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Genetic diversity and
diversification patterns of puma
(Puma concolor) populations in
the southern end of the
species distribution
Matias E. Mac Allister
1,2
*, Carlos E. Figueroa
1,2
, Regina Mazzei
1
,
Ramiro G. Tintorelli
2,3
, Diana B. Acosta
1,2
, Orlando Gallo
4
,
Diego Castillo
2,5
, Emiliano Pinardi
1,2
,
Virginia D. Zelada Perrone
1,2
, Alejandro Rodrı
´guez
6
,
Juan I. Zano
´n Martı
´nez
2,7
, Mariano L. Merino
1,8
,
Juan I. Tu
´nez
2,9
, Alejandro Travaini
2,10
and Gabriela P. Ferna
´ndez
1
*
1
Centro de BioInvestigaciones (CeBio‑CICBA), Universidad Nacional del Noroeste de la Provincia de
Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencias del Noroeste de la Provincia de
Buenos Aires (CITNOBA‑CONICET), Pergamino, Argentina,
2
Consejo Nacional de Investigaciones
Científicas y Técnicas (CONICET), Buenos Aires, Argentina,
3
Laboratorio de Bioingeniería, Instituto
Tecnológico de Buenos Aires (ITBA), Buenos Aires, Argentina,
4
Department of Biology and
Biotechnologies “Charles Darwin”, University of Rome La Sapienza, Rome, Italy,
5
Laboratorio de
Genética para la Conservación (GENCON), Instituto de Ciencias Biológicas y Biomédicas del Sur
(INBIOSUR)-CONICET, Departamento de Biología, Bioquímica y Farmacia, Universidad Nacional del
Sur, Bahía Blanca, Argentina,
6
Department of Conservation Biology and Global Change, Estación
Biológica de Doñana-CSIC, Sevilla, Spain,
7
Instituto Multidisciplinario sobre Ecosistemas y Desarrollo
Sustentable, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN),
Tandil, Argentina,
8
Comisión de Investigaciones Científicas (CIC) de la Provincia de Buenos Aires,
Buenos Aires, Argentina,
9
Grupo de Investigación en Ecología Molecular, Instituto de Ecología y
Desarrollo Sustentable (INEDES-UNLu-CONICET, Departamento de Ciencias Básicas, Universidad
Nacional de Luján), Lujan, Argentina,
10
Centro de Investigaciones Puerto Deseado, Universidad
Nacional de la Patagonia Austral (UNPA), Santa Cruz, Argentina
The puma (Puma concolor Linnaeus, 1771) is the top predator with the widest
distribution in America. Since the establishment of European settlers on the
American continent, puma populations have experienced significant
contractions and reductions in their original distribution. In Argentina, the
management of the conflict between humans and pumas (direct persecution
and habitat modification) focused on reduction or elimination methods, leading
to a drastic contraction, even total eradication, of puma populations as seen in
Patagonia and the eastern part of the country. Despite the lack of knowledge
about puma population demographic trends, there are taxonomic issues that
remain controversial and need to be resolved to implement appropriate
management and conservation measures. Therefore, the aim of this study was
to genetically characterize puma populations in the central-southern region of
Argentina using two mitochondrial markers, evaluate their demographic history,
compare our results at a macro-regional level, and discuss our findings in a
conservation and management context. A total of 203 individuals were used, and
a fragment of the control region and another of ND5 were sequenced. The
genetic variability obtained was moderate. Substitution rates for each locus and
the tMRCA were calculated from calibrated trees. In a concatenated tree, two
Frontiers in Ecology and Evolution frontiersin.org01
OPEN ACCESS
EDITED BY
Karine Frehner Kavalco,
Universidade Federal de Vic¸osa, Brazil
REVIEWED BY
Francisco Juan Prevosti,
Museo de Antropologı
´a y Ciencias Naturales
de la UNLAR, CONICET, Argentina
Victor Hugo Valiati,
University of the Rio dos Sinos Valley, Brazil
Igor Rodrigues-Oliveira,
Federal University of Minas Gerais, Brazil
*CORRESPONDENCE
Matias E. Mac Allister
macallistermaty@gmail.com
Gabriela P. Ferna
´ndez
gabriela.fernandez@nexo.unnoba.edu.ar
RECEIVED 21 May 2024
ACCEPTED 25 July 2024
PUBLISHED 10 September 2024
CITATION
Mac Allister ME, Figueroa CE, Mazzei R,
Tintorelli RG, Acosta DB, Gallo O, Castillo D,
Pinardi E, Zelada Perrone VD, Rodrı
´guez A,
Zano
´n Martı
´nez JI, Merino ML, Tu
´nez JI,
Travaini A and Ferna
´ndez GP (2024) Genetic
diversity and diversification patterns of puma
(Puma concolor) populations in the southern
end of the species distribution.
Front. Ecol. Evol. 12:1436320.
doi: 10.3389/fevo.2024.1436320
COPYRIGHT
© 2024 Mac Allister, Figueroa, Mazzei,
Tintorelli, Acosta, Gallo, Castillo, Pinardi,
Zelada Perrone, Rodrı
´guez, Zano
´nMartı
´nez,
Merino, Tu
´nez, Travaini and Ferna
´ndez. This is
an open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 10 September 2024
DOI 10.3389/fevo.2024.1436320
main phylogenetic clades were identified (posterior probability = 1), although a
reciprocal monophyly was not observed, with a divergence time of 228 thousand
years and a 95% CI [117–363 thousand years]. When evaluating population
structuring, three genetic clusters were found, one characteristic of the
Patagonian region and the others in the central part of the country. Calculating
the Ф
ST
values for pairs resulted in significant structuring between Patagonia and
the rest of the populations, suggesting the arid diagonal as a possible barrier to
gene flow. When evaluating the demographic history, neutrality tests would
support a recent expansion in Patagonia. These findings are crucial in defining
two distinct Management Units (MUs) in the southern part of puma distribution
and providing valuable information for management and conservation measures
for the species.
