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Research Paper

Bergmann’s Rule Examination based on the Morphology of the Long-tailed Dwarf Hamster (Cricetulus longicaudatus)

Y
Yao Xinhua1
https://orcid.org/0009-0008-3777-3994
C
Chang Jiahao1
https://orcid.org/0009-0007-2261-2883
C
Chen Yutong1
https://orcid.org/0009-0005-2242-2604
H
Huang Miao1
https://orcid.org/0009-0007-8238-4480
Y
Yang Xin’gen1
https://orcid.org/0000-0003-2550-8567
Z
Zhao Youyou1
https://orcid.org/0009-0001-1477-4782
R
Ren Yue1,*
https://orcid.org/0000-0002-5569-0171
1College of Plant Protection, Shanxi Agricultural University, Taiyuan, 030031, China.

ABSTRACT

Background: To investigate whether the variation in phenotypic traits of the long-tailed dwarf hamster (Cricetulus longicaudatus) across different environments in Shanxi province conforms to Bergmann’s rule. This study collected C. longicaudatus from 9 regions in Shanxi province, namely, Kelan county, Wutai county, Wuzhai county, Qin county, Shanyin county, Xi county, Lingchuan county, Loufan county and Zuoquan county.

Methods: We analyzed the differences in body and skull indicators of C. longicaudatus in different regions and combined environmental factors to explore whether these indicators conform to Bergmann’s rule.

Result: Significant inter-population differences were found in multiple phenotypic traits. Hierarchical clustering divided the nine populations into two groups: Group I (Zuoquan, Qin, Wutai, Lingchuan, Shanyin) and Group II (Loufan, Kelan, Wuzhai, Xi). Correlation analysis with environmental factors confirmed that phenotypic variation conformed to Bergmann’s rule, indicating geographic differentiation in C. longicaudatus across Shanxi.

INTRODUCTION

Phenotypic differentiation is fundamental to speciation and adaptive evolution (West-Eberhard, 2003).In rodents, morphological variations reflect genetic and ecological adaptation (Pergams and Lawler, 2009; Jia et al., 2024), with body and skull traits as key markers (Caumul et al., 2005). Their correlation aids classification (Xiong and Zhang, 2007; Tai et al., 2001; Ren et al., 2023), as shown in Eothenomys wardi (Zeng et al., 2013), penile morphology across eight species (Yang et al., 1992) and Apodemus sylvaticus mandible and molar analyses (Renaud and Michaux, 2003), revealing species status, intergeneric and intrageneric differences and island-related patterns (Michaux et al., 2007). Bergmann’s rule (larger body size in cold) and Allen’s rule (larger appendages in warm) (Bergmann, 1848; Allen, 1877) show variable conformity: Geomyidae follow Bergmann’s (Ashton et al., 2000), E. miletus only Allen’s (Ren et al., 2020) and some neither (Du et al., 2017). Widespread C. longicaudatus (Cricetidae) was collected from nine Shanxi regions (Kelan, Wutai, Wuzhai, Qin, Shanyin, Xi, Lingchuan, Loufan, Zuoquan) based on evidence of prior divergence.

MATERIALS AND METHODS

Experimental animal processing
 
This study was conducted at the Agricultural and Pastoral Rodent Pest Laboratory, Plant Protection College, Shanxi Agricultural University, Shanxi, China. In 2025, 101 adult male C. longicaudatus were collected from nine Shanxi counties: Kelan (KL, 1939.1, 13), Wutai (WT, 1427, 15), Wuzhai (WZ, 1460, 20), Qin (QX, 986, 6), Shanyin (SY, 1163.3, 7), Xi (XX, 1222.5, 15), Lingchuan (LC, 1359.2, 10), Loufan (LF, 1301, 7), Zuoquan (ZQ, 1128, 8). Individuals were identified as adults by developmental traits and housed individually to minimize reproductive effects. Sampling locations are shown in Fig 1.

Fig 1: Details of sampling sites.


