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\begin{document}

\title{Benefits and limitations of a new genome-based PCR-RFLP genotyping assay
(GB-RFLP): a SNP-based detection method for identification of species in
extremely young adaptive radiations}



\author[1]{Claudius Kratochwil}%
\author[2]{Andreas Kautt}%
\author[2]{Sina Rometsch}%
\author[3]{Axel Meyer}%
\affil[1]{University of Helsinki}%
\affil[2]{University of Konstanz}%
\affil[3]{ University of Konstanz}%


\vspace{-1em}



  
  \date{March 14, 2025}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
High-throughput DNA sequencing technologies make it possible now to
sequence entire genomes relatively easily. Complete genomic information
obtained by whole genome resequencing (WGS) can aid in identifying and
delineating species even if they are extremely young, cryptic or
morphologically difficult to discern and closely related. Yet for
taxonomic or conservation biology purposes WGS can remain
cost-prohibitive, too time-consuming, and often constitute a ``data
overkill''. Rapid and reliable identification of species (and
populations) that is also cost-effective is possible based on
species-specific markers that can be discovered by WGS. Based on WGS
data we designed a PCR restriction fragment length polymorphism
(PCR-RFLP) assay for 19 Neotropical Midas cichlid populations
(Amphilophus cf. citrinellus), that includes all 13 described species of
this species complex. Our work illustrates that identification of
species and populations (i.e., fish from different lakes) can be greatly
improved by designing genetic markers using available ``high
resolution'' genomic information. Yet, our work also shows that even in
the best-case scenario, when whole-genome resequencing information is
available, unequivocal assignments remain challenging when species or
populations diverged very recently, or gene flow persists. In summary,
we provide a comprehensive workflow on how to design RFPL markers based
on genome re-sequencing data, how to test and evaluate their
reliability, and discuss the benefits and pitfalls of our approach.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Historically morphological differences remain the basis for species
identification, taxonomic keys, and effort in species delimitation. Yet,
reliable classification of specimens can be complex due to many factors.
For example, when species are morphologically extremely similar or when
morphological characters are not expressed at a given life-history stage
(e.g., juveniles). In the last decade, the increasing affordability of
reduced-representation data (e.g., restriction-site-associated DNA
sequencing or target enrichment) or whole genome re-sequencing has
provided new possibilities to assign species not only based on
morphological or meristic characters, but also on genomic information.
In some instances, this has even greatly contributed to the discovery
and description of new (i.e., previously cryptic) species (Fennessy et
al., 2016; Nater et al., 2017). Genetic species assignment approaches
are also promising to add novel tools to aid in conservation efforts of
endangered species, but practical implementations often fail (Campbell
et al., 2019; Piertney, 2016; Shafer et al., 2015). A major disadvantage
of high-throughput sequencing techniques are the cost and time that is
needed to generate libraries, sequence them, and to analyze the data.
But, importantly, genomic data also allow for the identification of a
suite of informative, diagnostic genetic markers for species or
population assignment that can be genotyped using cheaper and faster
methods (Shafer et al., 2015).

Among all genetic variants, single-nucleotide polymorphisms (SNPs) are
clearly the most abundant (in the human population for example more than
95\% of all genetic variants are SNPs (Auton et al., 2015)) and
therefore powerful genetic markers for assigning populations or species.
Over the past 30 years, many methods have been developed to
cost-effectively genotype SNPs. One widely used fast method are PCR
restriction fragment length polymorphism (PCR-RFLP) markers (McKeown,
Robin, \& Shaw, 2015; Ota, Fukushima, Kulski, \& Inoko, 2007). Hereby, a
particular DNA fragment is first amplified by PCR. The resulting
amplicon is then digested using a restriction enzyme that cuts only one
allele at a diagnostic SNP (resulting in two fragments) but not the
other one (one fragment), due to an, ideally species-specific,
polymorphism in the enzyme's recognition site. Homozygous individuals
for either allele, as well as heterozygous individuals (three
fragments), can be easily distinguished from each other by gel
electrophoresis (see detailed description of the method in Ota et al.,
2007). Therefore, PCR-RFLP is an excellent method that can be used for
fast, cheap, and reliable genotyping of diagnostic markers.