KEYWORDS
Puma concolor, Argentina, mitochondrial markers, phylogeography, historical
demography, conservation genetics, management
Introduction
The Puma (Puma concolor), is the terrestrial mammal with the
largest distribution in the Western Hemisphere, occupying
historically the entire American continent, from Alaska and
northern Canada to the southern tip of Patagonia (Currier, 1983;
Sunquist and Sunquist, 2002). In the Neotropics, the Puma is
second in weight only to the jaguar (Panthera onca), with males
30–50% larger than females, reaching weights of up to 100 kg
(Eisenberg, 1989;Gay and Best, 1995;Jansen and Jenks, 2011).
Through its extensive distribution, pumas have adapted to living in
a wide variety of ecosystems (Beier, 2010), preying on a diverse
range of prey, from rodents to large ungulates (Iriarte et al., 1990).
The Puma is often considered a top predator and an umbrella
species in several regions (Caro and O’Doherty, 1999;Thorne et al.,
2006), playing an important role in biodiversity conservation and as
a regulator and shaper of ecosystems (Terborgh et al., 2001;Ripple
and Beschta, 2006;Terborgh et al., 2006).
Since European settlers established in the Americas, puma
populations have suffered large contractions and reductions in
their original distribution (Anderson et al., 2010). Direct
persecution by preying on domestic animals, reduction of prey
populations, and modification and destruction of habitats have been
the major factors reported in the decline and reduction of their
populations (Nielsen et al., 2015). It is known that puma
populations were extirpated in the eastern United States during
european colonization with a management focused on methods of
control or elimination (Gill, 2010). However, more recent
conservation plans are being implemented in North America,
designed to maintain viable puma populations as part of the
ecological community (Anderson et al., 2010). In South America,
particularly in Argentina, puma management was also focused on
reduction or elimination methods, where puma populations
suffered drastic contraction, even complete eradication, in their
distribution as occurred in Patagonia and eastern Argentina
(Chebez, 2009;De Lucca, 2010;Martı
nez et al., 2010;Chimento
and De Lucca, 2014;Brancatelli and Yezzi, 2017). Due to their main
function in regulating and structuring ecosystems, rewilding
programs are being carried out in Argentina with a central focus
on restoring large carnivores, such as the puma (Donadio
et al., 2022).
Despite this, the Puma is classified as “Least Concern”in the
IUCN Red list of Threatened Species (Nielsen et al., 2015), and by
the Argentine Society for the Study of Mammals (Sociedad
Argentina para el Estudio de los Mamı
feros, SAREM; De Angelo
et al., 2019) due to its broad distribution in the western hemisphere.
Although being persecuted and extirpated from many areas in the
past, Puma´s naturally recolonize environments from which it was
eliminated and/or colonize others little or heavily modified by
humans, ensuring the survival of this species (Jennings et al.,
2015;LaRue and Nielsen, 2016). However, there are exceptions,
such as the remaining population of Florida panthers (Roelke et al.,
1993), which suffered a strong reduction due to their persecution,
which generated drastic losses of genetic variability with
consequences on the fertility and survival of individuals (Culver
et al., 2008;Johnson et al., 2010;Hostetler et al., 2013;Saremi et al.,
2019;van de Kerk et al., 2019;Gustafson et al., 2021).
In Argentina, the Puma, along with other native predators, has
been heavily persecuted due to conflicts with farmers (e.g. Bellati
and Von Thüngen, 1990;Novaro and Walker, 2005;Walker and
Novaro, 2010;Guerisoli et al., 2017). Conflictive situations occur
especially in the central-south of the country, where its persecution
has been most intense and sustained over time, leading to local
extinctions (Bellati and Von Thüngen, 1990;Novaro and Walker,
2005;Chebez, 2009;De Lucca, 2010;Martı
nez et al., 2010;Walker
and Novaro, 2010;Chimento and De Lucca, 2014;Guerisoli et al.,
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org02
2020). In much of Patagonia, it is considered the primary predator
of sheeps (Llanos et al., 2019), replaced only by the Culpeo fox
(Lycalopex culpaeus) in areas where pumas were eradicated (Dı
az-
Ruiz et al., 2020). In the central area of the country it has also been a
significant game species (Walker and Novaro, 2010;Zanon
Martı
nez et al., 2016). Management actions undertaken by
regional authorities have been limited to compensating producers
for losses or offering rewards for hunted pumas (Provincial Law N°
XVII 52, 763, 2,373 and 2,539 for Chubut, Rı
o Negro, Santa Cruz
and Neuquen, respectively), with no further regulation beyond
accepting harvest levels empirically imposed by livestock
producers (Llanos et al., 2014).
Beyond the lack of knowledge regarding demographic trends for
the puma populations, there are taxonomic issues that remains
controversial. Based on morphological and geographical
distribution data, 36 puma subspecies have been described (Young
and Goldman, 1946;Neff, 1983), seven of which are found in
Argentina (Cabrera, 1958). Culver et al. (2000),basedon
mitochondrial and microsatellite markers data, differentiated six
phylogroups. They proposed these groups as distinct subspecies;
three of them are distributed in Argentina (Puma c. puma,P. c.
cabrerae and P. c. capricorniensis). On the other hand, a study
involving the almost complete distribution of the species suggests
the existence of three main genetic groups: North America, Central
America and South America (Caragiulo et al., 2014). The IUCN relies
on this study to recognize a single subspecies for all of South America,
Puma c. concolor (Linnaeus, 1771), and another for the rest of
America, Puma c. cougar (Kerr, 1792) (Kitchener et al., 2017).
Understanding patterns of genetic variability is essential for
reconstructing the evolutionary history of species, defining the
boundaries and distribution of different phylogroups, and thereby
conserving the genetic pool. However, there is a significant
knowledge gap regarding the genetic identity of puma
populations across many areas of their range (for example,
central and southern Argentina; Culver et al., 2000;Caragiulo
et al., 2014). Consequently, the implications for conserving the
genetic heritage of the species, particularly in the southern part of
the distribution, remain unclear (Gallo et al., 2021,2023). Molecular
tools, as mitochondrial DNA, haveproventobeeffectivein
revealing the genetic structure of populations, their evolutionary
history and the estimation of genetic variability indices at both the
intra and inter population level (Avise, 1998). Furthermore, they
have been widely used in phylogenetic and phylogeographic studies
in felids (e.g. Johnson et al., 2006;Gomez Fernandez et al., 2020),
especially in Puma (Culver et al., 2000,2008;Trigo et al., 2008;
Matte et al., 2013;Caragiulo et al., 2014) and to determine ancestral-
descendant relationships between taxa (Stoneking et al., 1991;
Avise, 1994;Jae-Heup et al., 2001;Johnson et al., 2006;Culver
et al., 2008).
