Phylogeny and evolution of habitat preference in Goniurosaurus (Squamata: Eublepharidae) and their correlation with karst and granite-stream-adapted ecomorphologies in species groups from Vietnam
expand article infoL. Lee Grismer, Hai Ngoc Ngo§|, Shuo Qi#, Ying-Yong Wang#, Minh Duc Le¤«», Thomas Ziegler|
‡ La Sierra University, Riverside, United States of America
§ Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| Cologne Zoo, Cologne, Germany
¶ University of Cologne, Cologne, Germany
# Sun Yat-sen University, Guangzhou, China
¤ University of Science, Hanoi, Vietnam
« Vietnam National University, Hanoi, Vietnam
» American Museum of Natural History, New York, United States of America
Open Access


Maximum likelihood (ML) and Bayesian inference (BI) analyses using two mitochondrial (16S and cyt b) and two nuclear (CMOS and RAG1) genes and 103 specimens recovered the first phylogenies of all 23 extant species of Goniurosaurus. The analyses strongly supported the recognition of four monophyletic species groups with identical inter-specific relationships within the kuroiwae, lichtenfelderi, and yingdeensis groups but discordant topologies at some nodes within the luii group. Both analyses recovered a polyphyletic G. luii with respect to G. kadoorieorum, and owing to the lack of diagnostic characters in the latter, it is considered a junior synonym of G. luii. A stochastic character mapping analysis of karst versus non-karst habitat preference suggested that karstic landscapes may have played a major role in the evolution and diversification of Goniurosaurus. A karst habitat preference is marginally supported as the most probable ancestral condition for Goniurosaurus as well as for the kuroiwae, luii, and yingdeensis groups. However, a non-karst habitat preference is marginally supported as the most probable ancestral condition for the lichtenfelderi group. Multivariate and univariate ecomorphological analyses of the karst-adapted G. catbaensis, G. huuliensis, and G. luii of the luii group and the granite-stream-adapted G. lichtenfelderii of the lichtenfelderi group demonstrated that their markedly statistically different body shapes may be an adaptive response that contributes to habitat partitioning in areas of northern Vietnam where they are nearly sympatric.


Asia, stochastic character mapping, systematics, synonymy, tiger geckos


Eublepharid geckos of the genus Goniurosaurus Barbour, 1908 comprise 23 saxicolous specialists (Uetz et al. 2021) that extend from the Ryukyu Archipelago in Japan, southward through East Asia to northern Vietnam. Goniurosaurus is a well-defined monophyletic group (Grismer 1988) comprised of four monophyletic species groups: the kuroiwae group containing six species endemic to the Ryukyu Archipelago, Japan; the lichtenfelderi group with five species from insular and mainland China and northern Vietnam; the luii group with eight species from northern Vietnam, some of its offshore islands, and southern China; and the yingdeensis group consisting of four species from southern China (Kurita et al. 2008; Nguyen et al. 2009; Nguyen 2011; Wang et al. 2013; Honda and Ota 2017; Liang et al. 2018; Qi et al. 2020a, 2020b; Zhu et al. 2020a, 2020b). Apart from these species, Goniurosaurus sinensis Zhou, Peng, Huo and Yuan, 2019 is likely a junior synonym of another species from Hainan Island, China and not included herein (Qi et al. in progress). Phylogenetic relationships within Goniurosaurus have never been strongly supported nor consistent among different studies (e.g. Wang et al. 2013; Liang et al. 2018; Qi et al. 2020a, 2020b; Zhu et al. 2020a, 2020b). This protracted state of discordance results, in part, from researchers focusing on different species groups as opposed to the entire genus, as well as using different genes or different combinations of genes with varying combinations of ingroup and outgroup species—all variables that bear significantly on tree construction (Wiens 1998; Zwickl et al. 2002; Heath et al. 2008; Wiens and Morrill 2011; Wainwright and Price 2016). The most commonly used genetic markers have been the mitochondrial genes 12S and 16S rRNA and cytochrome b (cyt b). Liang et al. (2018) were the first to address the challenges of properly aligning rRNA (Pyron et al. 2013) and constructed a well-supported mito-nuclear data set using 16S, cyt b, and the nuclear genes oocyte maturation factor MOS (CMOS), and recombination activating 1 (RAG1). Zhu et al. (2020b) also used this mito-nuclear combination, but examined only relationships within the lichtenfelderi group.

In an effort to continue building a more global understanding of the phylogenetic relationships within Goniurosaurus, we expanded the mito-nuclear data set of Liang et al. (2018) to include 103 individuals as opposed to 31 and 23 as opposed to 17 species, which for the first time, includes all extant species of the genus (Table 1). We used this phylogeny in a stochastic character state mapping (SCM) analysis (Revell 2012) of habitat preference to explore the role karstic landscapes may have played in the evolution and diversification of Goniurosaurus and if habitat preference coevolved with ecomorphology in near sympatric species of the luii and lichtenfelderi groups in Vietnam (Ngo et al. 2021; Figs 1, 2).

Figure 1. 

Distribution of the four recorded species of Goniurosaurus in Vietnam and China used in the ecomorphological analysis.

Figure 2. 

A1–A3. Photos of karst-adapted species (Goniurosaurus catbaensis, G. huuliensis, and G. luii, respectively), A4. Granite-stream-adapted species (G. lichtenfelderii), B1 – B3. Karst habitats of the luii group, B4. Granite-stream habitat of the lichtenfelderi group. Photos by Hai Ngoc Ngo.

Table 1.

Species and GenBank accession numbers of the sequenced specimens used herein.