Recently, we have sequenced 453 genomes of a very young species flock of
Nicaraguan Midas cichlid fishes (\emph{Amphilophus cf. citrinellus} )
(Kautt et al., 2020). This species complex includes, so far, 13
described species (Torres-Dowdall \& Meyer, in press). Two species
(\emph{A.s citrinellus} and \emph{A. labiatus} ) can be found in both
Great Lakes Managua and Nicaragua (Barluenga, St\selectlanguage{ngerman}ölting, Salzburger,
Muschick, \& Meyer, 2006). From there, seven crater lakes (Apoyeque,
Apoyo, As. León, As. Managua, Masaya, Tiscapa and Xiloá) have been
colonized (K. R. Elmer et al., 2014; Kathryn R. Elmer, Lehtonen, Fan, \&
Meyer, 2013; Kathryn R Elmer, Lehtonen, \& Meyer, 2009). In two of the
crater lakes, Apoyo and Xiloá, six and four endemic species have been
described, respectively (Barlow \& Munsey, 1976; Geiger, McCrary, \&
Stauffer Jr, 2010; Recknagel, Kusche, Elmer, \& Meyer, 2013; Stauffer
Jr, McCrary, \& Black, 2008; Stauffer Jr \& McKaye, 2002). In Crater
Lake As. Manuagua, another endemic species, \emph{A. tolteca} , has been
formally described (Recknagel et al., 2013), while species of the other
crater lakes await formal description (why we included them here as
`populations').

Crater lake populations and sympatric species therein clearly form
separate clusters using both RAD-sequencing data (Kautt,
Machado-Schiaffino, \& Meyer, 2018) and whole-genome data (Kautt et al.,
2020). While all crater lake populations and species differ
morphologically (Kathryn R. Elmer, Kusche, Lehtonen, \& Meyer, 2010;
Kautt et al., 2018), species assignment can be difficult, especially
when specimens are young, and particularly for the sympatric species
from crater lakes Apoyo and Xiloá. Therefore, methods to quickly
genotype fish using genetic markers would give additional confidence for
species assignments and allow identification of species also for
juvenile fish. This is important for certain research questions
including for example cohort analyses and unbiased frequency
estimations. Moreover, several of these species are protected or live in
protected environments where illegal fishing occurs. Cheap genotyping
assays with a fast turnaround time might contribute to conservation
monitoring.

The objectives of this study were therefore to (1) design a workflow to
screen for suitable GB-RFLP markers for species and population
assignment, (2) test \emph{in silico} if those markers would allow
unambiguous assignment and (3) to perform GB-RFLP assays on independent
samples (i.e., samples that have been not used for the design of the
markers in (1)) to test if the markers are suitable to assign species
and populations (i.e., lakes of origin).

\textbf{2. Materials and Methods}

2.1 Study system, samples and genomic data

The Midas cichlid species complex encompasses a total of --- based on
genetic clustering (Kautt et al., 2020) --- at least 19 genetically
distinguishable populations that include the 13 described species (Fig.
1). These include five crater lakes with only one species (Lakes
Apoyeque, As. León, As, Managua, Masaya, and Tiscapa), one crater lake
with six species (Lake Apoyo), one crater lake with four species (Lake
Xiloá), and the great lakes Managua and Nicaragua that both harbor the
same set of two species (\emph{A. citrinellus} and \emph{A. labiatus} ;
Fig. 1). To find species and population-specific markers, we used
genotype calls from a previous analysis of 453 re-sequenced Midas
cichlid genomes, including all aforementioned species and lake
populations (Kautt et al., 2020). Tissue samples were collected during
field trips to Nicaragua (years 2003--2018).

2.2 Screen for species and population specific variants

To screen for species- and population-specific markers, we estimated
population differentiation (F\textsubscript{ST}) for individual SNP loci
using \emph{vcftools} (Danecek et al., 2011) always comparing the
respective focal population (ingroup) to all other samples (outgroup)
(the workflow is also described in Fig. 2). For Crater Lakes Apoyo and
Xiloá, we compared the focal species (ingroup) against all other
sympatric species (outgroup). For the two great lake species, we used
instead genome-wide-association (GWA) data for lip shape, which is a
characteristic trait that differs between \emph{A. citrinellus}
and\emph{A. labiatus} and correlates with F\textsubscript{ST} in both
lakes (Kautt et al., 2020). For each species and population, we
extracted the 20 variants with the highest differentiation
(F\textsubscript{ST}) / genome-wide association (GWA).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1} The Midas cichlid species complex includes fish
inhabiting nine lakes (the two great lakes and seven crater lakes) and
comprises 13 described species. In this study we designed genome-based
PCR-RFLP (GB-RFLP) markers for population (lake specific genetic
markers) and species assignment (species specific genetic markers).

