.09 &
0.93*\tabularnewline
Number of elements (n) & 0.71* & 0.43 & -0.18\tabularnewline
\bottomrule
\end{longtable}

Table 2. Results of principal components analysis of body measurements
of white-eared ground-sparrow \emph{Melozone leucotis} . The asterisk
indicates the variables that have a larger contribution in each
component.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Body measurements & Principal component & Principal
component\tabularnewline
\midrule
\endhead
& PC1 & PC2\tabularnewline
Tarsus length (mm) & 0.73* & 0.28\tabularnewline
Tail length (mm) & 0.77* & 0.13\tabularnewline
Wing chord length (mm) & 0.75* & -0.15\tabularnewline
Exposed culmen length (mm) & 0.02 & 0.83*\tabularnewline
Beak width (mm) & 0.38 & -0.75*\tabularnewline
Beak depth (mm) & 0.65* & 0.11\tabularnewline
\bottomrule
\end{longtable}

Table 3. Mean \selectlanguage{ngerman}± SD of observed heterozygosity (\(H_{o}\)),
expected heterozygosity (\(H_{e}\)), inbreeding coefficient
(\(F_{\text{is}}\)), and allelic richness (\(A_{r}\)) for each
population assessed in two periods of time (2011-2012 and 2018-2022) for
white-eared ground-sparrow\emph{Melozone leucotis.} In parentheses the
number of females in each population.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Population & Time period & n & Ho & Ho & He & He & Fis & Fis &
Ar\tabularnewline
\midrule
\endhead
& & & Mean & SD & Mean & SD & Mean & SD &\tabularnewline
Monteverde & 2011-2012 & 12 (4) & 0.64 & 0.09 & 0.60 & 0.09 & -0.09 &
0.05 & 3.16\tabularnewline
Monteverde & 2018-2022 & 7(2) & 0.54 & 0.11 & 0.58 & 0.11 & 0.09 & 0.07
& 3.09\tabularnewline
Heredia & 2011-2012 & 7(3) & 0.59 & 0.12 & 0.49 & 0.10 & -0.20 & 0.05 &
2.72\tabularnewline
Heredia & 2018-2022 & 11(3) & 0.59 & 0.10 & 0.71 & 0.05 & 0.18 & 0.12 &
3.59\tabularnewline
Lankester & 2011-2012 & 11(5) & 0.55 & 0.08 & 0.52 & 0.08 & -0.07 & 0.09
& 2.64\tabularnewline
Lankester & 2018-2022 & 12(4) & 0.57 & 0.11 & 0.68 & 0.06 & 0.22 & 0.12
& 3.48\tabularnewline
UCR & 2011-2012 & 12(2) & 0.62 & 0.13 & 0.50 & 0.11 & -0.26 & 0.05 &
2.76\tabularnewline
UCR & 2018-2022 & 4 & 0.32 & 0.12 & 0.59 & 0.07 & 0.48 & 0.20 &
3.14\tabularnewline
\bottomrule
\end{longtable}

Table 4. Comparison of acoustic characteristics of solo songs between
two periods of time (2011 -- 2012, 2018 -- 2022) in four populations,
and their interaction of white-eared ground-sparrow \emph{Melozone
leucotis} , based on linear mixed-effects models. PC1 correlates with
the duration and number of elements, PC2 correlates with the minimum and
maximum frequency, and PC3 correlates withthe frequency of maximum
amplitude.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& PC1 & PC1 & PC1 & PC2 & PC2 & PC2 & PC3 & PC3 & PC3\tabularnewline
\midrule
\endhead
& DF & F & P & DF & F & P & DF & F & P\tabularnewline
Population & 3 & 1.67 & 0.18 & 3 & 10.58 & \textless{} 0.001 & 3 & 1.90
& 0.13\tabularnewline
Time period & 1 & 8.84 & \textless{} 0.05 & 1 & 2.86 & 0.09 & 1 & 1.38 &
0.24\tabularnewline
Population \selectlanguage{ngerman}× time period & 3 & 2.73 & \textless{} 0.05 & 3 & 1.04 & 0.38
& 3 & 3.65 & \textless{} 0.05\tabularnewline
\bottomrule
\end{longtable}

