Title: Genetic landscape clustering of a large DNA barcoding
dataset reveals shared patterns of genetic divergence among freshwater
fishes of the Maroni Basin
Short Title: DNA barcoding of Maroni freshwater fishes
Authors: Yvan Papa1,2*, Pierre-Yves Le
Bail3, Raphaël Covain1
1Department of Herpetology and Ichthyology, Museum of
Natural History of Geneva, Geneva, Switzerland
2School of Biological Sciences, Victoria University of
Wellington, Wellington, New Zealand
3INRA, Fish Physiology and Genomics UR 1037, Rennes,
France
*corresponding author (email: yvanpapa@gmail.com)
Abstract
The Maroni is one of the most speciose basins of the Guianas and hosts a
megadiverse freshwater fish community. Although taxonomical references
exist for both the Surinamese and Guyanese parts of the basin, these
lists were mainly based on morphological identification and there are
still taxonomical uncertainties concerning the status of several fish
species. Here we present a barcode dataset of 1,284 COI sequences from
199 freshwater fish species (68.86% of the total number of strictly
freshwater fishes from the basin) from 124 genera, 36 families, and 8
orders. DNA barcoding allowed for fast and efficient identification of
all specimens studied as well as unveiling a consequent cryptic
diversity, with the detection of 20 putative cryptic species and 5
species flagged for re-identification. In order to explore global
genetic patterns across the basin, genetic divergence landscapes were
computed for 128 species, showing a global trend of high genetic
divergence between the Surinamese south-west (Tapanahony and Paloemeu),
the Guianese south-east (Marouini, Litany, Tampok, Lawa…), and
the river mouth in the north. This could be explained either by lower
levels of connectivity between these three main parts or by the exchange
of individuals with the surrounding basins. A new method of ordination
of genetic landscapes successfully assigned species into cluster groups
based on their respective pattern of genetic divergence across the
Maroni Basin: genetically homogenous species across the basin were
effectively discriminated from species showing high spatial genetic
fragmentation and possible lower capacity for dispersal.
Keywords
Cytochrome c oxidase subunit I, species identification, French
Guiana, Suriname, genetic divergence, ichthyodiversity.
Introduction
With over 32,000 known species, fishes represent more than half of the
total number of vertebrates. Although 3,900 new species have been
described during the last decade (Nelson, Grande, & Wilson, 2016) and
100 are described per year in the Neotropics only (Birindelli &
Sidlauskas, 2018), the ultimate goal of cataloguing all fishes is still
far from being achieved. DNA barcoding using the mitochondrial
cytochrome c oxidase I gene, or COI (Hebert, Cywinska, Ball, &
DeWaard, 2003), has proven to be a powerful tool to quicken and
facilitate the global effort of species identification and discovery
(Barrett & Hebert, 2005; Goldstein & DeSalle, 2011; Gomes, Pessali,
Sales, Pompeu, & Carvalho, 2015). This has led to the foundation of the
BOLD platform, an ever-growing COI database of animal organisms
(Ratnasingham & Hebert, 2007). In this context, several studies have
already been carried out on the megadiverse Neotropical freshwater fish
community with focuses on rivers and lakes of Brazil (de Carvalho et
al., 2011; Nascimento et al., 2016; Berbel-Filho et al., 2018),
Argentina (Rosso, Mabragaña, González Castro, & Díaz de Astarloa, 2012;
Díaz et al., 2016), Mexico, and Guatemala (Valdez-Moreno, Ivanova,
Elías-Gutiérrez, Contreras-Balderas, & Hebert, 2009). All of them have
emphasised the efficacy of DNA barcoding for this model with successful
species discrimination rates ranging from 90 to 100%. In this context,
the Gui-BOL project by Covain et al.
(http://www.boldsystems.org/index.php/MAS_Management_DataConsole?codes=GBOL)
is a work group affiliated to the FishBOL campaign (Ward, Hanner, &
Hebert, 2009) that aims at building a reference DNA barcode database for
all fishes of the Guianas.
With a length of 400 km, a 68,700 km2 catchment area
and a mean discharge of 1780 m3/s, the Maroni River is
one of the largest rivers of the Guianas (Amatali, 1993; Négrel &
Lachassagne, 2000). The Maroni and the Mana rivers, which share the same
mouth estuary, constitute the Western French Guiana freshwater ecoregion
characterised by its specific faunistic assemblage and its high
endemism, and splits the fish fauna of Suriname from the one of French
Guiana (Lemopoulos & Covain, 2019). It is also a region of faunal
exchanges between the adjacent Surinamese Ecoregion to the west and the
Central and Eastern French Guiana ecoregions to the east. Strong
faunistic relationships with the Amazon Basin have also been
highlighted, and the Maroni is supposed porous to fish dispersal from
tributaries of the Amazon to the south (Cardoso & Montoya-Burgos, 2009;
Fisch-Muller, Mol, & Covain, 2018; Lemopoulos & Covain, 2019). The
Maroni River and its tributaries, among other watersheds of the Guiana
Shield, have been extensively studied these past decades in an effort to
inventory and describe its ichthyofauna. The latest complete checklists
of freshwater fishes of French Guiana (Le Bail et al., 2012) and
Suriname (Mol, Vari, Covain, Willink, & Fisch-muller, 2012) reported
the occurrence of 336 fish species in the Maroni Basin, including more
than 250 species strictly restricted to freshwaters, from 15 orders and
more than 50 families. This makes it the most speciose river basin of
both countries, hosting one third of the total number of fish species of
the Guiana Shield (Vari, Ferraris, Radosavljevic, & Funk, 2009).
However, several species catalogued in these checklists still have an
undefined status, while some other have been named with doubts on their
actual taxonomical identity. Some species identified based on
morphological and meristic methods display an intriguing patchwork
distribution across the Guianas (e.g. Leporinus nijssenidescribed from Suriname River with occurrences reported in Nickerie
River to the west of Suriname and Oyapock River to the east of French
Guiana (Mol, 2012)). Furthermore, few studies have used molecular
methods to better apprehend the faunistic richness of this basin, with
the exceptions of some enzymatic and molecular sequencing approaches onLeporinus (Planquette & Renno, 1990) and Loricariidae (Covain et
al., 2012, 2016; Fisch-Muller, Montoya-Burgos, Le Bail, & Covain, 2012;
Fisch-Muller et al., 2018; Weber, Covain, & Fisch-Muller, 2012) or
environmental DNA surveys (Cilleros et al., 2019; Murienne et al.,
2019). These elements make the stability of these reference lists
doubtful and incomplete, while they are essential for the management of
these natural areas currently facing growing anthropic pressure.
While the accurate assessment of species richness is a fundamental
prerequisite in the effective study and management of this megadiverse
river system, the extensive genetic and geospatial data provided by a
sampling campaign of this scale can also put into light global patterns
of genetic connectivity among populations. As an example, observation of
molecular data from the Guyanancistrus genus seems to indicate
that the Maroni could be divided into a West (Suriname) and an East
(Guianese) assemblage with lower genetic connectivity between them
(Fisch-Muller et al., 2018). Finding evidence for high genetic
divergence between these regions regardless of distance could strengthen
this hypothesis. There is a growing number of methods and tools
available to use genetic and spatial information in order to explore the
biogeographical patterns of species and populations (Chan, Brown, &
Yoder, 2011). One of them is the mapping of spatial patterns through
”genetic landscapes” (Manel, Schwartz, Luikart, & Taberlet, 2003). This
method can assist in identifying divergence hotspots (Wood et al., 2013)
and potential barriers to gene flow (Vodă, Dapporto, Dincă, & Vila,
2015) and has already been used on genetic distances between COI
sequences to provide a visual framework of genetic variation between
organisms across space (Arbeláez-Cortés, Milá, & Navarro-Sigüenza,
2014; Mamos, Wattier, Burzyński, & Grabowski, 2016).
The present study makes use of a large new dataset of DNA barcodes to
(1) assess the validity of the current references on the Maroni’s
freshwater fish species richness, (2) reveal the presence of genetic
heterogeneity in order to flag potential cryptic species, and (3)
investigate spatial genetic distribution within species that may reveal
obstacles to connectivity across the watershed, or the presence of
recent colonisation from adjacent rivers. The last point was approached
with a new method of multivariate clustering of genetic landscapes.
Materials and Methods
Ethics statement
No endangered or internationally protected species at time of collection
(local restrictions, IUCN or CITES listed species) were concerned by the
study. Most specimens and tissue samples were obtained from Museum
collections and/or by local populations or fishermen. No experimentation
was conducted on live specimens. For specimens and associated tissue
samples obtained from the field, specimens were collected and exported
with appropriate permits: Ministry of Agriculture, Animal Husbandry and
Fisheries to export fishes from Suriname in 2008. Material obtained from
the Parc Amazonien de Guyane (PAG) in 2014 and 2015 was collected under
the direct supervision of PAG authorities. When collecting occurred in
non-protected areas of French Guiana, sampled specimens were equally
declared to the French DEAL (French environmental protection ministry)
before export. Immediately after collection, fish were anesthetised and
sacrificed using water containing a lethal dose of eugenol (essential
oils of cloves). All the work has been conducted in accordance with
relevant national and international guidelines, and conforms to the
legal requirements (Directive 2010/63/EU of the European Parliament and
of the Council on the protection of animals used for scientific
purposes, the Swiss ordinance OPAn 455.1 of OSAV, and recommendations
and regulations of DT-OCAN).
