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Evolution within a language: environmental differences contribute to divergence of dialect groups
Terhi Honkola ORCID: orcid.org/0000-0002-3330-08571,2,
Kalle Ruokolainen3,
Kaj J. J. Syrjänen4,
Unni-Päivä Leino4,
Ilpo Tammi4,5,
Niklas Wahlberg1,6 &
Outi Vesakoski1
More than 7000 languages exist in the world today [1]. Many of these languages have emerged through a process of linguistic divergence, in which one linguistic unit separates over time into more or less distinct units. To comprehend the emergence of diversity, an understanding of the mechanisms of the divergence process is required. In biology, mechanisms of species divergence can be studied by focussing on population divergence within a framework of microevolution [2, 3]. Here, we adopted certain elements of a biological microevolutionary approach in studying the initial stages of linguistic divergence, i.e. the divergence of dialects.
Adopting elements from biological approaches to study language-related questions is possible due to the roughly analogous nature of species and languages [4, 5]. In both cases individuals carry heritable material (genetic material or linguistic information), which comprises various features (cf. loci in genetics) and variants therein (cf. alleles in genetics). As individuals may differ from each other in the variants they possess, they may be grouped together into populations and dialect groups based on the similarity of their variants (for more detailed discussion see [5]).
Linguistic divergence has been described as a process of "inter-group boundary formation", [6] referring to the linguistic differentiation of human populations due to constrained communication between them. Generally recognised factors restricting communication between human populations include environmental and geographical barriers (e.g. dense forests, swamps, mountains or oceans) [7,8,9] and geographical distance [7, 10,11,12,13]. The influence of these factors on the intensity of communication are further shaped by human-related factors such as social, cultural and political settings [7, 12, 13]. Barriers and geographical distance isolate speaker populations by acting as physical hindrances to movement, while human-related factors may also isolate populations via social group cohesion by encouraging communication within the groups and discouraging it between them.
These different isolating factors are not mutually exclusive [7, 12,13,14]. Nevertheless, their relative roles have not, to our knowledge, been attested quantitatively. Much of the work on linguistic divergence has been non-quantitative, and in the existing quantitative studies, only one factor at a time has commonly been studied [11]. Furthermore, it has not been quantitatively studied whether mere differences in environmental conditions may induce separation between speaker populations and thus, their language. This is, nevertheless, a plausible hypothesis, as environmental differences separate the populations of other species [15] and similar to these species, Homo sapiens is dependent on its environment.
In this study, we addressed and combined the two aspects that are fundamental in understanding the emergence of global linguistic diversity: linguistic divergence in the context of the ecological settings of the speaker populations and its relative role to other contributing factors. We achieved this by parallelling genetic variation clustered within populations with linguistic variation clustered within dialect groups. In practice, we studied whether linguistic divergence and the related isolation of speaker populations is explained by differences in ecological environment, as is done in evolutionary ecology when studying ecological speciation [16,17,18], or whether linguistic divergence is explained by differences in culture, geographical distance, and/or differences in administrative history.
In biology, the idea of population divergence via adaptation to differing ecological environments was brought up in the 1940s [19, 20], but studies on ecological speciation as a mechanism for population divergence have gained momentum only recently [18, 21]. In ecological speciation, the genetic differentiation of populations occurs through local adaptation in differing selective environments, even if there is ongoing gene flow [22]. Adaptation takes place in certain loci, but genetic differentiation via adaptation may also be detected indirectly in selectively neutral genetic markers, such as microsatellites [23, 24]. This is possible as neutral parts of the genome may differentiate due to genetic linkage [25]. Differentiation without genetic linkage is also possible if a divergent selection of multiple loci is strong enough to reduce the average rate of effective migration between environments [26]. Thus, sufficiently strong environmental differences may limit successful gene flow from one environment to another, eventually developing genetic differentiation between populations [24, 27, 28]. Accordingly, when gene exchange is stronger between similar environments and weaker when they differ, a pattern emerges where genetic differences between populations increase with environmental differences [23, 24]. This pattern can be called isolation by environment (IBE). Notably, IBE indicates that adaptive processes have contributed to the divergence process, as when IBE is detected the populations do not differentiate randomly but in relation to the differences in their biotic surroundings [23, 24, 29].
We hypothesise linguistic IBE would refer, correspondingly, to a situation where linguistic differences between speaker populations increase together with differences in their environmental conditions. Linguistic IBE could be interpreted as an outcome of reduced communication between speaker populations living in different environmental conditions. Following the biological inference, this would indicate that the disruption of contacts between speaker populations is due to environmental differences and adaptations related to these environments.
The next question then is, what type of adaptive process may be detected from linguistic IBE? Firstly, linguistic adaptations do not in general have effects on the biological fitness of language speakers, as "forms of words themselves do not have fitness implications" [30]. However, language is considered to be a part of human culture, and as it lacks the fitness effects, it may be seen as a neutral marker of cultural history of human populations [30]. Secondly, it is known that humans may adapt to their environment both genetically [31, 32] and culturally [33]. For example, subsistence strategies are cultural features which have a direct connection to the biological fitness of an individual [33]. Due to the connection between language and culture and the possible fitness implications of certain cultural features, we conclude that the adaptive process inferable from linguistic IBE is cultural adaptation and not linguistic adaptation. In other words, we are not studying whether language adapts to environment, but instead whether language speakers adapt to their local environment via their culture.
While language may be transmitted together with other cultural features, these may also be passed on separately from each other [34]. Therefore, our investigation also explored the strength of the relationship between linguistic and cultural differentiation among populations by measuring the correlation between linguistic and cultural differences. We refer to the pattern where linguistic differences increase together with cultural differences as isolation by culture (IBC).
Geographical distance can contribute to the separation of biological populations by limiting dispersal and gene flow, thus setting the stage for genetic drift within populations [23]. Genetic drift, or random changes in the allele frequencies of populations, represents the neutral process of evolution. In the absence of geographical barriers and selection pressures inducing divergence, neighbouring populations should share more gene flow than distant populations. In this scenario, the resulting pattern is a gradual increase in genetic differences as a function of increasing geographical distance [35], called isolation by distance (IBD). If IBD is observed independently of environmental differences, it is seen as a signal that neutral processes – rather than adaptation – are the major forces structuring the spatial pattern of genetic variation [29]. Correspondingly, linguistic IBD refers to a pattern where linguistic differences increase together with geographical distance [36].
Administrative histories have influenced the cultural and social interactions of humans, and thus their languages, throughout history [37, 38]. Their isolating effect may work in two ways: administrative borders may physically prevent contact, and social cohesion within administrative borders, for example due to kinship relationships and religious beliefs, may keep groups separated [39]. Here, we studied the role of administrative history in separating language speakers and their linguistic variants from each other. In practice, we studied whether differences in administrative history coincide with linguistic differences, a pattern we term "isolation by administrative history" (IBA).
Within the adopted population genetic framework, we can model the relative roles of different factors explaining linguistic divergence. In addition to that, we can make inferences about the relative roles of the processes driving linguistic divergence [29]. As explained above, we first studied the spatial covariation of linguistic differences and extra-linguistic factors. Following this, we inferred from the observed patterns of IBD and IBE the relative roles of neutral (IBD) and adaptive (IBE) processes contributing to the linguistic divergence. The larger relative role of environmental differences (IBE) suggests that linguistic divergence was caused by cultural adaptation of the language speakers to their local environment. However, if dialect divergence were coupled with geographical isolation, we would observe a marked relative role of geographical distance (IBD). In this case, neutral processes would act as the main driver of linguistic differentiation.