Table 5. Comparison of body measurements in two periods of time (2011 --
2012 vs 2018 -- 2022) in four populations, and their interaction of
white-eared ground-sparrows \emph{Melozone leucotis} , based on
generalized linear mixed models. PC1 correlates with tarsus length, tail
length, wing chord length, and beak depth, PC2 correlates with exposed
culmen length and beak width.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& Females & Females & Females & Females & Females & Females & Males &
Males & Males & Males & Males & Males\tabularnewline
\midrule
\endhead
& PC1 & PC1 & PC1 & PC2 & PC2 & PC2 & PC1 & PC1 & PC1 & PC2 & PC2 &
PC2\tabularnewline
& X2 & DF & P & X2 & DF & P & X2 & DF & P & X2 & DF & P\tabularnewline
Population & 0.75 & 3 & 0.86 & 1.72 & 3 & 0.63 & 2.28 & 3 & 0.52 & 0.62
& 3 & 0.89\tabularnewline
Time period & 0.24 & 1 & 0.63 & 1.25 & 1 & 0.26 & 0.001 & 1 & 0.97 &
0.005 & 1 & 0.94\tabularnewline
Population \selectlanguage{ngerman}× time period & 31.73 & 3 & \textless{} 0.001 & 2.68 & 3 &
0.44 & 27.14 & 3 & \textless{} 0.001 & 10.15 & 3 & \textless{}
0.05\tabularnewline
\bottomrule
\end{longtable}

Figure legends

Figure 1. STRUCTURE barplots for (a) 2011 -- 2012 populations of
white-eared ground-sparrow \emph{Melozone leucotis} into K = 2, and (b)
2018 -- 2022 populations of white-eared ground-sparrow \emph{Melozone
leucotis} into K = 2. Plots are sorted by sampling location, each bar
represents one individual, each color represent a cluster. MTV
Monteverde, HDA Heredia, UCR Universidad de Costa Rica, JBL Jard\selectlanguage{ngerman}ín
Botánico Lankester.

Figure 2. Box-plots of the variation in acoustic characteristics of
white-eared ground-sparrow \emph{Melozone leucotis} , based on linear
mixed-effects models. The responses are measured as principal components
scores. summarizing. (a -b) Duration and number of elements correlated
with PC1, © The minimum and maximum frequency correlated with PC2, and
(d) The frequency of maximum amplitude correlated with PC3. The box-plot
shows the median (central horizontal line), 75\textsuperscript{th} and
25\textsuperscript{th} percentile (top and bottom of the box) and the
maximum and minimum values (top and bottom whisker). Letters indicate
the values are significantly different from each other in post hoc
tests, and lack of letters mean that no significant differences were
found in the post hoc tests.

Figure 3. Variation in morphology of white-eared
ground-sparrow\emph{Melozone leucotis} , based on generalized linear
mixed models. The responses are measured as principal components scores;
summarizing (a) tarsus length, tail length, wing chord length, and beak
depth correlated with PC1 of females, (b) tarsus length, tail length,
wing chord length, and beak depth correlated with PC1 of males, (c) the
exposed culmen length and beak width correlated with PC2 of males. The
box-plot shows the median (central horizontal line), 75th and 25th
percentile (top and bottom of the box) and the maximum and minimum
values (top and bottom whisker). Letters indicate the values are
significantly different from each other in post hoc tests, and lack of
letters mean that no significant differences were found in the post hoc
tests.

Figure 4. Correlations between different sets of variables in
white-eared ground-sparrow \emph{Melozone leucotis.} (a) Song and
morphology distances, (b) morphology and genetic distances, and (c) song
and genetic distances. Linear regressions are shown, but statistical
signi\selectlanguage{english}ficance was evaluated using Mantel tests.

Data Accessibility

Cueva, L., Fuchs, E. J., Gilbert, B., Madrigal-Brenes, R., \& Sandoval,
L. (2024). Data from: Effect of spatial and temporal urban isolation on
the genetic diversity, acoustic variation, and morphological
characteristics of an urban survivor bird species. Zenodo.
https://doi.org/10.5281/zenodo.10472250

Competing interests

The authors declare no competing interests.

Author Contributions

L.C., E.J.F., G.B., and L.S. designed the study. L.C. and L.S. collected
the samples. L.C. and R.M.-B. performed lab work. L.C., E.J.F. and L.S.
performed statistical and genetic analyses. L.C. drafted the manuscript.
All authors contributed, completed, and approved the final manuscript.

Acknowledgements

We thank all Laboratorio de Ecologia Urbana y Comunicacion Animal
(LEUCA) member to collect the historical data of recordings, morphology,
and blood samples. We thank Raul Bartolo for field assistance. We thank
Universidad de Costa Rica, Jardin Botanico Lankester, and Estacion
Biologica Monteverde for the access to reserves. LS thanks to
Vicerrectoria de Investigacion for the financial support under
investigation grant numbers C1085, C2706, C2705, and C3025.\selectlanguage{english}
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