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\begin{document}

\title{A refined panel of 42 microsatellite loci to universally genotype
catarrhine primates}



\author[1]{Franziska Trede}%
\author[2]{Niels Kil}%
\author[2]{James Stranks}%
\author[3]{Andrew Connell}%
\author[1,2]{Julia Fischer }%
\author[4]{Julia Ostner}%
\author[4]{Oliver Schülke}%
\author[1,5]{Dietmar Zinner}%
\author[1]{Christian Roos}%
\affil[1]{German Primate Centre Leibniz Institute for Primate Research}%
\affil[2]{University of Göttingen}%
\affil[3]{University of Pennsylvania Perelman School of Medicine}%
\affil[4]{Georg-August-Universitat Gottingen Fakultat fur Biologie und Psychologie}%
\affil[5]{Baboon Group}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
1. Microsatellite genotyping is an important genetic method for a number
of research questions in biology. Given that the traditional fragment
length analysis using polyacrylamide gel or capillary electrophoresis
has several drawbacks, microsatellite genotyping-by-sequencing (GBS) has
arisen as a promising alternative. Although GBS mitigates many of the
problems of fragment length analysis, issues with allelic dropout and
null alleles often remain due to mismatches in primer binding sites and
unnecessarily long PCR products. This is also true for GBS in catarrhine
primates where cross-species amplification of loci (often human derived)
is common. 2. We therefore redesigned primers for 45 microsatellite loci
based on 17 available catarrhine reference genomes. Next, we tested them
in singleplex and different multiplex settings in a panel of species
representing all major lineages of Catarrhini and further validated them
in wild Guinea baboons (Papio papio) using faecal samples. 3. The final
panel of 42 microsatellite loci can efficiently be amplified with
primers distributed into three amplification pools. 4. With our
microsatellite panel, we provide a tool to universally genotype
catarrhine primates via GBS from different sample sources in a cost- and
time-efficient way, with higher resolution, and comparability among
laboratories and species.%
\end{abstract}%



\sloppy




\textbf{Introduction}

Microsatellites have been and are still widely applied in various
biological sciences including population genetics, kinship/pedigree
analysis, human and wildlife forensics, linkage analysis or disease
association studies (e.g. Cunningham, Dooley, Splan, \& Bradley, 2001;
Goodwin, Linacre, \& Hadi, 2011; Gulcher, 2012; Wasser et al., 2004).
Population genetic information obtained by microsatellite genotyping is
also important for monitoring wild populations in conservation contexts,
for reintroduction programs or to refine captive breeding management
(Arandjelovic \& Vigilant, 2018; Norman, Putnam, \& Ivy, 2019).
Microsatellites are also often the markers of choice to genetically
characterize (wild) populations in order to determine degrees of
population fragmentation and hybridization, dispersal patterns, mating
systems and reproductive success (e.g. Charpentier et al., 2012; De
Moor, Roos, Ostner, \& Schulke, 2020; Ferreira da Silva et al., 2018;
Kheng et al., 2017; McCarthy, Lester, Cibot, Vigilant, \& McLennan,
2020). The ongoing popularity of microsatellites is largely based on
their high abundancy in animal genomes (Hamada, Petrino, \& Kakunaga,
1982; Tautz \& Renz, 1984), the high levels of allelic diversity
(Ellegren, 2000) and the possibility to amplify them across related
species. Accordingly, microsatellites are preferred, for example over
SNPs, because of their higher statistical power per locus and their
cross-species amplifiability, particularly when applied to small sample
size datasets as typically found in forensic and kinship studies
(Barbian et al., 2018; Guichoux et al., 2011)

However, traditional microsatellite genotyping via fragment length
analysis (FLA) using polyacrylamide gel or capillary electrophoresis has
several disadvantages, such as fragment size homoplasy, allele calling
difficulties (stutter and split peaks, off-target PCR products),
laborious work and high laboratory costs, as well as poor comparability
of results among laboratories (De Barba et al., 2017; Guichoux et al.,
2011; Pasqualotto, Denning, \& Anderson, 2007). Even with attempts to
improve PCR amplification and more accurate/reliable genotyping
procedures (Arandjelovic et al., 2009; Buchan, Archie, Van Horn, Moss,
\& Alberts, 2005; Navidi, Arnheim, \& Waterman, 1992; Sefc, Payne, \&
Sorenson, 2003; Taberlet et al., 1996), many of the problems remained.