As our study object, we used dialects of the Finnish language. The dataset, a digitised edition of the Dialect Atlas of Finnish [5, 40, 41], represents linguistic variation in the Finnish language as it existed almost 100 years ago [42]. The data were collected per municipality (local administrative unit [5]; n = 471 in this study) and they represent linguistic variation within Finland before urbanisation, industrialisation and mass media had evened out dialect differences [43]. The Dialect Atlas of Finnish represents the spatial variation of language and regional dialects, and not social variation and sociolects. The dataset thus provides us an opportunity to study the initial stages of spatial separation of linguistic populations with a population genetic approach. We have shown earlier that this data is compatible with population genetic analyses [5].
We studied the association of extra-linguistic factors with linguistic divergence and therefore essentially also on the isolation of human populations. Extra-linguistic factors include geographical distances among the studied units, and data on 18 environmental and 22 cultural variables representing the environmental and cultural surroundings more than a century ago (Additional file 1: Table S1). We also compiled data on administrative history, including 16 sets of administrative borders from the thirteenth to the nineteenth century. Some variables within this dataset have a wider temporal coverage than the Dialect Atlas, (e.g. variation in soil types is considered to be unchanging), and the data aims to capture the extra-linguistic settings of Finland during the last one thousand years, during which the contemporary Finnish dialects have largely taken shape [44].
In our language data, the linguistic variants are documented per municipality instead of individual language speakers. Accordingly, in our analyses we parallelled municipalities and their municipal dialects with biological individuals. We clustered linguistic variation of municipal dialects into dialect groups in a similar way to how genetic variation of individuals would be clustered into populations. We inferred the dialect groups with a population genetic model-based clustering method (Fig. 1). We previously found that dialect groups produced by this method are largely in line with the traditional dialect divisions of Finnish [5].
Fourteen dialect groups of the Finnish language. Clustering produced with the admixture model of the Structure software package [66], showing the core areas of the dialect groups (areas with IC values ranging 0.75–1) with different colours. National and municipal borders are shown as they were in the 1920s. Municipalities shown in white represent either transitional dialect areas (IC value below 0.75), which were not included in the dialect group analyses, or the Swedish-speaking areas in the southern and western coasts, which are not covered by the Dialect Atlas [42]
We studied linguistic divergence at two spatio-linguistic resolutions: between municipal dialects (collected per municipality) and between dialect groups (clusters of municipal dialects). First, we studied the divergence of municipal dialects. We built one model where we explained linguistic differences between municipal dialects with environmental and cultural differences and with geographical distance, and another model where differences in administrative history were also included. We expected the role of geographical distance to be high due to spatial autocorrelation in linguistic variation [11]. Our expectations about the explanatory power of environmental and cultural differences, and of the differences in administrative history, were less clear. Next, we examined which factors have a role in the divergence of dialect groups. We did that with a model where we explained linguistic differences between dialect groups with environmental and cultural differences and with geographical distance. Thus, in total we had three different models: two with municipal dialects and one with dialect groups. The effect of administrative history was not studied with the dialect groups, as these typically encompass multiple administrative areas. Based on the relative roles of these factors in explaining the differences between the dialect groups, we made deductions about neutral and adaptive processes that might be responsible for inducing the divergence of dialect groups. We inferred the evolutionary processes from the analyses of the dialect groups only, as evolution is a property of a population (defined as a change of allele frequencies in a population), not of an individual (Table 1). Here we discuss in particular the role of natural environment as an isolating force, as this has been relatively unstudied compared to the isolating force of sociolinguistic processes [45].
Table 1 Factors and patterns studied, and the evolutionary processes inducing linguistic divergence
The data were analysed with multiple regression on distance matrices (MRM; [46, 47]), which allows simultaneous analysis of several explanatory variables. MRM is an extension of the Mantel test [48], where both the response and explanatory variables are dissimilarity matrices. Error probabilities of type one were estimated through permutations. Linguistic differences between the dialect groups were measured as FST values (introduced for languages in [5]). Linguistic differences between municipal dialects were calculated following Séguy's dialect distance metric, i.e. as a percentage of disagreeing linguistic features between pairs of municipal dialects [49]. We specifically chose the response variable to be linguistic difference and not language itself. This is because we are interested why the spoken language is sometimes more and sometimes less different between localities, not why a certain kind of language variety appears in a certain locality. In addition, by defining the response variable as a distance (or difference), we were also able to include the geographical distance, an often-discussed potential dispersal barrier [7, 10,11,12,13], as an explanatory variable in the multiple regression analysis. The other explanatory variables used in our analyses were a distance matrix representing administrative history, and 22 cultural and 18 environmental variables turned into distance matrices (see Methods and Additional file 1: Table S1).
In this study, we aim to resolve the inducers of linguistic divergence in the early stages of the divergence process. In other words, we want to disentangle the relative importance of different 1) extra-linguistic factors in the divergence of municipal dialects and dialect groups and 2) evolutionary processes prompting the divergence of dialect groups.
The three extra-linguistic factors (environmental, cultural and geographical distances) explained somewhat overlapping parts of the variation in linguistic differences (Fig. 2a and b). In all, these factors explained over half of the linguistic variance both at the levels of dialect groups and municipal dialects (53.4% and 53.7%, respectively). However, the relative explanatory powers of the three factors differed notably between these two levels (Fig. 2a and b). Adding administrative distances to the analyses of the municipal dialects (Fig. 2c) only slightly increased the overall explanatory power of the model (56.2%). Nevertheless, this addition markedly decreased the fraction of pure geographical distance compared to the analysis of municipal dialects where administrative distances were not included (Fig. 2b).
Partitioning the total variation in linguistic differences to components explained by variation in extra-linguistic factors. The extra-linguistic factors are environmental (E), cultural (C), geographical (D), joined environment-cultural (EC) and administrative (A) distances. (a) The relative proportions of E, C and D in explaining linguistic differences between the dialect groups. (b) The relative proportions of E, C and D, and (c) the relative proportions of A, D and EC in explaining linguistic differences between the municipal dialects. The values present the percentages of individual (a-c) and joint (d-g) contributions of explanatory factors; h refers to the amount of unexplained variation. Circle sizes roughly represent percentages of the total variation
Cultural differences explained the largest individual fraction of linguistic differences between the dialect groups (Fig. 2a), whereas they explained the smallest fraction of the linguistic differences between the municipal dialects (Fig. 2b). The cultural features included in the final models at both levels described land cover (farmed area and forest land) and house type (chimneyless huts), while in the analysis of the municipal dialects an additional feature related to a subsistence type was also included (slash-and-burn agriculture; Table 2). Thus, features related to land use were left in the models at both levels. To visualise the relationship of linguistic and cultural differences, we correlated the observed linguistic differences with values of linguistic difference predicted by the cultural differences left in the final models (Figs. 3a and 4a; features in the final models are indicated in Table 2). Cultural differences predicted the differences between the dialect groups well (Fig. 3a), but they predicted differences between the municipal dialects well only when the cultural differences were moderate or large (Fig. 4a).
