Forensic Anthropology Vol. 5, No. 3: 239–250 DOI: 10.5744/fa.2020.2023a © 2022 University of Florida Press Population variation can be explored using metric analysis of the human cranium (Algee- Hewitt 2016; Hefner et al. 2016; Howells 1973; Relethford 1994; 2010; Roseman & Weaver 2004; Wrobel et al. 2018). In forensic anthropology, these analy- ses are incorporated in the estimation of ancestry, or broad geo- graphical origin (Algee- Hewitt 2017; Dunn et al. 2020; Hefner et al. 2016; Spradley 2014a; Stull et al. 2014). These studies rely on known data obtained from reference samples to create popu- lation affinity estimation models generating probabilistic state- ments for an unknown individual’s population affinity. The level of resolution in these models can range from broad to nar- row, generally representing some form similar to the three- or five- group ancestry, social constructs and peer- perceived classi- fications, or even finer population levels (Hefner 2018). Apply- ing these models expedites the identification process in forensic casework and increases our understanding of group similar- ity within localized geographical arenas or larger geopolitical boundaries. Using these data, we investigate intraregional vari- ation in a sample from Antioquia, Medellín, Colombia. Colombians account for approximately 2%, or roughly 1.1 million, documented immigrants to the U.S. each year (López 2015). In 2015, the U.S. was the second most common destina- tion for out- migration from Colombia (Carvajal 2017). Follow- ing current U.S. governmental classifications, Colombians are included in the broad demographic category Hispanic. This paper reinforces the argument for population- specific methods beyond Hispanic and emphasizes the need for repre- sentative comparative datasets. We agree with Ross et al. (2004) and Spradley (2014a, 2014b) that the term Hispanic is uninformative to the assessment of geographic origin in a forensic context. We present support for finer levels of resolution in po pulation affinity estimation, specifically for Hispanic groups. When we do use the term Hispanic in this paper, we apply it as it is used in the United States’ governmental classifi- cations to distinguish persons originating from Latin America. We understand this term is not reflective of self- identification or ethnicity. Materials and Methods Data used in this study include craniometric measurements collected from skeletal material originating in Antioquia, aDepartment of Anthropology, Michigan State University, East Lansing, MI 48824, USA bDepartamento de Antropología— FCSH, Universidad de Antioquia, Medellín, Colombia *Correspondence to: Kelly R. Kamnikar, Department of Anthropology, 655 Auditorium Drive, Michigan State University, East Lansing, MI 48824, USA e- mail: kamnikar@msu . edu Received 17 July 2019; Revised 29 April 2020; Accepted 02 May 2020 RESEARCH ARTICLE Craniometric Variation in a Regional Sample from Antioquia, Medellín, Colombia: Implications for Forensic Work in the Americas Kelly R. Kamnikara* ● Joseph T. Hefnera ● Timisay Monsalveb ● Liliana Maria Bernal Florezb ABSTRACT: Population affinity estimation is frequently assessed from measurements of the cranium. Traditional models place indi- viduals into discrete groups―such as Hispanic―that often encompass very diverse populations. Current research, including this study, challenges these assumptions using more refined population affinity estimation analyses. We examine craniometric data for a sample of individuals from different regions in Antioquia, Colombia. We first assessed the sample to understand intraregional variation in cranial shape as a function of birthplace or a culturally constructed social group label. Then, pooling the Colombian data, we compare cranial vari- ation with global contemporary and prehistoric groups. Results did not indicate significant intraregional variation in Antioquia; classifica- tion models performed poorly (28.6% for birthplace and 36.6% for social group). When compared to other groups (American Black, American White, Asian, modern Hispanic, and prehistoric Native American), our model correctly classified 75.5% of the samples. We further refined the model by separating