bio-ecological-genomics-species-delimitation

name	bio-ecological-genomics-species-delimitation
description	Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.
tool_type	mixed
primary_tool	ASAP

Version Compatibility

Reference examples tested with: ASAP (current CLI), mPTP (current C++), bPTP (Python from github.com/zhangjiajie/PTP), splits 1.0+ for GMYC, BPP 4.7+, SNAPP/BEAST 2.7+, DELINEATE (current Python), Dsuite 0.5+, ape 5.7+, fossil 0.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Species Delimitation

"Delineate species boundaries from my DNA barcoding or genomic data" -> Apply distance-based (ASAP) and tree-based (mPTP) methods for primary delimitation, multilocus coalescent (BPP) for genomic confirmation, DELINEATE for speciation-process modeling to address MSC oversplitting, integrative-taxonomy validation across multiple lines of evidence, and Dsuite to test introgression before claiming sister relationships.

• CLI: asap (downloadable C binary) OR web (https://bioinfo.mnhn.fr/abi/public/asap/) for single-locus primary delimitation
• CLI: mptp (https://github.com/Pas-Kapli/mptp) for tree-based multi-rate PTP (faster than bPTP)
• Python: bPTP (https://github.com/zhangjiajie/PTP) for legacy tree-based delimitation — NOTE: Python not R
• R: splits::gmyc() for GMYC on ultrametric trees
• CLI: bpp (https://bpp.github.io) for multilocus MSC delimitation; control-file driven
• CLI: Dsuite for D-statistics / f4-ratio / f-branch introgression testing

The Single Most Important Modern Insight -- MSC Methods Delimit Structure, NOT Species

Sukumaran & Knowles 2017 PNAS 114(7):1607-1612 established that BPP, BFD*, and other multispecies-coalescent methods delimit genetic structure, not species. The mathematical reason: the MSC model treats every panmictic population as a "species" in the parameterization. Applied to data with substructure (isolated demes within a species), MSC methods partition that structure into "species" — leading to systematic oversplitting in published literature, especially in lizards, frogs, geckos, and insects.

Modern best practice (post-2021):

1. Run MSC methods (BPP, BFD*) as PRELIMINARY enumeration of candidate population lineages
2. Apply DELINEATE (Sukumaran 2021 PLoS Comput Biol 17:e1008924) to test which lineages have completed speciation vs are in the speciation process
3. Validate via integrative taxonomy (Padial 2010; Carstens 2013) — morphological, ecological, geographic congruence

A second cornerstone: the barcoding gap is OFTEN absent in real data (Meyer & Paulay 2005 PLoS Biol 3:e422). ABGD and ASAP are theoretically grounded in the gap; when the gap is absent, both fail gracefully but their output is unreliable. Always inspect the pairwise distance histogram.

A third: Dsuite-checked introgression must precede sister-species claims. D-statistics, f4-ratio, and f-branch (Malinsky 2021) distinguish admixture from incomplete lineage sorting; without this check, ILS-driven discordance is misinterpreted as gene flow.

Algorithmic Taxonomy

Method	Input	Approach	Strength	Fails when
ASAP (Puillandre 2021)	Aligned sequences	Hierarchical-clustering distance partitioning with new scoring	Fast; modern; web + CLI	Single locus; works only if barcoding gap exists
ABGD (Puillandre 2012)	Aligned sequences	Distance gap-detection across priors	Legacy; superseded by ASAP	Less robust when gap is weak
GMYC (Pons 2006)	Ultrametric tree	Yule-coalescent transition threshold	Theoretical foundation	Requires time-calibrated tree
Multi-threshold GMYC (Fujisawa & Barraclough 2013)	Ultrametric tree	Per-lineage transition thresholds	Heterogeneous rates	Same; not always more powerful
bPTP (Zhang 2013)	Rooted phylogeny	Bayesian PTP MCMC	Posterior support per partition	Single intraspecific rate assumption; slower than mPTP
mPTP (Kapli 2017)	Rooted phylogeny	Multi-rate PTP, C++	Faster (5+ orders); per-species intraspecific rates	Heterogeneity-friendly; for shallow phylogenies bPTP may be more powerful
BPP A10/A11 (Yang & Rannala)	Multi-locus alignments	Bayesian multispecies coalescent	Rigorous, multilocus	Computationally heavy; oversplits per Sukumaran-Knowles
SNAPP (Bryant 2012)	Biallelic SNP data	Coalescent species-tree bypassing gene trees	High-power genomic	Cubic complexity; not for very large datasets
BFD* (Leache 2014)	SNP data via SNAPP	Bayes-factor delimitation	Genomic-scale species hypothesis testing	Same oversplitting risk
DELINEATE (Sukumaran 2021)	BPP output + guide tree	Speciation-process modeling	Distinguishes structure from species	Requires preliminary delimitation as input
Dsuite (Malinsky 2021)	VCF + populations	D-statistic, f4-ratio, f-branch	Tests introgression vs ILS	Tree must accurately reflect history