Table 2 Environmental and cultural features remaining in the final models
Observed vs predicted linguistic differences between the dialect groups. Predicted differences between the dialect groups calculated on the basis of (a) cultural differences (corresponds to fractions b, d, f and g in Fig. 2a), (b) environmental differences (corresponds to fractions a, d, e, and g in Fig. 2a), (c) environmental and cultural differences together (corresponds to fractions a, b, d, e, f and g in Fig. 2a), and (d) geographical distances (corresponds to fractions c, e, f and g in Fig. 2a). Cultural and environmental features are given in Table 2. N = 91, as pairing up the 14 dialect groups resulted with 91 linguistic difference values
Observed vs predicted linguistic differences between the municipal dialects. Predicted differences between the municipal dialects calculated on the basis of (a) cultural differences (corresponds to fractions b, d, f and g in Fig. 2b), (b) environmental differences (corresponds to fractions a, d, e and g in Fig. 2b), (c) environment-cultural differences (corresponds to fractions a, d, e and g in Fig. 2c), (d) administrative and geographical distances (fractions b, c, d, e, f and g in Fig. 2c). Cultural and environmental features are given in Table 2. Pairing up the 471 municipal dialects resulted in 110,685 linguistic difference values, from which we randomly sampled 5% of the data points for the graphs, leading to N = 5535
Environmental differences alone explained the second largest fraction of the linguistic differences at both levels: ca. 11 % of the differences between the dialect groups (Fig. 2a) and 6 % of differences between the municipal dialects (Fig. 2b). Environmental features in the final models of both levels described soil type and water systems (Table 2). Large and moderate differences in the environmental conditions predicted linguistic differences well at the dialect group level as well as at the municipal dialect level, while small differences in the environmental conditions were poor predictors of linguistic difference between the municipal dialects (Figs. 3b and 4b).
Environmental and cultural differences together explained over half of the differences between the dialect groups (Fig. 2a). The model fit assessed from the correlation between the predicted and the observed differences between the dialect groups improved when predictions were done with both environmental and cultural features left in the final models (Fig. 3c). In the models of the municipal dialects, environmental and cultural differences explained one fifth of the variation in the linguistic differences (Fig. 2b and c). In the model where environmental and cultural differences were analysed jointly (Fig. 2c), the features remaining in the model described land cover, soil type and water systems (Table 2). The model fit was good with moderate and large environmental and cultural differences, but failed to predict the differences in the municipal dialects with small differences in environmental and culture (Fig. 4c).
Geographical distance explained individually only a small part of the differences between the dialect groups (Figs. 2a and 3d). Instead, it explained by far the largest fraction of the linguistic differences between the municipal dialects (Fig. 2b), but the vast majority of this fraction was also explained by administrative distance (Figs. 2c and 4d). The large role of geographical distance at the municipal level is also seen as a highly significant positive spatial autocorrelation of all the dependent and explanatory variables remaining in the final model, especially within the range of 100 km (Fig. 5). At the dialect level, positive spatial autocorrelation is highly significant (p = 0.001) within a 100 km range only in linguistic differences between dialect groups (Fig. 6). Administrative distances alone explained a tiny fraction of the variation among the differences of the municipal dialects (Fig. 2c).
Spatial autocorrelation plots for features remaining in the final model of municipal dialect analysis. Autocorrelation coefficient (r; blue line) as a function of geographical distance for (a) linguistic differences, (b) difference in administrative history, difference in (c) percentage of forest land, (d) percentage of chimneyless huts, (e) slash-and-burn agriculture per 100 ha cultivated land, (f) percentage of clay, (g) percentage of bedrock and (h) percentage of lakes. The 95% confidence intervals calculated with permutation (red lines), and bootstrapped 95% confidence intervals (error bars) are also shown
Spatial autocorrelation plots for features remaining in the final model of dialect group analysis. Autocorrelation coefficient (r; blue line) as a function of geographical distance for (a) linguistic differences, differences in percentage of (b) farmed area, (c) chimneyless huts, (d) moraine, (e) bedrock and (f) difference in the length of rivers. The 95% confidence intervals calculated with permutation (red lines), and bootstrapped 95% confidence intervals (error bars) are also shown
We found that environmental differences explained linguistic differences between the dialect groups independently of other explanatory variables. Environmental differences explained even more of the linguistic differences than geographical distances did. In biology, such an observation of IBE suggests genetic differentiation of populations through adaptation to local environment [23, 24, 29], while the pattern of IBE may emerge also for example via assortative mating or phenotypic plasticity without genetic adaptation to the local environment [23].
We consider linguistic IBE to be an indication of a process of adaptation in which spatially varying environmental conditions have played a role. We suggest that what we see in our data is that humans have culturally adapted to different environments [33] and in that process, language has behaved as a neutral marker of cultural history for human populations [30]. In our model, cultural differences alone explained more of the linguistic differences between the dialect groups than environmental and geographical distances. This indicates a connection between neutral and non-neutral cultural features and further reinforces the conclusion that cultural adaptation has had a role in the divergence of Finnish dialect groups. In many cases it may be difficult to decouple linguistic and cultural features and infer the direction of causation. However, as we are connecting language and culture to the physical environment of the language speakers and as humans adapt to their environment with culture and not with language, we propose a hypothesis that cultural differences are the cause and linguistic differences are the effect, and not vice versa.
Cultural adaptation (or "cultural adjustment" or "cultural specialisation") in this context refers to the accumulation of skills that help people to live and survive in a certain environment, but which are not easily transferable to a different type of environment [50]. It parallels with biological adaptation, where a population is genetically specialised to a certain type of habitat where its individuals have better success than in other, different environments, and where the local individuals outcompete the immigrants coming from a different environment [51,52,53]. Cultural adaptations within Finland could include the subsistence strategies and farming practices used in different parts of the country depending e.g. on the local climate conditions and soil types [54, 55]. Furthermore, the availability of water systems naturally promotes the development of fishing techniques as part of livelihood. In the north, cattle farming, fishing and hunting were the most important subsistence strategies due to unfavourable conditions for crop farming. In the rest of the country, crop farming techniques have varied from several slash-and-burn techniques to field cultivation, depending on the soil and forest type [54, 55]. For example, the slash-and-burn techniques and crop varieties used in large parts of western Finland were not viable in the east, which is why another variant of slash-and-burn technique and specific varieties of crops more applicable to the eastern conditions were adopted from further east [56].
We propose that differences in cultural adaptations, such as subsistence strategies arising from variation in the environmental conditions, could have acted as nonphysical barriers and limited the contacts between groups. Once at least partial isolation has taken place, the languages of these groups may differentiate in time due to the usual processes of language change (e.g. borrowing, grammaticalisation, sound change and semantic change motivated by e.g. prestige and/or the principle of least effort [57]). This follows the logic presented by Michalopoulos [50] and Gavin et al. [58], who suggested that cultural specialisation may produce distinct cultural spheres, which may in turn lead to group boundary formation, with linguistic diversification as a side product. Finally, it could be said that the language-environment connection found with moderate and large differences in our models is notable, especially when considering that even the largest environmental and cultural differences within Finland are subtle compared to cases where several languages and larger geographical areas are studied [58].
The role of geographical distance in separating the Finnish dialect groups was relatively small. This can be explained at least partly by what is currently known about the arrival and the spread of the Finnish language within Finland. The speakers of the early forms of Finnish arrived in the southern parts of Finland from two directions, from the south across the Gulf of Finland, and from the southeast along the Karelian Isthmus. As a result, early forms of Finnish were spoken in both eastern and western areas about one thousand years ago [59]. The early language varieties spread throughout Finland from these speaker populations. The east-west division remained in the southern and central parts of the country and today it still remains the main linguistic division of Finnish [12]. The northern areas, instead, have had influence from both western and eastern dialects [12, 60]. As a result, the neighbouring eastern and western dialect groups in the different sides of the east-west border are geographically very close, but linguistically very different. In contrast, the northern and southern areas, which are geographically far from each other, share many linguistic features and have comparably small linguistic differences [5].