the pooled Hispanic sample into Mexican and Guatemalan samples, which produced a correct classification rate of 74.4%. These results indicate significant differences in cranial form among groups commonly united under the classi- fication “Hispanic” and bolster the addition of a refined approach to population affinity estimation using craniometric data. KEYWORDS: forensic anthropology, craniometric analysis, population affinity estimation, human rights, biological distance 240 Cranial Variation in Antioquia, Colombia TABLE 1—Interlandmark distances used in analysis. Abbreviation Measurement Abbreviation Measurement GOL XCB ZYB BBH BNL BPL MAB AUB NLH NLB WFB* FOB* cranial length cranial breadth bizygomatic breadth basion- bregma height cranial base length basion- prosthion length maxilla- alveolar breadth biauricular breadth nasal height nasal breadth minimum frontal breadth foramen magnum breadth OBB OBH EKB DKB FRC PAC OCC FOL MDH MAL* UFBR* UFHT* orbital breadth orbit height biorbital breadth interorbital breadth frontal chord parietal chord occipital chord foramen magnum length mastoid height maximum alveolar length upper facial breadth upper facial height Adapted from Langley et al. (2016). (*) indicates a measurement used in the intraregional Colombian analysis. TABLE 2—Colombian sample by birthplace and social group. Birthplace Social Group n Uraba A 6 Occidente A 17 Oriente B 29 Suroeste B 44 Nordeste C 6 Norte D 25 Valle de Aburra E 89 Total: 216 *Adapted from Monsalve & Hefner (2016). Medellín, Colombia; a sample from the Forensic Databank (FDB; Jantz & Moore- Jansen 1998); and a sample of prehis- toric Native American data from the Howells dataset (1973) and the National Museum of Natural History (JTH). A sep- arate discussion for each sample follows. Colombian Sample The craniometric data in the Colombian sample comprises 19 interlandmark distance measures (Table 1) from 243 indi- viduals (males = 172; females = 70; unknown = 1). This sample— referred to hereafter as the Colombian sample― originates from the osteological reference collection (N = 317) housed at the University of Antioquia, Medellín, Colombia (Monsalve & Hefner 2016). This collection contains individ- uals with known birth years between the 1950s and the cur- rent century, with age- at- death values ranging from birth to 99 years. Many of the individuals in the Colombian sample originate from the San Pedro Cemetery Museum and the Uni- versity Cemetery in the city of Medellín, but have birth loca- tions from multiple municipalities within the department of Antioquia (Table 2; Figure 1). To ensure ethical use of data, we only included those individuals who donated their remains or were donated responsibly by next of kin for skeletal research. Information on phylogeographical social classifica- tions were included in our analysis, although the nature of self- assignment or assignment after death is not known (see Table 2). As social group is used to distinguish living peo- ples in Colombia, we also test the classificatory power of those labels derived from the osteological collection. Monsalve and Hefner (2016) provide a discussion on the roles social groupings play in Antioquia. Lastly, we pooled the Colom- bian sample for comparison to other groups from the FDB, the Howells data, and the NMNH. Comparative Samples A subset of individuals from the FDB (n = 654), the Howells dataset (n = 268), and the NMNH (n = 60) were compared to the Colombian sample (see Table 3). The FDB is a repository for cranial and postcranial measurements from identified skeletal remains (Moore- Jansen & Jantz 1998). Our analysis includes samples representing American Black, American White, Asian, and individuals identified as Hispanic in the FDB. These samples were selected to reflect the current demo- graphic structure of the U.S. Data for the American Black, American White, and Asian samples in the FDB include sam- ples from the Terry, Bass, and Hamman- Todd Skeletal Collec- tions and identified casework submitted to the FDB by forensic practitioners across the U.S. The FDB Hispanic sample broadly reflects Hispanic individuals as defined by U.S. prac- tices, and includes individual with origins from a number of