Decision Tree by Scenario

Scenario	Recommended approach	Why
Primary species hypothesis from single locus	ASAP first; cross-check with mPTP	ASAP is the modern successor to ABGD; faster and better-scoring
Tree-based delimitation, heterogeneous intraspecific rates	mPTP	Per-species rate model; 5+ orders faster than bPTP
Tree-based delimitation, shallow phylogeny	bPTP (single-rate) or mPTP	When rates are similar, single-rate model may be more powerful
Time-calibrated tree from BEAST / chronos	GMYC (single or multi-threshold)	Requires ultrametric; both threshold variants worth comparing
Multilocus genomic delimitation	BPP A10/A11 + DELINEATE	BPP alone oversplits per Sukumaran-Knowles 2017
SNP-based species hypothesis test	SNAPP + BFD*	Coalescent species-tree from biallelic SNPs
Confirm BPP result is not oversplitting	DELINEATE (Sukumaran 2021)	Speciation-process modeling distinguishes structure from species
Test introgression before sister-species claim	Dsuite (D, f4-ratio, f-branch)	Distinguishes admixture from ILS
Cryptic species complex	Integrative taxonomy: genetic + morphological + ecological congruence	Single-method conclusion unreliable
ABGD/ASAP returns no clear partition	Inspect pairwise distance histogram; do not force partition	Barcoding gap may be absent (Meyer & Paulay 2005)
Recently-diverged populations (Ne*t << 1)	Caution; MSC methods very prone to oversplit	Incomplete lineage sorting indistinguishable from shallow structure
Conservation management unit definition	Moritz 1994 ESU/MU framework, NOT MSC delimitation	Sukumaran-Knowles caveat applies

ASAP — The Modern Successor to ABGD

Goal: Primary species hypothesis from a single-locus alignment via hierarchical clustering of pairwise distances, scored by a new asap-score (Puillandre 2021).

Approach: Run ASAP via the web interface (https://bioinfo.mnhn.fr/abi/public/asap/) OR the downloadable C binary. ASAP ranks candidate partitions by asap-score (lower = better); always inspect the top 5-10 partitions, not just the best, and check for large score gaps that signal robust partitioning. Output: ranked species partitions with asap-score, p-value, and threshold distance per partition.

# ASAP CLI (downloadable C binary)
# Substitution model: K2P standard for COI; p-distance for very closely related; JC for general
./asap -d K2P -o asap_results/ aligned_sequences.fasta

# Inspect output
# - asap_output.csv: ranked partitions by asap-score (lower = better)
# - Always look at top 5-10 partitions; large score gaps = robust partitioning
# - The "best" partition is the top-ranked; report secondary partitions too

# Python ASAP-style analysis for inspection (NOT a substitute for ASAP)
# Useful for understanding the distance landscape
from Bio import AlignIO
from Bio.Phylo.TreeConstruction import DistanceCalculator
from scipy.cluster.hierarchy import fcluster, linkage
from scipy.spatial.distance import squareform
import numpy as np
import matplotlib.pyplot as plt

alignment = AlignIO.read('aligned_sequences.fasta', 'fasta')
calc = DistanceCalculator('identity')  # use K2P-friendly model in practice
dm = calc.get_distance(alignment)

names = [r.id for r in alignment]
n = len(names)
dist_array = np.zeros((n, n))
for i in range(n):
    for j in range(n):
        dist_array[i][j] = dm[names[i], names[j]]