While pure geographical distance had only a small influence on separating the dialect groups, it explained the largest fraction of the linguistic differences between the municipal dialects. Furthermore, linguistic differences between municipalities showed strong spatial structure over short geographical distances. Similar differences in linguistic IBD across two levels have been detected in Northern Island Melanesia, where linguistic IBD was detected at a local scale but not at a wider scale [61]. On the other hand, linguistic IBD has been found in a global sample of typological features between languages [36]. Thus, in general, the findings regarding the spatial scale at which linguistic IBD has been detected are not consistent.
When we added administrative history to the model of differences between the municipal dialects, the relative importance of pure geographical distance decreased dramatically. Geographical distances and differences in administrative history then jointly explained the majority of the variation in the linguistic differences, while the individual fractions of geographical distance and administrative differences were comparably small. Geographical distance physically limits dispersal between locations while administrative history may form both physical and nonphysical barriers. As the limits of dispersal between municipalities arise now via two mechanisms, they are likely to be more efficient limitations for dispersal than either of these would be alone.
In this study, we have adopted certain elements of the biological microevolutionary approach in order to study and understand the first steps of linguistic divergence. While new dialects, and linguistic diversity in general, may emerge also via contact (e.g. koineization [62]), we focus here only on divergence via isolation. Within this framework, we were able to examine the relative contributions of several factors and separate their individual and joint influences from each other; for example, to what extent do environmental differences contribute to the divergence of dialect groups, and how much of this is due to the geographical distance between locations. In addition, it allowed us to infer the relative contributions of underlying processes from the observed patterns in order to understand the mechanisms of linguistic divergence better. We inferred cultural adaptation from linguistic IBE, but we are not willing to try to pinpoint exactly which environmental or cultural differences would be the most relevant ones in this context, as several environmental and cultural variables are inherently interlinked with each other. Furthermore, to evaluate more thoroughly the inference of adaptive processes from the correlation of linguistic IBE, it would be important to study more specifically the connection between environmental variables and cultural adaptation. This way both neutral (language) and non-neutral (cultural variables under selection) variation would be covered in a comparable way, similar to how the study of both neutral and non-neutral genetic variation has been recommended when detecting the mechanisms behind IBE [29]. We found the role of geographical distance to be rather small in separating dialect groups. However, the relative contribution of geographic distance could be re-evaluated by use of a measure of functional connectivity [63]. It accounts for the time and difficulty of travel (i.e. where it is most cost-effective to move) and takes into consideration for example the promoting influence of waterways and the effects of administrative boundaries on human movement.
Our results suggest that mere differences in environmental conditions – and adaptive processes related to these – have a larger role in the divergence of the dialects groups than that of geographical distance even within an area of only modest environmental variation. We present here a hypothesis where ecological settings of the speaker populations contribute to the divergence of speaker populations, and put forward a framework with which it is possible to test it. This makes it possible to study the universality of this phenomenon and resolve whether it acts as one of the factors underlying the global patterns of linguistic diversity.
The linguistic data were obtained from the Dialect Atlas of Finnish [42] collected by Lauri Kettunen in the 1920s and 1930s. The Atlas contains 213 map pages showing various linguistic features (phonological, morphological and lexical) and their variants in 525 Finnish-speaking municipalities. Within each municipality, Kettunen recorded the linguistic variants of 1–4 informants. Therefore, each municipality has at minimum one variant and at maximum four variants of each linguistic feature. The number of linguistic variants per feature varied between 2 and 15 over the whole study area.
For the present analyses, we excluded the municipalities not within the administrative territory of Finland at the beginning of the twentieth century (Kven, Meänkieli, Ingrian, Karelian), and the islands in the Gulf of Finland, since the explanatory variables data did not cover these areas with uniform quality. Three municipalities in northernmost Lapland, with less than twelve documented features, were also excluded due to an insufficient amount of linguistic data. This reduced the number of municipalities included in this study to 471. The basic study unit of the Atlas is "the linguistic variant in a municipality" resulting in 100,323 potential study units. However, the data was missing from 5.8% of these.
To make the dialect atlas data appropriate for the population genetic analyses we parallelled municipalities and their municipal dialects (n = 471) with genomes of biological individuals, linguistic features (n = 213) with genetic loci, and the variants of these features (2–15) with alleles. Instead of using only one linguistic variant (allele) per municipality, we analysed the data in a diploid form and included two variants per municipality. A more detailed explanation of the data, its digitisation and its transformation to the diploid form is given in Syrjänen et al. [5]. The digitised atlas is archived in the AVAA-service (http://avaa.tdata.fi/).
We collected data for 22 cultural and 18 environmental explanatory variables from each municipality (Additional file 1: Table S1, Additional file 2). The cultural variables, which also included demographic variables, were related to human populations (including e.g. birth rates and per capita taxes), while the environmental variables represented the physical and natural environment of the municipality, for example temperature and topography. It is, however, important to note the interconnected nature of certain cultural and environmental variables. For example, the coverage of agricultural fields (classified as a cultural variable, since forests are converted into fields by humans) also depends on soil type and thermal conditions (classified as environmental variables). These data were mainly collected from statistical yearbooks going back approximately one century and historical atlases of Finland. However, data on relatively unchanging physical variables, such as soil type, lake coverage and topography were based on modern geographical databases (see Additional file 1: Table S1 for the full list of variables).
The data on administrative history were a compilation of administrative areas and their borders in the territory of Finland from the thirteenth to the nineteenth century. The data included the approximate eastern border after the second Swedish Crusade to Finland around 1250; the division established by the Treaty of Nöteborg between Sweden and the Novgorod Republic in 1323; provincial divisions from the years 1475, 1540, 1635, 1721, 1743, 1747, 1776, 1812 and 1831; bishoprics from the years 1554, 1850 and 1895; and judicial territories from 1634 and 1776 [43, 64, 65] (Additional file 2).
Geographical distances between each pair of municipal dialects were calculated between their respective municipal population centres (Additional file 3). Between the dialect groups, these distances were calculated between the frequency–weighted centroids of the core areas of each group (obtained from the Structure analysis, see below).
Calculating linguistic differences (dependent variables)
The Dialect Atlas data were turned into distance matrices for the distance-based analyses. The analyses were done separately for the data sets of municipal dialects and of dialect groups. In the analysis of municipal dialects, we used linguistic differences between all 471 municipal dialects as the dependent variable (Additional file 4). Linguistic differences were calculated for each pair of municipal dialects by summing the number of disagreements in dialectal features in that pair and dividing it by the total number of dialectal features. Thus, the calculation was a rough equivalent of Séguy's distance calculation formula [49].