With microsatellite genotyping-by-sequencing (GBS) using high-throughput
sequencing technologies most of the difficulties can be mitigated
(Barbian et al., 2018; Johannesen, Fabritzek, Ebner, \& Bikar, 2017;
Pimentel et al., 2018; Vartia et al., 2016). For instance, with GBS the
exact length of the microsatellite alleles can be determined, which is a
typical problem of FLA genotyping, particularly when alleles differ by
only one basepair (bp) (Barbian et al., 2018; Vartia et al., 2016).
Moreover, the nucleotide sequence is revealed so that cryptic alleles
(alleles with the same length but containing a nucleotide variant) can
be detected, resulting in an increased number of alleles and
consequently greater statistical power per locus.

Nevertheless, problems with null alleles due to relatively large PCR
products and allelic dropout as a result of primers binding in
unconserved regions remain with GBS (Pompanon, Bonin, Bellemain, \&
Taberlet, 2005). As many microsatellites can be cross-amplified in
phylogenetically related species, primers designed for one species are
often tested in related species and then applied if successfully
amplified and informative (i.e., polymorph) (Barbara et al., 2007; De
Barba et al., 2017). For example, various microsatellite loci
characterized for humans can be successfully amplified in non-human
catarrhine primates (Old World monkeys, apes) (Coote \& Bruford, 1996;
Ely, Gonzalez, Reeves-Daniel, \& Stone, 1998; Kayser et al., 1996;
Morin, Mahboubi, Wedel, \& Rogers, 1998; Newman, Fairbanks, Pollack, \&
Rogers, 2002; Roeder et al., 2009; Smith et al., 2000) and have been
used since then in numerous studies (e.g. Arandjelovic, Head, Boesch,
Robbins, \& Vigilant, 2014; Kopp, Fischer, Patzelt, Roos, \& Zinner,
2015; Minkner et al., 2018; St\selectlanguage{ngerman}ädele et al., 2019). Yet, attempts to
reduce PCR product size or to adapt primers specifically to the study
species have been rare (but see Bradley, Boesch, \& Vigilant, 2000;
Engelhardt, Muniz, Perwitasari-Farajallah, \& Widdig, 2017; Inoue,
Ogata, Seino, \& Matsuda, 2016).

In our study, we screened a total of 269 microsatellite loci, widely
targeted in catarrhine primates, and designed conserved primers for 45
loci based on available catarrhine genomes. We then tested the new
microsatellite panel in ten primate species representing all major
lineages of Catarrhini and further validated their applicability to low
quality DNA samples using faecal samples of wild Guinea baboons
(\emph{Papio papio} ).

\textbf{Material and Methods}

\emph{In silico selection of microsatellite loci}

We screened 269 human microsatellite loci widely used in catarrhine
population genetic studies. We extracted the human (GRCh38/hg38)
sequence of each locus with 500 bp flanking regions from GenBank
(https://www.ncbi.nlm.nih.gov/genbank/) and performed BLAT searches
against the 16 available (status: 5 December 2018) non-human Catarrhini
reference genomes (Table S1) using the UCSC (genome.ucsc.edu) or Ensembl
(ensembl.org) genome browsers with standard settings. In addition, we
checked the human sequence for repetitive elements (SINEs, LINES, etc.)
in flanking regions using the RepeatMasker Web Server
(http://www.repeatmasker.org/) with standard settings. We generated
alignments for each locus containing the 16 non-human catarrhine
species, the human and the human repeat-masked sequences with Muscle
3.8.31 (Edgar, 2004) in SeaView 4 (Gouy, Guindon, \& Gascuel, 2010) and
added published primer sequences to the alignments.

Loci were selected for further analysis if they fulfilled the following
criteria: (1) primer binding sites are not in repetitive elements thus
increasing locus-specific amplifiability and reducing the risk of
off-target PCR products particularly in multiplex PCR reactions; (2)
primer binding sites are conserved among catarrhines so that loci can be
universally amplified in this taxonomic group with \textgreater{}180
species (Mittermeier, Wilson, \& Rylands, 2013); (3) the microsatellite
motif is relatively short (max. 150 bp) to allow small amplicon size and
secure locus amplification from degraded DNA samples, such as faecal
samples; and (4) loci are evenly (1-3 loci per chromosome) distributed
throughout the genome (using the human genome as a reference) to avoid
potential linkage problems.