Species/Specimen 16s cytb CMOS RAG1
Goniurosaurus araneus AB308460
G. araneus ECNU-V0008 MT533259
G. araneus JFBM15830 HQ426537 HQ426286
G. bawanglingensis BL-RBZ-021 MH247190 MH247201 MH247212 MH247223
G. bawanglingensis BL-RBZ-022 MH247191 MH247202 MH247213 MH247224
G. bawanglingensis BL-RBZ-023 MH247192 MH247203 MH247214 MH247225
G. bawanglingensis BL-RBZ-024 MH247193 MH247204 MH247215 MH247226
G. bawanglingensis SYS r002162 MT995758 MT995773
G. catbaensis G33 MW741550 MW650944
G. catbaensis G34 MW741551 MW650945
G. catbaensis G35 MW650946
G. catbaensis MHNG 2699.49 EU499389
G. gezhi ECNU-V0038 MT533260
G. gezhi ECNU-V0040 MT533261
G. gezhi ECNU-V0042 MT533262
G. gezhi ECNU-V0046 MT533263
G. gezhi ECNU-V0047 MT533264
G. gollum SYS r002420 MT995784 MT995787 MW727559 MW727594
G. gollum SYS r002421 MT995785 MT995788 MW727560 MW727595
G. gollum SYS r002422 MT995786 MT995789 MW727561 MW727596
G. hainanensis BL-RBZ-041 MH247194 MH247205 MH247216 MH247227
G. hainanensis BL-RBZ-042 MH247195 MH247206 MH247217 MH247228
G. hainanensis SYS r000349 KC765080
G. hainanensis JK1 AB308458
G. huuliensis Gohu AB853453 AB853479
G. huuliensis G21 MW650936
G. huuliensis G23 MW650937
G. huuliensis G24 MW650938
G. kadoorieorum ECNU-V0058 MT533258
G. kadoorieorum ECNU-V0060 MT533265
G. kadoorieorum ECNU-V0061 MT533266
G. kuroiwae Goku1 Northern Okinawa AB853448 AB853473
G. kuroiwae Goku2 Southern Okinawa AB853445
G. kuroiwae Goor1 Southern Okinawa AB853446 AB853467
G. kwanghua ECNU-V0003 MK782788 MK782782 MK782776 MK782770
G. kwanghua ECNU-V0004 MK782789 MK782783 MK782777 MK782771
G. kwanghua ECNU-V0005 MK782790 MK782784 MK782778 MK782772
G. kwangsiensis ECNU-V0009 MK782786 MK782780 MK782774 MK782768
G. liboensis SYS r000217 KC900230
G. lichtenfelderii ECNU-V0007 MK782785 MK782779 MK782773 MK782767
G. lichtenfelderii IEBR 3692 JF799756
G. luii ECNU-V0012 MK782787 MK782781 MK782775 MK782769
G. luii Golu2 EF081254
G. luii Golu3 AB853452 AB853478
G. luii SYSr 000255 KC765083
G. luii SYSr 000256 KC765084
G. luii ZFMK 87057 EU499391
G. luii TG00795 HQ426287
G. orientalis Goku3 AB853446
G. orientalis Goor2 AB853443 AB853461
G. orientalis Goor3 AB853462
G. sengokui Gose1 AB853444 AB853463
G. sengokui Gose2 AB853464
G. sengokui KUZ 62087 HQ876393
G. splendens Gosp1 AB853451 AB853477
G. splendens Gosp2 AB853449
G. splendens Gosp3 AB853450
G. toyamai Goto1 AB853447 AB853468
G. toyamai Goto2 AB853469
G. variusi SYS r002331 MT995754 MT995769 MW727556 MW727590
G. variusi SYS r002332 MT995755 MT995770
G. variusi SYS r002333 MT995753 MT995768
G. variusi SYS r002362 MT995756 MT995771 MW727557 MW727592
G. variusi SYS r002363 MT995757 MT995772 MW727558 MW727593
G. variusi SYS r002485 MW721828 MW727532 MW727562 MW727597
G. variusi SYS r002486 MW721829 MW727533 MW727563 MW727598
G. yamashinae Goya1 AB853442 AB853460
G. yamashinae Goya2 AB853441 AB853459
G. yamashinae Goya3 AB853458
G. yingdeensis Field number DYA01 MW721830 MW727534 MW727574 MW727605
G. yingdeensis Field number DYA02 MW721831 MW727535 MW727575 MW727606
G. yingdeensis Field number HS01 MW721832 MW727536 MW727576 MW727607
G. yingdeensis Field number HS02 MW721833 MW727537 MW727577 MW727608
G. yingdeensis Field number LT01 MW721834 MW727538 MW727580 MW727611
G. yingdeensis Field number LT02 MW721835 MW727539 MW727581 MW727612
G. yingdeensis SYS r000549 KC765082
G. yingdeensis SYS r000550 KC900231
G. yingdeensis SYS r001271 MT995759 MT995774 MW727547
G. yingdeensis SYS r001272 MT995760 MT995775 MW727548
G. yingdeensis SYS r001493 MT995761 MT995776 MW727551
G. yingdeensis SYS r0002115 MT995762 MT995777
G. zhelongi Field number HW01 MW721838 MW727540 MW727578 MW727609
G. zhelongi Field number HW02 MW721839 MW727541 MW727579 MW727610
G. zhelongi Field number MDA01 MW721836 MW727542 MW727582 MW727613
G. zhelongi Field number MDA02 MW721837 MW727543 MW727583 MW727614
G. zhelongi Field number TZ01 MW721840 MW727544 MW727584 MW727615
G. zhelongi Field number TZ02 MW721841 MW727545 MW727585 MW727616
G. zhelongi SYS r000816 KJ423105 MT995778 MW727570
G. zhelongi SYS r001491 MT995763 MT995779 MW727549
G. zhelongi SYS r001492 MT995764 MT995780 MW727550
G. zhelongi SYS r002108 MT995765 MT995781
G. zhoui BL-RBZ-001 MH247196 MH247207 MH247218 MH247229
G. zhoui BL-RBZ-004 MH247197 MH247208 MH247219 MH247230
G. zhoui BL-RBZ-006 MH247198 MH247209 MH247220 MH247231
G. zhoui BL-RBZ-007 MH247199 MH247210 MH247221 MH247232
G. zhoui BL-RBZ-008 MH247200
G. zhoui SYS r002213 MT995766 MT995782 MW727553
G. zhoui SYS r002214 MT995767 MT995783 MW727554
Eublepharis macularius AB853454 AB853480