# Inspect distance histogram for barcoding gap (often ABSENT per Meyer & Paulay 2005)
upper = dist_array[np.triu_indices(n, k=1)]
plt.hist(upper, bins=50)
plt.xlabel('Pairwise distance')
plt.ylabel('Frequency')
plt.title('Distance histogram — look for bimodality (gap)')
plt.savefig('barcoding_gap_histogram.pdf')

# Hierarchical clustering at threshold scan
condensed = squareform(dist_array)
Z = linkage(condensed, method='average')
thresholds = np.arange(0.01, 0.10, 0.005)
for t in thresholds:
    clusters = fcluster(Z, t=t, criterion=)
    ()

mPTP — Multi-Rate PTP for Heterogeneous Intraspecific Rates

Goal: Delimit species from a phylogeny by detecting the transition from speciation to coalescent branching rates, allowing per-species intraspecific rates (Kapli 2017).

Approach: Run mPTP on a rooted ML tree (e.g., from RAxML or IQ-TREE). mPTP's multi-rate model is more flexible than bPTP's single-rate; for shallow phylogenies with similar rates, bPTP may be competitive. mPTP is C++ (5+ orders of magnitude faster than bPTP), available from https://github.com/Pas-Kapli/mptp.

# mPTP installation: build from C source
# git clone https://github.com/Pas-Kapli/mptp
# cd mptp && ./autogen.sh && ./configure && make

# Run mPTP ML inference
mptp --ml --tree_file rooted_tree.nwk --output_file mptp_ml

# MCMC for posterior support
mptp --mcmc 1000000 --mcmc_sample 1000 --mcmc_burnin 100000 \
     --tree_file rooted_tree.nwk --output_file mptp_mcmc

# Output: species partition with branch annotations
# > 0.95 posterior support: strong; 0.80-0.95: moderate; < 0.80: uncertain

bPTP — Legacy Tree-Based Delimitation (Python, NOT R)

Goal: Bayesian Poisson Tree Processes delimitation with single intraspecific rate (Zhang 2013).

Approach: Run bPTP via the Python package from https://github.com/zhangjiajie/PTP (NOTE: this is a Python tool, NOT an R package; install.packages('PTP') does not exist). For heterogeneous-rate data, prefer mPTP.

# bPTP Python install (NOT R)
pip install git+https://github.com/iTaxoTools/PTP-pyqt5

# Run bPTP MCMC
python -m PTP.PTP -t rooted_tree.nwk -o bptp_results \
    --type bayesian --ngen 100000 --burnin 0.1 --seed 42

# Output: posterior support per species partition (> 0.95 strong)
# bPTP over-splits when populations have strong geographic substructure
# Cross-check with ASAP and/or mPTP for consensus

GMYC with Mandatory Ultrametric Tree

Goal: Detect the threshold on an ultrametric phylogeny where branching transitions from interspecific (Yule) to intraspecific (coalescent).

Approach: Convert ML tree to ultrametric with ape::chronos (penalized likelihood) or BEAST (Bayesian time-calibration), then run GMYC. Single-threshold vs multi-threshold; multi-threshold handles heterogeneous rates across the tree.

library(splits)
library(ape)

tree <- read.tree('rooted_tree.nwk')

# Convert to ultrametric (REQUIRED for GMYC)
# chronos: penalized likelihood; lambda=1 mid-clock
# For rigorous time-calibration, use BEAST2 instead
ultrametric_tree <- chronos(tree, lambda = 1)
class(ultrametric_tree) <- 'phylo'
stopifnot(is.ultrametric(ultrametric_tree))

# Single-threshold GMYC
gmyc_single <- gmyc(ultrametric_tree, method = 'single')
summary(gmyc_single)
cat('GMYC species (single-threshold):', gmyc_single$entity[1], '\n')
cat('LR test p-value:', gmyc_single$p.value[1], 


gmyc_multi  gmycultrametric_tree method  
summarygmyc_multi


species_single  spec.listgmyc_single
species_multi  spec.listgmyc_multi

BPP A10/A11 — Multilocus MSC Delimitation with Calibrated Priors

Goal: Bayesian multilocus species delimitation under the multispecies coalescent (Yang & Rannala 2010; current Flouri 2018 BPP v4).