2.3 Screen for RFLP alleles

To screen for RFLP alleles, we extracted 801 bp flanking the target
variants (focal marker \selectlanguage{ngerman}± 400 bp) using samtools faidx from the \emph{A.
citrinellus} reference genome (Kautt et al., 2020). Genotypes for all
loci were extracted from an in-house filtered statistically-phased
variant call format (vcf) file (Kautt et al., 2020) using tabix.
Nucleotide sequences and genotypes of variants (using \emph{vcfR} (Knaus
et al., 2020)) were loaded into R (R Development Core Team, 2019). For
each locus, we generated a sequence with the alternative allele
(alternative sequence) -- for this initial analysis we did not use
phased haplotypes. Next, we generated a list of the recognition sites of
237 commercially available restriction enzymes and performed \emph{in
silico} restriction digests of reference and alternative sequences for
each locus. For each locus, we identified restriction enzymes cutting
one of the alleles but not the other one and generated a list of all
enzymes, including information on how many restriction sites could be
found within the 801 bp reference sequence. For every locus, we then
sorted the restriction sites by number of cutting sites within the 801
bp sequence.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2} Workflow of the whole genome re-sequencing-based
design of PCR-RFLP markers (GB-RFLP). A. Markers were designed based on
genetic differentiation (F\textsubscript{ST}) or genome-wide
genotype-phenotype association (GWA) data of a 453-genome dataset using
pairwise ingroup--outgroup comparisons. B. Variants were screened for
RFLPs with one allele (but not the other) being cut by a restriction
enzyme. C. Target variants were filtered based on the presence of
additional restriction sites and additional SNPs within the restriction
site. Quality control was performed by plotting allele frequencies and
haplotype networks. D. Likelihoods that a genotype corresponds to a
population (or not) were calculated based on population-specific allele
frequencies. Percentage of correct assignments together with false
negative and false positive rates were based on bootstrapping of
genotypes (informed by empirical allele frequencies in the genomic
dataset). E. Primers were designed based on 801 bp sequences (core SNP
+/- 400 bp) in a way that the restriction enzyme would generate two
fragments with a \textasciitilde{}1:2 length ratio. F. PCR conditions
were tested and optimized. G. Restriction digest was performed on 8
ingroup samples and 5--8 outgroup samples. Genotypes were determined by
visual inspection. These data were used to calculate the number of
correct assignments as well as rates of false positives and negatives
(as for the bootstrapping dataset in D).

2.4 Filtering and quality control

We filtered our genetic marker set in several ways. First, we removed
variants that had missing data across the dataset and avoided markers
that were on the same chromosome (to avoid that the markers are in
linkage disequilibrium). Second, we selected 800 bp loci with a single
restriction sites (or a maximum of two for a few). Third, we avoided
genetic markers that had other variants in the flanking region (\selectlanguage{ngerman}±5 bp).
Based on the position of the restriction enzyme recognition sequence, we
extracted the exact haplotypes for all sequenced genomes, estimated
allele frequencies across the dataset and constructed haplotype networks
(Fig. S1 and S2). Two RFLP loci had one additional SNP in the
recognition sequence. However, these SNPs did not affect the RFLP. In
one case the second SNP was fully linked to the target SNP (Fig. S2 L).
In the other case there were two haplotypes that both were not cut by
the enzyme and both more common in the outgroup (Fig. S1 O).

2.5 Primer design

Primers were designed using Primer3 (Sequences are summarized in Table
S1). Briefly, we used the 801 bp sequence and designed the primers
asymmetrically around the target SNP, i.e., that approximately one third
(or two thirds) of the amplicon is 5' of the target SNP to avoid that
the fragments have the same size after digestion. If the previous
analysis indicated additional restriction sites in the 801 bp sequence
we confirmed that the restriction sites are either outside of the
amplicon, or at least do not complicate the detection of the RFLP (e.g.,
for the Lake Managua marker Chr3:33,260,056 the alleles resulted in 402,
106, and 50 bps or 448 and 106 bps long fragments).

DNA extraction and PCR

DNA was extracted as described previously (Kratochwil \& Rijli, 2014).
Briefly, we incubated fin or muscle tissue for 2h on a shaking incubator
at 55°C using 200 \selectlanguage{greek}µ\selectlanguage{english}g/ml proteinase K (Sigma) in 500 \selectlanguage{greek}µ\selectlanguage{english}l extraction buffer
(100 mM Tris--HCl pH 8.5, 200 mM NaCl, 5 mM EDTA, 0.2 \% SDS) in 1.5ml
tubes. Tubes were centrifuged at 12,000--16,000 \selectlanguage{ngerman}× g. 500 \selectlanguage{greek}µ\selectlanguage{english}l isopropanol
were added to the supernatant and centrifuged again. The supernatant was
removed, and the pellet was washed with 500 \selectlanguage{greek}µ\selectlanguage{english}l of 70\% ethanol. The
pellet was air dried and diluted in 500 \selectlanguage{greek}μ\selectlanguage{english}l 10 mM Tris-Cl, pH 8.5. We did
not use EDTA as it might affect restriction enzyme activity. PCRs were
performed for eight samples (with the exception of a few cases where we
did not have enough individuals) of the target population (ingroup) and
5--8 samples that did not belong to the target population (outgroup).
For the outgroups, we tried to use representatives of each lake (for the
lake comparisons, i.e., population comparisons) or other species living
in sympatry (for the species comparisons). We did not include samples
that were part of the previously-generated genome re-sequencing dataset.
For PCRs, we used 2\selectlanguage{greek}µ\selectlanguage{english}l template and the standard protocol of DreamTaq DNA
Polymerases (Thermo Fisher) with a total volume of 20\selectlanguage{greek}µ\selectlanguage{english}l, an annealing
temperature of 58\selectlanguage{ngerman}°C, 30 cycles and 30s extension time.