For the analysis of the dialect groups, the data of municipal dialects were clustered. We did this with a Bayesian clustering technique implemented in Structure (v.2.3.3) [66], a model-based method designed to cluster population genetic data. The suitability of this method of analysis for dialect data has been shown by Syrjänen et al. [5]. Our dataset has less data (471 municipal dialects) than that in Syrjänen et al. ([5]; 525 municipal dialects); for this reason we re-ran the clustering analyses for this study. We clustered the linguistic data into 1–20 clusters (i.e. dialect groups) to estimate the optimal number of dialect groups for the data. We used the admixture model, which allows individuals to originate from more than one population (i.e. in the case of our linguistic data it allows the linguistic variants of a municipal dialect to originate from more than one dialect group). Each municipal dialect was therefore assigned a proportionate membership (a value of Inferred Cluster (IC) ranging from 0 to 1) of each of the dialect groups, allowing the appearance and illustration of transitional dialect areas between the core areas of the dialect groups. We repeated the analysis for each K value ten times to ensure that the results were consistent. The burn-in period was set at 10,000 generations and the number of MCMC repetitions after burn-in at 100,000 generations.
We estimated the optimal number of dialect groups with both the ΔK and the ln Pr (X|K) methods [67]. The results were largely similar to those in Syrjänen et al. ([5]; Fig. 5). Here we considered K = 14 to be the optimal number of dialect groups, as K = 14 got consistently higher maximum likelihood values (6 out of 10 repetitions) than K = 15 did (1 out of 10). Thus, the number of studied units in our analysis of dialect groups was 14. Of the ten repetitions of K = 14 we took the individual run with the highest likelihood to represent the dialect groups. To highlight linguistic differences we used only the core areas of our dialect groups in our analyses (Fig. 1). These were determined by including the municipalities with IC-values > 0.75 in the core areas of the dialect groups while discarding the rest that belonged to the transitional dialect areas.
We used the linguistic differences between the dialect groups as our dependent variable. The differences between the dialect groups were calculated as FST values (GenAlEx, v.6.41) [68, 69] between each pair of the cores of the dialect groups obtained from the Structure analysis (Fig. 1; Additional file 5). We borrowed the FST formula from population genetics, where it provides a measure of genetic differentiation between populations [5]. A higher number of differing linguistic variants produced a larger linguistic difference with both distance metrics (Séguy and FST).
Environmental, cultural, geographical and administrative distances (independent variables)
To compare the differences in environmental and cultural conditions with the linguistic differences between the dialect groups and between the municipal dialects, we transformed all the cultural and environmental variables into Euclidean distance matrices.
For the analyses of municipal dialects, we calculated Euclidean distances between each pair of municipalities for each of the 40 variables (e.g. the difference in the mean temperature (in °C) between each pair of municipalities). For the analyses of the dialect groups, we used data on 33 environmental and cultural variables (see Additional file 1: Table S1, Additional file 5). We first calculated averages of these variables for each core dialect area (e.g. the average temperature of Häme). Instead of averages, we calculated sums of core municipalities for the total population number and area of the municipality (Additional file 1: Table S1). Subsequently, these values were transformed into Euclidean distances between each pair of dialect groups for each of the 33 variables.
Geographical distances between the municipal dialects were calculated as planar straight-line distances between municipal population centres. These were determined manually as the location of the largest or main settlement in each municipality at the turn of the nineteenth and twentieth century [70].
For dialect groups, geographical distances were calculated between the dialect group frequency (IC) –weighted centroids of the core areas of the dialect groups (based on values of IC > 0.75) (Additional file 5). These centroids were determined as a weighted arithmetic means from the coordinates of the municipal population centres within the dialect group and the IC value of the dominant dialect group in these municipalities. Thus, instead of having the geographical centroid of the dialect group, the centroid was shifted towards the municipalities with the highest IC values. A planar distance matrix was calculated between these weighted centroids.
The administrative data were turned into a Jaccard distance matrix to represent differences in administrative histories between each pair of municipalities. The variables were coded in a binary form where a municipality either belonged to a particular administrative area, e.g. the province of Savonlinna in year 1475 (=1) or it did not (=0) (Additional file 2). This binary coding was turned into Jaccard index values by taking a pair of municipalities, summing the number of times these municipalities had belonged to different administrative areas (over all the 16 administrative divisions used here), and dividing it with the sum of the administrative areas to which either one or both of the municipalities had belonged. As a result, municipalities with different administrative histories received large Jaccard index values (cf. [71]). All distance calculations (except the FST) were performed with R [72].
Autocorrelation coefficients were calculated for different distance classes (100 km each) with GenAlEx 6.5 [68, 69]. The upper and lower bounds of 95% confidence intervals were obtained with 999 random permutations, and the 95% error bars for each distance class with 999 bootstrap trials.
We used multiple regression on distance matrices (MRM; [46, 47]) to analyse whether linguistic differences were explained by geographical distances, differences in environmental and cultural conditions or differences in administrative history. We first transformed geographical distance matrices into log10-values for a better fit of the MRM model. We then excluded the cultural and environmental features that did not correlate with linguistic differences or correlated only due to geographical distance. This was done by conducting one-tailed partial Mantel tests [73] between the linguistic differences and each of the explanatory variables with the effect of the logarithm of the geographical distance taken into account. The tests were conducted with 1000 permutations each with the 'ecodist package' [74] for R [72] at both the levels of municipal dialects and dialect groups.
Finally, we determined the multicollinearity of all the remaining cultural and environmental variables. We studied the multicollinearity of the variables at the municipality level with Spearman correlations. In cases of high correlations (Spearman correlation coefficient > 0.7 [75], see Additional file 6: Table S2), variables which had the highest correlations with several other variables were excluded, and only one of the inter-correlated factors was left in the model to represent these variables. In total five environmental and five cultural variables were left for the MRM analysis at the level of municipal dialects (Additional file 1: Table S1). A similar selection for uncorrelated explanatory variables was conducted for dialect groups but with a higher limit for correlations (correlation coefficient > 0.9; see Additional file 7: Table S3). Altogether five environmental and three cultural variables were left for the model selection at the dialect group level (Additional file 1: Table S1).
With these procedures, we selected for MRM analyses those environmental and cultural features which correlated with linguistic differences but were not too correlated with each other. Prior to analysing the data with MRM, the features were standardised to make the coefficients mutually comparable.
MRM analyses and variation partitioning
To determine the relative impacts of the differences in the explanatory variables on linguistic differences, we ran three sets of MRM analyses: one for differences between the dialect groups and two for differences between the municipal dialects.
In the analyses of the dialect groups, we explained the differences between dialect groups with environmental and cultural differences and with geographical distance. Here, MRM was first used in model selection to find the environmental and cultural features that best explained the linguistic differences and that would therefore be included in the final environmental model and the final cultural model. Model selection was done separately for environmental and cultural features with a backward elimination procedure [46], starting with five environmental and three cultural features (Additional file 1: Table S1). We removed the feature with the lowest coefficient in each round until the R2 value dropped dramatically, resulting with a positive regression coefficient for all the features remaining in the models. Hereby, the final environmental and the final cultural model had the largest explanatory power with the least number of features. From this analysis, we obtained the R2 values for the final individual models of environment (including two features) (E) and culture (three features) (C). We also ran the MRM to obtain R2 for the logarithm of the geographical distance. We then combined the features left in the final individual models to calculate the explanatory powers of the combined models: "environment and culture model" (EC; five features), "environment and geographical distance model" (ED; three features), "culture and geographical distance model" (CD; four features) and "environment, culture, and geographical distance model" (ECD; six features).
In the first set of analyses of the municipal dialects, we explained linguistic differences between the municipal dialects with environmental and cultural differences and with geographical distance. Here, to obtain the final environmental and cultural models we again began with model selection, starting with five environmental and five cultural features for the municipalities (Additional file 1: Table S1). The final environmental and cultural models included three features each and their R2 values were calculated. As above, we used MRM to get R2 values for the logarithm of the geographical distance individually, and for the combined models (EC, six features; ED, four features; CD, four features; ECD; seven features).