For loci which passed the selection criteria, we designed new primers
using Primer-Blast (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). To
allow for multiplexing, primers were designed to have similar annealing
temperatures. Locus specificity of primers was checked by BLAT search
against the 17 available catarrhine genomes. As primer-binding sites
were not always fully conserved among the 17 catarrhines, primers of 21
loci were designed with wobble positions. To simplify library
preparation for GBS, we added adapter nucleotide sequences to the 5' end
of the locus-specific primers (5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3'
to forward primers, 5'-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3' to reverse
primers; locus-specific primers are provided in Table S2).

\emph{Laboratory work}

First, we tested in singleplex reactions for the locus-specificity of
selected primers and their universal applicability to catarrhine species
in a panel representing all major lineages of catarrhines (Table 1).
High-quality DNA from a male of each of the ten species was obtained
from the Gene Bank of Primates at the German Primate Center. PCRs were
performed in total volumes of 25 \selectlanguage{greek}µ\selectlanguage{english}l containing 1x QIAGEN Multiplex PCR
Master Mix (QIAGEN), 0.4 \selectlanguage{greek}µ\selectlanguage{english}M of each primer and 50 ng genomic DNA.
Cycling conditions comprised of 15 min at 95\selectlanguage{ngerman}°C, 30 cycles each with
denaturation at 94°C for 30 sec, annealing at 57°C for 90 sec and
extension at 72°C for 90 sec, and a final extension step of 10 min at
72°C. All reactions were run together with no-template controls (NTCs)
to check for contamination. PCR performance was checked on 2\% agarose
gels stained with ethidium bromide (Carl Roth GmbH). Sequencing of
singleplex PCR reactions was omitted.

Next, we tested for the possibility of running multiplex PCR reactions
to reduce overall laboratory work and costs. Therefore, we pooled either
all 45 primer pairs in a single PCR reaction (1-pool approach) or
divided them into five PCR reactions each containing nine primer pairs
(5-pool approach) or three PCR reactions containing 18 and 2 x 12 primer
pairs (3-pool approach; for rationale of pooling and locus exclusion see
Results). Amplifications were conducted as described for the singleplex
PCRs (same PCR set-up, DNA samples, cycling conditions, NTCs), but with
different primer concentrations (see Tables S3-S5 for pooling schemes
and concentrations of single primers within pools). To minimize PCR
errors, we ran PCR reactions in two independent replicates. PCR
performance was again checked on 2\% agarose gels. Replicate PCR
products (including the NTCs) were pooled and then cleaned with the
MinElute PCR Purification Kit (QIAGEN). DNA concentrations were measured
with a Qubit 3.0 (ThermoFisher Scientific) and 100 ng were subjected to
indexing PCR. Indexing PCR was performed in total volumes of 25 \selectlanguage{greek}µ\selectlanguage{english}l
containing 1x KAPA HiFi HotStart ReadyMix (Roche), 0.4 \selectlanguage{greek}µ\selectlanguage{english}M of each
indexing primer and 100 ng purified PCR product. Cycling conditions
comprised of 45 sec at 98\selectlanguage{ngerman}°C, 4 cycles each with denaturation at 98°C for
15 sec, annealing at 62°C for 30 sec and extension at 72°C for 30 sec,
and a final extension step of 1 min at 72°C. Subsequently, indexed PCR
products were purified with the MinElute PCR Purification Kit (QIAGEN)
and ran on a Bioanalyzer 2100 (AGILENT) to check for PCR performance and
molarity. Libraries were diluted to a final concentration of 10 nM and
then pooled and sequenced with 51 cycles forward and 251 cycles reverse
on Illumina's MiSeq desktop sequencer.

To check for Mendelian inheritance and whether our new microsatellite
panel is also applicable to low-quality and -quantity DNA as typically
extracted from fecal samples (Monteiro et al., 1997; Perry, Marioni,
Melsted, \& Gilad, 2010), we tested our panel in 12 fecal samples of
wild Guinea baboons. The samples comprised of six males and two
`families' each composed of a male, a female and their known offspring.
DNA from these 12 specimens was previously genotyped via FLA at 24
microsatellite loci (Dal Pesco, 2019). The amplification procedure and
follow-up steps for the applied 3-pool approach were the same as
described above, but the number of cycles in the initial amplification
was increased to 40, the total DNA amount was increased to 200 ng, and
each PCR was performed in triplicates.