2.6 PCR-RFPL analysis

17 \selectlanguage{greek}µ\selectlanguage{english}l of the PCR amplicons were digested using the respective
restriction enzymes (AccI, AciI, AleI, AlwNI, ApoI, AvaI, BbvI, BccI,
BceAI, BclI, BsaAI, BsaBI, BsaI, BsiEI, BsmI, BspMI, BsrI, BstAPI,
HaeII, HaeIII, HgaI, HinfI, HpaII, HphI, NcoI, NspI, PleI, PstI, PvuII,
RsaI, Tth111I, XcmI; all from New England Biolabs) and recommended
concentrations. PCR products were digested for 4h or overnight to avoid
partial digestion and loaded on a 1\% agarose gel. Genotypes were
determined by visual inspection of gels and gel photographs (Fig. S3 and
S4). Sometimes, despite long digestion time, we observed incomplete
digestion. Genotypes were assigned as follows: 1) A lack of a smaller
fragment was always interpreted as a homozygous genotype. 2) A band at
the position of the uncut amplicon together with a second band was
interpreted as a heterozygous genotype, but only if the uncut band was
stronger in intensity (as the same number of longer-sized DNA molecules
results in a stronger signal). 3) Complete lack of a band at the size of
the uncut fragment or a band that had less intensity than the digested
fragments were interpreted as a homozygous genotype for the alternative
allele.

2.7 Estimation of RFLP marker quality

To estimate the reliability of individual markers, we first calculated
discovery and false discovery rates based on allele frequencies. To do
so, we first calculated allele frequencies by lake (lake comparisons) or
sympatric species (species comparisons). Based on the allele frequency,
we calculated expected genotype frequencies for all populations
individually (assuming Hardy--Weinberg equilibrium) (Table S2). To
evaluate the quality of the markers, we used these frequencies to
calculate the chance that an individual with a particular genotype is
from a particular population or not (without specifying which and
assuming a 50:50 chance that the individual is from the focal population
or not) (Table S2).

We used a bootstrap approach and randomly picked one million genotypes
(i.e. individuals) from the ingroup (target population) and one million
from the outgroups (again with an equal chance for each
population/sympatric species to be picked) based on their relative
frequencies and calculated how often a particular population/sympatric
species would have been assigned correctly (correctly assigned), how
often an ingroup individual would have been assigned to an outgroup
(false negative) and how often an outgroup individual would have been
assigned to the ingroup (false positive) (Fig. 3, Table S3). The same
approach was then used based on our PCR-RFPL data (Table S3): False
negatives were ingroup individuals that were incorrectly assigned as
outgroup individuals, false positives were outgroup individuals that
were incorrectly assigned as ingroup individuals. The proportion of
correctly assigned individuals was calculated by taking the mean of the
percentage of correctly assigned ingroup and the percentage of correctly
assigned outgroup individuals (to make these estimates comparable to the
estimates based on the bootstrapping dataset --- some analyses were
imbalanced with a different number of ingroup and outgroup individuals).

\textbf{3. Results}

3.1 GB-RFLP marker analysis for lake of origin assignment

As a first step, we chose RFLP markers that are specific for lakes
(i.e., allowing to distinguish one population {[}ingroup{]} from all
other populations {[}outgroup{]}) (Fig. 1 and Fig. 2). Although not all
of them are (yet) described as separate species, genetic differentiation
is very high among most of the lake populations --- mainly because the
crater lakes are completely isolated (and therefore not permitting gene
flow) and because their founder populations were small (30--850
individuals), resulting in strong genetic drift (and thereby more
alternatively fixed alleles) (Kautt et al., 2020). Most variants have
likely been recruited from the standing genetic variation present in the
source populations that was introduced into the crater lakes with the
colonizers, as there was too little time for a substantial amount
of\emph{de novo} mutations to occur --- all crater lakes have been
colonized less than 5,000 years ago (Kautt et al., 2020; Kautt,
Machado-Schiaffino, \& Meyer, 2016; Kautt et al., 2018). The two great
lakes Managua and Nicaragua are not isolated from each other, but
intermittently connected by a river (Rio Tipitapa) resulting in limited
ongoing gene flow (based on demographic models from (Kautt et al., 2020)
for \emph{A. citrinellus} approximately 1 in 12000 individuals per
generation).

