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.
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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.
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 to verify parameters
?function_name
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: 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):
Run MSC methods (BPP, BFD*) as PRELIMINARY enumeration of candidate population lineages
Apply DELINEATE (Sukumaran 2021 PLoS Comput Biol 17:e1008924) to test which lineages have completed speciation vs are in the speciation process
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
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 landscapefrom 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.idfor r in alignment]
n = len(names)
dist_array = np.zeros((n, n))
for i inrange(n):
for j inrange(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='distance')
print(f'Threshold {t:.3f}: {len(set(clusters))} groups')
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],'\n')# Multi-threshold for heterogeneous rates
gmyc_multi <- gmyc(ultrametric_tree, method ='multiple')
summary(gmyc_multi)# Compare single vs multi-threshold; report both
species_single <- spec.list(gmyc_single)
species_multi <- spec.list(gmyc_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 chainsfor i in 1 2 3 4; do
bpp --cfile bpp_control.bpp --seed $((1000 * i)) --out bpp_run_$i.txt &
donewait# 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.