Materials and methods

Genetic data and phylogenetic analyses

Genomic DNA was extracted from muscle tissue samples, using a DNA extraction kit from Tiangen Biotech (Beijing) Co., Ltd. Primers used for 16S were r16S-5L (5’- GGTMMYGCCTGCCCAGTG -3’) and 16Sbr-H (5’- CCGGTCTGAACTCAGATCACGT-3’) (Palumbi et al. 1991), for cyt b the primers were L14731 (5’- TGGTCTGAAAAACCATTGTTG-3’) (Honda et al. 2014) and H15149m (5’- GCMCCTCAGAAKGATATTTGYCCTCA-3’) (Chambers and MacAvoy 1999), for CMOS the primers were FU-F (5’- TTTGGTTCKGTCTACAAGGCTAC -3’) and FU-R (5’- AGGGAACATCCAAAGTCTCCAAT -3’) (Gamble et al., 2008) , and for RAG1 the primers were R13 (5’- TCTGAATGGAAATTCAAGCTGTT -3’) and R18 (5’- GATGCTGCCTCGGTCGGCCACCTTT -3’) (Groth and Barrowclough 1999). The PCR procedure was performed with an initial denaturation at 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min, followed by a final extension at 72 °C for 10 min (Liang et al. 2018). PCR products were purified with spin columns and then sequenced with forward primers using BigDye Terminator Cycle Sequencing Kit as per the guidelines on an ABI Prism 3730 automated DNA sequencer by Shanghai Majorbio Bio-pharm Technology Co., Ltd.

We constructed Maximum Likelihood (ML), Bayesian Inference (BI), and Bayesian Evolutionary Analysis by Sampling Trees (BEAST) phylogenetic trees using a concatenated data set composed of 3070 base pairs (bp) of the mitochondrial genes, 16S (633 bp) and cyt b (1075 bp), and the nuclear genes, CMOS (472 bp) and RAG1 (890), from 103 specimens of 23 species of Goniurosaurus with varying degrees of sequence coverage across the samples (Table 1). Concatenation followed the comparison of separate gene trees to confirm there were no major discordances. One species, Eublepharis macularius, served as an outgroup (Grismer 1988; Jonniaux and Kumazawa 2008) to root the trees. Sequence data and GenBank accession numbers are listed in Table 1.

A Maximum likelihood (ML) analysis partitioned by gene was implemented using the IQ-TREE webserver (Nguyen et al. 2015; Trifinopoulos et al. 2016) preceded by the selection of substitution models using TIM2+F+I+G4 for 16S and cyt b and HKY+F for CMOS and RAG1. To avoid over parameterization, protein coding genes were not partitioned by codon. One-thousand bootstrap pseudoreplicates via the ultrafast bootstrap (UFB: Hoang et al. 2018) approximation algorithm were employed, and nodes having UFB values of 95 and above were considered strongly supported (Minh et al. 2013). We considered nodes with values of 90–94 as well-supported. A Bayesian inference (BI) analysis was carried out in MrBayes 3.2.3. (Ronquist et al. 2012) on XSEDE using the CIPRES Science Gateway (Cyberinfrastructure for Phylogenetic Research; Miller et al. 2010) employing default priors and models of evolution that most closely approximated those selected by the BIC and used in the ML analysis. Two independent Markov chain Monte Carlo (MCMC) analyses for each data set were performed—each with four chains, three hot and one cold. The MCMC simulations ran for 100 million generations. Trees were sampled every 10,000 generations, and the first 10% of the trees from each run from each data set were discarded as burn-in. The parameter files from both runs were checked in Tracer v1.6 (Rambaut et al. 2014) to ensure that convergence and stationarity of all parameters had effective sample sizes (ESS) well-above above 200. Post-burn-in sampled trees from each respective run were combined and 50% majority-rule consensus trees were constructed. Nodes with Bayesian posterior probabilities (BPP) of 0.95 and above were considered highly supported (Huelsenbeck et al. 2001; Wilcox et al. 2002). We considered nodes with values of 0.90–0.94 as well-supported.

An input file was constructed in Bayesian Evolutionary Analysis Utility (BEAUti) v. 2.4.6 using a relaxed lognormal clock with unlinked site models, linked trees and clock models, and a Yule prior and run in BEAST on CIPRES (Cyberinfrastructure for Phylogenetic Research; Miller et al. 2010). bModelTest was used to numerically integrate over the uncertainty of substitution models of each gene while simultaneously estimating phylogeny using Markov chain Monte Carlo (MCMC). MCMC chains were run for 100,000,000 generations and logged every 10,000 generations. The BEAST log file was visualized in Tracer v. 1.6.0 (Rambaut et al. 2014) to ensure effective sample sizes (ESS) were well-above 200 for all parameters. A Maximum clade credibility tree using mean heights at the nodes was generated using TreeAnnotator v.1.8.0 (Rambaut and Drummond 2014) with a burn-in of 1000 trees (10%). Nodes with BPPs of 0.95 and above were considered strongly supported (Huelsenbeck et al. 2001; Wilcox et al. 2002). We considered nodes with values of 0.90–0.94 as well-supported.

Ancestral state reconstruction

The BEAST tree was converted to newick format and pruned using the drop.tip () command (Paradis and Schliep 2018) in the R package ape [v.3.4.3] to include only the earliest diverged individual of each species. Habitat preference (karst or non-karst; see below) was mapped onto the tree using SCM implemented in the R package Phytools (Revell 2012) in order to derive probability estimates of the ancestral states at each node. A transition rate matrix was identified that best fit the data by comparing the corrected Akaike Information Criterion (AICc) values in the R package ape (Paradis and Schilep 2018). Three transition rate models were considered: a 2-parameter model having different rates for every transition type (the ARD model); a single-parameter model with equal forward and reverse rates between states (the symmetrical rates SYM model); and a single rate parameter model that assumes equal rates among all transitions (ER). Lastly, an MCMC approach was used to sample the most probable 1000 trait histories from the posterior using the make.simmap () command and then summarized them using the summary () command.