Considering the context and the limited knowledge of Puma
genetics in Argentina, the objective of this study was to genetically
characterize puma populations in the central-southern region of the
country using two mitochondrial markers. We aimed to assess its
demographic history, comparing our results at a macro-regional
level, and discussing our insights into a conservation and
management context.
Materials and methods
Study area
The study area includes central and southern Argentina
between 31°15’and 51°51’south latitude and 72°40’to 57°52’
west longitude (Figure 1). The study area encompasses seven of
the eleven phytogeographic provinces identified by Oyarzabal et al.
(2018) in Argentina: Patagonian, Patagonian Monte, Monte, High
Andean, Chaco, Pampas, and Espinal; which represents six
ecoregions: Patagonian Steppe, Patagonian woodlands, Argentine
Low Monte, Espinal, Pampa and Dry Chaco (Brown et al., 2006).
An important biogeographic region that crosses the study area in a
northwest-southeast direction, and covers many deserts (e.g.
Sechura, Atacama, Monte and the Patagonian Desert) is the so-
called “South American Arid Diagonal”(“Dry Diagonal”;Figure 1).
This area, with an extension spanning from the northern coasts of
Peru to the Patagonian coasts of Argentina (Bruniard, 1982)is
characterized by being a region of scarce (to null) rainfall,
transitioning between drier conditions towards the South and
more humid towards the north (Bruniard, 1982).
In the last century, the whole study area experienced a significant
reduction of its wildlife due to the advancement of livestock and
agricultural activities, affecting most of Patagonia (Novaro and
Walker, 2005), the Pampas region (Parera, 2002;De Lucca, 2010,
2011), and a considerable portion of Entre Rı
os province. Lately,
sheep production covered the 82.3% (7,101,717 heads) of farmlands
in Argentina (INDEC, 2021), with a higher concentration in the
southern provinces of Patagonia and Buenos Aires. The study area
also includes 49% (1,208,347 heads) of goat production, concentrated
in the provinces of Neuquen and Mendoza (70.8%). Fifty-seven
percent (23,034,785 heads) and 62% (2,232,766 heads) of cattle and
pig populations, respectively, are concentrated in the provinces of
Cordoba, Santa Fe, and Buenos Aires, where the highest value was
reported also for horse population (21%; INDEC, 2021).
Central-south Argentina corresponds to the country’s
agriculture core zone (northern Buenos Aires, southern Cordoba
and Santa Fe province), with more than 24,115,143 (65.7% of the
country) hectares of production located in our study area (INDEC,
2021). Additionally, Patagonia region is characterized by an
extensive presence of mining and oil companies (INDEC, 2017),
which have an important role in modifying the natural habitats and
influencing puma presence and behavior.
Finally, game hunting is also a common practice, particularly in
La Pampa province (Walker and Novaro, 2010;Zanon Martı
nez
et al., 2016).
Sample collection
Tissue samples were collected in the field (individuals run over
or hunted) or from samples previously deposited in biological
collections (Supplementary Table S1). The last ones were mainly
represented by material collected under the provincial legal hunting
regimes of Santa Cruz, Neuquen, Chubut, and Rı
o Negro province
(Supplementary Table S1). Tissue samples were preserved in 96%
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org03
alcohol. For each scat sample, we collected approximately 0.5 mL of
fecal material and stored it at ambient temperature in 2 sterile, 2-mL
screw-top tubes filled with dimethyl sulfoxide saline solution (DETs
buffer; Seutin et al., 1991).
All samples were georeferenced (Supplementary Table S1) and
stored at-20°C until their characterization at the Bioresearch Center
(Pergamino, Argentina).
Mitochondrial DNA extraction and
PCR amplification
Genomic DNA from tissue samples was obtained following
extraction protocols with CTAB (Doyle and Doyle, 1987)orthe
Phenol-Chloroform protocol (Sambrook and Russell, 2006). For fecal
samples the Guanidinium Thiocyanate/Silica method (GuSCN—Boom
et al., 1990) was employed. After that, DNA was purified using a
commercial kit (Monarch Genomic DNA Purification Kit, BioLabs),
following the manufacturer’s recommendations.
A 750 bp fragment of the NADH dehydrogenase subunit 5 gene
(ND5) and a 430 bpfragment of the conserved mitochondrial control
region (CR) were amplified using the ND5-DF1 and ND5-DR1
primers (Trigo et al., 2008), and PDL3N (Culver et al., 2008), and
RCP_R (5´-GTCCTGTGACCATTGACTGA-3´, self-designed)
primers, respectively. PCR amplifications were performed in a final
volume of 20 mL, containing 25–100 ng of template DNA, 0.2 mMof
each primer, 0.2 mM dNTP, 1x TAS reaction buffer, 1.5 mM MgCl
2
,
0.5U of Taq T-Plus DNApolymerase and ultrapure sterile water to
came to final volume. Thermocycling conditions for ND5 were as
described in Tchaicka et al. (2007). For the control region,
thermocycling consisted of 94°C for 2 min, followed by 30 cycles of
denaturing at 94°C for 45 s and annealing at 62°C for 45 s and
extension at 74°C for 1.5 min. Negative controls were included in all
PCR runs to check for contamination.
Amplification success was confirmed by electrophoresis on 1%
(w/v) agarose gel, stained with ethidium bromide and visualized
under UV light. Amplification products were purified using 10U of
Exonuclease I and 1U of FastAp thermosensible alkaline phosphatase
(ThermoFisher Scientific). The purified DNA products were sent to
an external laboratory (Macrogen Co. Ltd., South Korea) for direct
sequencing using the same oligonucleotide primers.
The obtained sequences for both markers were visualized and
aligned using the Clustal W algorithm and checked for accuracy and
edited using BioEdit (Hall, 2004).
Data analyses
Genetic diversity
Genetic variability was estimated by determining the number of
haplotypes (Ha), polymorphic sites per sampling location (SP),
haplotype diversity (Hd), nucleotide diversity (p), and the mean
number of pairwise differences (k) using DnaSP 6.0 (Rozas
et al., 2017).