Specimens collection and sampling
area
All 1,284 specimens were collected on 83 sampling points across the
Maroni River (1 to 18 specimens per location per species) and some of
its main tributaries including Tapanahony, Paloemeu, Lawa, Litany,
Tampok and Marouini rivers, as well as some more remote headwaters
locations like Mitaraka Mountains or Saül uplands (Figure 1). Specimens
were collected between 1997 and 2015 as part of a broader project on the
ichthyological biodiversity and the ichthyofaunistic assemblages of the
Guianese ecoregions (sensu Lemopoulos & Covain, 2019). A piece
of fin or muscle tissue was collected from each specimen and stored in
80% ethanol at -20°C. To conform to the Barcoding Of Life
recommendations (Ratnasingham & Hebert, 2007) and provide vouchered
references, 764 specimens were fixed in 5% formaldehyde at room
temperature or in ethanol at -20°C for long-term conservation and
deposited in the MNHG fish collection. Six specimens were stored in the
Museum of National History of Paris, six in Auburn University of
Alabama, and two in the Academy of Natural Sciences of Philadelphia. The
remaining 506 specimens are vouchered by tissue only, sometimes
completed by a photograph. Most of these were large specimens returned
to local fishermen.
Fish preliminary identification and Maroni species
coverage
Fish were morphologically identified at the species level based on
literature (Planquette, Keith, & Le Bail, 1996; Keith, Le Bail, &
Planquette, 2000; Le Bail, Keith, & Planquette, 2000; Mol, 2012). Fish
taxonomical classification follows Le Bail et al. (2012) and Mol et al.
(2012), with taxonomic updates following Fricke, Eschmeyer, & Van Der
Laan (2019) (Table 1). Out of the 264 freshwater fish species certainly
known to occur in the Maroni Basin according to the last checklists (Le
Bail et al., 2012; Mol et al., 2012), 174 were collected, representing
65.91% of the total number of already known species (Supplementary
Material 1). Additionally, five species collected during this study were
not known to occur or were considered dubious in the basin in the 2012
checklists (Charax gibbosus , Guyanancistrus megastictus ,Krobia guianensis , Poecilia bifurca, and Tomeurus
gracilis ).
Extraction, PCR amplification, and DNA
sequencing
Total genomic DNA was extracted with the E.Z.N.A. Tissue DNA Kit (Omega
Biotek) following the instructions of the manufacturer. The PCR
amplifications of COI were carried out using the Taq PCR Core Kit
(Qiagen) following Covain et al. (2012). Primers and their taxonomic
targets are listed in Table 2. Cycles of amplification were programmed
following the profile: (1) 3 min at 94 °C, (2) 30 s at 94 °C (initial
denaturing), (3) 40 s with annealing temperature ranging between 51 and
54 °C depending on primers used, (4) 40 s to 1 min at 72 °C
(elongation), (5) 10 min at 72 °C (final elongation). Steps 2–4 were
repeated 40 times (42 with 5COI-F / COI-R3). Some samples were amplified
by Touchdown PCR following the protocol of Korbie & Mattick (2008).
Purification and sequencing of PCR products were performed at Eurofins
Genomics (France) and Macrogen Europe (The Netherlands) using Sanger
method (Sanger, Nicklen, & Coulson, 1977). DNA sequences were edited
using BioEdit 7.2.5. (Hall, 1999) and aligned with MUSCLE (Edgar, 2004).
Edited sequences were deposited on BOLD with corresponding vouchers.
Fish molecular identification and barcode
analysis
The barcode sequence data was used in conjunction with morphology and
known geographical distribution (an integrative approach similar to
Gomes et al. (2015) and Pugedo, de Andrade Neto, Pessali, Birindelli, &
Carvalho (2016) to flag potential identifications errors, identify
unknown specimens (e.g. juveniles), and detect putative cryptic species.
Molecular identification was performed with BOLD Identification System
(www.boldsystems.org), BLAST search (Altschul, Gish, Miller, Myers, &
Lipman, 1990) on NCBI (http://www.ncbi.nlm.nih.gov/BLAST) and by
neighbour-joining tree based identification. Specimens showing a
combination of unexpected intra-specific genetic divergence (both in our
dataset and on the global BOLD database), overlooked morphological cues,
and support from distribution patterns were flagged as putative cryptic
species, given provisional new names, and treated as distinct species in
all subsequent analyses.
All genetic distance analyses were performed under the Kimura
two-parameter (K2P) substitution model (Kimura, 1980), as it is a
standard metric in barcode studies (Ward, 2009; Díaz et al., 2016).
Sequence divergences at the Species, Genus, and Family level were
estimated using the BOLD Distance Summary Tool (BOLD Aligner, pairwise
deletions). BOLD’s Barcode Gap Analysis (same parameters) was used to
investigate species who do not comply with barcode gap (i.e. for which
distance to the Nearest Neighbour (NN) is lower than the standard
barcode threshold of 2% or lower than the maximum intra-specific
distance). The BIN Discordance Report tool was used to analyse the final
dataset using the clustering method provided by BOLD: the Barcode Index
Number (BIN, Ratnasingham & Hebert, 2013) which is the standard method
in barcode studies to attribute each specimen to a new or pre-existing
operational taxonomic unit. A neighbour-joining dendrogram of
BOLD-aligned K2P distances was built with BOLD’s Taxon ID Tree and
modified with MEGA 7.0.26 (Kumar, Stecher, & Tamura, 2016) to visualise
total clustering of BINs and species.
Genetic divergence landscape
analysis
Patterns of genetic divergence among species were represented by genetic
landscapes using the Inverse Distance Weighting (IDW) interpolation as
in Vandergast, Perry, Roberto, & Hathaway (2011). We focused on
intra-specific genetic divergence instead of genetic variation because
of the relatively low number of specimens per species collected at each
sampling point and used K2P distances as the metric to stay consistent
with the barcoding approach. Genetic landscape analysis (Supplementary
Material 2) was performed in R v3.5.0 (R Core Team, 2018) based on the
location data (catch coordinates) of the 1,284 specimens and their
respective barcode sequences. In order to take into account the
intra-locality sequence variation of specimens, all equal pairs of
coordinates within species were added a constant term of one or more
fifth digit on their latitude value. This transformation only changed
the recorded sampling point by a few meters, which conforms to the
reality of the fishing area on the field. Matrices of pairwise K2P
distances were computed among specimens within each species with ape 5.3
(Paradis & Schliep, 2018). The 55 species with less than three
specimens and the 16 species with only one sampling point were discarded
from landscape analysis.
A Mantel test was performed with 9,999 permutations using ade4 1.7-13
(Dray & Dufour, 2007) to evaluate absence of relationships between
geographic and non-null K2P genetic distances for each species. All
obtained p-values were corrected for multiple comparisons with the false
discovery rate method (Benjamini & Hochberg, 1995). If a species showed
a corrected p-value lower than 0.05, actual K2P distances were corrected
by fitting a linear model between genetic and geographic distances,
residuals were then used to compute the IDW interpolation to remove
effect of inter-location distances in genetic distance. If the p-values
were greater or equal to 0.05, IDW was performed directly on uncorrected
K2P distances.
For IDW, midpoints coordinates between pairs of samples locations
connected by a Delaunay triangulation (i.e. the smallest network with
non-overlapping edges) were extracted for each species with the phylin
package 2.0 (Tarroso, Carvalho, & Velo-Antón, 2019). Respective K2P
distance values (or geographically corrected K2P distances) between pair
of samples were assigned to each respective midpoint, and IWD (as
implemented in phylin with default weighting method “Shepard”) was
used to interpolate the Z values of each coordinate in the grid within
the whole species’ sampling area. Real and interpolated K2P distance
values or residuals were normalised to enable comparison of
heterogeneous genetic divergence rates among species and then projected
on basin maps using the raster package 2.9-5 (Hijmans, 2019). IDW
interpolations of species for which all pairwise distances were equal to
zero were calculated and projected directly using these raw zero values.
The global patterns of genetic divergence across the basin
(“multispecies landscape”) were visualised by projecting the
arithmetic mean of all previously generated normalised overlapping
surfaces.
We then sought to ordinate genetic landscapes based on similar genetic
divergence patterns in order to explore shared trends among species and
ultimately assign each species to a broader cluster group. Ordination
was performed through Principal Component Analysis (PCA). For this, a
genetic landscape data table was compiled with Z values as estimates of
genetic divergence for each species listed in rows and each grid cell in
the basin in columns, resulting in a table of 129 rows and 60,675
columns. Because genetic landscapes had different sizes depending of the
species sampling area, many Z values were missing in the table. These
missing values were replaced by the values for the corresponding
coordinate pair from the multispecies landscape with MCRestimate package
2.38-0 (Johannes et al., 2018). The multispecies landscape was then
treated as the “null genetic landscape” of the basin. The PCA was
performed using the variance-covariance matrix of the data table using
ade4 1.7-13. Since the variables consisted of tens of thousands of
coordinate points, their projection on the principal components were
visualised as points with two separate colour gradient codes
respectively representing latitudes (from red to orange to yellow) and
longitude (purple to blue to green). In order to cluster genetic
landscapes into general patterns, the canonical pairwise distance matrix
of the row coordinates of informative axes of the PCA was submitted to
an agglomerative hierarchical clustering analysis. These axes were
chosen in order to explain most of inertia without being affected by
artefactual effects. The suitability of eight clustering methods were
compared, and the Weighted Pair Group Method with Arithmetic Mean
(WPGMA) algorithm (McQuitty, 1966) on Euclidean distances was retained
for having the highest cophenetic correlation coefficient of 0.83. The
package pvclust 2.0-0 (Suzuki & Shimodaira, 2006) was used to calculate
two types of p-values on each cluster node via multiscale bootstrap:
Approximately Unbiased (AU) (Shimodaira, 2002, 2004) and Bootstrap
Probability (BP) (Efron, 1979; Felsenstein, 1985). The analysis was
performed using 999 pseudoreplicates. The “mean landscapes” of the
main resulting clusters were visualised by projecting the arithmetic
mean of all genetic landscapes nested in said clusters.