In the second set of analyses of the municipal dialects, we explained linguistic differences between the municipal dialects by administrative differences (A), geographical distances (D) and a joined environment-culture model. Here, model selection was done jointly for environmental and cultural features with a backward elimination procedure starting with 10 features in total. Four features (three environmental and one cultural) were left in the final environment-culture (EC) model (Additional file 1: Table S1). Again, R2 values were calculated for each of the individual models (A, D, EC) and the combined ones (AD; two features, AEC; five features, DEC; five features, ADEC; six features). MRM calculations were performed with the 'ecodist package' of R [74], with 1000 permutations each.
MRM models show the relative importance of different explanatory factors. However, to resolve how much different factors explain of the linguistic variation individually (e.g. pure environmental effect) and how much is explained jointly by two or three factors (e.g. environment explains together with geographical distance), we performed variation partitioning [47, 76]. These fractions were calculated from the R2 values obtained from MRM analyses (for the methodology, see Macía et al. [77] and Heikkinen et al. [78]).
IBA:
Isolation by administrative history
IBC:
Isolation by culture
IBD:
IBE:
Isolation by environment
MRM:
Multiple regression on distance matrices
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We thank M. Heikkilä, K. Korhonen, J. Santaharju, M. Dunn, A. Mäkilähde and two anonymous reviewers for comments on the manuscript; Q. Atkinson, I. Herlin, A. Kalske, S. Korpinen, L. Laukkanen and J. Pettay for useful discussions; T. Suutari and Institute for the Languages of Finland for providing the language data; U. Määttä for helping to get funding for the research.
This work was supported by working grants from Kone Foundation (TH, KS, OV), Finnish Cultural Foundation (Varsinais-Suomi Regional Fund) and Ella & Georg Ehrnrooth Foundation, and salary from University of Turku Graduate School (TH). These funding bodies did not have any specific role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
All data generated or analysed during this study are included in this published article and its additional files.
Department of Biology, FI-20014 University of Turku, Turku, Finland
Terhi Honkola
, Niklas Wahlberg
& Outi Vesakoski
Institute of Estonian and General Linguistics, Jakobi 2, University of Tartu, 51014, Tartu, Estonia
Department of Geography and Geology, FI-20014 University of Turku, Turku, Finland
Kalle Ruokolainen
Faculty of Communication Sciences, FI-33014 University of Tampere, Tampere, Finland
Kaj J. J. Syrjänen
, Unni-Päivä Leino
& Ilpo Tammi
Council of Tampere Region, FI-33201 Tampere, Tampere, Finland
Ilpo Tammi
Department of Biology, Sölvegatan 37, Lund University, 223 62, Lund, Sweden
Niklas Wahlberg
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TH, KR, OV and NW formulated the study question. UL assisted in acquiring the linguistic dataset and in transferring it into an open database. IT and KS generated the materials. TH, KR, OV, KS and IT conducted the analyses, and TH, KR, OV and IT interpreted the results. TH, KR, KS, UL, IT, NW and OV took part in writing and commenting on the manuscript. All authors read and approved the final manuscript.
Correspondence to Terhi Honkola.
Table S1. Explanatory variables and their usage in the analyses. * Abbreviations used in Tables S2 and S3. Variables without abbreviations were eliminated in the partial Mantel analyses. † Variables which were left after the partial Mantel test and correlation observations and were inserted to MRM analyses. ‡ Variables left in the final models. § Variables from which averages and sums were calculated for the municipality data. ¶ Sums of per municipality (instead of average) values were calculated for these variables. (DOCX 19 kb)
Environmental, cultural and administrative data used in the analyses of municipal dialects. Raw data of 40 environmental and cultural variables (sheet 1), and binary data on administrative history (sheet 2) used in the analyses of municipal dialects in 471 municipalities. (XLS 805 kb)
Geographical distances between municipalities. Planar straight-line distances between municipal population centres (in metres). (CSV 1490 kb)
Linguistic distances between municipalities. Rough equivalents of Séguy's dialect distance metric, i.e. a percentage of disagreeing linguistic features between pairs of municipal dialects. (CSV 1277 kb)
Datasets of the dialect group analyses. Linguistic FST distances (sheet 1), geographical distances (sheet 2) and raw data of 33 environmental and cultural variables (sheet 3) used in the analyses of the dialect groups. (XLS 52 kb)
Table S2. Spearman correlations of municipality raw data for variables remaining after partial Mantel test. (DOCX 16 kb)
Table S3. Pearson and Spearman correlations of the 14 dialect groups for variables remaining after partial Mantel test. *Spearman correlations. (DOCX 13 kb)
Dialect divergence
Linguistic microevolution
Variation partitioning
Human ecology
Environmental variation
Cultural adaptation
Speciation and evolutionary genetics


text:
What Is John Doing After 'The Bachelorette'? Bach Nation Isn't Done With Him Yet
By Kelly Schremph
Craig Sjodin/ABC
When it comes to shows like The Bachelor and The Bachelorette, some of the contestants prove to have more impressive jobs than others. However, upon first stepping out of the limo, it became clear that John Graham from Becca's Bachelorette season had reached a whole different level of success. In fact, he even told the Bachelorette that very night that he "created Venmo," the popular app people use to instantly send money back and forth to one another. Unfortunately, his resume wasn't enough to impress Becca, who eliminated him from the competition halfway through the season. So what is John doing now after The Bachelorette, you may be wondering? Did he immediately go back to focusing on his budding career or is there something new for him on the horizon? Fans will be pleased to know that it doesn't look like ABC is done with him just yet.
The network announced back in June that John joined the cast of Bachelor in Paradise Season 5, giving him another chance to find love through the use of roses and date cards. And while viewers will have to watch the season in its entirety to know if this journey proves to be more successful for him, you won't actually have to wait until then to see him again. John will also be appearing on Becca's Men Tell All special on Monday, July 30, giving all of the eliminated contestants a chance to tell their side of the story and even ask Becca some questions that's been on their minds.
Suffice to say, John is still very much apart of Bachelor Nation right now, which is definitely a good thing. Right from the start, he proved to be a very sweet and genuine guy, who caused zero drama and seemed to have no other motive than to win over Becca's heart. All in all, he was basically the perfect guy, though evidently the spark between him and the Bachelorette just wasn't strong enough to go the distance. Hopefully, his time in Paradise will prove to have better results.
Considering that filming The Bachelorette and Bachelor in Paradise happen fairly close together, John probably didn't return to work after his elimination — at least not full-time anyway. According to his very own Medium post, after working at Venmo for three years, John went on to become a product engineer for Fin and has been working there ever since. Perhaps he's been doing some work remotely for the company in between his Bachelor-related responsibilities. Or maybe he's just on a hiatus for the time being while enjoying his newfound fame. Either way, he seems to be having a great time outside of work, if his Instagram account is any indication.
From going on hiking adventures to eating delicious ice cream or simply sharing photos of himself with his fellow Bachelor comrades, John's social media is jam-packed with fun activities. If this keeps up, he may never want to go back to work — or maybe all of this will inspire him to tackle a new career path in the entertainment industry. John seems to succeed in anything he puts his mind to, so I wouldn't put it past him.