countries (e.g., Costa Rica, El Salvador, Guatemala, Hondu- ras, Mexico, Puerto Rico, and Panamá) (Jantz & Ousley 2005). Due to small sample sizes (n < 2) for some countries in the FDB Hispanic sample, we limited our samples to the Mexican and Guatemalan individuals. We also compiled a third com- parative sample representing prehistoric Native Americans derived from the Howells dataset and the NMNH. This sam- ple was included to attempt to reflect groups with similar pop- ulation histories to indigenous populations in the Americas. Statistical Analyses All analyses were conducted using R Statistical Software (R Core Team 2018), a free software environment for statisti- cal computing and graphics. To remove the influence of sex on the data, all observations were first scaled and centered (set- ting mean to 0 and standard deviation to 1). For the majority of the sample, complete observation was the norm. However, some of the samples did have missing data. This includes (percent missing): Asian (9%), Colombian (33%), and prehis- toric Native American (8%). Individuals with ten or more missing ILDs were removed entirely from the analysis. Miss- ing values were imputed, by variable, using the mice Kamnikar et al. 241 package (van Buuren & Groothuis- Oudshoorn 2011). This method is appropriate for imputation of several data types, including continuous data. We used the predictive mean matching approach to select a random observation from the pool of observed values (by variable) to replace a missing value (van Buuren & Groothuis- Oudshoorn 2011). Outliers were identified using Cook’s distance. Birth- place and social group were tested among the intraregional sample for associations with craniometric measurements using a multivariate analysis of variance (MANOVA) and pairwise analysis of variance (ANOVA). The MANOVA was used to examine the effect of social group and birth- place on cranial morphology, while the pairwise ANOVAs examined the individual relationships between the ILDs and the grouping variables. A canonical (linear discriminant function [LDFA]) analy- sis quantified the relationship between samples and measured variability within and between each to develop prediction equa- tions. For intraregional tests, the dependent variables were birthplace and social group. In the more comprehensive com- parative analysis, geographic origin was the dependent variable. In both LDFAs, prior probabilities were set to simulate equal probability of group membership. One analysis explored classi- fication accuracies for the American Black, American White, Asian, pooled Colombian, prehistoric Native American, and pooled Hispanic samples; a second analysis separated the His- panic sample into Colombian, Guatemalan, and Mexican groups. All models were cross- validated using a leave- one- out (LOOCV) procedure. Finally, we calculated Mahalanobis dis- tances (D2) using the HDMD package (McFerrin 2013) to assess similarity/dissimilarity and explore potential group relatedness. Results Intraregional Variability within Antioquia Cook’s distance identified six potential outliers; however, four were from the Uraba (n = 6) region and one from the Nordeste (n = 6) region. Due to these already small samples, the outli- ers were not removed. The MANOVA results indicate significant effects (α = 0.05) for both birthplace and social group. The pairwise ANOVAs iden- tified significant interaction between (1) birthplace and BBH, FIG. 1—Map of Antioquia, Colombia (Monsalve & Hefner 2016). TABLE 3—Sample distribution. Sample Size males females unknown total Colombian 172 70 1 243 American White 126 74 200 American Black 101 99 200 Asian 151 8 1 160 Native American 146 175 7 328 Arikara 42 27 69 ― Santa Cruz 51 51 102 ― Blackfeet 25 45 70 ― Indian Knoll 11 16 27 ― Smithsonian (JTH) 17 36 7 60 ― Hispanic 94 Mexican 74 9 83 ― Guatemalan 6 5 11 ― Total: 1,198 *The subset of individuals in the American Black, American White, and Asian groups were randomly selected using the rand() function in Excel. 