Results
Barcode dataset
The 1,284 COI sequences collected from the Maroni Basin generated a
final barcode data library of 199 species (125 Genera, 36 Families, and
8 Orders) after integrative re-identification (Figure 2). Five out of
these species were flagged for being putative re-identifications from
the latest Maroni checklists, with the nominal species being presumably
absent from the basin (Table 3), and 20 are putative cryptic species
that were revealed by our integrative approach for identification, among
which 13 are already BIN-concordant on the BOLD database (Table 3). All
species in the data set belong to the class Actinopterygii with the
exception of Potamotrygon marinae from the class Elasmobranchii.
The number of individuals per species ranged from 1 to 59 (mean=6.45)
with 169 species represented by more than one specimen (84.92%). All
final sequences were devoid of stop codons, insertions, or deletions.
Mean nucleotide frequencies for the total alignment were 24.7% adenine,
27.0% cytosine, 18.4% guanine and 29.9% thymine. The mean K2P genetic
divergence was 0.32% within species, 12.46% within genus, and 19.61%
within family (Table 4).
Species delimitation
Barcode Gap Analysis showed a distance to the Nearest Neighbour (dNN)
greater than 2% and greater than the maximum intra-specific distance
for 187 species out of 199. Low dNN (<2%) was observed in
only six pairs of species: Ancistrus cf. leucostictus /
Ancistrus temminckii (dNN=1.84%), Corydoras geoffroy /
Corydoras aff. geoffroy (dNN=0.12%), Guyanancistrus
brevispinis / Guyanancistrus nassauensis (dNN=0.62%),Hypostomus plecostomus / Hypostomus watwata (dNN=1.94%),Melanocharacidium sp. 1 / Melanocharacidium sp. 2
(dNN=0.97%) and Pimelodella geryi / Pimelodella aff.geryi (dNN=1.47%). Among them, maximum intra-specific distance
exceeded or was equal to their dNN in the following two species only:Corydoras geoffroy (NN=Corydoras aff. geoffroy ) andGuyanancistrus brevispinis (NN=Guyanancistrus nassauensis )
(Table 5).
The neighbour-joining dendrogram showed no overlap between any species,
except only for Corydoras aff. geoffroy , which was nested
within the Corydoras geoffroy cluster (Figure 3, Supplementary
Material 3). Two hundred twenty nine BINs were assigned by BOLD to the
total number of samples in the dataset, among which 171 were
taxonomically concordant (represented by 1,195 specimens) and 55 were
singletons (i.e. were assigned to only one specimen from this dataset).
The remaining three BINs (32 specimens) showed some discordance and were
represented by three pairs of species that shared the same BIN in our
dataset: Guyanancistrus brevispinis / Guyanancistrus
nassauensis , Corydoras geoffroy / Corydoras aff.geoffroy and Melanocharacidium sp. 1 /Melanocharacidium sp. 2 (Figure 3). Two specimens (oneSerrasalmus rhombeus and one Peckoltia otali ) were not
assigned any BINs, probably due to a high proportion of ambiguous bases
for the latter (14Ns/652bp). However, both specimens clustered perfectly
with their conspecifics, so they can be reasonably considered well
identified.
Among concordant BINs, species that were assigned more than one BIN in
our dataset are: Aequidens tetramerus , Bryconamericus
guyanensis , Characidium zebra , Cleithracara maronii ,Crenicichla multispinosa , Eigenmannia virescens ,Erythrinus erythrinus , Farlowella reticulata ,Gymnotus anguillaris , Hemiodus huraulti , Hoplias
malabaricus , Hypopygus lepturus , Ituglanis amazonicus ,Moenkhausia moisae , Moenkhausia oligolepis ,Nannostomus bifasciatus , Pimelodella leptosoma ,Poptella brevispina with two BINs; Cetopsidium orientale ,Crenicichla albopunctata, Helogenes marmoratus ,Hyphessobrycon roseus , Phenacogaster wayana ,Pimelodella cf. cristata with three BINs, andGasteropelecus sternicla with four BINs (Figure 3, Supplementary
Material 3). This result suggest that these 25 species could hide some
unexpected diversity and that some of them may be potential cryptic
species complexes. However, they were not flagged as such for the
current study due to the lack of strong morphological and / or molecular
evidence.
Genetic divergence landscape
analysis
A genetic landscape was produced for 128 species out of 199
(Supplementary Material 3). Twenty-one of them showed no genetic
divergence across the basin, while 107 displayed various patterns.
Fifteen out of these 107 species showed a significant relationship
between genetic and geographic distances after false discovery rate
correction (Bryconops affinis , Bryconamericus guyanensis ,Corydoras geoffroy , Crenicichla multispinosa ,Cteniloricaria platystoma , Gasteropelecus sternicla ,Geophagus harreri , Harttia guianensis , Lithoxusaff. planquettei , Metaloricaria paucidens ,Moenkhausia grandisquamis , Myloplus ternetzi ,Poptella brevispina , Pseudancistrus barbatus andRineloricaria aff. stewarti 3). Accordingly, Z values for
these species were computed on K2P distances between residuals of the
linear model instead of the raw K2P distances. Z values of the
multispecies landscape ranged from 9.43E-06 to 0.81 and number of
species contributing to the calculation of each cell ranged from one to
95 (Figure 4). The lowest and highest mean genetic divergences were
located at specific points of the basin with a low number of species
sampled, i.e. the mouth of the Maroni River and the Saül uplands. The
remaining of the map had a better species coverage and showed that the
highest mean genetic divergences were observed between the West Upper
Maroni (Tapanahony and Paloemeu rivers) and the East Upper Maroni (Lawa,
Litany, Tampok and Marouini rivers), as well as between the West Upper
Maroni and the Lower Maroni and Nassau Mountains. Relatively high
divergence was also observed between the East Upper Maroni and the Lower
Maroni, as well as between the Tampok and the Marouini. As a whole, the
basin was divided into three large regions displaying high genetic
divergences between each other without influence of geographic
distances: the West Upper Maroni, the East Upper Maroni, and the Lower
Maroni, with West Upper Maroni being the most divergent of the three.
Ordination of genetic landscapes
patterns
Most of the genetic landscape structures were explained by the first
three axes of the PCA, which accounted for 40.61%, 16.8%, and 7.70%
of total inertia respectively (Figure 5). The multispecies landscape
computed on the mean Z values of all analysed species was always
projected at the centre of axes and effectively acted as a “null
landscape” from which other landscapes were ordinated. Axis 1 aligned
species with globally low genetic divergence across the basin in
negative values (Pimelodus ornatus , Hypostomus
gymnorhynchus , Ageneiosus inermis , Serrasalmus
rhombeus …) with species with high genetic divergences in
positive values (Helogenes marmoratus , Metaloricaria
paucidens , Bryconamericus guyanensis , Curculionichthyssp. Maroni…). Examination of variables revealed that most
loadings were positive with only few of them around zero or weakly
negative, revealing potential size effect in the ordination along the
first axis (Figure 6). Accordingly, this axis was discarded from further
clustering analyses. Axis 2 mostly expressed a latitudinal influence in
landscape ordinations. Landscapes projected on negative values of this
axis had lower genetic divergence in the north. They included high
coverage landscapes with global genetic divergence that was either
mostly low (Auchenipterus nuchalis , Serrasalmus
rhombeus …) or mostly high (Platydoras costatus, Myloplus
ternetzi… ). Conversely, landscapes with higher genetic
divergences in the north were projected in positive values of axis 2,
including high coverage landscapes like Gasteropelecus sterniclaor Leporinus fasciatus and low coverage ones likeProchilodus rubrotaeniatus or Leporinus granti (Figure 5).
Projection of variables supported the split between the south (red) in
negative values and the north (yellow) in positive values (Figure 6).
Axis 3 displayed a pattern of opposition between higher genetic
divergence in the west (Pimelodella leptosoma ,Guyanancistrus brevispinis , Cteniloricaria platystoma ,Curculionichthys sp. Maroni…) in negative values and
higher genetic divergence in the east (Triportheus brachipomus ,Jupiaba keithi , Semaprochilodus varii …) in positive
values (Figure 5). This pattern was supported by variable projections
with most western longitudes (green) projected in negative values and
eastern longitudes (purple) in positive values (Figure 6).