The coding of habitat preference for each species was determined from the literature and field observations of the authors (Table 2). A species’ habitat preference was coded as “karst” if it had a strong association with karstic habitats. Many such species may range into forested areas or even other rock types (granite or volcanic) if nearby but their densities are much greater in karstic areas. A species was coded as “non-karst” if detected only in forested areas or forested areas with other rock types (e.g. granite). These species never show any strong preference for karstic microhabitats even if such habitats exist within their range.

Table 2.

Habitat preferences of the species of Goniurosaurus.

Species 1° habitat 2° habitat Source
kuroiwae group
G. splendens karst forest Nakamura and Ueno (1963), H. Ota pers. comm., L. Grismer pers. obs.
G. toyamai forest H. Ota pers. comm.
G. kuroiwae North forest H. Ota pers. comm., L. Grismer pers. obs.,
G. kuroiwae South karst forest Nakamura and Ueno (1963), H. Ota pers. comm., L. Grismer pers. obs.
G. yamashinae karst forest H. Ota pers. comm., L. Grismer pers. obs.,
G. sengokui karst forest Werner et al. (2004), H. Ota pers. comm.
G. orientalis karst H. Ota pers. comm.
yingdeensis group
G. gollum karst Qi et al. (2020a)
G. yingdeensis karst granite Wang et al. (2010), S. Qi pers. obs.
G. zhelongi karst granite S. Qi, pers. obs., Wang et al. (2014)
G. variusi karst Qi et al. (2020b)
lichtenfelderi group
G. bawanglingensis granite karst Grismer et al. (2002), Orlov et al. (2008)
G. zhoui karst granite Zhou et al. (2018), S. Qi pers. obs.
G. kwanghua karst granite Zhu et al. (2020)
G. lichtenfelderii granite Orlov et al. (2008)
G. hainanensis granite volcanic S. Qi pers. obs., L. Grismer pers. obs.
luii group
G. catbaensis karst Ziegler et al. (2008), Ngo et al. (2019a)
G. gezhi karst Zhu et al. (2020)
G. araneus karst Grismer et al. (1999)
G. kadoorieorum karst Yang and Chan (2015)
G. huuliensis karst Orlov et al. (2008)
G. luii karst Grismer et al. (1999), Vu et al. (2006)
G. liboensis karst Wang et al. (2013)
G. kwangsiensis karst Yang and Chan (2015)

Morphological data and analyses

An ecomorphological analysis was conducted using four of the five recorded species from Vietnam (Grismer et al. 1999; Vu et al. 2006; Orlov et al. 2008; Ziegler et al. 2008; Nguyen et al. 2009; Nguyen 2011; Orlov et al. 2020) for which there existed a substantially large morphometric data set (Ngo et al. 2021): the karst-adapted G. catbaensis Ziegler, Nguyen, Schmitz, Stenke, and Rösler, 2008, G. huuliensis Orlov, Ryabov, Nguyen, Nguyen, and Ho, 2008, and G. luii Grismer, Viets, and Boyle, 1999 of the luii group and the granite stream-adapted G. lichtenfelderii (Mocquard, 1897) of the lichtenfelderi group (Figs 1, 2). A total of 486 live individuals and 54 museum specimens of four species were examined for morphological data, comprising 194 individuals of G. catbaensis (21 juveniles, 93 females, and 80 males), 80 individuals of G. huuliensis (two juveniles, 46 females, and 32 males), and 88 individuals of G. luii (11 juveniles, 43 females, and 34 males) of the luii species group and 178 individuals of G. lichtenfelderii (14 juveniles, 72 females, and 92 males) of the lichtenfelderi group.

Measurements were taken with dial calipers to the nearest 0.1 mm on the right side of each individual. Abbreviations are as follows: snout-vent length (SVL), from tip of snout to vent; axilla to groin length (AG), from posterior edge of forelimb insertion to anterior edge of hind limb insertion; maximum body width (BW), greatest width of torso, taken at level of midbody; maximum body height (BH), from dorsal surface of body to belly; internarial distance (ID), distance between nares; head length (HL), from the tip of snout to posterior edge of occiput; maximum head width (HW); cheek height (CH), from posterior edge of labial to top of head at parietal region; interorbital distance (IO), distance between posteriormost points of eyes; diameter of auditory meatus (AD); snout to eye distance (SL), measured from tip of snout to anteriormost point of eye; diameter of eye (ED), greatest diameter of eye; eye to ear distance (EE), from posterior margin of eye to posterior margin of ear; forelimb length (FLL), from axilla to the tip of the fourth finger; hind limb length (HLL), from groin to the tip of the fourth toe. To remove potential effects of allometry, size was adjusted using the following equation: Xadj=log(X)-β[log(SVL)-log(SVLmean)], where Xadj=adjusted value; X=measured value; β=unstandardized regression coefficient for each population; and SVLmean=overall average SVL of all populations (Thorpe 1975, 1983; Turan 1999; Lleonart et al. 2000)—accessable in the R package GroupStruct (available at The morphometrics of each species were adjusted separately and then concatenated so as not to conflate intra- with interspecific variation (Reists 1986). All data were then scaled to their standard deviation to insure they were analyzed on the basis of correlation and not covariance and were log-transformed to insure they were normally distributed.

An analysis of variance (ANOVA) was performed on a data set coded for species to search for the presence of statistically significant mean differences (p < 0.05) among characters across the selected subset of species in the luii and lichtenfelderi groups. Character means bearing statistical differences among species were subjected to a TukeyHSD test to ascertain which species pairs differed significantly from each other for those particular characters. A Student t-test was also performed on a second data set coded for only habitat preference (karst versus non-karst) to search for the presence of statistically significant mean differences (p < 0.05) among the same subsets of species coded for habitat. Violin plots with inserted boxplots were generated in order to visualize the range, frequency, mean, 50% quartile, and degree of differences between the dependent variables for both data sets bearing statistically different mean values.

The morphospatial clustering of the two separate data sets (species and habitat preference) were visualized using principal component analysis (PCA) along the ordination of the first two principal components (PC) using the Adegenet package in R (Jombart et al. 2010) and implemented by the prcomp () command. The data were log-transformed prior to analysis in order to normalize their distribution so as to ensure characters with very large or very low values could not over-leverage the results owing to intervariable nonlinearity. All statistical analyses were performed using R.3.1.2 (R Core Team 2018).