       
Environmental data (latitude, altitude, temperature) came from local sources and CMA (http://sx.cma.gov.cn/) based on 2025 records. All animal procedures complied with China’s Ministry of Science and Technology regulations (Approval No. SXAU-EAW-2024M.JE.004022321).
 
Morphological measurements
 
Captured samples were quarantined for flea control, weighed and transferred to the laboratory. After euthanasia, morphological indicators were measured immediately. Body weight was measured using an electronic balance (0.001 g precision). External traits (0.01 cm) included BL, TL, LFL/RFL, LHL/RHL, LEL/REL (Gao, 2017; Ren, 2022). Skull measurements included CL, CBL, PNL, ZW, LD, SKW, CH, EBL, UCL_L/LCL_L (Yang et al., 2005; Xia et al., 2006). All measurements were performed by a single experimenter. Traits were classified as body or cranial traits.
 
Statistical methods
 
Data were analyzed using SPSS 27.0 and R 4.5.1. Normality tests confirmed all variables met normality assumptions. One-way ANOVA with Levene’s test and Tukey’s HSD compared phenotypic traits among populations. PCA and cluster analysis revealed differentiation patterns; morphological and geographical distance matrices were compared via Mantel test (Mantel, 1967; Legendre and Legendre, 2012). Phenotypic differentiation coefficient (VST = δ2t/s / (δ2t/s + δ2t)) was calculated, where δ2t/s and δ2t represent among- and within-population variance components (Pfrender and Lynch, 2000); this coefficient, analogous to genetic differentiation, is widely applied in population studies (Meng et al., 2013; Ren et al., 2025). Coefficient of variation (CV = S/X, S = standard deviation, X = trait mean) assessed trait dispersion. Spearman correlations examined trait–environment relationships (latitude, altitude, temperature) to test Bergmann’s rule.

RESULTS AND DISCUSSION

Analysis of the differences in body and skull indices among populations
 
ANOVA revealed significant inter-population differences in multiple phenotypic traits (Table 1). Body weight showed no significant difference among regions (P>0.05). Body length varied significantly (P<0.01), with XX population the largest (significantly greater than WZ, LC, LF and ZQ) and ZQ/WZ the smallest. Tail length was greatest in QX and smallest in WT. Tail-to-body ratio differed highly significantly (P<0.01), with LF, QX and ZQ significantly higher than WT, which was the lowest. Forelimb lengths differed significantly (P<0.01), with WZ and KL significantly longer than SY and WT; hindlimb lengths also differed significantly (P<0.01), with QX and WZ significantly longer than WT. Ear length differed significantly (P<0.01), with KL and XX significantly longer than LC, WZ and ZQ (LC shortest). Ear width differed significantly (P<0.01), with LF (left) and QX (right) largest and WT smallest.

Table 1: Analysis of variation in body metrics and skull metrics of long-tailed dwarf hamsters (Cricetulus longicaudatus) from different regions.


       
Most cranial traits showed no significant regional differences (P>0.05), except cranial height (P<0.01), which was highest in XX and significantly higher than in LC, QX and WT. Other traits (cranial length, condylobasal length, occipitonasal length, zygomatic width, interorbital width, skull width, ear bulla length and upper/lower tooth row length) showed no significant differences (P>0.05).
 
Analysis of phenotypic trait variation within and between populations
 
Variance component analysis for 22 traits (Table 2) revealed significant variance distribution differences among populations. Random error: 46.64%-91.01% (mean 69.91%); within-population: 4.71%-9.12% (mean 6.92%); among-population: 0-48.75% (mean 23.17%). Among-population variance generally exceeded within-population variance for most traits.

Table 2: Variance components and inter-population phenotypic differentiation coefficients VST of phenotypic traits.