242 Cranial Variation in Antioquia, Colombia BNL, BPL, MAB, and NLH, and (2) social group and BBH, BPL, MAB, and NLH. The LDFA tested the influence of birthplace on cranial variability. Figure 2 illustrates the group centroids and their relationships. The overall correct classification rate by birth- place using LOOCV was 28.6%; an accuracy above random allocation (~14%). Correct classifications range from 16.7% (Nordeste, Uraba) to 37.1% (Valle de Aburra) (Table 4). Here, the regions with the highest accuracies were Valle de Aburra followed by Occidente (28.6%) and Norte (28.0%). Uraba and Nordeste had the lowest classification accuracy at 16.7%, just above random, which could indicate disparate populations or issues related to sample size (n = 6) for both groups (Figure 2). To understand if social designations influence craniomet- ric variability (i.e., gene flow), a second LDFA was performed using social group categories. Performance rates were not high, although group classification increased to 36.6%. The highest classification accuracy was 43.8% (Table 5). Figure 3 shows the centroids for each social group. In order to understand overall similarity, a Mahalanobis distance (D2) matrix was calculated using birthplace and social group (Tables 6 and 7). Here, Suroeste and Oriente are similar, while the rest of the groups appear as separate branches. Uraba is most unlike other groups (Figure 4). Social groups ‘A’ and ‘D’ are most similar to each other, as are ‘B’ and ‘E’; however, social group ‘C’ is distinct from all other groups (Figure 5). Variability among the Colombian Sample and Comparative Samples The LDFA of the Colombian and the comparative samples perform well (Figure 6). The American Black, American White, and Colombian sample exhibit slight overlap, but over- all good separation. There is significant overlap between the Hispanic, prehistoric Native American, and Asian samples. Table 8 provides the classification accuracies for each group, with an overall model accuracy of 74.7%. The lowest classi- fication rate is for the combined Hispanic group (50.0%), while all other groups had classification accuracies higher than 71.0%. An additional LDFA on the refined Hispanic dataset has a similar overall classification rate (71.4%). Figure 7 illustrates the separation of the LDFA on the test sample. However, group classification rates vary considerably (Table 9). The FIG. 2—The first two discriminant axes, by birthplace. Kamnikar et al. 243 TABLE 4—Classification rate of Colombian sample by birthplace (LOOCV). Nordeste Norte Occidente Oriente Suroeste Uraba Valle de Aburra CCR (%) Nordeste 1 0 0 2 2 0 1 16.7 Norte 3 7 6 3 2 1 3 28.0 Occidente 0 2 4 4 2 1 3 28.6 Oriente 5 5 2 6 7 2 2 20.7 Suroeste 6 7 1 7 9 6 8 20.5 Uraba 0 1 0 1 2 1 1 16.7 Valle de Aburra 10 13 7 8 12 6 33 37.1 Overall 28.6 TABLE 5—Classification rate of the Colombian sample by social group (LOOCV). Group A Group B Group C Group D Group E CCR (%) Group A 5 5 1 5 4 25.0 Group B 11 23 13 14 12 31.5 Group C 0 4 1 0 1 16.7 Group D 6 3 3 10 3 40.0 Group E 11 14 10 15 39 43.8 Overall 36.6 FIG. 3—The first two discriminant axes, by social group. 244 Cranial Variation in Antioquia, Colombia Colombian sample classifies moderately well (84.7%) with eight individuals misclassifying as Mexican and two indi- viduals misclassifying as Guatemalan, the two other His- panic reference samples (Table 9). The Mahalanobis distances among the refined sample are presented in Table 10. The D2 values for the Colombian group was closest to American Whites and Mexicans, fol- lowed by the Prehistoric Native Americans and Guatema- lans, then the Mexican and Asian and American Black samples (Figure 8). FIG. 4—Unrooted dendrograms projecting the Mahalanobis distances. TABLE 6—Mahalanobis distance (D2) matrix on birthplace. Nordeste Norte Occidente Oriente Suroeste Uraba Nordeste — Norte 12.41 — Occidente 12.67 6.39 — Oriente 9.61 8.21 9.21 — Suroeste 10.96 8.99 9.54 7.52 — Uraba 19.70 14.32 13.32 14.52 10.94 — Valle de Aburra 11.18 10.07 10.52 9.81 7.02 12.30 TABLE 7—Mahalanobis distance (D2) matrix on social group. A B C D A — B 7.62 — C 13.84 9.78 — D 7.27 7.87 12.41 — E 9.26 7.37 11.18 10.07 FIG. 5—Unrooted dendrograms projecting the Mahalanobis distances; see Table 2 for relationship between social group and birthplace. Discussion Colombian Cranial Variation To understand cranial variation and its relationship to social and