\emph{Bioinformatic analysis}

The data analysis was performed using the software package CHIIMP
v.3.0.0 (Barbian et al. 2018). The raw data (FASTQ files) as well as all
input files (config-file, sample-file, locus-attributes-file) are
available in the online supplement resources. As our microsatellite
panel included several di-repeat loci, which stutter more frequently
than tetra-repeats, we increased the stutter count ratio to 0.70
(stutter.count.ratio\_max: 0.7). We further implemented a broad range of
possible allele lengths in the locus attributes by setting the length
buffer to 100 bp. This ensured the inclusion of all tested species even
if the allele sizes at a given locus varied between species according to
the available reference genomes. The minimum number of reads per locus
was set to 100 (counts.min: 100). All other parameters were set to
default.

With the current version of CHIIMP, wobble positions in primer sequences
cannot be accounted for. Hence, for loci with a wobble position in a
primer sequence, alternative nucleotides of the wobble are erroneously
recognized as different alleles. Moreover, the repeat motif needs to be
specified in CHIIMP, but as repeat motifs can vary in the investigated
species, correct (orthologous) reads remain unrecognized for some
species if CHIIMP is fed with a wrong repeat motif. Due to these
reasons, the output for all loci was checked manually and corrected if
needed. Additionally, we screened the processed reads for the general
level of amplification per locus and the occurrence of PCR artifacts
(off target amplification, primer dimer, false primer pairings, etc.).

\textbf{Results}

\emph{In silico selection of microsatellite loci}

In total, 217 of the 269 investigated loci were not optimal for
microsatellite genotyping of catarrhines. For 147 of them, one or both
primer binding sites or the complete locus were located in repetitive
elements. This increases the likelihood to amplify various off-target
PCR products, particularly in multiplex settings when many primers that
can bind multiple times in the respective genome are combined in a
single PCR reaction. For an additional 32 loci, we could not find
conserved primer binding sites near the microsatellite and a further 15
loci contained relatively long microsatellite repeat regions for one or
more species, resulting in long PCR products (\textgreater{}250 bp).
Longer PCR products are often difficult to generate if only degraded DNA
material is available and can result in null alleles. Further problems
included, for instance, the location of loci directly next to each other
on the same chromosome and thus increasing the risk of linkage.
Additionally, double entries of loci under different names or gaps in
some of the reference genomes (especially for Y-chromosomal loci)
impeded the screening process. A full list of screened loci including
the respective reasons for their exclusion is provided in Table S6. Of
the 52 loci which fulfilled our criteria, we selected 45 (1-3 loci per
human chromosome including gonosomes) for downstream analyses.

The newly designed primers for the 45 loci (consisting of di-, tri- and
tetra-repeats) amplify PCR products between 56 - 215 bp (according to
available genome data; Table S2). Compared to the original published
primers, we were able to reduce PCR product sizes by 2 - 225 bp (mean
75.9 bp) in 37 loci whereas for five loci, the new primers amplify a
moderately longer fragment (elongation by 2~-~15 bp; mean 7.6 bp). PCR
product size for the remaining three loci did not change. As primer
binding sites were not always perfectly conserved among the 17
investigated catarrhine reference genomes, primers for 21 loci contain
wobble positions. Mismatches in primer binding sites, found only in a
few (1 - 2) of the investigated species, were neglected in primer design
and probably result in less efficient or no amplification of the
respective locus in the given species (0 - 12 loci with mismatches per
species, mean 3.4; Table S2).

\emph{Singleplex PCR test}

Singleplex PCR reactions of the 45 loci in ten species representing all
major lineages of catarrhines were run on agarose gels and resulted, for
all loci and species, in PCR products within the expected size range
with no signs of amplifying any off-target PCR products (data not
shown). Thus, locus-specificity and universal applicability of our
primer set to catarrhine primates was indicated.

\emph{Multiplexing approaches}

Sequenced alleles ranged in size from 71 bp (D3s1768) to 211 bp
(D12s372) and nine loci contained cryptic alleles in at least one
species (Tables S7-S10). The level of amplification and obtained
sequence reads varied across samples/species and loci in all three
approaches. The amplification of all loci in one pool (1-pool approach)
was least effective, resulting in the lowest number of amplified loci
(mean 25.9) and alleles (mean 36.9; Table 2). In some cases, the reason
for allelic dropouts could be attributed to wrong primer pairing/primer
mismatches (primer-dimer or off-target amplification of short products).
Most loci amplified less efficiently than in the other two approaches
and some (N=11) failed to amplify at all. Only nine loci recovered the
same number of alleles as in the 5-pool approach. Interestingly, even
though the number of amplified alleles was reduced from a mean of 60.1
to 36.9 compared to the 5-pool approach, the level of heterozygosity was
not affected to the same extent with a reduction from 49.8 to 43.5\%
(Table 2).