       
Body weight showed the lowest VST (6.96%), with random error variance at 90.39%. Body surface traits showed high differentiation: VST values for body length, tail length and tail-to-body ratio were 75.64%, 88.68% and 91.11%, respectively. Limb length VST ranged from 86.78% (left forelimb) to 91.37% (right hindlimb); ear-related traits all exceeded 86% (right ear width at 88.73%). For skull traits, skull width (SKW) and cranial height (CH) showed moderately high VST (77.90% and 79.01%), indicating stable inter-population differentiation. However, cranial length (33.02%), condylobasal length (18.50%) and occipitonasal length (30.56%) showed lower VST. Zygomatic width, interorbital width and upper/lower tooth row length had zero among-population variance and VST = 0, indicating no detectable inter-population differentiation.
       
CV for each trait varied significantly among populations (Table 3), with mean CV ranging from 2.17% to 19.08%. Body weight was highest (19.08%), followed by tail length (13.90%), tail-to-body ratio (13.77%) and body length (8.82%). Limb length mean CV: 8.14%-9.88%. Ear length: 10.56% (left) and 10.35% (right); ear width: 8.93% (left) and 9.31% (right). Skull traits showed low variation: cranial length (4.17%), condylobasal length (4.19%), occipitonasal length (3.84%), skull width (2.17%), cranial height (3.06%). Ear bulla length (6.54%) was relatively high among skull traits. Upper and lower tooth row lengths were lowest (2.63% and 2.54%).

Table 3: Coefficients of variation (CV) of phenotypic traits in long-tailed dwarf hamster (Cricetulus longicaudatus) populations.


 
Principal component analysis and cluster analysis of morphological indices
 
PCA revealed PC1, PC2 and PC3 explained 51.24%, 28.01% and 8.99% of variation (cumulative 88.24%, Fig 2). In PC1-PC2 space (Fig 2A), populations formed concentrated clusters with distinct shifts (especially PC1) and some overlap (PC2), suggesting phenotypic similarities. PC1-PC3 (Fig 2B) was consistent with PC1-PC2, with PC3 reflecting secondary trait differences. Hierarchical clustering (Fig 3) divided populations into Group I (QX, ZQ, LC, SY, WT) and Group II (WZ, LF, KL, XX); Group I (except SY) were mainly in the Taihang Mountains, Group II in the Lvliang Mountains.

Fig 2: Principal component analysis (PCA) of phenotypic traits in long-tailed dwarf hamsters (Cricetulus longicaudatus).



Fig 3: Dendrogram of hierarchical cluster analysis for long-tailed dwarf hamster (Cricetulus longicaudatus) populations.


 
Bergmann’s rule testing and analysis of C. longicaudatus morphology
 
Phenotypic indicators were grouped by function and morphology into body surface traits (BL, TL, TL/BL, BW, LIMBS, EAR_SIZE) and skull traits (further integrated into length, width, height and functional axes) to reduce single-indicator interference.
       
Spearman correlations between 22 traits and 6 environmental factors (altitude, humidity, latitude, longitude, temperature, wind speed) are shown in heatmap (Fig 4). Trait responses varied significantly. Wind speed and latitude showed relatively concentrated correlations with multiple traits (P<0.05), while altitude, humidity, longitude and temperature showed no correlations with most traits. Latitude was significantly negatively correlated with cranial height (P<0.01, R = -0.46), skull width (P<0.05, R = 0.30), ear length (P<0.01, RLEL = -0.29, RREL = -0.08), ear width (P<0.01, RLEW = -0.41, RREW = -0.34) and forelimb length (P<0.01, RLFL = -0.30, RRFL = -0.31). Wind speed was significantly positively correlated with skull width (P<0.05, R = -0.30), tail length (P<0.01, R = 0.27), tail-to-body ratio (P<0.01, R = 0.29), ear width (P<0.01, RLEW = 0.38, RREW = 0.43) and limb length (PLFL < 0.05, RLFL = 0.26; PRFL < 0.01, RRFL = 0.27; PLHL < 0.01, RLHL = 0.44; PRHL < 0.01, RRHL = 0.43), but negatively correlated with body length (P<0.05, R = -0.23). Tail length was negatively correlated with altitude (P < 0.01, R = -0.29). Cranial height and ear width were negatively correlated with humidity (P<0.05, RCH = -0.32; RLEW = -0.24, RREW = -0.22). Ear bulla length was negatively correlated with longitude (P<0.05, R = -0.29), but positively correlated with temperature (P<0.05, R = 0.26), as was body length (P<0.05, R = 0.24). Other traits showed no significant correlations (P>0.05).