To assess the performance of markers, we calculated their predictive
power (see detailed description in Materials and Methods section) based
on bootstrapping using allele frequencies from a previous genomic study
that was based on 453 re-sequenced genomes (Kautt et al., 2020) as well
as GB-RFLP assays of additional samples in this study (that were not
part of the resequenced genomes from the (Kautt et al., 2020) study, but
morphologically assigned to lakes and species). In line with the
discussed potential effect of the described demographic parameters
(Kautt et al., 2020), RFLP markers were most powerful for crater lakes.
For ten out of the twelve RFLP markers for Crater Lakes Apoyeque (Fig.
S3E), Apoyo (Fig. S3G, H), As. Managua (Fig. S3I, J), As. Le\selectlanguage{ngerman}ón (Fig.
S3K, L), Masaya (Fig. S3M, N), Tiscapa (Fig. S3O, P), and Xiloá (Fig.
S3Q) more than 90\% of the samples were correctly assigned to the lake
(Table 1). The second Apoyeque marker (87\%, Fig. S3F) and second Xiloá
marker (69\%, Fig. S3R) performed less well. For the great lakes Managua
(both 62\%) and Nicaragua (87\% and 69\%) the percentage of correctly
assigned samples was --- as expected --- lower (Fig. S3A--D), as many
variants are shared between the two great lakes or can be found at least
in one of the crater lakes (as they have been colonized from the great
lakes).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3} Comparison of the percentage of correctly assigned
samples (A, D), false negatives (B, D) and false positives (C, E) using
lake--specific GB-RFLPs (A--C) and species-specific GB-RFLPs. On the
left side of the plots are always estimates based on bootstrapping data
using allele frequencies from a previously generated genomic dataset are
on the left, data from GB-RFLP analyses on the right. Color-code in D--E
indicates the populations of Crater Lake Apoyo (red), Xilo\selectlanguage{ngerman}á (blue) and
the great lake species \emph{A. citrinellus} and \emph{A.
labiatus}(grey).

Most markers showed similar accuracy as in the bootstrapping dataset
that was based on allele frequencies in the respective lakes with 1/18
markers performing much worse (\textgreater{}20\% fewer correctly
assigned samples; Fig. S3R) in the RT-RFLP assay than in the
bootstrapping dataset (Fig. 3A--C). We also tested whether a combination
of two markers would improve accuracy. For two populations, Great Lake
Nicaragua (69\% instead of 62.5\%) and Crater Lake Masaya (100\% instead
of 94\%), we found a marginal improvement of correct assignments (Table
S4).