The WPGMA of axes 2 and 3 of the PCA clustered the 129 genetic
landscapes (including the multispecies) into nine main cluster groups
noted A to I (Figure 7). Twenty-eight of the clusters, comprising 106
landscapes, were supported by internal nodes all having an AU greater
than 95%, but nodes supports were strong overall with most of the nodes
having an AU greater than 80%. Group A included two small strongly
supported clusters. The first cluster included Guyanancistrus
brevispinis and Pimelodella leptosoma , the two species with the
lowest loadings on axis 3. As stated above, both landscapes displayed a
strong genetic isolation between the Upper West and the rest of the
basin. The second cluster included the four landscapes with the lowest
loadings on axis 2, i.e. landscapes where the global genetic divergence
across the basin is mostly low, but the highest divergence is in the
south (Auchenipterus nuchalis , Serrasalmus rhombeus ,Hypostomus gymnorhynchus …). Group B was a strongly
supported cluster of the four species with the greatest genetic
divergence in the north, which accordingly had the highest loadings on
axis 2 (Gasteropelecus sternicla, Helogenes marmoratus,
Eigenmannia virescens, and Leporinus fasciatus ). Group C
included 15 species that also projected on positive values of axis 2 and
displayed particularly high genetic homogeneity in the south, with the
notable exceptions of Curculionichthys sp. Maroni andCteniloricaria platystoma , which should probably be part
of their own separate cluster (cf. genetic landscape patterns in
Supplementary Material 4). Group D grouped together the species that
displayed the highest genetic heterogeneity among southeast locations
compared to the rest of the basin, and had the highest loadings on axis
3 (e.g. Ageneiosus inermis , Semaprochilodus
varii …). On the opposite, species in group E all showed the
highest genetic divergence between the south-west and the rest of the
basin (e.g. Moenkhausia oligolepis , Bryconamericus
guyanensis , Anostomus brevior …) with negative loadings on
both axes 2 and 3. Group F contained landscapes which all projected on
negative values of axis 2, with patterns very similar to group E in that
they all showed a high Upper West / Upper East divergence (e.g.Hypopomus artedi , Pimelodella cf. cristata… )
but also included more patchy patterns (e.g. Moenkhausia
intermedia , Nannostomus bifasciatus …). Group G is a small
strongly supported cluster characterised by a high genetic homogeneity
in the Upper East, as shown by low values on axis 2.
Groups H and I consist of 63 landscapes (almost half of the total) that
were either too small in area, displayed unusual patterns, or consisted
of species that were from under-sampled parts of the basin (i.e. not the
mouth, the Tapanahony or the Saut Wayo / Langa Sula region). As
expected, the multispecies landscape was part of one of these groups
(group H), being the null landscape from which all others where compared
in the multivariate analysis. Accordingly, the six species with the
smallest sampling areas were grouped with it in a strongly supported
cluster. Landscapes from groups H and I were all projected close to the
centre of axes two and three (but not always on axis 1), and nodes
within these groups were generally less well supported than in the rest
of the tree.
Discussion
Checklists updates: Re-identified
species
Five species that were thought to be present in the Maroni according to
the current checklists are now strongly suspected to be different
species after examination of their respective COI haplotype compared to
con-specific individuals from other basins. We thus flagged the
specimens caught in the Maroni with the following provisional names:Imparfinis aff. pijpersi , Lithoxus aff.planquettei , Loricaria aff. nickeriensis ,Pachypops aff. fourcroi , and Pimelodella aff.cristata (Table 3). Pachypops aff. fourcroi has
black spots on the back that are not present in the nominal species.Pimelodella aff. cristata is one of the two putative
species that was probably misidentified as Pimelodella cristatain the Maroni (the other being P . cf. cristata that is
here considered as a cryptic species of P. aff. cristata ,
see below). Tree based identification on the global database shows that
this cluster of specimens is widely distributed (several rivers of
Suriname and French Guiana) and actually branches far from P.
cristata and P. cf. cristata , with its sister barcode
species being Pimelodella macturki. However, although allP . aff. cristata on BOLD share the same BIN, it also
includes two specimens identified as P. vittata and sevenPimelodella sp., making it currently discordant.
Checklists updates: Putative new cryptic
species
The 13 putative new species that were BIN concordant on the BOLD
database (Table 3) are as follows: (1) Ancistrus sp. is
represented by one specimen from the remote Mitaraka Mountains that had
a similar morphology to Ancistrus temminckii . However, Tree Based
Identification analysis (Figure 3, Supplementary Material 3) placed it
as a distinct sister group to a clade composed of Ancistrus cf.leucostictus and A. temminckii. (2) Bryconops aff.melanurus : two specimens from Langa Sula and Saut Wayo (Marouini)
showed a dNN greater than 12% with the four Bryconops melanurusthat were caught in the same two locations. They share the same BIN with
four specimens from Sinnamary, Suriname and Mana rivers that were also
re-identified as B. aff. melanurus on BOLD database. (3)
The eight specimens of Corydoras aff. guianensis ,
differing from the nominal species by a faint black margin along the
dorsal-fin spine, constituted the sister group of the blunt snoutedCorydoras aff. breei (lineage 9 in Alexandrou & Taylor
(2011)) and the nominal species C. guianensis in our dataset with
a minimum K2P distance of respectively 5.49% and 5.13%. The fiveC. guianensis specimens available were caught in the Tapanahony
and Paloemeu rivers in Suriname (type locality of C. guianensisbeing Nickerie River in Suriname) while the eight C. aff.guianensis came from the east tributaries in French Guiana. (4)Cyphocharax aff. spilurus 2 was first morphologically
identified as C. spilurus but showed a dNN of 13.84% with all
other C. spilurus and 8.59% with C . cf. spilurus(see below), while the shortest dNN was 7.40% with C.
biocellatus. This specimen was caught in the same location as sevenC. spilurus in the Tapanahony, while the two other C.
spilurus were caught in the east tributaries. It displays a large dark
spot on the caudal peduncle, and shares a BIN on the BOLD database with
another Cyphocharax aff. spilurus 2 from Sipaliwini River
(Suriname). Although not present in the two checklists, C. aff.
spilurus 2 was already known to occur in the Maroni and suspected to
live in sympatry with C. spilurus (Planquette et al., 1996). This
putative species is different from C. aff. spilurus sensuLe Bail et al. (2012) described from Approuague and Iracoubo. (5) The
three juvenile specimens of Guianacara sp. Tapanahony showed a
minimum dNN of 3.26% with G. owroewefi from our dataset. They
were all caught in the Paloemeu River in sympatry with G.
owroewefi . None of them display the diagnostic pattern of G.
oelemariensis described from upper Marowijne River in Suriname and were
thus treated as an unknown species. (6) Hemigrammus aff.guyanensis from Litany River likely corresponds to the form
illustrated for H. guyanensis in Planquette et al. (1996).
However, the true H. guyanensis is illustrated in Mol (2012) asH . aff. ocellifer . Specimens from Tapanahony River
correspond to this form and are here re-identified as H.
guyanensis . They are characterised by a longitudinal black line, and a
distinct iridescent spot on upper part of caudal peduncle. Specimens
from Litany lack this last characteristic. (7) Leptocharacidiumsp.: This non-identified specimen caught in Wayu Camp (Paloemeu) and
firstly assigned to Melanocharacidium was peculiar in showing a
very high genetic distance from all other closest species in our dataset
(dNN=17.71% with Parodon guyanensis instead of another
Crenuchidae species) and on the BOLD database (dNN=14.04% with aCharacidium sp. from Bolivia). It also has an unusual morphology,
i.e. a small operculary spot and a longitudinal line, but lacks the
diagnostic two unbranched rays in pelvic fins. It is tentatively placed
in Leptocharacidium , awaiting further analyses. (8) Six specimens
first identified as Moenkhausia collettii from Litany and Sector
Apsik were renamed Moenkhausia aff. collettii after
showing a very high dNN of 15.33% with previously recorded M.
collettii from French Guiana in our dataset. (9) Nannacara sp.
Litany was represented by a single specimen in his own BIN. It is the
first occurrence of a Nannacara found in the Maroni basin and
showed a minimum dNN of 10.49% with other Nannacara from the
BOLD database, including the two Nannacara species that occur in
French Guiana: N. aureocephalus and N. anomala . (10) The
ten Pimelodella geryi specimens clustered into two clear groups
in the neighbour-joining tree (Supplementary Material 3). The first
group included specimens from the west (Tapanahony) and the east of the
basin, while the second cluster included only specimens from the west.
Although the dNN between these two groups is relatively low (1.47), the
second cluster exclusive to the west basin has its own concordant BIN
and was flagged as a potential P. aff. geryi . (11)Pimelodella cf. cristata : Although Pimelodella
cristata is present in the Maroni checklists, tree based identification
of available close specimens from BOLD database coupled with observation
of catch localities seem to indicate that the nominal species, described
from Takutu River in Guyana, is present in the Corantijn River in
Suriname but not in any basin of French Guiana. Instead, two potentially
undescribed species formerly identified as P. cristata occur in
the Maroni: Pimelodella cf. cristata andPimelodella aff. cristata . According to Tree Based
Identification, Pimelodella cf. cristata , which is present
in several rivers of Suriname as well as the Maroni, is the closer of
the two from the nominal P . cristata (although its NN is
actually Pimelodella sp.). It is represented by five specimens in
our dataset that are split in three concordant BINs. (12) One
unidentified Pimelodella sp. from Paloemeu shows a high distance
with its NN (P . geryi , 8.59) and its sister group
(P . megalops , 9.58) in our dataset. Tree based
identification on BOLD database showed that this specimen is most
closely related to another group of unidentified Pimelodella from
another basin in Sipaliwini. (13) Three unidentifiedRineloricaria specimens were caught in the Paloemeu River and
were named Rineloricaria aff. stewarti 2. They constitute
a sister species of an undescribed species from western Suriname. Their
dNN in our dataset was 9.95% with Rineloricaria aff.stewarti 3 (Rineloricaria aff. stewarti sensu Le
Bail et al. (2012)), a species broadly distributed in French
Guiana and eastern Suriname.