The best results, i.e. the highest amplification levels for loci (mean
40.2) and alleles (mean 60.1), were generated applying the 5-pool
approach (Table 2). Nevertheless, we observed again primer-dimers and
short off-target PCR products potentially as a result of interacting
primers from different loci. Moreover, three loci (D11s1366, D12s67.2,
and D15s1007) neither amplified in the 1-pool nor in the 5-pool approach
and were excluded from further testing. To further improve amplification
success and to reduce primer interactions among primers of different
loci (based on the knowledge obtained from the 1-pool and 5-pool
approaches), we distributed the 42 remaining loci into three
amplification pools containing 18, 12 and 12 loci, respectively (Table
S5). Using the 3-pool approach, we were able to largely minimize primer
interactions, but amplification success for loci (mean 37.8) and alleles
(mean 55.5) per species was slightly reduced compared to the 5-pool
approach, but higher than in the 1-pool approach (Table 2).

\emph{Degraded DNA samples}

For the degraded DNA samples, we applied the 3-pool approach. The
amplification from fecal samples was successful except for four (out of
42) loci (two autosomal and two gonosomal loci; Table S10). The number
of loci and alleles amplified per sample was comparable to the results
obtained from high-quality DNA samples (Table 2). However, ten of the 42
amplified loci were monomorphic in our \emph{P. papio} population, i.e.,
all twelve individuals showed the same allele. The remaining 32 loci
showed a level of 46.3\% heterozygosity (Table 2).

All autosomal loci were in accordance with Mendelian inheritance besides
D7s503 and D13s1291. For D7s503, the two alleles with the highest read
counts for male MRX (99/109) did not match one of the two alleles of his
offspring THL (111/113; mother MMI: 103/113). A closer look at the data
revealed that MRX also had many reads for allele 111 (only 23 reads less
than for allele 109), indicating that the allele 109 of MRX was likely
an overamplified stutter sequence. For D13s1291, the two alleles of male
MLK (130b/132b) did not match with his offspring PTC (130a/132a; mother
LCY: 128/130a). MLK was the only individual with these two cryptic
alleles (each with a GA point mutation) and further showed reads with
130 and 132 bp length without this mutation. At this point, we can
neither exclude the occurrence of a PCR artifact in this particular
case, nor that this locus indeed does not follow the rules of Mendelian
inheritance.

\textbf{Discussion}

From a set of 269 microsatellite loci widely applied in catarrhine
primates, we selected a total of 45 loci that can be universally applied
to genotype catarrhine primates. Due to the relatively small amplicon
sizes, even low-quality DNA could be genotyped and since the selected
loci were evenly distributed throughout the genome (at least according
to the human genome), the risk of linkage was significantly reduced.
Moreover, our panel could be multiplexed to a great extent. The testing
of different multiplex settings revealed that a 5-pool approach produced
the best result, but that a 3-pool approach containing one pool of 18
and two of 12 loci is the best compromise between locus amplification
efficiency and laboratory effort and costs.

We tested the panel with high-quality DNA samples from all major
lineages of catarrhines in multiplex settings and revealed successful
amplification rates of 33 to 41 (average 38) loci per species (Table 2).
We additionally showed the applicability of the 3-pool approach to
degraded DNA samples such as fecal samples, which is a common material
in many non-invasive wildlife studies (Carroll et al., 2018; Waits \&
Paetkau, 2005). The results for fecal samples were similar to the
results of the high-quality samples (Table 2). All loci, besides D7s503
and D13s1291, were in accordance with Mendelian inheritance,
demonstrating the suitability of the new microsatellite panel for
kinship and relatedness analyses.

Through multiplexed GBS, cryptic alleles can be detected (Barbian et
al., 2018; Sarhanova, Pfanzelt, Brandt, Himmelbach, \& Blattner, 2018;
Vartia et al., 2016), and even in our test panel of only ten catarrhine
species with one individual each, we found cryptic alleles at nine loci
(Tables S7-S10). As more individuals per species get tested, this number
will most likely increase and provide further accuracy and a higher
statistical power of our panel.

Another advantage of GBS is that the resulting genetic data, in form of
allele sequences, is independent of the used sequencing platform. Thus,
data produced by different laboratories can be easily shared and
compared. By applying validated bioinformatics pipelines, such as the
CHIIMP pipeline (Barbian et al., 2018), one can further ensure that the
resulting data are reproducible and less prone to arbitrary allele
calling by different researchers while still allowing the customization
of e.g. filtering parameters to fit different datasets.