\textbf{TABLE 1} Tested RFLP markers, their location in the reference
genome, used restriction enzyme, quality of the marker, and correctly
assigned individuals (in \%). Quality was assessed based on a
combination of the predicted and tested number of correctly assigned
specimens (++++: \textgreater{}99\%, +++: \textgreater{}95\%, ++:
\textgreater{}90\%, +: \textgreater{}80\%, --: \textless{}80\%). Ingroup
means ``within test population'', outgroup ``not within test
population''. For lake markers (above line) the outgroup contains all
samples, for species markers (below line) only sympatric species within
the same respective crater lake.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Genotype (\% correctly assigned)}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Genotype (\% correctly assigned)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Marker (Test population \textbar{} coordinates)}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Enzyme}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Marker quality}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{ingroup}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{outgroup}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Managua \textbar{} Chr3:33,260,056\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HaeII\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
402/402 (99.8\%) 550/402 (96.3\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
550/550 (73.8\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Managua \textbar{} Chr21:22,155,441\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BclI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
400/400 (98.9\%) 529/400 (88.2\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
529/529 (82.1\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Nicaragua \textbar{} Chr11:21,205,346\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsrI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
395/395 (98.9\%) 544/395 (86.7\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
544/544 (85.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Nicaragua \textbar{} Chr17:561,899\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsaAI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
400/400 (99.2\%) 551/400 (89.2\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
551/551 (88.9\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Apoyeque \textbar{} Chr13:8,711,066\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
PleI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
607/607 (100\%) 607/367 (51.3\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
367/367 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Apoyeque \textbar{} Chr11:1,877,014\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
PvuII\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
585/585 (99.7\%) 585/363 (59.6\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
363/363 (99.9\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Apoyo \textbar{} Chr18:5,677,584\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HpaII\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
523/523 (100\%) 523/362 (59.4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
362/362 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Apoyo \textbar{} Chr11:1,173,519\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ApoI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
402/402 (100\%) 533/402 (86.5\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
533/533 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake As. Managua \textbar{} Chr7:6,889,897\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HaeII\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
480/480 (99.9\%) 480/361 (84.6\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
361/361 (99.7\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake As. Managua \textbar{} Chr23:32,083,942\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
NcoI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
196/196 (100\%) 557/196 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
557/557 (98.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake As. Le\selectlanguage{ngerman}ón \textbar{} Chr18:14,951,950\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
530/530 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
405/405 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake As. Le\selectlanguage{ngerman}ón \textbar{} Chr8:21,669,333\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BceAI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
500/500 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
500/188 (100\%) 188/188 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Masaya \textbar{} Chr20:9,539,893\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BccI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
400/400 (100\%) 553/400 (99.5\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
553/553 (84.7\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Masaya \textbar{} Chr11:20,264,687\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HaeIII\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
492/492 (100\%) 492/363 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
363/363 (82.4\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Tiscapa \textbar{} Chr18:17,567,949\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HphI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
411/411 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
528/528 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Tiscapa \textbar{} Chr14:11,451,235\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
AvaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
524/524 (100\%) 524/401 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
401/401 (99.8\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Xilo\selectlanguage{ngerman}á \textbar{} Chr3:13,299,069\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
543/543 (100\%) 543/408 (97.1\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
408/408 (99.2\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lake Xilo\selectlanguage{ngerman}á \textbar{} Chr14:29,887,480\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HgaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
506/506 (99.6\%) 506/377 (63.4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
377/377 (99.6\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. citrinellus / A. labiatus \textbar{} Chr8:11,495,129\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
AccI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
602/602 (98.5\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
602/351 (71\%) 351/351 (99.8\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. citrinellus / A. labiatus \textbar{} Chr8:11,381,985\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
NspI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
348/348 (99.5\%) 480/348 (57.4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
480/480 (99.1\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. astorquii \textbar{} Chr24:27,792,838\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsmI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
370/370 (100\%) 515/370 (96.8\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
515/515 (94.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
A. astorquii \textbar{} Chr11:4,091,881\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
AlwNI\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
536/536 (99.4\%) 536/389 (67\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
389/389 (99.2\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. chancho \textbar{} Chr17:14,664,535\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
PstI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
397/397 (99.9\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
520/397 (100\%) 520/520 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. chancho \textbar{} Chr3:25,801,703\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
AciI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
112/112 (99.5\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
488/112 (100\%) 488/488 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. flaveolus \textbar{} Chr15:22,314,952\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ApoI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
538/538 (95.2\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
538/185 (100\%) 185/185 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. flaveolus \textbar{} Chr20:3,776,504\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HinfI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
524/524 (98.3\%) 524/397 (74.7\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
397/397 (91.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. globosus \textbar{} Chr3:19,696,124\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsaBI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
536/536 (100\%) 536/197 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
197/197 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. globosus \textbar{} Chr12:23,301,195\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
HgaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
382/382 (100\%) 517/382 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
517/517 (99.4\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. supercilius \textbar{} Chr2:17,623,789\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
RsaI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
583/583 (100\%) 583/199 (96.4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
199/199 (96.1\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. zaliosus \textbar{} Chr24:14,867,912\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BbvI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
531/531 (99.9\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
531/413 (100\%) 413/413 (100\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. amarillo \textbar{} Chr6:811,192\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BsiEI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
514/514 (100\%) 514/169 (86.8\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
169/169 (99.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. sagittae \textbar{} Chr18:24,041,527\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BstAPI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
535/535 (99.3\%) 535/171 (71.7\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
171/171 (97.6\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. viridis \textbar{} Chr5:15,701,465\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
XcmI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
175/175 (100\%) 563/175 (96.2\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
563/563 (93.4\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. viridis \textbar{} Chr16:24,619,817\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BspMI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
566/566 (99.8\%) 566/185 (82.3\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
185/185 (97.8\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. xiloaensis \textbar{} Chr4:5,998,299\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Tth111I\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
512/512 (100\%) 512/155 (100\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
155/155 (98.5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
A. xiloaensis \textbar{} Chr8:24,801,784\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
AleI\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
593/593 (99.8\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
593/391 (53.4\%) 391/391 (99.9\%)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

3.2 GB-RFLP marker analysis for the assignment of sympatric crater lake
species

Next, we designed markers to distinguish the sympatric species of crater
lakes Apoyo (\emph{A. astorquii} , \emph{A. chancho} , \emph{A.
flaveolus} , \emph{A. globosus} , \emph{A. supercilius} and \emph{A.
zaliosus} ) and Xilo\selectlanguage{ngerman}á (\emph{A. amarillo} , \emph{A. sagittae} ,
\emph{A. viridis} and \emph{A. xiloaensis} ). Although all sympatric
species clearly form separate genetic clusters, the number of shared
alleles is extensive due to ongoing gene flow and/or recent divergence
(Kautt et al., 2016, 2018). Indeed, there are no alternatively fixed
differences, but we show that there are some nearly fixed ones based on
which we should reach 90 to 100\% accuracy --- however it is possible
that these predictions might be limited because our genomic samples do
not accurately enough reflect the actual population frequencies.