geographical categories in Antioquia, several statistical models explored the interaction of cranial morphology to a priori group labels. The models did not perform well when birthplace or social group were used to ‘identify’ potential subsamples within the Antioquian sample. Heavy overlap of all groups in the LDFA suggest relative homogeneity across the groups in Antioquia. Interestingly, when plotting birth- place (see Figure 2), the ellipses for the Uraba and Nordeste cohorts exhibit the least amount of overlap compared to the other groups. This may indicate differences in cranial shape that make Uraba and Nordeste more unique, but the small sample size (n = 6) and the inclusion of outliers may play a Kamnikar et al. 245 role in these distributions. Socially defined category ‘B’ (Ori- ente and Suroeste) and ‘E’ (Valle de Aburra), are geograph- ically proximate and exhibit heavy overlap in the LDFA plot and are likewise similarly positioned in the D2 plot (see Figure 5). Social groups ‘A’ (Uraba and Occidente) and ‘D’ (Norte) did not exhibit much overlap in the LDFA, despite geographical proximity; however, these two groups were more similar on the D2 plot (see Figure 5). Meaningful pat- terns did not emerge to relate craniometric variables to birth- place or social group categories. Additional study with a larger sample size could clarify or support our results. Classifying individuals by birthplace or a peer- perceived social category using cranial measurements does not work well in Antioquia. While this system can be used with some success in places like the U.S. and South Africa (Stull et al. 2014) where social race is used by the legal and law enforce- ment community as a ‘culturally constructed labeling system’ (Sauer 1992:109), it cannot be applied in Colombia, despite distinguishing individuals by social group or ethnicity (Mon- salve & Hefner 2016). Using MMS data, Monsalve and Hef- ner (2016) found no significant differences in trait expression by birthplace, but more intraregional variation (46.0%) could be explained with MMS trait data compared to the cranio- metric data (26.0%) used in this study. Monsalve and Hefner (2016) identified three main clusters (Occidente- A, Sur- oeste- B, and Uraba- A; Valle de Aburra- E and Nordeste- C; FIG. 6—The first two discriminant axes for the six- group analysis. TABLE 8—Classification rate of Colombian and comparative groups (LOOCV). American Black American White Asian Colombian Hispanic Prehistoric Native American CCR (%) American Black 142 14 3 6 22 10 72.1 American White 10 148 3 16 11 6 76.3 Asian 4 5 113 2 24 11 71.0 Colombian 7 19 2 205 8 2 84.4 Hispanic 9 9 11 2 45 14 50.0 Prehistoric Native American 15 9 31 1 20 251 76.7 Overall: 74.7% 246 Cranial Variation in Antioquia, Colombia FIG. 7—The first two discriminant axes for the seven- group analysis. TABLE 9—Classification rate of Colombian and comparative groups; refining the Hispanic group (LOOCV). American Black American White Asian Colombian Guatemalan Mexican Prehistoric Native American CCR (%) American Black 139 14 3 6 9 18 8 70.1 American White 10 145 3 16 4 9 6 75.1 Asian 3 5 110 1 10 20 10 69.2 Colombian 6 18 2 206 2 8 1 84.7 Guatemalan 0 1 1 0 5 3 3 38.5 Mexican 10 8 6 2 13 31 9 39.2 Prehistoric Native American 15 10 27 0 33 13 229 70.0 Overall: 71.4% TABLE 10—Mahalanobis distance (D2) matrix on the refined sample. American Black American White Asian Colombian Guatemalan Mexican American Black — American White 11.64 — Asian 14.92 13.85 — Colombian 15.85 13.56 15.34 — Guatemalan 16.34 17.17 13.60 15.12 — Mexican 11.65 11.78 7.82 13.86 10.62 — Prehistoric Native American 13.26 15.27 12.41 14.80 10.65 9.52 Kamnikar et al. 247 and Oriente- B and Norte- D) based on mid- facial and vault characteristics. These clusters are not present within our results, indicating craniometric variation is not measurably different across groups or adequately captured by the ILDs used in this study. Further attempts to classify based on socially defined groups did not improve classifications with MMS data, which is similar to results reported here. Craniometric analysis of the Colombian sample as a sin- gle dataset produced promising results. While other research- ers noted