Phylogenetic relationships

The ML, BI, and BEAST analyses recovered strong nodal support (UFB 98–100/BPP 1.00) for the monophyly of all four species groups with the kuroiwae group being the strongly supported (100/1.00) sister group to the remaining three groups (Fig. 3). The ML analysis weakly recovered (88) the lichtenfelderi and yingdeensis groups as sister lineages but the BEAST analysis recovered this relationship with strong support (1.00; Fig. 4). The BI analysis recovered the luii and yingdeensis groups as sister lineages, although the support is so low (0.51), the three groups effectively form a polytomy. The ML and BI analyses recovered the identical inter-specific relationships within the species groups but discordant relationships with the BEAST analysis regarding the luii group. The ML and BI analyses recovered a poorly supported (G. catbaensis (G. araneus, G. gezhi)) clade but the BEAST analysis recovered G. catbaensis as the strongly supported (0.99) sister species to the remainder of the luii group species (Figs 3, 4, respectively). All analyses recovered a polyphyletic Goniurosaurus luii with respect to G. kadoorieorum (not shown in the pruned tree of Fig. 4). The mito-nuclear data set of Liang et al. (2018) differed from all the above analyses in that their ML and BI analyses (79/0.99) placed the yingdeensis group as the sister group to a sister lineage comprised of the luii group and lichtenfelderi group (87/1.00).

Figure 3. 

Mito-nuclear maximum likelihood topology with ultrafast bootstrap values (UFB) and Bayesian posterior probabilities (BPP) at the nodes. All species except Goniurosaurus luii had strong nodal support (100/1.00) for their monophyly. The inset in the luii species group is a section of the BI analysis showing the non-monophyly of G. luii with respect to G. kadoorieorum. Colored species are those used in the ecomorphological analyses.

Ancestral state reconstruction

The AICc scores for the three transition rate models of the SCM analysis were ARD = 34.547134 and SYM and ER = 32.099451. The SCM analysis using either the SYM or ER model suggests that a karst habitat preference is the most probable ancestral condition for Goniurosaurus (57.0% probability), the kuroiwae group (62.7%), the luii group (90.0%), and the yingdeensis group (95.7%; Fig. 4). The probable ancestral condition for the lichtenfelderi group is non-karst (55.4%). The karst habitat preference of G. kwanghua and G. zhoui of the lichtenfelderi group is considered to have evolved independently given that the ancestral condition of the lichtenfelderi group and that of the most recent common ancestor of the sister species G. lichtenfelderii and G. hainanensis was not karst-adapted (Fig. 4).

Figure 4. 

Stochastic character map of karst and non-karst habitat preferences on a BEAST topology. Percentages at the nodes are the probability of the ancestral habitat preference. Numbers at the nodes are BPP values. Colored species are those used in the ecomorphological analyses.


In both the species and habitat preference PCA analyses, PC1 accounted for 49.1% of the variation in the data set and loaded most heavily for limb length (FLL and HLL), snout length (SL), eye diameter (ED), interorbital distance (IO), head width (HW), and head length (HL). PC2 accounted for an additional 13.3% of the variation and loaded most heavily for body width (BW) and body height (BH) (Figs 5, 6; Table 4).

Figure 5. 

A. Principal component analysis of the karst-adapted species G. catbaensis, G. huuliensis, and G. luii of the luii group and the granite-stream-adapted G. lichtenfelderii of the lichtenfelderi group. B. Violin plots overlain with box plots showing the range, frequency, mean (white dot), and 50% quartile (black rectangle) of the size-adjusted morphometric characters.

The PCA analysis of the karst-adapted Goniurosaurus catbaensis, G. huuliensis, and G. luii of the luii group demonstrates that their body shapes greatly overlap in morphospace despite there being several slight, but statistically significant mean differences among them (Fig. 5; Table 3). Additionally, none of the plots of the karst-adapted species overlap with that of the granite stream-adapted species G. lichtenfelderii along the ordination of PC1.

Table 3.

Difference, lower and upper ranges, and adjusted p values of statistically significant mean differences between species pairs for each character based on ANOVA and subsequent TukeyHSD analyses.