Four putative cryptic species were BIN concordant in our dataset but not
on the BOLD database (Table 3): (1) Cyphocharax cf.spilurus sister group was a group composed of C.biocellatus specimens in our dataset, as opposed to the C .spilurus cluster that branched further. However, this specimen
showed morphological differences (e.g. absence of the lateral spot) withC. biocellatus and a high dNN of 7.27. On the BOLD
database, it shares a BIN with five other C. cf. spilurusfrom other rivers in Suriname and seven specimens from Guyana identified
as C. spilurus by other authors. (2) While all Eigenmanniacf. limbata specimens shared a BIN with specimens identified asE. limbata and E . nigra , tree based
identification strongly implies that E. limbata is a
Surinamese species while E. cf. limbata is a French
Guianese species. Moreover, all E . cf. limbata specimens
display a black humeral spot that is not present in E. limbata.(3) Gymnotus aff. carapo: one specimen from Wawapsi Creek
(Paloemeu) showed a dNN of 7.30% with the G. carapo specimens in
our dataset. It shares the same BIN with three specimens from Brazil
identified as G. carapo on BOLD database. However, tree based
identification shows that G. aff. carapo and these three
Brazilian specimens constitute a group situated very far from all othersG. carapo from several locations (Suriname, Brazil, Guyana and
French Guiana) on BOLD database and that their sister group is composed
of several G . pantana and unidentified Gymnotus .
Gymnotus carapo is known to be a widespread species in South
America and contains several sub-species (Craig, Crampton, & Albert,
2017). (4) Despite a similar colour pattern with the nominal species,
one Sternopygus cf. macrurus from Tampok shows a high dNN
of 13.96% with other S. macrurus specimens from our dataset.
Tree based identification shows that K2P distances among specimens
identified as S. macrurus on BOLD database are particularly high
and that this species seems to consist of a complex of several
operational taxonomic units. The two closest S. macrurus to ourS. cf. macrurus came from Suriname and Ecuador.
Three putative cryptic species showed BIN discordance in our dataset
after integrative identification (Table 3). (1) The two Corydorasaff. geoffroy specimens shared the same BIN as C.
geoffroy . They are also nested in the C. geoffroy complex in the
neighbour-joining dendrogram. However, there is still strong assumptions
that it could be a cryptic species. The main hint is that some
unidentified Corydoras that live in sympatry with C .geoffroy in the Litany River (Sector Apsik) display a dark
lateral stripe that is not present on C. geoffroy . Although these
striped specimens share the same mitochondrial DNA sequence as C.
geoffroy , we decided to flag these specimens as C. aff.geoffroy waiting for further evidence on the status of these
morphs. (2) Melanocharacidium sp. 1 (two specimens from Wawapsi
Creek, Paloemeu) and sp. 2 (one specimen from Wayu Camp, Paloemeu
and two from Sector Apsik, Litany) share the same BIN and are a
different group than other Melanocharacidium species on BOLD
database with a dNN of 16.30% with M. blennioides . They were
kept split as sp. 1 and sp. 2 as the Wayu Camp specimen in
the Surinamese Paloemeu River displays a much shorter genetic distance
with the two Litany specimens in French Guiana than with the other two
specimens from Paloemeu, which could indicate a potential genetic flow
barrier between sp.1 and sp.2 although they can occur in the same river.
Species from the Crenuchidae family are still under-studied, as
«undescribed Crenuchidae may be present in Suriname, especially in the
Interior of the country» (Mol, 2012).
Barcode Gap
Apart from the new putative species cited above, only three pairs of
species from the final dataset were below the 2% barcode gap (Table 5).
Among them, the only ones that shared the same BIN in our dataset and on
BOLD were Guyanancistrus brevispinis and G. nassauensis(dNN=0.62%). However, G. nassauensis is morphologically very
distinct from G. brevispinis and has been formally described in
detail (Fisch-Muller et al., 2018). Moreover, mitochondrial
introgression between the two species has been reported, which explains
this shared COI haplotype. The remaining two pairs, Ancistrus cf.leucostictus / Ancistrus temminckii (dNN=1.84%) andHypostomus plecostomus / Hypostomus watwata (dNN=1.94%)
still bear little doubt on their species delimitation. Each of the four
species have their own associated BIN, and their dNN is still relatively
high and close to the classical 2%. Moreover, Hypostomus is
known to be a genera with low inter-specific COI divergence (de Queiroz
et al., 2020). Overall, results from our dataset conform to the now
well-established notion that the 2% barcode gap is a good start to flag
low inter-species divergence (only six pairs of species show a dNN lower
than <2% in our dataset), but that it does not always apply
to some groups of fish (Pereira, Hanner, Foresti, & Oliveira, 2013; de
Queiroz et al., 2020) and thus should not be interpreted as a
stand-alone metric.
Genetic divergence landscape
analysis
The multispecies landscape (Figure 4) computed using all available
species, provided a mean reference pattern of genetic divergences across
the Maroni basin. This pattern supports evidence for the hypothesis of
lower levels of genetic connectivity between the west and the east of
the basin. Especially, the Tapanahony / Paloemeu rivers in Suriname
showed the highest mean divergence with both the east and the north with
three divergence hotspots (average Z values between 0.5 and 0.6 in
yellow in Figure 4). To assess the extent of this West-East bipartition,
we briefly compared intra-basin and inter-basin genetic divergence of 31
species with enough sampling coverage across the Guianas on BOLD
(Supplementary Material 5). Two groups were detected: a western group
that includes the West Maroni (Tapanahony) and nine basins from Suriname
and Guyana, and an eastern group that includes the East and North Maroni
areas as well as six basins of French Guiana. The Mana (east) and the
Corantijn (west) were the only rivers that were not assigned to their
respective expected groups, but both are highly connected to the Maroni,
either through the mouth (Mana) or through putative shared waters in the
upstream Surinamese areas (Corantijn). This global bi-partition could be
a consequence of multiple entries of species from the western and
eastern rivers in the Maroni. However, although the trend is detectable,
very few individuals among the species investigated displayed a lower
genetic divergence with individuals from surrounding basins than with
individuals from the Maroni (Supplementary Material 6), implying that
faunal exchanges were rather ancient. The hypothesis of past migrations
out of the Maroni to colonise surrounding basins, as it may have
happened several time in the Guyanancistrus brevispinis complex
(Fisch-Muller et al., 2018), might fit better with the general lack of
highly divergent haplotypes in the basin (but see Ageneiosus
inermis , Gymnotus aff. carapo or Sternopygus cf.macrurus above for examples of recent entries from headwaters of
Amazonian tributaries).
Ordination of individual patterns of the different species relative to
the mean pattern reveals individual trends, and their respective
contribution to the consensus (Figure 5). The method of genetic
landscape reconstruction is sensitive to the sampling coverage of each
species (all species not being collected in every place) given that
genetic landscapes are interpolations from point data. Accordingly,
resulting ordination patterns are highly dependent on the number and
distribution of specimen captures across the landscape. However, if
species displaying a broader coverage are better represented further
from the centre of the axes, similarity in the grouping of patterns of
genetic divergences along axes seems preserved regardless of the species
sampling coverage. This result is reinforced by the hierarchical
classification that created clusters of similar trends expressed by the
different species independently from sampling coverage, providing a
relatively good confidence in the various reconstructed cluster groups
(Figure 7). For instance, cluster group D grouped species displaying
higher divergence in the south-eastern part of the basin, a pattern
contrasting with other results, including both species with broad
sampling (Ageneiosus inermis and Triportheusbrachipomus ) and species with smaller coverage
(Rineloricaria aff. stewarti 3, Hypomasticusdespaxi , Caenotropus maculosus , Jupiaba
keithi , or Semaprochilodus varii ). An example of pattern
revealed by the method is the higher genetic divergence in the west of
the basin observed in several species (G. brevispinis andP. leptosoma from group A and all species from group E (Figure
7)). This observed pattern could result from the progressive
establishment of hotspots of mutual exclusion among populations linked
to more ancient and favoured dispersal routes from the Maroni Basin,
something that has also been suggested for G. brevispinis , which
first dispersed from the lower Maroni to the west toward the Suriname
River and then toward Upper Corantijn River (Fisch-Muller et al., 2018).
A rapid examination of the clusters showed that they contain most
taxonomic groups, including Siluriformes, Characiformes, Gymnotiformes,
Cichliformes, etc. (Figure 7). In addition, comparison of local
communities revealed equally likely distributed patterns among faunistic
assemblages (Supplementary Material 5). The community of Langa Sula in
the mainstream of Marouini River in French Guiana and the community of
Wawapsi Creek, a small forest creek tributary of the Paloemeu River in
Suriname comprise around 60 species each, but only 16 of them are shared
between both. In both communities, including among shared species, all
patterns of genetic divergence were present. Confrontation of pattern
distribution relative to the taxonomy (which could be considered a proxy
of the phylogeny) reveals weakly overdispersed patterns (i.e. weak
negative autocorrelation of divergence patterns with the taxonomy) with
absence of taxonomic structure among patterns (i.e. non-significant
autocorrelation). Overdispersion of traits in regard of a phylogeny is
often considered as a signature of competitive interactions driving
community assemblage rules, meaning that dissimilar traits are expected
to co-occur (Cavender‐Bares, Ackerly, Baum, & Bazzaz, 2004;
Cavender-Bares, Kozak, Fine, & Kembel, 2009; Pausas & Verdú, 2010).