FIGURE 1
Sample distribution map. The main productive and hunting activities in the study area are identified. The animal silhouettes, the light green ellipse,
and the orange ellipse refer to the primary animal production areas, the country’s core cultivation zone, and the region with the highest hunting
activity, respectively. The black dotted line indicates South American Arid Diagonal. SC, Santa Cruz; Ch, Chubut; RN, Río Negro; N, Neuquen;
M, Mendoza; LP, La Pampa; BA, Buenos Aires; ER, Entre Ríos; Cor, Córdoba; SL, San Luis; Cat, Catamarca and SJ, San Juan.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org04
Phylogenetic analysis
Prior to any analysis, the retention of phylogenetic signals was
checked for both data sets (ND5 and CR) using the Xia et al. (2003)
test, implemented in the DAMBE program (Xia and Xie, 2001).
This test estimates a sequence saturation index (Iss) and compares it
to a critical saturation index (Iss.c) generated by a randomization
process with 95% confidence. In this context, this test analyzes
whether the observed Iss is significantly less than the estimated Iss
(Iss.c). IssSym, assuming a symmetric topology, and IssAsym,
assuming an asymmetric topology represent such estimated value;
both topologies are taken into account. Our sequences are suitable
for a phylogenetic study since they meet this condition
(Supplementary Table S2).
Likewise, we examined the congruence of substitution rates
between each data sets using the partition homogeneity test (Farris
et al., 1995), as implemented in PAUP* (Swofford, 1998). Their
congruence (P = 0.48) allowed the concatenated fragment (ND5
+CR) to be used for subsequent analysis.
Additionally, the molecular evolution models that best fit both
datasets were estimated using the Corrected Akaike Information
Criterion (AICc) through the jModelTest software (Posada, 2008).
Phylogenetic inferences were conducted independently for each
dataset (ND5 and CR) using five calibration points (Table 1)
through the BEAST 2.5.2 software (Bouckaert et al., 2019). In
both cases, sequences from the superfamily Feloidea (Carnivora -
Feliformia) retrieved from the GenBank database were included
(Supplementary Table S3). The family Felidae (subfamilies Felinae
and Pantherinae) was used as the ingroup and Prionodontidae as
the outgroup (Supplementary Table S3).
The log-normal distribution was employed for the calibration
points, enforcing monophyly for each of these nodes. A relaxed
lognormal clock model and a calibrated birth-death branching rate
were utilized (Heled and Drummond, 2014).
For each dataset, two independent runs of 5x10
7
Markov chain
Monte Carlo (MCMC) generations were executed, sampling every
5,000 generations. Mutation rates were estimated for both markers.
Convergence of the posterior distribution for all runs (ESS values >
200 for each dataset) was determined using Tracer 1.7.1 (Rambaut
et al., 2018). Log files and trees were combined using LogCombiner
2.5.2 (Drummond and Rambaut, 2007), trees were summarized
using the maximum clade credibility (MCC) option in Tree
Annotator 2.5.2 (Bouckaert et al., 2019), and the final tree was
visualized in FigTree 1.4.4 (Rambaut, 2018).
The estimated mutation rates and time to the most recent
common ancestor (tMRCA) were used to perform a Bayesian
phylogeny considering only puma samples from concatenated
sequences (n = 141; 901 pb) using BEAST 2.5.2 software
(Bouckaert et al., 2019). For this dataset, two independent runs of
5x10
7
MCMC generations were executed, sampling every 5,000
generations. Two separate partitions were employed: ND5 and CR,
utilizing substitution models and mutation rates that were
estimated for each marker in the calibrated phylogenies.
Genetic structure and phylogeography
Haplotype networks were constructed using the median-joining
algorithm (Bandelt et al., 1999) using the PopART software (Leigh
and Bryant, 2015). One network was created using haplotypes from
the concatenated sequence dataset, while the other included
haplotypes obtained for ND5 along with those obtained by Matte
et al. (2013). The latter was conducted to integrate our data into a
South America context. Finally, in order to polarize the ND5
haplotype network, a short sequence (240pb) from an ancient
southern patagonian puma specimen (Puma concolor, GenBank
ID: KU884292.1; Metcalf et al., 2016) was incorporated into
the analyses.
To analyze the genetic structure and identify the possible
existence of differentiated genetic groups, the concatenated
sequence dataset was used to perform the clustering analyzes. The
calculation was performed by testing the number of clusters (K)
from 2 to 10, with five replicates for each K, through a Bayesian
analysis using BAPS v6 (Corander et al., 2008). Additionally, an
Analysis of Molecular Variance (AMOVA) was conducted using
Arlequin3.5software(Excoffier and Lischer, 2010). The
significance of the observed Ф-statistics was tested using the null
distribution generated from 10,000 nonparametric random
permutations of the data matrix variables. Population pairwise
Ф
ST
values were also calculated using Arlequin 3.5, applying
Bonferroni correction (Rice, 1989). Based on the subspecies
proposed by Culver et al. (2000), three groups were considered in
the AMOVA analysis: one in Patagonia corresponding to P. c.
concolor, another in central Argentina corresponding to P. c.
cabrerae, and a third group (hereafter referred to as the ‘buffer
zone’) containing sequences from a 100 km strip on either side of
the distribution boundary assigned to the subspecies. This area was
arbitrarily defined to visualize better the patterns of puma
variability in the southern part of its distribution.
Historical population dynamics
The demographic history of populations was studied using two
different methods. First, Tajima’sD(Tajima, 1989), Fu’sFs(Fu,
1997) and Fu and Li´s (Fu and Li, 1993) neutrality tests were carried
out using Arlequin 3.5. The analysis was performed using 1000
iterations for the three data sets (ND5, CR, and concatenated
sequences). Significant negative values of Tajima’s D and Fu’sFs
are indicative of an excess of low-frequency mutations and are
consistent with a demographic expansion or purifying selection.
TABLE 1 Calibration nodes used in this study (A–E), ages are expressed
in million years ago (mya), lognormal distribution was used in every node
and publication reference of each one is shown.