Approach: Configure BPP A10 (joint delimitation + species tree estimation) with priors on theta and tau ESTIMATED FROM DATA — NOT defaults. Yang 2015 Curr Zool 61:854-865 gives explicit guidance: thetaprior = G(2, 2000) for small/moderate data; tighter for large genomic data. Wrong priors dominate the analysis.

# BPP control file for A10 analysis
# CRITICAL: priors below are CALIBRATED to data, not defaults
# Defaults are tuned for primate-mammal data; insects/fish/plants need adjustment

seed = 12345

seqfile = alignment.phy
Imapfile = imap.txt                   # map individuals -> putative species
outfile = bpp_results.txt
mcmcfile = bpp_mcmc.txt

speciesdelimitation = 1 0 2 0.5       # 1 = delimitation mode; rjMCMC parameters
speciestree = 1                       # 1 = estimate species tree
speciesmodelprior = 1                 # 1 = uniform prior on rooted trees

species&tree = 4  speciesA speciesB speciesC speciesD
                  10 8 12 6
                  ((speciesA, speciesB), (speciesC, speciesD));

# CALIBRATED theta prior (per-locus nucleotide diversity)
# G(2, 2000) -> mean = 0.001 (calibrate from observed per-locus pi)
# Yang 2015 Curr Zool 61:854 has taxon-specific recommendations
thetaprior = 2 2000

# CALIBRATED tau prior (divergence times)
# Calibrate from observed maximum genetic distance / mutation rate
tauprior = 2 2000

# MCMC
nsample = 100000
sampfreq = 2
burnin = 50000

# Run multiple independent chains; check convergence

# Run BPP with multiple independent chains
for i in 1 2 3 4; do
    bpp --cfile bpp_control.bpp --seed $((1000 * i)) --out bpp_run_$i.txt &
done
wait

# Compare chains; posterior probabilities must agree across chains
# PP > 0.95 for a delimitation: strong support
# PP 0.50-0.95: moderate; integrate other evidence

DELINEATE — Addressing the Sukumaran-Knowles Oversplitting

Goal: Test whether candidate lineages from BPP represent fully-formed species OR incomplete-speciation structure (Sukumaran 2021).

Approach: Provide DELINEATE with a guide tree of population lineages and a hypothesis about which are species. The output: probability that each population lineage is a true species vs an incipient lineage. THE post-2021 best practice complement to BPP.

# DELINEATE (https://github.com/jsukumaran/delineate)
# Python tool
pip install delineate

# Configure delineate input
# - guide tree: rooted Newick with population lineages as tips
# - constraint file: which tips are confirmed species, which to test
delineate-estimate partitions \
    --tree-file population_lineage_tree.nwk \
    --config-file delineate_config.json \
    --output-prefix delineate_run

# Output: probability that each lineage is a species
# Use ALONGSIDE BPP; do not claim species from BPP alone post-2021

Dsuite — Introgression vs Incomplete Lineage Sorting

Goal: Test for introgression before claiming sister-species relationships, distinguishing admixture from ILS.

Approach: Compute D-statistic (ABBA-BABA), f4-ratio (admixture fraction), and f-branch (Malinsky 2021 Mol Ecol Resour 21:584-595) on a VCF with population assignments. Non-zero D indicates introgression between H3 and one of H1/H2.

# Dsuite (https://github.com/millanek/Dsuite)
# Sets file: 3 columns (sample_id, population/species, optional outgroup tag)
# Tree file: Newick with population names matching the sets file

# All-trio D-statistic
Dsuite Dtrios -t species_tree.nwk sets.txt input.vcf.gz

# F-branch decomposition (more interpretable than raw D)
Dsuite Fbranch species_tree.nwk Dtrios_output_tree.txt > fbranch.txt

# Plot f-branch matrix
dtools.py fbranch.txt species_tree.nwk

# D > 0 with |Z| > 3: significant ABBA-BABA imbalance -> introgression
# f4-ratio gives the admixture fraction estimate

Per-Method Failure Modes

MSC delimitation oversplits population structure as species

Trigger: Running BPP, BFD*, or other multispecies-coalescent methods on data with within-species substructure (isolated demes), then claiming the delimited lineages are species.

Mechanism: The MSC model treats every panmictic population as a "species" in its parameterization (Sukumaran & Knowles 2017). When applied to data with population substructure, MSC partitions that structure into "species."