Overall, the quality of the species-specific RFLP markers (Fig. S4) was
much lower than the lake specific markers (Fig. S3). The percentage of
correctly assigned species ranged from 50\% (equal to a random
assignment) to 100\%. Only two markers achieved values above 90\%
(\emph{A. chancho} , Fig. S4F and \emph{A. viridis} , Fig. S4O). In
contrast to the lake-specific markers, species specific-markers showed a
much lower accuracy in the GB-RFLP assay compared to the bootstrapping
dataset with 12/16 samples having \textgreater{}20\% less correctly
assigned samples than in the bootstrapping dataset (Fig. 3D--F), which
is substantially different from the lake-specific markers (1/18). The
combination of markers did not improve the accuracy.

3.3 GB-RFLP marker analysis for the assignment of the great lake species
A. citrinellus and A. labiatus

Lastly, we designed markers for the two great lake species. As genetic
differentiation between these species is very low (0.015--0.02) compared
to the crater lake or allopatric species pairs (0.07--0.5), we used SNPs
that we have identified based on genome-wide association mapping for the
species--defining trait: lip size (\emph{A. labiatus} has thick,
hypertrophic lips that \emph{A. citrinellus} lacks) (Kautt et al., 2020;
Machado-Schiaffino et al., 2017). As the phenotype is largely driven by
a single locus, the variants were on the same chromosome (distance:
113kb). For the two species-specific markers that we designed, we
predicted an accuracy of 92 and 99\%, respectively. When tested, the
GB-RFLP markers obtained similar accuracies of 87 and 81\%, respectively
(Fig. S4a, b and Fig. 3d--f). Interestingly, even though both loci are
in proximity on the same chromosome, combination of both markers lead to
an accuracy of 100\%.

\textbf{4. Discussion}

Despite the increase in large-scale genomic data, PCR-RFLPs are still
widely used as diagnostic markers for the detection and species
assignment of parasites (Pegg et al., 2016), disease-causing pathogens
(Kato et al., 2019), microbiota (Baffoni et al., 2013), toxic
dinoflagellates (Lozano-Duque, Richlen, Smith, Anderson, \& Erdner,
2018) as well as animals using different tissue samples (Larraín,
González, Pérez, \& Araneda, 2019), scat samples (Mukherjee, Cn, Home,
\& Ramakrishnan, 2010) or environmental DNA (eDNA) (Clusa, Ardura,
Fernández, Roca, \& García-Vázquez, 2017). Once markers are identified
it is a fast, cheap, and reliable technique, but the design of PCR-RFLP
markers is usually time consuming, especially if many species and
populations are being compared and/or highly differentiated markers are
difficult to find. Here, we introduce a streamlined workflow to identify
PCR-RFLPs from whole genome re-sequencing data (GB-RFLPs). We note that
the same approach could be applied to RAD-seq, exome sequencing, or
other forms of targeted genomic data (Fig. 2).

Our study yielded promising results for diverged populations from
different lakes without ongoing gene flow, as represented by all seven
Nicaraguan crater lakes. While populations could be assigned with more
than 90\% accuracy to two crater lakes (Apoyeque and As. Managua), our
markers even yielded 100\% assignment accuracy for populations of the
remaining five crater lakes (Table S4). Results were less clear for
populations with ongoing gene flow and/or large population sizes, in
particular the Great Lakes Nicaragua and Managua, for which
population-specific markers performed poorly (between 62 and 86\%
assignment accuracy, Table S4). This was not unexpected as we know that
many alleles are shared between the great lakes and chances are high
that alleles found in one of the great lakes can at least be found in
one of the crater lakes that was colonized from this older source
population. Therefore, although we could assign individual samples using
whole-genome (Kautt et al., 2020) or RAD-seq data (Kautt et al., 2018),
single- or double marker approaches are not sufficient to unambiguously
differentiate between Lake Managua or Lake Nicaragua Midas cichlid
populations. A similar problem can be observed for the species--specific
markers for the species of Crater Lakes Apoyo and Xiloá. Also here,
species clearly form pronounced clusters using whole-genome (Kautt et
al., 2020) or RAD-seq marker sets (Kautt et al., 2016). Yet,
particularly in the sympatric scenario, where speciation occurred within
the last 5,000 years (Kautt et al., 2020) and in at least one case gene
flow persists (Kautt et al., 2020; Kautt et al., 2018), there might be a
strong ascertainment bias when focusing on single SNPs --- as it has
been intensively discussed for SNP datasets from humans (Clark, Hubisz,
Bustamante, Williamson, \& Nielsen, 2005). In line with this caveat,
indeed species-specific markers, with a few exceptions (\emph{A.
chancho} and \emph{A. viridis} ), performed less reliably (12/14 markers
have \textless{}90\% correct assignments; Table S4). Interestingly, the
genetic markers for the great lake species that show extremely low
genetic differentiation (FST\textasciitilde{}0.02) perform quite well
(87\% and 81\% correctly assigned), particularly when combined (100\%
correctly assigned) (Fig. S4). This can be explained by the different
approach that was taken here. We designed markers based on the cognizant
of our prior knowledge of the genomic basis of the species-defining
trait of \emph{A. labiatus} : hypertrophied, thick lips. As the trait
and the underlying associated SNPs (lip size variation links to only a
single locus in most populations; (Kautt et al., 2020)) are almost
alternatively fixed between these species, the marker seem to be most
powerful to reliably assign species. While signals for gene flow
between\emph{A. labiatus} and \emph{A. citrinellus} can be detected in
most of the genome, this is not true for the lip locus on chromosome 8,
where also the genetic markers are located.