Point Node mya Reference
AFelidae 26 ± 4 Peigne, 1999;Bellani, 2019
BFelinae 7.17 ± 1.85 Bellani, 2019
CPanthera
Lineage 5.03 ± 0.93 Tseng et al. 2014;Bellani, 2019
DLynx Lineage 3.80 ± 2 Bellani, 2019
EPuma Lineage 3.60 ± 0.20 Barry, 1987;Werdelin et
al. 2010
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org05
Second, to estimate the shape of the population change over time,
the Bayesian Skyline Plot (BSP) implemented in BEAST 2.5.2
(Bouckaert et al., 2019) was carried out only for the concatenated
sequences dataset. The molecular evolution models for each locus
were calculated using the Corrected Akaike Information Criterion
(AICc) through the jModelTest software (Posada, 2008). Four
independent runs of 5x10
7
MCMC generations were executed,
sampling every 5,000 generations. Skyline reconstruction was
performed in Tracer 1.7.1 (Rambaut et al., 2018), and the median
and 95% credibility interval were plotted as a time function.
Results
Genetic diversity
We obtained 162 and 180 sequences for ND5 and CR fragments,
respectively (Table 2;Supplementary Table S1). The ND5 sequences
showed 14 variable sites (10 unique polymorphisms, and four
informative by parsimony) defining 10 haplotypes. For the CR set of
sequences,11polymorphicsites(oneuniquepolymorphismand10
informative by parsimony) and 11 haplotypes were obtained.
Furthermore, 22 variable sites (10 unique polymorphisms and 12
informative due to parsimony) and 17 haplotypes were obtained for
the concatenated sequences dataset (n = 141) (Supplementary Table S4).
The full set of mtDNA haplotypes generated for each locus (eleven for
RC and ten for ND5) were deposited in GenBank under accession
numbers: PP952688 to PP952689 and PP952691 to PP952698 for ND5
and PP952699 to PP952709 for CR sequences (Supplementary Table S1).
For the three sets of mitochondrial sequences, congruent values
of genetic variability were observed (Table 2). Particularly for the
concatenated sequences dataset, both the haplotype and nucleotide
diversity were moderate (Hd = 0.641 ± 0.040 and p= 0.0051 ±
0.0016), with lower values in Patagonia (Hd = 0.163 ± 0.054 and p=
0.0003 ± 0.0006) and higher in central Argentina (Hd = 0.806 ±
0.079 and p= 0.0048 ± 0.0013). The presence of a high number of
haplotypes in La Pampa and southern San Luis provinces stands out
from the rest of the provinces (Supplementary Table S1).
Phylogenetic analysis
The phylogenetic relationships obtained from both ND5 and
CR datasets, were highly congruent with each other. The different
families and subfamilies of the superfamily Feloidea were recovered
with high posterior probability values (Supplementary Figure S1)
and the puma lineage was a very well-supported monophyletic clade
with maximum posterior probability (=1) in both phylogenies.
Mutation rates estimated from the calibrated phylogenies were
0.0115 (SD: 0.0002) substitutions per site per million years for CR
and 0.0190 (SD: 0.00007) for ND5. The tMRCA estimated for the
puma lineage was 3.616 [3.385–3.870] and 3.623 [3.390–3.840]
million years for ND5 and CR, respectively.
Using the mutation rate obtained for both molecular markers
from the calibrated phylogenies and the tMRCA for the puma
lineage, three independent Bayesian trees were made from the
concatenated set of sequences, and a consensus tree was obtained
for the puma lineage (Figure 2). Two monophyletic clades are
TABLE 2 Genetic diversity and neutrality test for P. concolor populations.
Marker N Ha SP Hd ± SD p± SD k Fu´s Fs Fu and Li´s D Fu and Li´s F Tajima´s D
CR
Patagonia 109 2 1 0.105 ± 0.039 0.0004 ± 0.0007 0.105 -0.324 0.488 0.219 -0.504
Buffer 46 7 8 0.755 ± 0.042 0.0128 ± 0.0023 3.564 2.466 1.304 2.041* 2.657
Center Arg. 25 8 11 0.680 ± 0.098 0.0100 ± 0.0032 2.713 -0.644 0.964 0.706 -0.230
Total 180 11 11 0.571 ± 0.037 0.0098 ± 0.0021 2.721 0.754 0.665 0.955 1.031
ND5
Patagonia 96 2 4 0.021 ± 0.020 0.0001 ± 0.0012 0.083 -0.642 -3.855* -3.750* −1.783**
Buffer 42 5 9 0.609 ± 0.055 0.0033 ± 0.0011 2.157 2.185 -1.811 -1.414 0.134
Center Arg. 24 8 10 0.757 ± 0.075 0.0028 ± 0.0013 2.089 -2.011 -1.615 -1.675 -1.021
Total 162 10 14 0.476 ± 0.039 0.0027 ± 0.0013 1.762 -0.706 -4.927* -4.072* -0.743
CR-ND5
Patagonia 81 4 6 0.163 ± 0.054 0.0003 ± 0.0006 0.241 -2.015 -3.509* -3.498* -1.734**
Buffer 37 10 15 0.820 ± 0.042 0.0040 ± 0.0010 5.790 1.541 0.292 0.989 1.918
Center Arg. 23 11 20 0.806 ± 0.079 0.0048 ± 0.0013 4.316 -1.707 -0.658 -0.801 -0.749
Total 141 17 22 0.641 ± 0.040 0.0051 ± 0.0016 4.639 -0.053 -2.967* -2.011 0.463
Number of sequences (N), haplotypes (Ha) and polymorphic sites per sampling location (SP), haplotype diversity (Hd), nucleotide diversity (p) with their respective standard deviations (SD), k:
mean number of pairwise differences. *P < 0.05; **P < 0.01.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org06
distinguished with an estimated divergence time of 0.228 million
years with a 95% CI [0.117–0.363 million years]. Clade I correspond
mainly to individuals from Patagonia, while Clade II corresponds to
those of central (Figure 2;Supplementary Table S1). The divergence
time of the pumas that form clade I and Clade II are 0.046 million
years with a 95% CI [0.010–0.094] and 0.172 million years with a
95% CI [0.068–0.231], respectively (Figure 2).