Symptom: Many "species" delimited; each with low sample size; lineages defined by geography rather than morphology; BPP posterior support is high but biological reality is unclear.

Fix: Run DELINEATE (Sukumaran 2021) on the BPP output to test which lineages have completed speciation. Validate via integrative taxonomy (Padial 2010): require congruence across genetic + morphological + ecological evidence before publishing species.

ABGD/ASAP forced to partition data without a barcoding gap

Trigger: Reporting "best partition" from ABGD or ASAP when the pairwise distance histogram is unimodal (no gap).

Mechanism: ABGD and ASAP detect a gap in the pairwise-distance distribution that separates intra- from inter-specific divergences. Meyer & Paulay 2005 PLoS Biol 3:e422 demonstrated the gap is frequently absent.

Symptom: Distance histogram is unimodal (no clear bimodality); ASAP partitions have similar scores across many K values; ABGD priors disagree.

Fix: Inspect the distance histogram first; if no clear gap, do NOT report ASAP/ABGD results as definitive; integrate other lines of evidence.

bPTP oversplit on populations with strong substructure

Trigger: Running bPTP on a tree where the same biological species has multiple isolated demes contributing genetic structure.

Mechanism: bPTP detects branching-rate shifts as species boundaries; isolated demes within a species show coalescent-rate branching that bPTP partitions as separate species.

Symptom: More bPTP species than morphologically recognized; species correspond to geographic regions.

Fix: Cross-check with ASAP (more conservative) and with mPTP at multi-rate option; treat bPTP/mPTP as preliminary; require congruence with other lines of evidence.

BPP with default theta and tau priors

Trigger: Running BPP without calibrating priors to the data; using priors copied from a primate/mammal example file.

Mechanism: BPP results depend critically on theta (per-locus nucleotide diversity) and tau (divergence time) priors. Default priors are typically tuned for mammal data; for insects, fish, plants with different mutation-rate and Ne contexts, defaults can dominate the posterior.

Symptom: Posterior support clusters at 1.0 OR 0.0; results inconsistent with morphological / ecological data; sensitivity to alternative priors is very high.

Fix: Calibrate priors from data per Yang 2015 Curr Zool 61:854: estimate per-locus heterozygosity for theta mean; estimate divergence times from observed maximum genetic distances and mutation rate.

Reporting D-statistic without f-branch

Trigger: Reporting raw D-statistics for many taxa without decomposing introgression patterns.

Mechanism: D-statistic measures asymmetric site-pattern counts; with taxon-rich datasets, D > 0 can be confounded by ancient introgression in the outgroup or by ghost lineages. f-branch (Malinsky 2018) decomposes admixture across the tree more interpretably.

Symptom: Many trios with D > 0; difficult to identify which specific introgression event drives the pattern.

Fix: Report f-branch decomposition alongside D; check for consistent patterns across phylogenetic levels.

Quantitative Thresholds

Threshold	Value	Source / rationale
COI K2P "species" cutoff	typically 2-3% pairwise distance (variable)	Animal barcoding convention; verify per taxon
ASAP score gap signaling robust partition	Large drop between consecutive scores	Puillandre 2021; inspect top 5-10 partitions
bPTP/mPTP posterior support	> 0.95 strong; 0.80-0.95 moderate; < 0.80 uncertain	Standard Bayesian inference
GMYC LR test p-value	< 0.05 detects significant transition	Pons 2006
BPP posterior probability	> 0.95 strong; 0.50-0.95 moderate; < 0.50 uncertain	Yang 2015
BPP minimum independent chains	4 with different seeds	Convergence check
Dsuite D significance		Z
Sukumaran-Knowles validation rule	DELINEATE OR integrative-taxonomy congruence required post-2021	Modern best practice
Integrative taxonomy congruence	Genetic + morphological + ecological all support hypothesis	Padial 2010; Carstens 2013

Common errors

Error	Cause	Solution
ABGD/ASAP returns no clear partition	Barcoding gap absent in data	Inspect distance histogram; do not force partition
bPTP install error in R	bPTP is Python, NOT R	pip install git+https://github.com/iTaxoTools/PTP-pyqt5
GMYC error "not ultrametric"	ML tree directly fed without chronos	Run ape::chronos() first or use BEAST output
BPP all PP = 1.0 (oversplit)	Default priors too informative; population substructure	Calibrate priors from data; run DELINEATE alongside
BPP no convergence across chains	Insufficient burnin or sampling	Increase nsample and burnin; multiple chains
Dsuite format error	Sets file column ordering	First column = sample ID, second = species/population
mPTP not found	Build from source github.com/Pas-Kapli/mptp	pip install mptp does not exist
ASAP web upload "too many sequences"	> 10^4 sequences	Use downloadable C binary instead of web