Fig 4: Heatmap of Spearman correlation coefficients among morphological variables.


       
Mantel tests (Fig 5) showed significant correlations mainly between specific environmental gradients and composite traits. Altitude correlated positively with LIMBS and EAR_SIZE; latitude correlated with body weight (strong), body length (weak) and tail/tail-body (weak but stable). For skull traits, latitude correlated positively with all four composite axes (length, width, height, functional), indicating synchronous skull changes along latitude. Longitude and humidity showed weaker/fewer correlations. Overall, trait-environment correlations concentrated between altitude and body surface traits and latitude and skull structure, reflecting differential trait responsiveness.

Bergmann’s rule (body size) and Allen’s rule (appendage length) (Bergmann, 1848; Allen, 1877) have species- and gradient-specific applicability (Tabh and Nord, 2023; Valladares-Gómez et al., 2024). Our latitude-body size correlations align with most rodents (Ashton et al., 2000; Balčiauskas et al., 2025; Tomlinson and Withers, 2009), supporting Bergmann’s rule and altitude-driven skull selection. However, Allen’s rule was contradicted - limb/ear sizes increased with altitude, not latitude, consistent with exceptions in Chilean rodents and A. draco (Valladares-Gómez et al., 2024; Cui et al., 2020).

Fig 5: Mantel test of phenotypic and geographic distances in long-tailed dwarf hamsters (Cricetulus longicaudatus).


       
Differential growth rates drive morphological diversity, with plastic contributions nonlinear and energy-dependent (Tabh and Nord, 2023). The appendage-altitude positive correlation in C. longicaudatus may reflect allometric scaling with cooling, as in A. agrarius, Rattus norvegicus and Niviventer confucianus, with climate effects mediated via body size (Geng and Zhu, 2024). Bird analyses show similar variation across temperature gradients (Frӧhlich et al., 2023), suggesting this correlation reflects locomotor maintenance rather than thermoregulation. Allometric ratios vary among rodent groups (Rosalino et al., 2013), suggesting species-specific patterns.
       
Environmental gradients drive differentiation via selection and gene flow (Price et al., 2003; Rodríguez-Cajarville et al., 2022; Cao et al., 2026). High VST (>75%) in body/skull traits, with among-population variance exceeding within-population variance, mirrors patterns in Toona ciliata (Wang et al., 2019), Tilia mandshurica (Mu et al., 2023) and rodent skulls (Monteiro et al., 2005; Kang et al., 2024), indicating geographic isolation dominates. However, food/competition can outweigh temperature (Cui et al., 2022), adjusting niche width (Stephens et al., 2019) and foraging (Manlick et al., 2021); thus ecological factors beyond climate matter. Body weight (VST = 6.96%) is season/reproduction-sensitive and not a sole indicator; food availability may drive size changes (Alston et al., 2023), so multiple linear indicators are preferable.

CONCLUSION

Phenotypic variation in C. longicaudatus across nine Shanxi regions supports Bergmann’s rule (larger body size in colder regions) but not Allen’s rule; appendage elongation with altitude likely reflects allometric growth. These findings enrich understanding of rodent adaptation to climatic gradients and provide baseline data for broader ecogeographic studies.

ACKNOWLEDGEMENT

The present study was supported by National Natural Science Foundation of China (32501395), Basic Research Program of Shanxi Province, China (Project No. 202303021221093 and 202303021212109); China Postdoctoral Science Foundation Funded Project (Project No. 2024M761890).
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
All animal procedures for experiments were strictly in compliance with the People’s Republic of China’s Ministry of Science and Technology’s Regulations on the Administration of Laboratory Animals (SXAU-EAW-2024M.JE.004022321).