Genetic structure and phylogeography
The two main groups recovered in the phylogenetic tree can be
observed in the haplotype network obtained from the concatenated
sequences (Figure 3A). It can be seen that there are seven
haplotypes (RCND_4, RCND_5, RCND_7 and RCND-14 to 17)
that are found only in the central region of the country. In the buffer
zone, haplotypes from both central Argentina and Patagonia are
found, and four haplotypes exclusive to that region (RCND_6 and
RCND_11 to 13; Figure 3A). Only one shared haplotype was
observed in the three regions analyzed (RCND_2).
In the network that groups the haplotypes for ND5 obtained in
this work together with those obtained by Matte et al. (2013)
(Supplementary Figure S2), is observed that Patagonia and central
Argentina are separated by a number of mutations higher than the
observed between each of these areas and the central haplotype of
Brazil (ND_11; Supplementary Figure S2). By reducing the ND5
fragment to 240 bp to include the sample from the ancient puma
specimen, 12 haplotypes were obtained. The ancient puma
clustered together with individuals from central Brazil, central
Argentina and one sample from Buffer zone (Supplementary
Figure S3).
The Bayesian analysis of population clustering resulted in three
genetic clusters and four individuals not assigned to any of them
(Log marginal likelihood = -323.583; Figure 3B). While clusters II
(n =33) and III (n = 8) are found exclusively in central Argentina,
cluster I (n = 96) occupies all Patagonia and few localities in the
buffer zone and central Argentina (Figure 3C). Besides, the pairwise
Ф
ST
values showed a significant structuring between Patagonia and
the other genetic clusters (Supplementary Table S5).
Historical population dynamics
For the entire study area, the expansion hypotheses was
supporting from the concatenated loci with negative values for
the neutrality tests of Fu (Fs = -0.053), Fu and Li’s F (-2.011), and Fu
and Li’s D (-2.967), although only the last one was significant (p-
value ≤0.05; Table 2). The Tajima test showed a positive but non-
significant value for D (D = 0.463; P = 0.67). In the Patagonian
region, negative values were observed for all tests, with only the
Fu’sFsbeingnon-significant for the population expansion
signal (Table 2).
Concerning the results for each locus, a similar situation was
observed for ND5, with negative and significant values in Patagonia
and globally. Still, no sign of expansion at any scale was observed for
CR (Table 2).
FIGURE 2
Bayesian consensus time-tree of Puma concolor based on concatenated mtDNA (ND5 and control region) sequences. Values above nodes
correspond to posterior probabilities > 0.60. The age of the most recent common ancestor of P. concolor was estimated at 0.228 Ma (95% highest
posterior density = 0.117 to 0.363 Ma). The green, red and blue boxes refer to clusters I, II and III, respectively. The black arrows mark haplotypes
that belong to one clade but are also found in the biogeographic zone of the other clade.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org07
The demographic scenario presented by the Bayesian Skyline
Plot for all individuals denotes a demographic stability with a slight
trend to increase near 7,000–8,000 years ago (Figure 4).
Discussion
Starting from a representative sample of Patagonia and Central-
Western Argentina, our work addresses for the first time the
matrilineal history of the Puma in the region. Our results reveal
the low genetic variability of the species throughout the south of its
distribution, slight signs of expansion processes, as well as genetic
differentiation between populations north and south of the South
American Arid Diagonal.
Genetic diversity
The genetic variability found in this study was moderate
compared to that reported for pumas (Matte et al., 2013)andother
felines (Trigo et al., 2008;Gomez Fernandez et al., 2020). Seventeen
haplotypes (concatenated loci) were identified, while 22 were found
in the sampling across all of South America by Matte et al. (Matte
et al., 2013; ND5), and 11 haplotypes by Culver et al. (Culver et al.,
2000; ND5, 16S, and ATPase-8), and Caragiulo et al. (Caragiulo et al.,
2014; Cytochrome b, 12S, 16S, and ATPase-6), providing the
potential of the marker used in this work for phylogenetic studies.
If we compare our results for concatenated loci with those
obtained for Southwestern South America (SWSA, that includes our
region called Patagonia and southern and northern Chile) by Matte
et al. (2013) we can observe, that despite using two loci, the
variability of haplotypes and nucleotides found (Hd = 0.163 ±
0.054 and p= 0.0003 ± 0.0006) is lower than Matte et al (Matte
et al., 2013; Hd = 0.595 ± 0.073 and p= 0.00334 ± 0.00032). When
comparing our results only for ND5, it can be seen that the
variability is even lower. This difference can be explained by the
variability contributed by the haplotypes from northern Chile
(Matte et al., 2013), absent in our study.
Thisreductioningeneticvariabilitycanbeattributedtoseveral
main factors, among them, species tend to be less variable at the edges
of their distribution due to the founder effect and genetic drift, as was
found for pumas in previous works (Culver et al., 2000;Matte et al.,
2013). Similar results were reported for the Geoffroy’scat,Leopardus
geoffroyi, which shows greater variability in the center of its distribution
than in peripheral areas (Gomez Fernandez et al., 2020). On the other
hand, Puma may have undergone a recent bottleneck as those detected
in the north and central Patagonia mainly caused by intense human
persecution (Gallo et al., 2020,2021). In addition, it is necessary to
consider that the desertification caused by the livestock activity (Del
Valle et al., 1998;Aagesen, 2000;Oliva et al., 2016) could have led to a
decrease in abundance and composition of plant species in Patagonia, a
lower abundance of prey and ultimately a decline in biodiversity (Peri
et al., 2016). This impoverishment of the Patagonian ecosystem could
lead to a reduction in connectivity and gene flow between feline
populations (Gallo et al., 2020,2021).
In relation to the pumas of the central region of Argentina, our
estimates of genetic variability (Ha = 11; Hd = 0.806 ± 0.079) were
similar to those obtained by Matte et al. (2013) for Central-
Southern South America (CSSA: central and northern Argentina,
Uruguay, southern Bolivia and central-western Brazil; Ha = 6; Hd =
FIGURE 3
Phylogeography. (A) Haplotype network. (B) mtDNA genetic clusters obtained by BAPS (best K = 3, Log marginal likelihood = -323.583). (C) Distribution of
the Clusters in the study area. Dotted line indicates South American Arid Diagonal.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org08
0.810 ± 0.078), even though our study region is approximately half
of the study area covered by Matte et al. (2013). For this reason, we
consider that it is necessary to expand sampling to the north of the
country to be able to characterize the complete diversity in the south
of the species distribution.