Based on our results, we conclude that the design of markers based on
whole genome data is a powerful approach in an effort to distinguish
clearly differentiated species or populations or rare cases where we
have loci with high local differentiation that can be used as markers.
For populations with ongoing gene flow or instances where the population
constitutes the source population (both applies for Great Lakes
Nicaragua and Managua) the single/double-GB-RFLP marker approach
performs poorly --- likely because our genomic samples that we used for
the design of the markers gives only an estimate of the `true'
population allele frequencies (i.e., markers that seem perfect based on
our limited genomic data are in reality not markers that can
unambiguously differentiate populations). The same is true for sympatric
species (Crater Lake Apoyo and Xiloá) without localized differentiation
(as opposed to differentiation found between \emph{A. labiatus}
and\emph{A. citrinellus} ). To make reliable species identification
possible, multi-marker assays might be necessary for some instances.
These would likely not require the complete set of markers found via
RAD-seq or WGS analyses but could be applied with a selected set of
markers. Here, one approach would be to use those SNPs that load most
heavily on the first principal components of Crater Lakes Apoyo and
Xiloá (based on Kautt et al., 2020; Kautt et al., 2016, 2018) thereby
giving most power to distinguish the sympatric species. Such very
cost-effective targeted multi-SNP genotyping panels have been used, for
example, for 217 SNPs to assign salmons to particular populations
(Aykanat, Lindqvist, Pritchard, \& Primmer, 2016) and might be an
excellent approach, also for the Midas cichlid system. Lastly, this set
of RFLPs is now available as a resource for conservation purposes to for
example identify individual samples on fish markets, but also for cohort
and mark-recapture studies. This study therefore also presents a
workflow how use genomic resources for the generation of applicable
low-budget approaches for species assignment. Our study therefore
introduces a new methodological approach for such an effort, as
implementation of approaches that can help `real-world conservation
issues' often fail as previously discussed (Shafer et al., 2015).

In summary, we tested a set of 36 PCR-RFLP loci that we designed based
on whole genome re-sequencing data to genetically assign Midas cichlid
species and populations. While our analyses reveal limitations for the
assignment of species and populations with ongoing gene flow and/or
extreme recent divergence, genome-based designed PCR-RFLPs (GB-RFLP)
have great benefits when populations with robust genome-wide (allopatric
populations) or local differentiation (\emph{A. citrinellus} and\emph{A.
labiatus} ) have to be identified.

\textbf{Acknowledgements}

The authors want to thank Sylvia Finkernagel for her crucial technical
contributions. The authors also wish to thank the animal research
facility (TFA) of the University of Konstanz. This work was supported by
a European Research Council Advanced Grant (ERC ``GenAdap'' 293700) to
AM and grants from the Deutsche Forschungsgemeinschaft to AM and CFK (KR
4670/2-1, KR 4670/4-1), as well as a grant from the Baden-Württemberg
Foundation to CFK.

\textbf{Conflict of interest}

The authors declare that there are no conflict of interests.

\textbf{Authors contributions}

Sina Rometsch and Claudius Kratochwil collected data. Claudius
Kratochwil and Andreas Kautt conducted the analysis. Claudius Kratochwil
wrote the manuscript. All authors contributed to the manuscript. Axel
Meyer and Claudius Kratochwil designed and coordinated the study.

\textbf{Data Accessibility statement}

All data to reproduce these results are part of the submitted
manuscript. The genomic data this study is based on has been previously
described in Kautt et al. 2020 and has been deposited at
DDBJ/ENA/GenBank under accession JACBYM000000000 and PRJEB38173.

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