Present faunistic assemblages result from interaction of multifactorial
processes acting at different scales and operating over a wide range of
time and space (Lowe & McPeek, 2014; Mittelbach & Schemske, 2015;
Lemopoulos & Covain, 2019). In this context, dispersal abilities seem
to be a key factor in species distributions, contributing to
colonisation of new areas, range shifts and gene mixing among
populations (Jønsson et al., 2016). Accordingly, observed genetic
divergences among species likely result from limitations to dispersal,
possibly related to competition, within the basin, responsible to
limitation to gene flows and driving individual species response in
assembly processes.
Conclusion
The 1,284 COI sequences introduced in this study represent the first
extensive fish barcode dataset of the Maroni and the first dataset of
this scale for the Guiana Shield. Using barcoding as a secondary tool to
identify specimens during this study has been a success. Although the
majority of specimens could be identified on the field with morphologic
and meristic methods only, this traditional way of identification has
its limits: post-larvae or small damaged specimens are sometimes
impossible to classify based on morphological keys (Ward et al., 2009).
Species for which identification relies heavily on pigmentation (e.g.Moenkhausia and Jupiaba ) are also highly problematic when
identification is performed long after sampling because conservation in
ethanol and formaldehyde results in the loss of pigments from the
specimens. By building a DNA barcode library and relying on other
sequences deposited by the scientific community, it was possible to
easily verify some dubious identifications and pinpoint some overlooked
diversity in our data. More than two thirds of the freshwater fishes of
the Maroni now have at least one reference sequence available to
facilitate identification. Biodiversity assessment in rivers have
recently shown a trend of shifting from traditional “sampling by
catch” methods to the less destructive use of environmental DNA
metabarcoding (Taberlet, Coissac, Hajibabaei, & Rieseberg, 2012), a
method that is currently being trialled in French Guiana rivers
(Cilleros et al., 2019; Murienne et al., 2019). This method relies on
solid reference databases to identify environmental DNA sequences to the
species level or below, and our dataset is a step forward towards this
goal.
The present study also updated the 2012 checklists in several ways. Five
species absent or dubious in these checklists have been confirmed to
occur in the Maroni. Five more species have been flagged as being in
need of formal re-identification, and 20 new putative cryptic species
have been added to the Maroni inventory. They all require further
investigations to better assess their taxonomical status. Other updates
include the change of taxonomical status of three recently described
species (Distocyclus guchereauae Meunier, Jegu, & Keith 2014,Guyanancistrus nassauensis Mol, Fisch-Muller & Covain 2018, andMastiglanis durantoni De Pinna & Keith 2019) and the new
assignation of “doubtful” status to 13 species, based on the authors’
extensive observations of the basin since 2012 (Supplementary Material
1).
We showed that the use of genetic landscape divergence is an efficient
way to explore shared patterns of genetic connectivity when sampling is
spatially extensive but number of specimens per locations is relatively
low. Using a multivariate analysis (PCA) coupled with a hierarchical
clustering method (WPGMA) proved to be an efficient way to classify
these landscape patterns in order to facilitate the construction of
species-specific genetic connectivity hypotheses. While this methodology
is in no way restricted to river networks (on the opposite, land and sea
may even be better suited to it), using this methodology on different
basins and at different times could assist in the detection of habitat
fragmentation, one of the biggest threats that freshwater ecosystems are
currently facing. More extensive sampling, i.e. by using environmental
DNA, would allow the application of this clustering landscape approach
to compare levels of genetic diversity (as opposed to divergence) across
the basin, another important indicator of the resilience capacity of
species in an ecosystem.
Acknowledgements
We are grateful to François Meunier, Yves Fermon, and Philippe Keith,
MNHN; Mark Sabaj and John Lundberg, ANSP; Philippe Gaucher, CNRS Guyane;
Régis Vigouroux, Philippe Cerdan✝, and Sébastien Le Reun, Hydreco
Guyane; Chrystelle Delord, Marie Le Noc, Marie Nevoux and Jean-Marc
Roussel, INRA, Rennes; Michel Jégu, IRD; Maël Dewynter, Benjamin Adam
and Antoine Baglan, Biotope Guyane; Frédéric Melki, Biotope France; Jan
Mol, University of Suriname; Kenneth Wan Tong You and Paul Ouboter,
NZCS; Juan Montoya-Burgos, UNIGE; Sonia Fisch-Muller and Claude Weber,
MHNG; Raphaelle Rinaldo and Guillaume Longin, Parc Amazonien de Guyane;
Gregory Quartarollo and the Guyane Wild Fish Association ; Olivier
Tostain, Ecobios Cayenne; Jonathan W. Armbruster, Auburn University;
Sébastien Brosse, University of Toulouse ; as well as all our friends
from Maroni River communities, Wayanas, Tekos, and Bushinengues for
their contribution to field collection of specimens and logistic
assistance. The French Guiana DEAL, PAG, and Préfecture; and the
Surinamese Ministry of Agriculture, Animal Husbandry and Fisheries
provided the necessary authorisations and collecting permits. Océane
Leclercq, Sabrina Grillard, and Alexandre Lemopoulos, MHNG are
acknowledged for laboratory assistance. Océane Leclercq benefited from a
grant of the Biotope Foundation for Biodiversity, Mèze, France.
References
Alexandrou, M. A., & Taylor, M. I. (2011). Evolution, ecology and
taxonomy of the Corydoradinae revisited. In I. A. M. Fuller & H. G.
Evers (Eds.), Identifying Corydoradinae catfish: Aspidoras,
Brochis, Corydoras, Scleromystax, C-numbers & CW-numbers: supplement
1. (pp. 101–114). Kidderminster, England: Ian Fuller Enterprises.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J.
(1990). Basic local alignment search tool. Journal of Molecular
Biology , 215 (3), 403–410.
http://doi.org/10.1016/S0022-2836(05)80360-2
Amatali, M. (1993). Climate and surface water hydrology. In P. E.
Ouboter (Ed.), The Freshwater Ecosystems of Suriname (Vol. 70,
pp. 29–51). Dordrecht: Springer Netherlands.
http://doi.org/10.1007/978-94-011-2070-8
Arbeláez-Cortés, E., Milá, B., & Navarro-Sigüenza, A. G. (2014).
Multilocus analysis of intraspecific differentiation in three endemic
bird species from the northern Neotropical dry forest. Molecular
Phylogenetics and Evolution , 70 (1), 362–377.
http://doi.org/10.1016/j.ympev.2013.10.006
Barrett, R. D. ., & Hebert, P. D. . (2005). Identifying spiders through
DNA barcodes. Canadian Journal of Zoology , 83 (3),
481–491. http://doi.org/10.1139/z05-024
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery
rate: a practical and powerful approach to multiple testing.Journal of the Royal Statistical Society: Series B
(Methodological) , 57 (1), 289–300.
http://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Berbel-Filho, W. M., Ramos, T. P. A., Jacobina, U. P., Maia, D. J. G.,
Torres, R. A., & Lima, S. M. Q. (2018). Updated checklist and DNA
barcode-based species delimitations reveal taxonomic uncertainties among
freshwater fishes from the mid-north-eastern Caatinga ecoregion,
north-eastern Brazil. Journal of Fish Biology , 93 (2),
311–323. http://doi.org/10.1111/jfb.13758
Birindelli, J. L. O., & Sidlauskas, B. L. (2018). Preface: How far has
Neotropical Ichthyology progressed in twenty years? Neotropical
Ichthyology , 16 (3). http://doi.org/10.1590/1982-0224-20180128
Bivand, R., Keitt, T., & Rowlingson, B. (2019). rgdal: Bindings for the
“Geospatial” Data Abstraction Library. Retrieved from
https://cran.r-project.org/package=rgdal
Cardoso, Y. P., & Montoya-Burgos, J. I. (2009). Unexpected diversity in
the catfish Pseudancistrus brevispinis reveals dispersal routes in a
Neotropical center of endemism: the Guyanas Region. Molecular
Ecology , 18 (5), 947–964.
http://doi.org/10.1111/j.1365-294X.2008.04068.x
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A., & Kembel, S. W.
(2009). The merging of community ecology and phylogenetic biology.Ecology Letters , 12 (7), 693–715.
http://doi.org/10.1111/j.1461-0248.2009.01314.x
Cavender‐Bares, J., Ackerly, D. D., Baum, D. A., & Bazzaz, F. A.