Phylogenetic analysis
Both phylogenies (ND5 and CR) using five calibration points
based on fossil data (Table 1,Supplementary Figure S1), showed
topologies and divergence times consistent with each other
(confidence intervals broadly overlapping in all estimates) and
comparable with those published in previous studies (Culver
et al., 2000;Johnson et al., 2006). However, the estimated tMRCA
for the puma lineage obtained from ND5 (3.616 [3.385–3.870]) and
from CR (3.623 [3.390–3.840] million years) were lower than those
previously reported: 4.2 million years (Culver et al., 2000) and 4.92
(3.86–6.92) (Johnson et al., 2006). Despite this, the divergence time
for pumas obtained from the concatenated dataset (0.228 [0.117–
0.363] million years) is similar to the estimate by both Matte et al.
(2013) for South American pumas (0.211 [0.091–0.353] million
years) or Culver et al. (2000) across the entire distribution (0.318
million years). Although, our results present more narrow
confidence intervals, thereby confirming that the inclusion of
several calibration points located adjacently on deep nodes of the
phylogenetic tree allows greater precision in estimating divergence
times (Zheng and Wiens, 2015;Caraballo and Rossi, 2018).
Moreover, by obtaining similar divergence times and observing in
the haplotype network for ND5 that the groups are well
differentiated (Supplementary Figure S2), we can see that the
separation of the phylogroups obtained in this work is as ancient
as the diversification of all South American pumas.
Genetic structure and phylogeography
The diversification in two main clades observed in the Bayesian
Tree (Figure 2), as well as in the haplotype network topology
(Figure 3A), shows a high correspondence with the population
groups defined north and south of the South American Arid
Diagonal. Bruniard (1982) has proposed that this area, a
transition zone between drier conditions towards the South and
more humid towards the north, could be a divisor between ‘good’
and ‘poor’niches for many species, and lead to the evolution of
different adaptive forms in response to these environmental
conditions. The Arid Diagonal has proven to be a distribution
limit for some felid species such as Panthera onca (Seymour, 1989)
and Herpailurus yagouaroundi (Luegos Vidal et al., 2017), but not
for others as Leopardus geoffroyi (Gomez Fernandez et al., 2020)or
Leopardus colocola (Santos et al., 2018;Do Nascimento et al., 2020).
This limit coincides with the one proposed by Culver et al. (2000)
for the subspecies P. c. puma and P. c. cabrerae, although the Negro
river, a geographical barrier proposed in that work, is not supported
by our results or those reported by Gallo et al. (2021). In fact, there
is evidence of this species’ability as a swimmer, so inferences of this
nature should be reviewed (Elbroch et al., 2010). Additionally, these
phylogroups also match the boundaries between Puma c. hudsoni
and Puma c. pearsoni proposed by Cabrera (1958), but not for the
Patagonian subspecies (Puma c. araucana and Puma c. pearsoni).
This might be a result of the limited number of samples available for
the Patagonian Forests (the biogeographic region where Puma c.
araucana is distributed, according to Cabrera (1958), or because we
cannot genetically differentiate them with the molecular markers
employed in this study.
The ND5 haplotype network, including the sample from the
ancient puma specimen, indicates low genetic variation in the ND5
gene among modern pumas (Metcalf et al., 2016). However, these
results are not robust due to the use of only a very small fragment of
the gene. Despite this, the inclusion of this sample allowed us to
polarize the network, showing that pumas from central Argentina
(Figure 2) and central Brazil are older than those from Patagonia
(Matte et al., 2013).
We did not find individuals belonging to clusters II and III
(characteristic of central Argentina) below this diagonal; however,
we found individuals from Cluster I (widely represented in the
Patagonian Region) above it. These results are in coincidence with
those obtained by Gallo et al. (2021) from microsatellite loci, who
FIGURE 4
Bayesian Skyline Plot. Bold line indicates the mean of Ne (effective population size) through time and the colored areas represent the 95% highest
posterior densities over the mean estimates along the coalescent history of the species.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org09
estimated that the migration rate from western to eastern
populations was almost three times greater than in the opposite
direction, which can be explained by the effect of anthropogenic
disturbances on the puma’s dispersal capacity. It is noteworthy that
of the 11 individuals from Cluster I found north of the Arid
Diagonal, three of them (belonging to the haplotype RCND_2)
are outside the Buffer zone, and only one was sampled in the
Northeast of Buenos Aires province, and more than 800 km distant
from the whole (Patagonian) group. This individual may have
migrated from the south, traveling several kilometers in search of
prey (Walker and Novaro, 2010), or it may have been an illegal
movement of a Patagonian puma such as those that moved towards
hunting reserves in La Pampa (ZanonMartı
nez et al., 2023).
Consequently, it is imperative to improve sampling efforts in the
region to substantiate these hypotheses.
Historical population dynamics
Although the expansion signals from the neutrality tests are not
strongly supported by the Bayesian Skyline Plot, a slight trend of
demographic expansion is seen around 7,000–8,000 years ago, after
the Last Glacial Maximum (31–18 thousand years ago) and the Late
Glacial (18–12 thousand years ago), which extended from Alaska to
southern Chile (Late Pleistocene; Clapperton, 2000). These results
would be consistent with those obtained for the Puma in South
America (Matte et al., 2013) or North America (Culver et al., 2000)
and for L. geoffroyi (Gomez Fernandez et al., 2020). This expansion
could be the result of the extinction of large carnivores and
competitors, which coexisted with the puma in the late
Pleistocene in Patagonia, such as the saber-toothed cat (Smilodon)
and the Patagonian panther (Panthera onca mesembrina)(Turner
and Anton, 1997;Borrero, 2009;Prieto et al., 2010;Prevosti and
Martin, 2013;Metcalf et al., 2016;Prevosti and Forasiepi, 2018); as
well as the expansion in Patagonia of their prey, such as Lama
guanicoe, which shows signs of expansion between 8,000 to 10,000
years ago (Moscardi et al., 2020). This pattern of expansion in
Patagonia, following the Last Glacial Maximum, has also been
observed in human populations, which have been associated with
the extinction of megafauna and the expansion of their main food
source, Lama guanicoe (Metcalf et al., 2016;Perez et al., 2016;
Moscardi et al., 2020).