Although we recommend the 3-pool approach, the amplification success of
individual loci can be improved, for example, by amplifying all loci in
individual reactions and then pooling before or after the indexing PCR.
However, this would largely increase workload in the laboratory and
costs. It is also important to check which loci are polymorphic in the
species of interest, so that monomorphic loci can be excluded from
large-scale population genetic investigations. Likewise, as several
species exhibit mismatches in primer binding sites (0 - 12 loci with
mismatches per species), primer design for a given species can be
adjusted and optimized, which becomes easier to do with an increasing
number of sequenced catarrhine genomes.

In summary, with our microsatellite panel, we provide a tool to
universally genotype catarrhine primates via GBS from samples of varying
DNA quality in a cost- and time-efficient way; with higher resolution,
better comparability among laboratories, and largely mitigated problems
of traditional FLA.

\textbf{Acknowledgements}

We thank C. Schwarz and K.N. Poulle for the valuable support during
laboratory work. This work was supported by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation)---project
number 453 254142454/GRK 2070.

\textbf{Data Accessibility}

The sequencing data have been submitted to the NCBI Sequence Read
Archive (SRA) under the accession number \#\#\#\#. Input files for the
bioinformatics analysis (config-, sample- and locus-attributes-files)
are available online in the supporting information.

\textbf{Author Contributions}

Conceptualization, CR; Investigation, CR, FT, JS and NK; Writing --
Original Draft, CR, DZ and FT; Writing -- Review \& Editing, CR, DZ, FT,
JF, JS, JO, NK and OS; Funding Acquisition JF, JO and OS; Software, AJC.

\textbf{ORCID}

Franziska Trede: 0000-0003-3690-1006

Niels Kil: 0000-0002-8823-2599

James Stranks: 0000-0002-5557-9051

Andrew Jesse Connell: 0000-0002-9393-3074

Julia Fischer: 0000-

Julia Ostner: 0000-0001-6871-9976

Oliver Schülke: 0000-0003-0028-9425

Dietmar Zinner: 0000-0003-3967-8014

Christian Roos: 0000-0003-0190-4266

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\textbf{Tables}

Table 1 Catarrhine species used to test the new microsatellite panel.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Family & Subfamily & Tribe & Species\tabularnewline
\midrule
\endhead
Hominidae & Homininae & & \emph{Pan troglodytes}\tabularnewline
& & & \emph{Gorilla gorilla}\tabularnewline
& Ponginae & & \emph{Pongo abelii}\tabularnewline
Hylobatidae & & & \emph{Hylobates lar}\tabularnewline
Cercopithecidae & Colobinae & Presbytini & \emph{Trachypithecus
obscurus}\tabularnewline
& & & \emph{Pygathrix nemaeus}\tabularnewline
& & Colobini & \emph{Colobus guereza}\tabularnewline
& Cercopithecinae & Cercopithecini & \emph{Cercopithecus
diana}\tabularnewline
& & Papionini & \emph{Papio papio}\tabularnewline
& & & \emph{Macaca mulatta}\tabularnewline
\bottomrule
\end{longtable}

Table 2 Number of amplified loci and alleles, as well as the level of
heterozygosity per species generated in three approaches with high
quality DNA (blood) and degraded DNA from fecal samples.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& \textbf{High-quality DNA} & \textbf{High-quality DNA} &
\textbf{High-quality DNA} & \textbf{Degraded DNA}\tabularnewline
\midrule
\endhead
\textbf{High-quality DNA} \textbf{High-quality DNA} \textbf{High-quality
DNA} \textbf{Degraded DNA} 5-pool approach (45 loci) 1-pool approach (45
loci) 3-pool approach (42 loci) 3-pool approach (42 loci) mean number of
loci amplified per sample/species (range) 40.2 (37-43) 25.9 (21-32) 37.8
(33-41) 38.8 (35-41) mean number of alleles amplified per sample/species
(range) 60.1 (53-69) 36.9 (31-45) 55.5 (46-68) 52.2 (47-56) mean level
of heterozygosity per sample/species (range) 49.8\% (23.3-69.4\%) 43.5\%
(22.6-71.4\%) 47.1\% (19.5-66.7\%) 34.3\%
(23.1-47.4\%)\textsuperscript{+} 46.3\% (31.0-64.3\%)\textsuperscript{++}
& & & &\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
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\end{document}