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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    Research Paper

    Bergmann’s Rule Examination based on the Morphology of the Long-tailed Dwarf Hamster (Cricetulus longicaudatus)

    Y
    Yao Xinhua1
    https://orcid.org/0009-0008-3777-3994
    C
    Chang Jiahao1
    https://orcid.org/0009-0007-2261-2883
    C
    Chen Yutong1
    https://orcid.org/0009-0005-2242-2604
    H
    Huang Miao1
    https://orcid.org/0009-0007-8238-4480
    Y
    Yang Xin’gen1
    https://orcid.org/0000-0003-2550-8567
    Z
    Zhao Youyou1
    https://orcid.org/0009-0001-1477-4782
    R
    Ren Yue1,*
    https://orcid.org/0000-0002-5569-0171
    1College of Plant Protection, Shanxi Agricultural University, Taiyuan, 030031, China.

    ABSTRACT

    Background: To investigate whether the variation in phenotypic traits of the long-tailed dwarf hamster (Cricetulus longicaudatus) across different environments in Shanxi province conforms to Bergmann’s rule. This study collected C. longicaudatus from 9 regions in Shanxi province, namely, Kelan county, Wutai county, Wuzhai county, Qin county, Shanyin county, Xi county, Lingchuan county, Loufan county and Zuoquan county.

    Methods: We analyzed the differences in body and skull indicators of C. longicaudatus in different regions and combined environmental factors to explore whether these indicators conform to Bergmann’s rule.

    Result: Significant inter-population differences were found in multiple phenotypic traits. Hierarchical clustering divided the nine populations into two groups: Group I (Zuoquan, Qin, Wutai, Lingchuan, Shanyin) and Group II (Loufan, Kelan, Wuzhai, Xi). Correlation analysis with environmental factors confirmed that phenotypic variation conformed to Bergmann’s rule, indicating geographic differentiation in C. longicaudatus across Shanxi.

    INTRODUCTION

    Phenotypic differentiation is fundamental to speciation and adaptive evolution (West-Eberhard, 2003).In rodents, morphological variations reflect genetic and ecological adaptation (Pergams and Lawler, 2009; Jia et al., 2024), with body and skull traits as key markers (Caumul et al., 2005). Their correlation aids classification (Xiong and Zhang, 2007; Tai et al., 2001; Ren et al., 2023), as shown in Eothenomys wardi (Zeng et al., 2013), penile morphology across eight species (Yang et al., 1992) and Apodemus sylvaticus mandible and molar analyses (Renaud and Michaux, 2003), revealing species status, intergeneric and intrageneric differences and island-related patterns (Michaux et al., 2007). Bergmann’s rule (larger body size in cold) and Allen’s rule (larger appendages in warm) (Bergmann, 1848; Allen, 1877) show variable conformity: Geomyidae follow Bergmann’s (Ashton et al., 2000), E. miletus only Allen’s (Ren et al., 2020) and some neither (Du et al., 2017). Widespread C. longicaudatus (Cricetidae) was collected from nine Shanxi regions (Kelan, Wutai, Wuzhai, Qin, Shanyin, Xi, Lingchuan, Loufan, Zuoquan) based on evidence of prior divergence.

    MATERIALS AND METHODS

    Experimental animal processing
     
    This study was conducted at the Agricultural and Pastoral Rodent Pest Laboratory, Plant Protection College, Shanxi Agricultural University, Shanxi, China. In 2025, 101 adult male C. longicaudatus were collected from nine Shanxi counties: Kelan (KL, 1939.1, 13), Wutai (WT, 1427, 15), Wuzhai (WZ, 1460, 20), Qin (QX, 986, 6), Shanyin (SY, 1163.3, 7), Xi (XX, 1222.5, 15), Lingchuan (LC, 1359.2, 10), Loufan (LF, 1301, 7), Zuoquan (ZQ, 1128, 8). Individuals were identified as adults by developmental traits and housed individually to minimize reproductive effects. Sampling locations are shown in Fig 1.

    Fig 1: Details of sampling sites.