References

• de Queiroz K (2007) Species concepts and species delimitation. Syst Biol 56(6):879-886. doi:10.1080/10635150701701083
• Pons J, Barraclough TG, Gomez-Zurita J et al. (2006) GMYC original. Syst Biol 55(4):595-609. doi:10.1080/10635150600852011
• Fujisawa T, Barraclough TG (2013) Revised GMYC. Syst Biol 62(5):707-724. doi:10.1093/sysbio/syt033
• Puillandre N, Lambert A, Brouillet S, Achaz G (2012) ABGD original. Mol Ecol 21(8):1864-1877. doi:10.1111/j.1365-294X.2011.05239.x
• Puillandre N, Brouillet S, Achaz G (2021) ASAP. Mol Ecol Resour 21(2):609-620. doi:10.1111/1755-0998.13281
• Zhang J, Kapli P, Pavlidis P, Stamatakis A (2013) PTP / bPTP. Bioinformatics 29(22):2869-2876. doi:10.1093/bioinformatics/btt499
• Kapli P, Lutteropp S, Zhang J et al. (2017) mPTP. Bioinformatics 33(11):1630-1638. doi:10.1093/bioinformatics/btx025
• Yang Z, Rannala B (2010) BPP original. Proc Natl Acad Sci USA 107(20):9264-9269. doi:10.1073/pnas.0913022107
• Yang Z (2015) BPP review with prior guidance. Curr Zool 61(5):854-865. doi:10.1093/czoolo/61.5.854
• Flouri T, Jiao X, Rannala B, Yang Z (2018) BPP v4 genomic-scale. Mol Biol Evol 35(10):2585-2593. doi:10.1093/molbev/msy147
• Bryant D, Bouckaert R, Felsenstein J et al. (2012) SNAPP. Mol Biol Evol 29(8):1917-1932. doi:10.1093/molbev/mss086
• Leache AD, Fujita MK, Minin VN, Bouckaert RR (2014) BFD*. Syst Biol 63(4):534-542. doi:10.1093/sysbio/syu018
• Sukumaran J, Knowles LL (2017) MSC delimits structure, not species. Proc Natl Acad Sci USA 114(7):1607-1612. doi:10.1073/pnas.1607921114
• Sukumaran J, Holder MT, Knowles LL (2021) DELINEATE (speciation-process modeling). PLoS Comput Biol 17(5):e1008924. doi:10.1371/journal.pcbi.1008924
• Padial JM, Miralles A, De la Riva I, Vences M (2010) Integrative future of taxonomy. Front Zool 7:16. doi:10.1186/1742-9994-7-16
• Carstens BC, Pelletier TA, Reid NM, Satler JD (2013) How to fail at species delimitation. Mol Ecol 22(17):4369-4383. doi:10.1111/mec.12413
• Malinsky M, Matschiner M, Svardal H (2021) Dsuite. Mol Ecol Resour 21(2):584-595. doi:10.1111/1755-0998.13265
• Malinsky M, Svardal H, Tyers AM et al. (2018) Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow (f-branch original). Nat Ecol Evol 2(12):1940-1955. doi:10.1038/s41559-018-0717-x
• Meyer CP, Paulay G (2005) Barcoding-gap-absence critique. PLoS Biol 3(12):e422. doi:10.1371/journal.pbio.0030422

Related Skills

• ecological-genomics/edna-metabarcoding - Generate barcode sequences from environmental samples for primary delimitation
• ecological-genomics/conservation-genetics - Population-level genetic assessment (use ESU/MU framework, NOT species delimitation, for management units)
• phylogenetics/tree-io - Tree input/output for tree-based delimitation methods
• phylogenetics/modern-tree-inference - ML and Bayesian tree construction for delimitation input
• database-access/entrez-fetch - Retrieve barcode sequences from GenBank for reference comparison