Implications for conservation
and management
Genetic approaches constitute a fundamental tool in
determining taxonomic limits, mainly in cases where traditional
taxonomy (based on morphological characters) does not allow
identifying discontinuities in gene flow (Frankham et al., 2002).
In this sense, our work constitutes a powerful tool that provides
preliminary but strong guidance for decision makers.
Our results allow us to identify two major genetic groups,
Patagonia and Central-West Argentina, whose geographic
boundary partially corresponds with the South American Arid
Diagonal. Although these clades recovered in the Bayesian tree
cannot be formally defined as Evolutionarily Significant Units
(ESUs) sensu Moritz (Moritz, 1994a,b) due to their lack of
reciprocal monophyly, these phylogroups agree with those
proposed by Culver et al. (2000) for the subspecies Puma c. puma
and Puma c. cabrerae. Given that they show divergence on haplotype
frequencies and significant genetic structure, they should be
considered different Management Units (MU) (Moritz, 1994a). We
consider it is necessary to preserve these MUs, which inhabit well-
differentiated environments, and therefore could be carriers of
important genetic variability from an adaptive point of view.
It is remarkable that in the region of the country where sport
hunting represents a significant economic activity (Walker and
Novaro, 2010) (see Figure 1), high variability and unique haplotypes
were recorded. This may be due to potential trafficking of pumas
from other provinces to supply the demands of hunting reserves.
For this reason, it is essential to develop and implement
management plans for puma populations, alongside strict control
over hunting reserves (Zanon Martı
nez et al., 2016). By doing so, we
would not only be protecting the region’s top carnivore but possibly
also benefiting all species sharing the same habitat (Logan and
Sweanor, 2001;Sergio et al., 2006), including the culpeo fox, one of
the most persecuted species in Patagonia (Dı
az-Ruiz et al., 2020),
and the Pampas fox (Lycalopex gymnocercus), which also
experiences significant hunting pressure (Porini and Ramadori,
2007). The presence and abundance of herbivores (Logan and
Irwin, 1985;Turner et al., 1992) and plant communities (Schaefer
et al., 2000) might benefit as well of the new conservation strategies.
This study also collected samples in the center and east of the
Pampas ecoregion where pumas had not been recorded until ten years
ago (Chimento and De Lucca, 2014;Nielsen et al., 2015). Based on our
results from the maternal lineage, we cannot rule out any of the
hypotheses proposed by Chimento and De Lucca (2014),regardingthe
origin of the pumas that recolonized the Center and East of the
Pampean ecoregion, since the haplotypes found could originate
either from the South of Buenos Aires or from Cordoba province. It
cannot be excluded that these populations have always existed in the
area but were not previously detected due to insufficient research.
In this sense, we highlight the need to increase sampling across
understudied areas of the country and to implement the use of other
markers such as microsatellites or SNPs in order to refine the
population and demographic approaches. The use of more variable
markers will allow us to elucidate the origin of the pumas that
colonized (or recolonized) previously inhabited regions. Also, this
would be useful to provide refined answers regarding the existence
of illegal traffic of puma individuals in hunting reserves. On the
other hand, the use of genomic approaches (e.g. Rad-seq SNPs) will
allow us to test biogeographical hypotheses regarding the adaptive
divergence of populations north and south of the arid diagonal.
Data availability statement
The original contributions presented in the study are publicly
available. This data can be found here: NCBI GenBank, accession
PP952688, PP952689, PP952691–PP952709.
Mac Allister et al. 10.3389/fevo.2024.1436320
Frontiers in Ecology and Evolution frontiersin.org10
Ethics statement
Ethical approval was not required for the study involving
animals in accordance with the local legislation and institutional
requirements because our work was based on the use of feces and
tissue samples. Tissue samples were collected in the field
(individualsrunoverorhunted)orfromsamplespreviously
deposited in biological collections. Skin samples were mainly
represented by material collected under the provincial legal
hunting regimes of Santa Cruz, Neuquen, Chubut, and Rı
o
Negro province.
Author contributions
MMA: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Writing –original draft, Writing –
review & editing. CF: Writing –review & editing. RM: Writing –
review & editing. RT: Writing –review & editing. DA: Writing –review
& editing. OG: Writing –review & editing. DC: Writing –review &
editing. EP: Writing –review & editing. VZ: Writing –review &
editing. AR: Writing –review & editing. JZ: Writing –review & editing.
MLM: Writing –review & editing. JT: Writing –original draft, Writing
–review & editing. AT: Writing –original draft, Writing –review &
editing. GF: Conceptualization, Data curation, Formal analysis,
Funding acquisition, Investigation, Methodology, Project
administration, Resources, Supervision, Writing –original draft,
Writing –review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This work
was supported by the Subsidios de Investigacion Bianuales (SIB)
(Expte0600/2019 and EXP-2093/2022) provided by the Universidad
Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA)
and the Spanish National Research Council (CSIC) through the
programme Lincglobal 2021 (grant INCGLO0028). MMA, CF, RT,
DA, DC, EP, VZ, JZ, JT and AT were funded by Argentine national
funds through Consejo Nacional de Investigaciones Cientı
ficas y
Tecnicas (CONICET); MLM was funded by Buenos Aires province
funds through Comision de Investigaciones Cientı
ficas (CIC), AR
was funded by Spanish National Research Council (CSIC) and GF
was funded by Universidad Nacional del Noroeste de la Provincia
de Buenos Aires (UNNOBA).
Acknowledgments
Authors thank the Direccion de Fauna y Flora Silvestre of
Buenos Aires and Chubut provinces, and the Secretarı
ade
Ambiente y Desarrollo Sustentable of Rı
o Negro province for
samples and collection permits. The colleagues of Centro de
Bioinvestigaciones (CEBIO) for the support and discussions
during the development of this study. A. Andrade, E. Furlan, L.
Ali Moran, D. Kloster, B. Carpinetti, C. Allende, G, Buzetti, A.
Lavore, M. Monteverde, E. Prodan, A. Gloazzo and M. Faillafor the
help in obtain samples; A. Baricalla by bioinformatics support, D. A.
Caraballo, C. S. Carnovale for the help with the calibration of the
trees and E. Ibañez for help with feces extractions.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fevo.2024.1436320/
full#supplementary-material
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