Then again, there's always the possibility of him becoming the next Bachelor. (Hey, a girl can dream, right?) Either way, fans will certainly get to see a lot more of John this summer, both on the Men Tell All and Bachelor in Paradise. These truly are blessed times.

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text:
A Division of the
Timothy Theaker, Mayor
Weather forecast Mansfield, Ohio ▸
Civil Air Patrol
Mansfield's Early Aviation History
By Scott Schaut, Curator, Mansfield Memorial Museum
Since early man looked to the skies to watch the birds, humans have been fascinated with flying. The people of Mansfield were no different. Mansfield's first experience with flying came on Saturday, July 17, 1852 when John Wise brought his balloon, the Jupiter, to the city square. He inflated the balloon and ascended into the sky while hundreds of spectators watched in amazement. Five years later, Wise delivered the first U.S. Airmail in the Jupiter. After the Civil War, Mansfield's citizens became accustomed to balloonists at the County Fair. The balloons came in all shapes, colors and sizes. In these annual events, aerialists would perform acrobatics from the balloons to the amusement of the crowds.
Frank Purdy Lahm was born in Mansfield on November 17, 1877 and in his early years attended Mansfield Schools. Lahm's father had a great influence in his son's interest in aeronautics and trained him in night ballooning, which helped him get his ballooning license in 1905. The following year, Lieutenant Lahm entered the first Gordon-Bennett Race which started in Paris, France. Lahm as the pilot and copilot Hersey won the race by traveling slightly over 400 miles to the west coast of England near the North Sea. This race is still held today. Lahm went on to distinguish himself in early aviation history when he and Orville Wright set the world's record for a two-man flight of one hour, 12 minutes, and 40 seconds in 1908. Frank Lahm was trained to fly by Wilbur Wright in a lesson that lasted a little more than three hours, eventually earning Lahm the Aero Club of America Pilot's license number 2. He was instrumental in purchasing the first planes for the fledgling Army Air Corps from the Wright Brothers. His career spanned another 4 decades with distinction until his retirement on November 20, 1941 as a Brigadier General. His contributions to aviation are recognized worldwide. Frank Lahm is a son of Mansfield that we are all proud of.
The first aeroplane that Mansfield and Richland County saw was the landing of the historic cross-county flight of Cal Rogers in 1911. The Vin-Fiz soda company sponsored a cash prize of $50,000.00 for the first person to fly across the United States coast-to-coast. Rogers landed his plane in Mansfield on September 30, 1911 when he was in need of directions and gas. After an hour on the ground, he took off and flew twice around the city before heading to Marion to the delight of thousands of Mansfielders. The following year, the Mansfield News sponsored the News Aviation Meet at the fairgrounds. Two Curtis Biplanes eventually made it to the meet after a delay of several weeks due to the damage of one of the planes. On June 21-22, 1912, the two pilots, Beckwith Havens and Charles F. Walsh, delighted the spectators with takeoffs, landings, and acrobatics over the fairgrounds. They also delivered the first airmail to Mansfield by taking a bag of mail and dropping it on the Post Office building roof that was just below Fourth Street on Main. Four months later, Charles F. Walsh died in New Jersey in the same airplane he used in Mansfield.
During World War I, several individuals in Mansfield distinguished themselves in the Army Air Corps including Alan Tappan and Mud Gardner, who also served in World War II. Both would have a profound impact on the establishment of the Mansfield Airport in later years. Another man who was interested in early aviation was James L. Zimmerman, who wasn't born in Mansfield, but eventually made the city his home. Growing up in Nebraska, Zimmerman saw an article in the August 1909 issue of Popular Mechanics on how to build a glider. He was successful in the construction and flying of the glider in 1910. He was a pilot in World War I and was one of the pilots that testified to the U.S. Senate on the disposition of the Army Air Corps airplanes that had been destroyed in Europe instead of being shipped back to the States. He was called up to active service and flew in Europe in World War II. There are many other men who served in World War I in the Army Air Corps that eventually made Mansfield their home and made their mark in early aviation history.
Frank Lahm and Orville Wright on September 9, 1908, when Frank Lahm became the first military passenger in the world.
Close-up view of the Curtis Flyer at the Aviation Meet in 1912.
Mansfield was on the forefront of modernization and in 1927 and 1928 saw a construction boom of many large buildings including the Farmers Bank, Richland Bank, the Leland Hotel, J.C. Penney (known today as the Barrington Building), and the new Hospital. Thus, it was decided to build and establish an airport for the community allowing Mansfield to keep pace with larger cities. The men credited in making the airport a reality were C. H. Keating, general chairman; Alan Tappan, assistant chairman; Sam Isaly, manufactures; C. M. Crafton, merchants; C. H. Huston, attorneys, Dr. Hattery, physicians and dentists; John Bissman, wholesalers; W. H. Goodenough, utilities; C. K. Smaltz, public; R. E. Moorehead, auto dealers and accessories; J. J. Jones, finances; and C. E. Nail, insurance and real estate. This committee raised the monies to build Mansfield Airport. Neil Robinson negotiated the land purchase and Leslie K. Wagner prepared the legal obligations. The original estimate for grading, tiling, erecting the hangar, and preparing the field was $15,000.00. The hope for the airport was to have airmail service and to help businesses make quicker contacts for the local industry to grow faster. Another aspect proposed was air service from Mansfield to Detroit.
The dream for Mansfield to have an airport became a reality with the opening of the new airport on May 9, 1928. Col. Clarence D. Chamberlin, along with the Flying Circus, conducted the dedication which entertained a crowd of thousands. The Flying Circus was made up of WWI pilots and performed stunts, or barnstorming, around the country. Included in the festivities was an autogyro, the Goodyear Blimp Pilgrim, parachuting, and many airplanes of different types on display. The Mansfield Aerial Service could take you on a ride in a Curtis JN-4 for $2.50. During one of these passenger rides, Mansfield experienced its first airplane accident when James L. Zimmerman, the pilot, experienced a downdraft and forced the plane down prematurely hitting a fence. Mrs. Harry Young was slightly injured, while Cal Walter and Loren Cook were unharmed. A Ford Tri-motor also took passengers on a flying tour over Mansfield. The oldest person to take a ride on one of the planes was Mrs. R. A. Garret, 72, mother-in-law to Mayor J. Earl Ports, who enjoyed the flight and became an aviation enthusiast. With the depression the following year, many hopes for the use and expansion of the airport waned.
Mansfield hosted the Air Races on July 24-26, 1931. Jimmy Doolittle participated in the races, and Blanche Noyes flew an autogyro. The race was a five mile triangular course set up with three pylons over the airport and surrounding fields. There were 40 to 50 planes that competed for a cash prize of $2,000.00. The 112th Observation Squadron, from Cleveland, brought some of their planes to show off their new aircraft. The first hangar cornerstone was laid during the three day race.
On February 18, 1934, a lone Army Air Corps Mail plane, Douglas 0-38F, piloted by Lieut. J. H. Gibson was traveling to Columbus in a fierce winter storm and lost his way. Almost out of fuel, he took the plane to a height of 2,000 feet and bailed out, surviving with only minor injuries. The plane crashed 5 miles out of town on the Freund farm. This is the first official record of a plane accident in the Mansfield area.
The first delivery by plane to Mansfield was Gardenias on July 20, 1946.
Lines waiting to view planes on display during the airport dedication on July 20-21, 1946.