axilla-groin (AG) difference lower range upper range p adjusted
huuliensis-catbaensis 0.095633573 0.072292064 0.118975082 3.37E-10
lichtenfelderi-catbaensis –0.075967576 –0.094200659 –0.057734493 3.37E-10
luii-catbaensis –0.029676257 –0.05225407 –0.007098445 0.004206065
lichtenfelderi-huuliensis –0.171601149 –0.195246969 –0.14795533 3.37E-10
luii-huuliensis –0.12530983 –0.152447204 –0.098172456 3.37E-10
luii-lichtenfelderi 0.046291319 0.023399042 0.069183596 1.60E-06
body width (BW)
huuliensis-catbaensis 0.106179184 0.066412755 0.145945613 4.37E-10
lichtenfelderi-huuliensis –0.083314667 –0.123599543 –0.043029791 8.67E-07
luii-huuliensis –0.121548632 –0.167781995 –0.075315269 5.34E-10
body height (BH)
huuliensis-catbaensis 0.094637915 0.046801122 0.142474708 2.84E-06
lichtenfelderi-huuliensis –0.07317021 –0.121630666 –0.024709755 0.000647886
luii-huuliensis –0.125013692 –0.180629845 –0.069397539 7.11E-08
luii-lichtenfelderi –0.051843482 –0.098759605 –0.004927359 0.02359844
internarial distance (ID)
lichtenfelderi-catbaensis –0.082396274 –0.105742691 –0.059049857 3.37E-10
luii-catbaensis –0.076638786 –0.105548379 –0.047729192 4.74E-10
lichtenfelderi-huuliensis –0.096677642 –0.126954757 –0.066400527 3.37E-10
luii-huuliensis –0.090920154 –0.125668004 –0.056172303 5.78E-10
head length (HL)
huuliensis-catbaensis 0.075818967 0.058801575 0.092836359 3.37E-10
lichtenfelderi-catbaensis –0.162875997 –0.176169033 –0.149582961 3.37E-10
luii-catbaensis –0.029098886 –0.045559496 –0.012638276 3.82E-05
lichtenfelderi-huuliensis –0.238694964 –0.255934217 –0.221455712 3.37E-10
luii-huuliensis –0.104917853 –0.124702663 –0.085133043 3.37E-10
luii-lichtenfelderi 0.133777111 0.117087238 0.150466985 3.37E-10
head width (HW)
huuliensis-catbaensis 0.036775074 0.019138869 0.05441128 6.89E-07
lichtenfelderi-catbaensis –0.158637831 –0.172414249 –0.144861413 3.37E-10
luii-catbaensis –0.065002064 –0.082061241 –0.047942887 3.37E-10
lichtenfelderi-huuliensis –0.195412905 –0.213279039 –0.177546772 3.37E-10
luii-huuliensis –0.101777138 –0.122281395 –0.081272881 3.37E-10
luii-lichtenfelderi 0.093635767 0.07633899 0.110932545 3.37E-10
head height (HH)
huuliensis-catbaensis 0.108413032 0.073172094 0.143653969 3.37E-10
lichtenfelderi-catbaensis –0.032237965 –0.059766217 –0.004709713 0.014142658
lichtenfelderi-huuliensis –0.140650997 –0.176351381 –0.104950613 3.37E-10
luii-huuliensis –0.14002168 –0.180993602 –0.099049757 3.37E-10
cheek height (CH)
huuliensis-catbaensis 0.069595379 0.028246161 0.110944597 0.000101199
lichtenfelderi-catbaensis –0.14807373 –0.180373429 –0.11577403 3.37E-10
luii-catbaensis –0.057345812 –0.09734215 –0.017349473 0.001381599
lichtenfelderi-huuliensis –0.217669109 –0.259557409 –0.175780808 3.37E-10
luii-huuliensis –0.126941191 –0.175014741 –0.078867641 5.00E-10
luii-lichtenfelderi 0.090727918 0.050174509 0.131281326 8.27E-08
interorbital distance (ID)
huuliensis-catbaensis 0.05692565 0.034698839 0.079152461 9.29E-10
lichtenfelderi-catbaensis –0.211451872 –0.228814215 –0.194089528 3.37E-10
luii-catbaensis –0.03636425 –0.057863835 –0.014864664 9.22E-05
lichtenfelderi-huuliensis –0.268377522 –0.29089411 –0.245860933 3.37E-10
luii-huuliensis –0.093289899 –0.1191313 –0.067448499 3.37E-10
luii-lichtenfelderi 0.175087622 0.15328859 0.196886655 3.37E-10
snout length (SL)
huuliensis-catbaensis 0.108547374 0.083797596 0.133297152 3.37E-10
lichtenfelderi-catbaensis –0.20706889 –0.226402034 –0.187735746 3.37E-10
lichtenfelderi-huuliensis –0.315616264 –0.340688712 –0.290543816 3.37E-10
luii-huuliensis –0.125435452 –0.154210112 –0.096660792 3.37E-10
luii-lichtenfelderi 0.190180812 0.16590737 0.214454254 3.37E-10
ear diameter (ED)
huuliensis-catbaensis –0.137477526 –0.199198043 –0.075757008 9.51E-08
lichtenfelderi-catbaensis –0.338691314 –0.386903935 –0.290478693 3.37E-10
luii-catbaensis –0.365692271 –0.425393393 –0.305991149 3.37E-10
lichtenfelderi-huuliensis –0.201213788 –0.263738975 –0.138688602 3.37E-10
luii-huuliensis –0.228214745 –0.299972435 –0.156457055 3.37E-10
eye to ear distance (EE)
huuliensis-catbaensis 0.085091637 0.059731157 0.110452117 3.37E-10
lichtenfelderi-huuliensis –0.06878479 –0.094475902 –0.043093678 4.25E-10
luii-huuliensis –0.101777804 –0.131262479 –0.072293128 3.37E-10
luii-lichtenfelderi –0.032993014 –0.057865404 –0.008120623 0.00377334
eye diameter (ED)
huuliensis-catbaensis 0.085091637 0.059731157 0.110452117 3.37E-10
lichtenfelderi-huuliensis –0.06878479 –0.094475902 –0.043093678 4.25E-10
luii-huuliensis –0.101777804 –0.131262479 –0.072293128 3.37E-10
luii-lichtenfelderi –0.032993014 –0.057865404 –0.008120623 0.00377334
forelimb length (FLL)
huuliensis-catbaensis –0.137477526 –0.199198043 –0.075757008 9.51E-08
lichtenfelderi-catbaensis –0.338691314 –0.386903935 –0.290478693 3.37E-10
luii-catbaensis –0.365692271 –0.425393393 –0.305991149 3.37E-10
lichtenfelderi-huuliensis –0.201213788 –0.263738975 –0.138688602 3.37E-10
luii-huuliensis –0.228214745 –0.299972435 –0.156457055 3.37E-10
hindlimb length (HLL)
huuliensis-catbaensis –0.137477526 –0.199198043 –0.075757008 9.51E-08

The PCA analysis using habitat preference as the dependent variable among the four species, showed that the karst-adapted and granite-stream-adapted species plot separately as before along taxonomic lines and that collectively, the former have significantly longer axilla-groin lengths (AG); longer, wider, and thicker heads (HL, HW, and CH); longer snouts (SL); longer limbs (FLL and HLL); wider interorbital distances (IO); larger eyes (ED) and larger ear openings (AD) (Fig. 6). Many of these characters—longer head and snout, larger eyes, longer trunk, longer limbs—occur in many other distantly related karst-adapted species of Cyrtodactylus (Grismer et al. 2016a, 2020b; Kaatz et al. 2021; Nielsen and Oliver 2017), indicating that these are convergent adaptions to a karstic life style within and between the gekkotan families.

Figure 6. 