(2004). Phylogenetic Overdispersion in Floridian Oak Communities.The American Naturalist , 163 (6), 823–843.
http://doi.org/10.1086/386375
Chan, L. M., Brown, J. L., & Yoder, A. D. (2011). Integrating
statistical genetic and geospatial methods brings new power to
phylogeography. Molecular Phylogenetics and Evolution ,59 (2), 523–537. http://doi.org/10.1016/j.ympev.2011.01.020
Cilleros, K., Valentini, A., Allard, L., Dejean, T., Etienne, R.,
Grenouillet, G., … Brosse, S. (2019). Unlocking biodiversity and
conservation studies in high‐diversity environments using environmental
DNA (eDNA): A test with Guianese freshwater fishes. Molecular
Ecology Resources , 19 (1), 27–46.
http://doi.org/10.1111/1755-0998.12900
Covain, R., Fisch-Muller, S., Montoya-Burgos, J. I., Mol, J. H., Le
Bail, P.-Y., & Dray, S. (2012). The Harttiini (Siluriformes,
Loricariidae) from the Guianas: a multi-table approach to assess their
diversity, evolution, and distribution. Cybium , 36 (1),
115–161.
Covain, R., Fisch-Muller, S., Oliveira, C., Mol, J. H., Montoya-Burgos,
J. I., & Dray, S. (2016). Molecular phylogeny of the highly diversified
catfish subfamily Loricariinae (Siluriformes, Loricariidae) reveals
incongruences with morphological classification. Molecular
Phylogenetics and Evolution , 94 , 492–517.
http://doi.org/10.1016/j.ympev.2015.10.018
Craig, J. M., Crampton, W. G. R., & Albert, J. S. (2017). Revision of
the polytypic electric fish Gymnotus carapo (Gymnotiformes, Teleostei),
with descriptions of seven subspecies. Zootaxa , 4318 (3),
401–438. http://doi.org/10.11646/zootaxa.4318.3.1
de Carvalho, D. C., Oliveira, D. A., Pompeu, P. S., Leal, C. G.,
Oliveira, C., & Hanner, R. (2011). Deep barcode divergence in Brazilian
freshwater fishes: the case of the São Francisco River basin.Mitochondrial DNA , 22 (sup1), 80–86.
http://doi.org/10.3109/19401736.2011.588214
de Queiroz, L. J., Cardoso, Y., Jacot-des-Combes, C., Bahechar, I. A.,
Lucena, C. A., Rapp Py-Daniel, L., … Montoya-Burgos, J. I.
(2020). Evolutionary units delimitation and continental multilocus
phylogeny of the hyperdiverse catfish genus Hypostomus. Molecular
Phylogenetics and Evolution , 145 , 106711.
http://doi.org/10.1016/j.ympev.2019.106711
Díaz, J., Villanova, G. V., Brancolini, F., del Pazo, F., Posner, V. M.,
Grimberg, A., & Arranz, S. E. (2016). First DNA Barcode Reference
Library for the Identification of South American Freshwater Fish from
the Lower Paraná River. PLOS ONE , 11 (7), e0157419.
http://doi.org/10.1371/journal.pone.0157419
Dray, S., & Dufour, A.-B. (2007). The ade4 Package: Implementing the
Duality Diagram for Ecologists. Journal of Statistical Software ,22 (4), 1–20. http://doi.org/10.18637/jss.v022.i04
Edgar, R. C. (2004). MUSCLE: multiple sequence alignment with high
accuracy and high throughput. Nucleic Acids Research ,32 (5), 1792–1797. http://doi.org/10.1093/nar/gkh340
Efron, B. (1979). Bootstrap methods: Another look at the jackknife.Annals of Statitics , 7 (1), 1–26.
http://doi.org/10.1214/aos/1176344552
Felsenstein, J. (1985). Confidence limits on phylogenies: an approach
using the bootstrap. Evolution , 39 (4), 783–791.
http://doi.org/10.1111/j.1558-5646.1985.tb00420.x
Fisch-Muller, S., Mol, J. H. A., & Covain, R. (2018). An integrative
framework to reevaluate the Neotropical catfish genus Guyanancistrus
(Siluriformes: Loricariidae) with particular emphasis on the
Guyanancistrus brevispinis complex. PLOS ONE , 13 (1),
e0189789. http://doi.org/10.1371/journal.pone.0189789
Fisch-Muller, S., Montoya-Burgos, J. I., Le Bail, P.-Y., & Covain, R.
(2012). Diversity of the Ancistrini (Siluriformes: Loricariidae) from
the Guianas: the Panaque group, a molecular appraisal with descriptions
of new species. Cybium , 36 (1), 163–193.
Fricke, R., Eschmeyer, W. N., & Van Der Laan, R. (2019). Eschmeyer’s
Catalog of Fishes: Genera, Species, References. Retrieved May 24, 2019,
from
http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp
Gerritsen, H. (2018). mapplots: Data Visualisation on Maps. Retrieved
from https://cran.r-project.org/package=mapplots
Goldstein, P. Z., & DeSalle, R. (2011). Integrating DNA barcode data
and taxonomic practice: Determination, discovery, and description.BioEssays , 33 (2), 135–147.
http://doi.org/10.1002/bies.201000036
Gomes, L. C., Pessali, T. C., Sales, N. G., Pompeu, P. S., & Carvalho,
D. C. (2015). Integrative taxonomy detects cryptic and overlooked fish
species in a neotropical river basin. Genetica , 143 (5),
581–588. http://doi.org/10.1007/s10709-015-9856-z
Hall, T. (1999). BioEdit: a user-friendly biological sequence alignment
editor and analysis program for Windows 95/98/NT. Nucleic Acids
Symposium Series , 41 , 95–98.
Hebert, P. D. N., Cywinska, A., Ball, S. L., & DeWaard, J. R. (2003).
Biological identifications through DNA barcodes. Proceedings of
the Royal Society of London. Series B: Biological Sciences ,270 (1512), 313–321. http://doi.org/10.1098/rspb.2002.2218
Hijmans, R. J. (2019). raster: Geographic Data Analysis and Modeling.
Retrieved from https://cran.r-project.org/package=raster
Johannes, M., Ruschhaupt, M., Froehlich, H., Mansmann, U., Buness, A.,
Warnat, P., … Beissbarth, T. (2018). MCRestimate:
Misclassification error estimation with cross-validation.
Jønsson, K. A., Tøttrup, A. P., Borregaard, M. K., Keith, S. A., Rahbek,
C., & Thorup, K. (2016). Tracking Animal Dispersal: From Individual
Movement to Community Assembly and Global Range Dynamics. Trends
in Ecology & Evolution , 31 (3), 204–214.
http://doi.org/10.1016/j.tree.2016.01.003
Keith, P., Le Bail, P.-Y., & Planquette, P. (2000). Atlas des
poissons d’eau douce de Guyane. Tome 2, Fascicule I: Batrachoidiformes,
Mugiliformes, Beloniformes, Cyprinodontiformes, Synbranchiformes,
Perciformes, Pleuronectiformes, Tetraodontiformes. Paris, France:
Muséum national d’histoire naturelle.
Kimura, M. (1980). A simple method for estimating evolutionary rates of
base substitutions through comparative studies of nucleotide sequences.Journal of Molecular Evolution , 16 (2), 111–120.
http://doi.org/10.1007/BF01731581
Korbie, D. J., & Mattick, J. S. (2008). Touchdown PCR for increased
specificity and sensitivity in PCR amplification. Nature
Protocols , 3 (9), 1452–1456.
http://doi.org/10.1038/nprot.2008.133
Kumar, S., Stecher, G., & Tamura, K. (2016). MEGA7: Molecular
Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.Molecular Biology and Evolution , 33 (7), 1870–1874.
http://doi.org/10.1093/molbev/msw054
Le Bail, P.-Y., Covain, R., Jégu, M., Fisch-Muller, S., Vigouroux, R.,
& Keith, P. (2012). Updated checklist of the freshwater and estuarine
fishes of French Guiana. Cybium , 36 (1), 293–319.
Le Bail, P.-Y., Keith, P., & Planquette, P. (2000). Atlas des
poissons d’eau douce de Guyane. Tome 2, Fascicule II - Siluriformes .
Paris, France: Muséum national d’histoire naturelle.
Lehner, B., Verdin, K., & Jarvis, A. (2008). New global hydrography
derived from spaceborne elevation data. Eos, Transactions American
Geophysical Union , 89 (10), 93–94.
http://doi.org/10.1029/2008EO100001
Lemopoulos, A., & Covain, R. (2019). Biogeography of the freshwater
fishes of the Guianas using a partitioned parsimony analysis of
endemicity with reappraisal of ecoregional boundaries.Cladistics , 35 (1), 106–124.
http://doi.org/10.1111/cla.12341
Lowe, W. H., & McPeek, M. A. (2014). Is dispersal neutral? Trends
in Ecology & Evolution , 29 (8), 444–450.
http://doi.org/10.1016/j.tree.2014.05.009
Mamos, T., Wattier, R., Burzyński, A., & Grabowski, M. (2016). The
legacy of a vanished sea: a high level of diversification within a
European freshwater amphipod species complex driven by 15 My of
Paratethys regression. Molecular Ecology , 25 (3), 795–810.
http://doi.org/10.1111/mec.13499
Manel, S., Schwartz, M. K., Luikart, G., & Taberlet, P. (2003).
Landscape genetics: combining landscape ecology and population genetics.Trends in Ecology & Evolution , 18 (4), 189–197.
http://doi.org/10.1016/S0169-5347(03)00008-9
McQuitty, L. L. (1966). Similarity Analysis by Reciprocal Pairs for
Discrete and Continuous Data. Educational and Psychological
Measurement , 26 (4), 825–831.
http://doi.org/10.1177/001316446602600402
Mittelbach, G. G., & Schemske, D. W. (2015). Ecological and
evolutionary perspectives on community assembly. Trends in Ecology
& Evolution , 30 (5), 241–247.
http://doi.org/10.1016/j.tree.2015.02.008
Mol, J. H. A. (2012). The Freshwater Fishes of Suriname . Leiden,
Netherlands: Brill Academic Pub.