           
    Environmental data (latitude, altitude, temperature) came from local sources and CMA (http://sx.cma.gov.cn/) based on 2025 records. All animal procedures complied with China’s Ministry of Science and Technology regulations (Approval No. SXAU-EAW-2024M.JE.004022321).
     
    Morphological measurements
     
    Captured samples were quarantined for flea control, weighed and transferred to the laboratory. After euthanasia, morphological indicators were measured immediately. Body weight was measured using an electronic balance (0.001 g precision). External traits (0.01 cm) included BL, TL, LFL/RFL, LHL/RHL, LEL/REL (Gao, 2017; Ren, 2022). Skull measurements included CL, CBL, PNL, ZW, LD, SKW, CH, EBL, UCL_L/LCL_L (Yang et al., 2005; Xia et al., 2006). All measurements were performed by a single experimenter. Traits were classified as body or cranial traits.
     
    Statistical methods
     
    Data were analyzed using SPSS 27.0 and R 4.5.1. Normality tests confirmed all variables met normality assumptions. One-way ANOVA with Levene’s test and Tukey’s HSD compared phenotypic traits among populations. PCA and cluster analysis revealed differentiation patterns; morphological and geographical distance matrices were compared via Mantel test (Mantel, 1967; Legendre and Legendre, 2012). Phenotypic differentiation coefficient (VST = δ2t/s / (δ2t/s + δ2t)) was calculated, where δ2t/s and δ2t represent among- and within-population variance components (Pfrender and Lynch, 2000); this coefficient, analogous to genetic differentiation, is widely applied in population studies (Meng et al., 2013; Ren et al., 2025). Coefficient of variation (CV = S/X, S = standard deviation, X = trait mean) assessed trait dispersion. Spearman correlations examined trait–environment relationships (latitude, altitude, temperature) to test Bergmann’s rule.

    RESULTS AND DISCUSSION

    Analysis of the differences in body and skull indices among populations
     
    ANOVA revealed significant inter-population differences in multiple phenotypic traits (Table 1). Body weight showed no significant difference among regions (P>0.05). Body length varied significantly (P<0.01), with XX population the largest (significantly greater than WZ, LC, LF and ZQ) and ZQ/WZ the smallest. Tail length was greatest in QX and smallest in WT. Tail-to-body ratio differed highly significantly (P<0.01), with LF, QX and ZQ significantly higher than WT, which was the lowest. Forelimb lengths differed significantly (P<0.01), with WZ and KL significantly longer than SY and WT; hindlimb lengths also differed significantly (P<0.01), with QX and WZ significantly longer than WT. Ear length differed significantly (P<0.01), with KL and XX significantly longer than LC, WZ and ZQ (LC shortest). Ear width differed significantly (P<0.01), with LF (left) and QX (right) largest and WT smallest.

    Table 1: Analysis of variation in body metrics and skull metrics of long-tailed dwarf hamsters (Cricetulus longicaudatus) from different regions.


           
    Most cranial traits showed no significant regional differences (P>0.05), except cranial height (P<0.01), which was highest in XX and significantly higher than in LC, QX and WT. Other traits (cranial length, condylobasal length, occipitonasal length, zygomatic width, interorbital width, skull width, ear bulla length and upper/lower tooth row length) showed no significant differences (P>0.05).
     
    Analysis of phenotypic trait variation within and between populations
     
    Variance component analysis for 22 traits (Table 2) revealed significant variance distribution differences among populations. Random error: 46.64%-91.01% (mean 69.91%); within-population: 4.71%-9.12% (mean 6.92%); among-population: 0-48.75% (mean 23.17%). Among-population variance generally exceeded within-population variance for most traits.

    Table 2: Variance components and inter-population phenotypic differentiation coefficients VST of phenotypic traits.