heavy overlap with Hispanic craniometric data (namely Mexican and Guatemalan samples) (Dudzik & Jantz 2016; Hughes et al. 2019), the Colombian sample, e.g., “His- panic”, did not overlap or misclassify considerably with these groups. This pattern was evident in the LDFA classification models, where Colombians most frequently misclassified as American Whites. Despite these results, classification accu- racies were high for all groups except Mexican and Guate- malan samples. D2 scores for the Colombian sample were closest to the American White and Mexican samples, followed by the Prehistoric Native American, Guatemala, Asian, then American Black samples. Proximity to and misclassifications in the American White sample suggest a significant European ancestral component in Antioquia. Morphological and genetic research in Colombia suggests a diverse population structure corresponding to African, European, Mesoamerican, and South American origins (Bryc et al. 2010; Lopez et al. 2012). FIG. 8—Unrooted dendrograms projecting the Mahalanobis distances (Pre NA = Prehistoric Native American; Colom = Colombian; Black = American Black; White = American White; Guate = Guatemalan). In Antioquia, geographical barriers, most notably the Andes mountain range, have contributed to population isolation and development of the paisa, a distinct regional identity based on socio- political processes and ancestral origins. The paisa includes a racial hierarchical component (Posada 2003), cor- responding to ‘whiteness’ or ‘white’ ethnic groups (Álvarez 1996). Genetic studies in Antioquia demonstrate a significant European component, with smaller contributions from Afri- can and Native American groups (Bravo et al. 1996; Carvajal- Carmona et al. 2000; Sandoval et al. 1993). A significant genomic contribution from a male, Spanish founder popula- tion, persists in the Y- chromosome of individuals in the region today (Carvajal- Carmona et al. 2000). Interestingly, the majority of mitochondrial DNA contributions came from four Amerindian founder linages (Carvajal- Carmona et al. 2000). Because craniometric data is highly heritable (Adhikari et al. 2016; Relethford & Harpending 1994; Roseman & Weaver 2004; Šešelj et al. 2015), and genetic structure in the Antioquian region indicates a large European contribution through migration (Bravo et al. 1996; Carvajal- Carmona et al. 2000), it is not surprising that the cranial phenotype is com- parable to the American White sample of the groups tested. Both populations are genetically descended from European populations (Bryc et al. 2010, 2015), and therefore, are more similar to each other than other groups used in this analysis. Migration and restricted gene flow in Antioquia could explain the separation between the Colombian sample and the Asian- derived samples (Asian, Guatemalan, Mexican and prehis- toric Native American). Genetic studies identify a mix of African, European, and Native American genetic contribu- tions to populations in Antioquia (Bryc et al. 2010; Wang et al. 2008), which could explain these results. Comparing our results to Monsalve and Hefner’s (2016) conclusions, we found that craniometric data has a higher classification accuracy (74.7%: pooled Hispanic data; and 71.4%: separate Hispanic groups) when compared with results derived with Artificial Neural Network analysis used on cranial MMS traits (48.0%). In their study, Colombians mis- classified as American Black (10.0%), American White (16.6%), and Hispanic (10.0%) individuals (Monsalve & Hef- ner 2016). Our craniometric analysis shows less frequent mis- classification for the same groups: American Black (2.5%), American White (7.4%), Asian (0.8%), prehistoric Native American (0.4%), and the two Hispanic groups: Mexican (3.2%) and Guatemalan (0.8%). Further comparison of the spatial distribution using the two approaches illustrates sim- ilar patterning. The PCA using cranial MMS data identified four clusters: 1) Hispanic and American White, 2) Pacific Islander and Asian, 3) American Black, and 4) Colombian. The D2 results from this metric- based study identified three clusters: 1) Asian and Mexican, 2) Guatemalan and prehis- toric Native American 3) American Black, American White, and