The airport in the late 1930s was still small and inadequate for any large commercial ventures. James "Bud" Harrington had established the Harrington Air Service with several hangars. It was the first private training center in the country for Navy pilots and over 1,500 were trained under the War Department's Civil Pilot Training Program. In the early war years, the Navy was considering three super-ports for the possible dispersal of military aircraft in case of an invasion of either coast by the Axis Powers. Although not from Mansfield, Congressman Harry McGregor of the 17th took an interest in the Mansfield airport and in early 1944 went to the U.S. government on behalf of the city to get funding for the airport expansion. The government at this time had almost abandoned the Mansfield airport because it wasn't essential to the war program. Helping McGregor to raise local interest and support were James Harrington, John G. Routzon, J. Harry McGregor, Col. Alan Tappan, C. H. Huston (these two men were part of the original founding of the airport), Russel D. Porch, Neil Robinson, Howard Sword, C. H. Henkel, J. Gil Baird and W. J. Locke. McGregor took all the information directly to President Roosevelt who announced shortly after the meeting that the project would continue to expand the runway and other improvements for the Mansfield airport.
On July 20-21, 1946, over 95,000 people came to the two day celebration to participate in the dedication of the new Airport that included longer runways and better facilities. J. Gil Baird and Col. Alan Tappan, Chairman and Vice Chairman of the dedication committee, helped coordinate the logistics between the city, Navy, and Army. The cost of this new airport was $2,000,000.00. Over 250 planes participated in the event with many on the ground for the spectators to view and go through. As in the original opening in 1928, there were two minor accidents with no injuries. Two notables present at the dedication were Brig. Gen. Frank P. Lahm and Maj. Gen. Thomas J. Hanley (former Commander of the Air Service Command in the CBI theater during the war.) The Ohio Dawn Patrol flew 150 planes in an aerial parade over the airport. From the Aircraft Carrier Sicily, the Navy VC Squadron 42 participated in aerial stunts with the Marine Squadron VMF-244. With the renovation of the airport, the Mansfield Weather Station found a permanent home. Seventy top rank military planes participated in the air show highlighted by B-29 flyovers and the new Army's P-80 Shooting Star which stole the show. The first cargo plane departed for Los Angeles on the first day, starting what was hoped to be the beginning of passenger and cargo flights to and from Mansfield. Two air deliveries were made: one of 7,500 pounds of fresh Bing Cherries from Wenatchee, Washington, and the other was fresh Gardenias from San Francisco. United and American Airlines each brought their top of the line aircraft to promote air travel. United Airlines Mainliner was the first passenger plane to depart Mansfield with passengers from the airport. Passenger flights from Mansfield became a reality with three airlines servicing the airport: United, TWA, and Lake Central Airlines. In 1952, a round trip ticket from Mansfield via Pittsburgh to Washington, D.C. and back cost $45.20.
Thanks go to the foresight of Alan P. Tappan, a WWI and WWII Army Air Corps Pilot, who was instrumental in petitioning the government to establish an Air Guard Base in Mansfield. Through heavy negotiations on the part of the city and civil groups, the 164th Air National Guard Fighter Squadron became a reality. Hangar No. 7 was leased from Richland Aviation as temporary quarters for the Guard until the permanent buildings could be built. On June 20, 1948 J. Gil Baird officiated the ceremonies with a C-47 as a backdrop, while Maj. Gen. C. W. Goble swore in and enlisted 145 officers on the airport grounds. Thirty P-51 fighter planes, two F-4Us and two PBMs from Air Guard squadrons in four states soared over the proceedings.
1852 Jupiter Balloon
John Wise and the balloon Jupiter in Mansfield City Square, July 17, 1852.
1911 Cal Rogers
Cal Rogers taking off from a field near Mansfield on September 30, 1911.
1912 Beckwith Havens
Beckwith Havens with his Curtiss Aeroplane at the Manstield News Aviation Meet of 1912. Held at the Richland County Fairgrounds on Springmill.
1912 Curtis Flyer
Curtis Flyer landing in a field near the fairgrounds, 1912.
1912 Charles F. Havens
Charles F. Havens taking off from the Race Track at the Richland County Fairgrounds, 1912.
1912 Charles F. Walsh
Charles F. Walsh at his Curtiss Aeroplane at the Mansfield News Aviation Meet of 1912.
1913 Balloonist
Balloonist exhibition at the 1913 Richland County Fair.
1918 James Zimmerman
James Zimmerman's License issued during WWI.
1928 Flying Circus
The Flying Circus on display at the opening of the Mansfield Airport on May 8, 1928.
1928 Goodyear Blimp Pilgrim
The Goodyear Blimp Pilgrim flying over the city during the festivities of May 8, 1928.
Goodyear Blimp, Pilgrim , during the opening of the new airport on May 8, 1928.
1934 Crash
The crash of the Douglas O-38F Army Air Corps Mail Plane on the Freund farm just outside Mansfield. This was the first recorded crash in the National Aviation Records.
1946 Navy Observation
July 20-21, 1946 a Navy Observation is taking off for its aerials over the crowds.
1940s James Harrington's Hangers
James Harrington's Air Service hangers and planes, early 1940s.
1946 First Delivery to Mansfield
1946 Shooting Star
The P-80, Shooting Star, taking off for combat maneuvers at the dedication in 1946.
Closer view of the P-80 Shooting Star.
1946 Rededication
An orderly chaos of cars parked for the celebration on July, 1946.
Planes on display and crowds waiting to view the interiors of several planes.
1946 Food Service
Harrington Manufacturing of the food service buffets for the DC-4 Skymasters.
1946 Bing Cherries
Display of the Bing Cherries that were freshly delivered from Washington State in July 1946.
1946 United Airlines
United Airlines display in one the quansit huts on the airport grounds during the celebration in 1946 promoting the new air service to Mansfield.
1940s Skyway Club
The new Skyway Club was built at a cost of $125,000 and served as a restaurant and clubhouse for the airport.
Mansfield saw its first helicopter and how it could maneuver on July, 1946.
1948 Induction
The induction of the 145 Officers and Enlisted men to the new 164th Ohio Air National Guard Fighter Squadron on July 20, 1948.
1940s Richland County P-47
P-47 with Richland County, Ohio markings.
1948 German Buzz Bomb
wA German WWI V-1 Buzz Bomb in the City Square during the recruitment for the Air National Guard 1948.
1952 TWA Passenger Flight
A TWA passenger flight landing at Mansfield Airport in 1952.
Mansfield Lahm Regional Airport
2000 Harrington Memorial Rd
Mansfield, OH 44903
airport@ci.mansfield.oh.us
© 2021 Mansfield Lahm Regional Airport


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Measuring Players' Losses in Experimental Games
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Levine, David
Fudenberg, Drew
Drew Fudenberg
David Knudsen Levine
In some experiments rational players who understand the structure of the game could improve their payoff. We bound the size of the observed losses in several such experiments. To do this, we suppose that observed play resembles an equilibrium because players learn about their opponents' play. Consequently, in an extensive-form game, some actions that are not optimal given the true distribution of opponents' play could be optimal given available information. We find that average losses are small: $0.03 to $0.64 per player with stakes between $2 and $30. In one of the three experiments we examine, this also implies a narrow range of outcome.
Levine, David & Fudenberg, Drew, 1997. "Measuring Players' Losses in Experimental Games," Scholarly Articles 3160492, Harvard University Department of Economics.
Handle: RePEc:hrv:faseco:3160492
File URL: http://dash.harvard.edu/bitstream/handle/1/3160492/fudenberg_measuring.pdf
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