           
    Body weight showed the lowest VST (6.96%), with random error variance at 90.39%. Body surface traits showed high differentiation: VST values for body length, tail length and tail-to-body ratio were 75.64%, 88.68% and 91.11%, respectively. Limb length VST ranged from 86.78% (left forelimb) to 91.37% (right hindlimb); ear-related traits all exceeded 86% (right ear width at 88.73%). For skull traits, skull width (SKW) and cranial height (CH) showed moderately high VST (77.90% and 79.01%), indicating stable inter-population differentiation. However, cranial length (33.02%), condylobasal length (18.50%) and occipitonasal length (30.56%) showed lower VST. Zygomatic width, interorbital width and upper/lower tooth row length had zero among-population variance and VST = 0, indicating no detectable inter-population differentiation.
           
    CV for each trait varied significantly among populations (Table 3), with mean CV ranging from 2.17% to 19.08%. Body weight was highest (19.08%), followed by tail length (13.90%), tail-to-body ratio (13.77%) and body length (8.82%). Limb length mean CV: 8.14%-9.88%. Ear length: 10.56% (left) and 10.35% (right); ear width: 8.93% (left) and 9.31% (right). Skull traits showed low variation: cranial length (4.17%), condylobasal length (4.19%), occipitonasal length (3.84%), skull width (2.17%), cranial height (3.06%). Ear bulla length (6.54%) was relatively high among skull traits. Upper and lower tooth row lengths were lowest (2.63% and 2.54%).

    Table 3: Coefficients of variation (CV) of phenotypic traits in long-tailed dwarf hamster (Cricetulus longicaudatus) populations.


     
    Principal component analysis and cluster analysis of morphological indices
     
    PCA revealed PC1, PC2 and PC3 explained 51.24%, 28.01% and 8.99% of variation (cumulative 88.24%, Fig 2). In PC1-PC2 space (Fig 2A), populations formed concentrated clusters with distinct shifts (especially PC1) and some overlap (PC2), suggesting phenotypic similarities. PC1-PC3 (Fig 2B) was consistent with PC1-PC2, with PC3 reflecting secondary trait differences. Hierarchical clustering (Fig 3) divided populations into Group I (QX, ZQ, LC, SY, WT) and Group II (WZ, LF, KL, XX); Group I (except SY) were mainly in the Taihang Mountains, Group II in the Lvliang Mountains.

    Fig 2: Principal component analysis (PCA) of phenotypic traits in long-tailed dwarf hamsters (Cricetulus longicaudatus).



    Fig 3: Dendrogram of hierarchical cluster analysis for long-tailed dwarf hamster (Cricetulus longicaudatus) populations.


     
    Bergmann’s rule testing and analysis of C. longicaudatus morphology
     
    Phenotypic indicators were grouped by function and morphology into body surface traits (BL, TL, TL/BL, BW, LIMBS, EAR_SIZE) and skull traits (further integrated into length, width, height and functional axes) to reduce single-indicator interference.
           
    Spearman correlations between 22 traits and 6 environmental factors (altitude, humidity, latitude, longitude, temperature, wind speed) are shown in heatmap (Fig 4). Trait responses varied significantly. Wind speed and latitude showed relatively concentrated correlations with multiple traits (P<0.05), while altitude, humidity, longitude and temperature showed no correlations with most traits. Latitude was significantly negatively correlated with cranial height (P<0.01, R = -0.46), skull width (P<0.05, R = 0.30), ear length (P<0.01, RLEL = -0.29, RREL = -0.08), ear width (P<0.01, RLEW = -0.41, RREW = -0.34) and forelimb length (P<0.01, RLFL = -0.30, RRFL = -0.31). Wind speed was significantly positively correlated with skull width (P<0.05, R = -0.30), tail length (P<0.01, R = 0.27), tail-to-body ratio (P<0.01, R = 0.29), ear width (P<0.01, RLEW = 0.38, RREW = 0.43) and limb length (PLFL < 0.05, RLFL = 0.26; PRFL < 0.01, RRFL = 0.27; PLHL < 0.01, RLHL = 0.44; PRHL < 0.01, RRHL = 0.43), but negatively correlated with body length (P<0.05, R = -0.23). Tail length was negatively correlated with altitude (P < 0.01, R = -0.29). Cranial height and ear width were negatively correlated with humidity (P<0.05, RCH = -0.32; RLEW = -0.24, RREW = -0.22). Ear bulla length was negatively correlated with longitude (P<0.05, R = -0.29), but positively correlated with temperature (P<0.05, R = 0.26), as was body length (P<0.05, R = 0.24). Other traits showed no significant correlations (P>0.05).