Colombian. Again, the separation of the Colombian sample 248 Cranial Variation in Antioquia, Colombia from the Hispanic samples in both analyses bolsters calls for the refinement of this category in population affinity estima- tion. We recommend that Colombians from Antioquia not be included under the broad heading Hispanic, but as a sepa- rate sample in comparative analysis. Implications for Future Work in the Region There are several implications for future forensic work in the Americas and within Colombia. While we could not distin- guish individuals by birthplace or social group using cranio- metric data, cranial variation is not necessarily homogeneous in Colombia. These results warrant additional testing of the Antioquian sample against other regions in the country. For example, mountainous and costal populations may differ from each other, which may be true for individuals born in cities like Barranquilla or Cartagena compared with Bogotá or Cali. Identification of larger, intraregional variation within Colom- bia may have direct implications for current migration events from Venezuela (Faiola 2018; Miami Herald 2018), potentially providing an option for estimating geographic origin in the region. Additionally, regional studies in Colombia may pro- vide useful for identification efforts for unidentified individ- uals from Colombia’s armed conflict, which potentially number into the 100,000s (Fondebrider 2016). We hope our results stimulate this type of research in the region and other countries, especially those involved in mass migration events, as a possible avenue for estimating geographic origin. All three Hispanic samples in this study (Mexico, Gua- temala, and Colombia) are grouped under one classificatory group within traditional population affinity estimation mod- els. However, the level of separation between the Colombian sample from the Mexican and Guatemalan samples war- rants a rethinking of population affinity estimation for this group. Our results support previous findings by Spradley et al. (Figueroa- Soto & Spradley 2012; Spradley 2014b; Tise et al. 2014) and Ross et al. (Ross et al. 2014; Humphries et al. 2015) suggesting populations in Latin America, while geographically proximate, show considerable cranial varia- tion. We suggest practitioners consider the origin of His- panic samples and the use of pooled data in three- or five- group models. Hispanic samples, such as the subsample from the FDB, comprise individuals from neighboring countries like Costa Rica, Cuba, El Salvador, Guatemala, Mexico, Puerto Rico, and Panamá (Anderson 2008). Differ- ences among these groups are certainly apparent at a finer level of refinement since each was subjected to a unique historical migration and evolutionary events. While the debate over appropriate and necessary levels of population affinity refinement for forensic casework is ongoing, this research bolsters support toward a more refined approach. The addition of this regional sample to modern, reference datasets allows for population specific models for Colombians (Hefner & Spradley 2018; Spradley 2016). Future research using this and other diverse samples from Latin America is poised to address current issues in foren- sic anthropology research and practice. Conclusion This study aimed to assess if craniometric analysis can be used to identify intraregional variation within Antioquia, Medellín, Colombia. While we failed to demonstrate sepa- ration based on birthplace or social labels, the pooled Colom- bian sample separated well from other comparative groups, including other populations traditionally classified under the term Hispanic. This study demonstrates heterogeneity within Latin American populations, offering further support to the call for refinement of the Hispanic category. Following the suggestions outlined in Hefner and Sprad- ley (2018), we advocate a broad- level analysis, followed by further refinement, especially within the Hispanic group des- ignation. As more data are collected on this diverse group and incorporated into reference databanks, researchers will iden- tify patterned differences and nuances within those samples. 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