Mol, J. H. A., Vari, R. P., Covain, R., Willink, P. W., & Fisch-muller,
S. (2012). Annotated checklist of the freshwater fishes of Suriname.Cybium , 36 (1), 263–292.
Murienne, J., Cantera, I., Cerdan, A., Cilleros, K., Decotte, J.,
Dejean, T., … Brosse, S. (2019). Aquatic eDNA for monitoring
French Guiana biodiversity. Biodiversity Data Journal , 7 ,
1–9. http://doi.org/10.3897/BDJ.7.e37518
Nascimento, M. H. S., Almeida, M. S., Veira, M. N. S., Limeira Filho,
D., Lima, R. C., Barros, M. C., & Fraga, E. C. (2016). DNA barcoding
reveals high levels of genetic diversity in the fishes of the Itapecuru
Basin in Maranhão, Brazil. Genetics and Molecular Research ,15 (3). http://doi.org/10.4238/gmr.15038476
Négrel, P., & Lachassagne, P. (2000). Geochemistry of the Maroni River
(French Guiana) during the low water stage: implications for water–rock
interaction and groundwater characteristics. Journal of
Hydrology , 237 (3–4), 212–233.
http://doi.org/10.1016/S0022-1694(00)00308-5
Nelson, J. S., Grande, T. C., & Wilson, M. V. H. (2016). Fishes
of the World (5th ed.). Hoboken, NJ: John Wiley & Sons.
Paradis, E., & Schliep, K. (2018). ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R. Bioinformatics ,35 (3), 526–528. http://doi.org/10.1093/bioinformatics/bty633
Pausas, J. G., & Verdú, M. (2010). The jungle of methods for evaluating
phenotypic and phylogenetic structure of communities. BioScience ,60 (8), 614–625. http://doi.org/10.1525/bio.2010.60.8.7
Pereira, L. H. G., Hanner, R., Foresti, F., & Oliveira, C. (2013). Can
DNA barcoding accurately discriminate megadiverse Neotropical freshwater
fish fauna? BMC Genetics , 14 (1), 20.
http://doi.org/10.1186/1471-2156-14-20
Planquette, P., Keith, P., & Le Bail, P.-Y. (1996). Atlas des
poissons d’eau douce de Guyane: Tome 1 . Paris: Muséum national
d’Histoire naturelle.
Planquette, P., & Renno, J.-F. (1990). Les Leporinus de la Guyane
française (Pisces , Characiformes , Anostomidae ), avec une note sur les
techniques d’identification des espèces. Revue Française
d’Aquariologie , 17 (2), 33–40.
Pugedo, M. L., de Andrade Neto, F. R., Pessali, T. C., Birindelli, J. L.
O., & Carvalho, D. C. (2016). Integrative taxonomy supports new
candidate fish species in a poorly studied neotropical region: the
Jequitinhonha River Basin. Genetica , 144 (3), 341–349.
http://doi.org/10.1007/s10709-016-9903-4
R Core Team. (2018). R: A language and environment for statistical
computing. Vienna, Austria: R Foundation for Statistical Computing.
Retrieved from https://www.r-project.org/
Ratnasingham, S., & Hebert, P. D. N. (2007). bold: The Barcode of Life
Data System (http://www.barcodinglife.org). Molecular Ecology
Notes , 7 (3), 355–364.
http://doi.org/10.1111/j.1471-8286.2007.01678.x
Ratnasingham, S., & Hebert, P. D. N. (2013). A DNA-Based Registry for
All Animal Species: The Barcode Index Number (BIN) System. PLoS
ONE , 8 (7), e66213. http://doi.org/10.1371/journal.pone.0066213
Rosso, J. J., Mabragaña, E., González Castro, M., & Díaz de Astarloa,
J. M. (2012). DNA barcoding Neotropical fishes: recent advances from the
Pampa Plain, Argentina. Molecular Ecology Resources ,12 (6), 999–1011. http://doi.org/10.1111/1755-0998.12010
Sanger, F., Nicklen, S., & Coulson, A. R. (1977). DNA sequencing with
chain-terminating inhibitors. Proceedings of the National Academy
of Sciences , 74 (12), 5463–5467.
http://doi.org/10.1073/pnas.74.12.5463
Shimodaira, H. (2002). An approximately unbiased test of phylogenetic
tree selection. Systematic Biology , 51 (3), 492–508.
http://doi.org/10.1080/10635150290069913
Shimodaira, H. (2004). Approximately unbiased tests of regions using
multistep-multiscale bootstrap resampling. Annals of Statitics ,32 (6), 2616–2641. http://doi.org/10.1214/009053604000000823
Stabler, B. (2013). shapefiles: Read and Write ESRI Shapefiles.
Retrieved from https://cran.r-project.org/package=shapefiles
Suzuki, R., & Shimodaira, H. (2006). Pvclust: an R package for
assessing the uncertainty in hierarchical clustering.Bioinformatics , 22 (12), 1540–1542.
http://doi.org/10.1093/bioinformatics/btl117
Taberlet, P., Coissac, E., Hajibabaei, M., & Rieseberg, L. H. (2012).
Environmental DNA. Molecular Ecology , 21 (8), 1789–1793.
http://doi.org/10.1111/j.1365-294X.2012.05542.x
Tarroso, P., Carvalho, S. B., & Velo-Antón, G. (2019). Phylin 2.0:
Extending the phylogeographical interpolation method to include
uncertainty and user-defined distance metrics. Molecular Ecology
Resources , (February), 1–14. http://doi.org/10.1111/1755-0998.13010
Valdez-Moreno, M., Ivanova, N. V., Elías-Gutiérrez, M.,
Contreras-Balderas, S., & Hebert, P. D. N. (2009). Probing diversity in
freshwater fishes from Mexico and Guatemala with DNA barcodes.Journal of Fish Biology , 74 (2), 377–402.
http://doi.org/10.1111/j.1095-8649.2008.02077.x
Vandergast, A. G., Perry, W. M., Roberto, L. V., & Hathaway, S. A.
(2011). Genetic landscapes GIS Toolbox: tools to map patterns of genetic
divergence and diversity. Molecular Ecology Resources ,11 (1), 158–161. http://doi.org/10.1111/j.1755-0998.2010.02904.x
Vari, R. P., Ferraris, C. J., Radosavljevic, A., & Funk, V. A. (2009).Checklist of the Freshwater Fishes of the Guiana Shield .Biological Society of Washington . Biological Society of
Washington. http://doi.org/10.2988/0097-0298-17.1.i
Vodă, R., Dapporto, L., Dincă, V., & Vila, R. (2015). Why Do Cryptic
Species Tend Not to Co-Occur? A Case Study on Two Cryptic Pairs of
Butterflies. PLOS ONE , 10 (2), e0117802.
http://doi.org/10.1371/journal.pone.0117802
Ward, R. D. (2009). DNA barcode divergence among species and genera of
birds and fishes. Molecular Ecology Resources , 9 (4),
1077–1085. http://doi.org/10.1111/j.1755-0998.2009.02541.x
Ward, R. D., Hanner, R., & Hebert, P. D. N. (2009). The campaign to DNA
barcode all fishes, FISH-BOL. Journal of Fish Biology ,74 (2), 329–356. http://doi.org/10.1111/j.1095-8649.2008.02080.x
Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R., & Hebert, P. D.
N. (2005). DNA barcoding Australia’s fish species. Philosophical
Transactions of the Royal Society B: Biological Sciences ,360 (1462), 1847–1857. http://doi.org/10.1098/rstb.2005.1716
Weber, C., Covain, R., & Fisch-Muller, S. (2012). Identity of
Hypostomus plecostomus (Linnaeus, 1758), with an overview of Hypostomus
species from the Guianas (Teleostei: Siluriformes: Loricariidae).Cybium , 36 (1), 195–227.
Wood, D. A., Vandergast, A. G., Barr, K. R., Inman, R. D., Esque, T. C.,
Nussear, K. E., & Fisher, R. N. (2013). Comparative phylogeography
reveals deep lineages and regional evolutionary hotspots in the Mojave
and Sonoran Deserts. Diversity and Distributions , 19 (7),
722–737. http://doi.org/10.1111/ddi.12022
Data Accessibility
All collection information and sequence data are available on the
Barcode of life Data System (BOLD) in the project “Gui-BOL Barcoding
Guianese fishes”. All BOLD accession numbers for this dataset are
available in Supplementary Material 6. R scripts used in the analyses
are available on GitHub (https://github.com/yvanpapa/Maroni_Barcode).
Author Contributions
Y.P. contributed to molecular data collection, designed and ran the
analyses, and wrote the manuscript. P.Y.L.B contributed to specimen
collection and reviewed the manuscript. R.C. conceptualised and
supervised the research, contributed to specimen and molecular data
collection, contributed to design of analyses and reviewed the
manuscript.
Tables
Table 1 . Species that have been taxonomically updated since
2012 Maroni checklists (Le Bail et al., 2012; Mol et al., 2012) to
follow current nomenclature. (†) Currently still referred asGlanidium leopardus in BOLD dataset. (‡) Currently still referred
as Mastiglanis cf. asopos in BOLD dataset.