    Fig 4: Heatmap of Spearman correlation coefficients among morphological variables.


           
    Mantel tests (Fig 5) showed significant correlations mainly between specific environmental gradients and composite traits. Altitude correlated positively with LIMBS and EAR_SIZE; latitude correlated with body weight (strong), body length (weak) and tail/tail-body (weak but stable). For skull traits, latitude correlated positively with all four composite axes (length, width, height, functional), indicating synchronous skull changes along latitude. Longitude and humidity showed weaker/fewer correlations. Overall, trait-environment correlations concentrated between altitude and body surface traits and latitude and skull structure, reflecting differential trait responsiveness.

    Bergmann’s rule (body size) and Allen’s rule (appendage length) (Bergmann, 1848; Allen, 1877) have species- and gradient-specific applicability (Tabh and Nord, 2023; Valladares-Gómez et al., 2024). Our latitude-body size correlations align with most rodents (Ashton et al., 2000; Balčiauskas et al., 2025; Tomlinson and Withers, 2009), supporting Bergmann’s rule and altitude-driven skull selection. However, Allen’s rule was contradicted - limb/ear sizes increased with altitude, not latitude, consistent with exceptions in Chilean rodents and A. draco (Valladares-Gómez et al., 2024; Cui et al., 2020).

    Fig 5: Mantel test of phenotypic and geographic distances in long-tailed dwarf hamsters (Cricetulus longicaudatus).


           
    Differential growth rates drive morphological diversity, with plastic contributions nonlinear and energy-dependent (Tabh and Nord, 2023). The appendage-altitude positive correlation in C. longicaudatus may reflect allometric scaling with cooling, as in A. agrarius, Rattus norvegicus and Niviventer confucianus, with climate effects mediated via body size (Geng and Zhu, 2024). Bird analyses show similar variation across temperature gradients (Frӧhlich et al., 2023), suggesting this correlation reflects locomotor maintenance rather than thermoregulation. Allometric ratios vary among rodent groups (Rosalino et al., 2013), suggesting species-specific patterns.
           
    Environmental gradients drive differentiation via selection and gene flow (Price et al., 2003; Rodríguez-Cajarville et al., 2022; Cao et al., 2026). High VST (>75%) in body/skull traits, with among-population variance exceeding within-population variance, mirrors patterns in Toona ciliata (Wang et al., 2019), Tilia mandshurica (Mu et al., 2023) and rodent skulls (Monteiro et al., 2005; Kang et al., 2024), indicating geographic isolation dominates. However, food/competition can outweigh temperature (Cui et al., 2022), adjusting niche width (Stephens et al., 2019) and foraging (Manlick et al., 2021); thus ecological factors beyond climate matter. Body weight (VST = 6.96%) is season/reproduction-sensitive and not a sole indicator; food availability may drive size changes (Alston et al., 2023), so multiple linear indicators are preferable.

    CONCLUSION

    Phenotypic variation in C. longicaudatus across nine Shanxi regions supports Bergmann’s rule (larger body size in colder regions) but not Allen’s rule; appendage elongation with altitude likely reflects allometric growth. These findings enrich understanding of rodent adaptation to climatic gradients and provide baseline data for broader ecogeographic studies.

    ACKNOWLEDGEMENT

    The present study was supported by National Natural Science Foundation of China (32501395), Basic Research Program of Shanxi Province, China (Project No. 202303021221093 and 202303021212109); China Postdoctoral Science Foundation Funded Project (Project No. 2024M761890).
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    All animal procedures for experiments were strictly in compliance with the People’s Republic of China’s Ministry of Science and Technology’s Regulations on the Administration of Laboratory Animals (SXAU-EAW-2024M.JE.004022321).

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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