A. Principal component analysis using karst habitat preference and granite stream habitat preference as the dependent variables for G. catbaensis, G. huuliensis, and G. luii of the luii group and G. lichtenfelderii of the lichtenfelderi group. B. Violin plots overlain with box plots showing the range, frequency, mean (white dot), and 50% quartile (black rectangle) of the size-adjusted morphometric characters. Asterisks denote characters bearing statistically significant mean differences between the karst and granite-stream-adapted species based on student t-tests. Upper photo from Vuu et al. (2008). Lower photo from Hai Ngoc Ngo.

Table 4.

Summary statistics and principal component analysis scores for the mensural characters for Goniurosaurus catbaensis, G. huuliensis, G. luii, and G. lichtenfelderii. Abbreviations are listed in the Materials and methods.

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14
Standard deviation 2.62151 1.36659 1.03962 0.92238 0.82762 0.77172 0.71922 0.62534 0.54279 0.51042 0.44460 0.42745 0.41145 0.18664
Proportion of variance 0.49088 0.1334 0.0772 0.06077 0.04893 0.04254 0.03695 0.02793 0.02104 0.01861 0.01412 0.01305 0.01209 0.00249
Cumulative proportion 0.49088 0.62428 0.70148 0.76225 0.81118 0.85371 0.89066 0.9186 0.93964 0.95825 0.97237 0.98542 0.99751 1
Eigenvalue 6.87232 1.86758 1.08081 0.85079 0.68496 0.59554 0.51728 0.39105 0.29462 0.26053 0.19767 0.18272 0.16929 0.03483
AG –0.24490 –0.08424 0.09672 0.10058 –0.56138 0.67337 –0.35690 0.01987 –0.04902 0.07718 –0.08231 0.03497 –0.02384 0.01371
BW –0.04928 –0.61441 –0.07471 0.28744 –0.08051 –0.01832 0.20859 –0.63240 –0.03995 –0.19837 0.15593 –0.11090 0.06221 0.01347
BH –0.04819 –0.61109 –0.04352 0.29637 0.08259 –0.06845 0.04189 0.71074 0.07724 0.07219 –0.03719 –0.04417 –0.03405 0.02105
ND –0.18533 –0.11312 0.64606 0.12685 0.26270 –0.29150 –0.55437 –0.15934 –0.06520 0.16090 –0.04518 0.00367 0.01346 –0.01680
HL –0.34765 0.02673 –0.05102 –0.02271 0.00465 –0.10546 –0.03065 0.01183 0.02638 –0.32873 0.17358 0.23473 –0.81798 0.00137
HW –0.33156 –0.06751 –0.07095 –0.08475 0.05312 –0.03718 0.30763 –0.17678 0.01022 0.48788 –0.67604 –0.04173 –0.21464 0.01521
CH –0.22962 –0.07645 –0.23672 –0.12992 0.73588 0.52586 –0.13009 –0.06367 0.04671 0.02514 0.139 0.0252 0.08193 –0.0003
IO2 –0.34276 0.00113 –0.05114 0.03079 –0.01618 –0.12015 0.10256 0.06796 –0.38288 –0.23806 –0.14615 0.67256 0.40575 –0.08672
SE –0.32091 0.01885 –0.14557 –0.08578 –0.10147 –0.18206 –0.18393 –0.02718 0.77823 –0.28404 –0.17039 –0.03379 0.27124 –0.00567
ED –0.32467 0.19657 0.01794 0.11714 0.06087 –0.00844 0.0395 0.12818 –0.36952 –0.45307 –0.20533 –0.65522 0.08037 –0.01989
EE –0.08674 –0.33966 0.34954 –0.81367 –0.09736 0.04251 0.1902 0.08589 –0.05072 –0.11731 0.09913 –0.09141 0.05499 0.02214
AD –0.19445 0.23327 0.54515 0.30447 0.06568 0.23322 0.5704 0.05955 0.28325 0.01759 0.2077 0.05512 0.05956 0.0013
FLL –0.35278 0.08503 –0.16262 –0.01768 –0.10677 –0.18636 –0.00643 0.02061 –0.09402 0.28874 0.3664 –0.07982 0.12433 0.73937
HLL –0.34892 0.04532 –0.17931 –0.04073 –0.1368 –0.17019 0.00654 0.02352 –0.04871 0.36988 0.42336 –0.16275 0.07515 –0.666


Geckos in general are particularly well-adapted to karstic landscapes (see Luu et al. 2016; Grismer et al. 2014, 2020a, 2021 and references therein; Google Scholar search using key words “karst” and “Gekkonidae”) and Goniurosaurus is no exception, being that 19 of its 23 species (83%) occupy karstic habitats (Grismer et al. 1994, 1999; Orlov et al. 2008; Ziegler et al. 2008; Yang and Chan 2015; Honda and Ota 2017; Zhou et al. 2018; Ngo et al. 2019a; Qi et al. 2020a, 2020b; Zhu et al. 2020a, 2020b). It is clear that karstic landscapes have played a significant role in the evolution and diversification of Goniurosaurus being that it is the probable ancestral habitat preference for the genus and three of the four species groups. Even the ancestor of the non-karst adapted ancestor of the lichtenfelderi group was karst-adapted (Fig. 4). Furthermore, within the species groups, the limited data herein would suggest that the karst-adapted species are specialized, range-restricted endemics (Grismer et al. 1994, 1999; Orlov et al. 2008; Ziegler et al. 2008; Yang and Chan 2015; Honda and Ota 2017; Zhou et al. 2018; Ngo et al. 2019a; Qi et al. 2020a, 2020b; Zhu et al. 2020a, 2020b). With the exception of G. lichtenfelderii, all the non-karst-adapted species are restricted to islands in the Ryukyu Archipelago (kuroiwae group) or Hainan Island (lichtenfelderi group). It may be that the absence of competition and/or predators in these insular habitats widened the fundamental niches of their ancestors and allowed some species to become more generalized in their habitat preference, which should be tested using new techniques combining phylogenetic history, character evolution, and ecological reconstruction programs.

Systematics of the luii group

The ML and BI analyses of Liang et al. (2018) and the BEAST analysis herein (Fig. 4) recovered Goniurosaurus catbaensis as the strongly supported sister species to the remainder of the luii group. Whereas the ML and BI analysis herein, recovered G. catbaensis as the very poorly supported (60/0.51) sister species of the G. araneus plus G. gezhi clade (Fig. 3). Given that three of the five analyses strongly supported the former relationship and two analyses poorly supported the latter, we prefer the placement of G. catbaensis as the sister species to the remainder of the luii group (Fig. 4). Given the very low nodal support of the latter, it essentially renders that portion of the tree a polytomy and as such, does not effectively contradict the strongly supported sister species position of G. catbaensis in the other trees.

Goniurosaurus kadoorieorum of the luii group (represented by only 16S) is nested within G. luii in both the ML and BI analyses, rendering G. luii polyphyletic (Fig. 3). The same relationship was recovered in the 16S phylogeny of Zhu et al. (2020a). This, and the lack of diagnostic characters separating G. kadoorieorum from G. luii (Yang and Chan 2015; Ngo et al. 2016), indicates the two species should be considered conspecific and as such, G. kadoorieorum Yang and Chan, 2015 is relegated here to a junior synonym of G. luii Grismer, Viets, and Boyle, 1999. In all analyses, G. huuliensis is consistently recovered as the sister species to G. luii sensu lato and its species status is not questioned (Figs 3, 4).


Wide-ranging more inclusive studies pertaining to ecosystems management are becoming commonplace in light of climate change and widespread habitat destruction. Such studies reconcile data from a broad range of disciplines in order to address issues that may bear on ecosystems management. Foundational to many of these studies is a basic understanding of species ecology and habitat preference—correlated here with ecomorphology (Cabral et al. 2009; Harfoot et al. 2014). Baseline information on habitat and microhabitat requirements of any species are paramount to understanding how they interact with, and navigate through, their environment (e.g. Grant and Grant 2008; Greene 2005; Losos 2010) and as such, the contextualization of ecosystem management may ultimately turn on these simple points (Meiri 2018; Sinervo et al. 2010).

Integrating the phylogenetic patterns of biodiversity and the morphological adaptations of habitat preference that, in part, underpin species radiations, can fundamentally contribute to conservation management programs (Grismer et al. 2020a, 2021; Erwin 1991; Vane-Wright et al. 1991; Williams et al. 1991; Vázquez and Gittleman 1998; Moritz et al. 2000; Forest et al. 2007; Sgro et al. 2010; Harvey et al. 2011; Rolland et al. 2012; Winter et al. 2012; Shaffer et al. 2015; Beaumont and Wang 2019; Fay et al. 2019; Holderegger et al. 2019)—especially in the karstic regions of northern Vietnam where anthropogenic impact is degrading the habitat and reducing the density of localized populations of Goniurosaurus (Ngo et al. 2019b). Northern Vietnam and many of its offshore islands in the Gulf of Tonkin, harbor large areas of fragmented karstic habitats scattered across their landscapes (Cerrano et al. 2006; Do 2001, 2014; Luo et al. 2016; Ngo et al. 2019a) that are inhabited by an exceptionally large number of endemic plants and animals (Do 2001; Sterling et al. 2006; Clements et al. 2008; Luo et al. 2016; von Oheimb et al. 2017). The obligate restriction of many species to fragmented karstic environments—such as all species of the luii and yingdeensis groups—functionally transforms these environments into habitat islands (Clements et al. 2006, 2008; von Oheimb et al. 2017), which in some cases, bear an unprecedented degree of range-restricted endemism (e.g. Sgro et al 2012; Harvey et al. 2011; Grismer et al. 2018a, 2021).

Unfortunately, Goniurosaurus species are particularly attractive (Fig. 2) and over-harvested for the illegal pet trade (Stuart et al. 2006; Yang and Chan 2015; Ngo et al. 2019b). This is an additional threat to these range-restricted endemics from imperiled karstic environments (Grismer et al. 1997; Orlov et al. 2008; Ziegler et al. 2008; Nakamura et al. 2014; Yang and Chan 2015; Honda and Ota 2017; Zhou et al. 2018; Ngo et al. 2019a; Qi et al. 2020a, b; Zhu et al. 2020a, b). In fact, in areas of China and Vietnam, many populations have suffered huge declines in numbers, or even extirpation at some localities, due to the illegal commercial pet trade (Stuart et al. 2006; Yang and Chan 2015; Ngo et al. 2019b, 2021). We hope that this study will bring more clarity to the plight of this genus and continue to serve ongoing conservation and management programs.


For supporting fieldwork and issuing relevant permits, we thank the authorities of the Cat Ba National Park (CBNP), Hai Phong City, Huu Lien Nature Reserve, Lang Son Province, Bai Tu Long National Park, Quang Ninh Province, Tay Yen Tu Nature Reserve, Bac Giang Province, Yen Tu Nature Reserve and the Management Department of Ha Long Bay, Quang Ninh Province. We are thankful to N. H. Nguyen, C. T. Pham, T.Q. Phan, H.M. Tran (IEBR, Hanoi), H.Q. Nguyen (VNMN, Ha Noi), H.T. Ngo (HUS, Hanoi) for assistance in the field and laboratory. We are grateful to T. Pagel and C. Landsberg (Cologne Zoo), S.V. Nguyen, T.Q. Nguyen (IEBR, Hanoi), L.V. Vu and T.T. Nguyen (VNMN, Hanoi) for their support of conservation–based biodiversity research in Vietnam. Field surveys were partially funded by Cologne Zoo, the Wildlife Conservation Society (“WCS”) John Thorbjarnarson Fellowship for Reptile Research Grant, the Mohamed bin Zayed Species Conservation fund (Project: 170515492) to H. N. Ngo, the Goniurosaurus yingdeensis Population Conservation Research of Guangdong Shimentai National Nature Reserve, China. Cologne Zoo is partner of the World Association of Zoos and Aquariums (WAZA): Conservation Project 07011, 07012 (Herpetodiversity Research, Amphibian and Reptilian Breeding and Rescue Stations). Researches of Hai Ngoc Ngo are funded by the German Academic Exchange Service (DAAD). We thank Dr. Hidetoshi Ota for his insight on the microhabitat preferences of the species of the Goniurosaurus kuroiwae species group.


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