Morphological variation in red-backed voles (Myodes gapperi) in Québec and western Labrador
Rodrigo Barata Souto Lima Department of Biology McGill University, Montreal
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Master of Science
Submitted August 2012
© Rodrigo Lima 2012
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Table of contents
Abstract ...... 4
Résumé ...... 5
Contribution of the authors ...... 6
Acknowledgements ...... 7
List of tables ...... 9
List of figures ...... 10
General introduction ...... 12
Literature review ...... 13 1 - Ecotypic variation ...... 13
2 - Environmental conditions in Québec ...... 19
2.1 - Climate ...... 19
2.2 - Ecozones ...... 20
2.3 - Primary productivity ...... 22
3 - Southern red-backed vole, Myodes gapperi (Vigors, 1830) ...... 22
3.1 - Origin and evolution ...... 22
3.2 - Distribution and preferred habitats ...... 24
3.3 - Diet ...... 25
3.4 - Home range ...... 27
3.5 - Activity pattern ...... 27
3.6 - Physiology ...... 28
4 - Morphometrics ...... 30
4.1 - A brief history of morphometrics ...... 30
4.2 - Applications of morphometrics ...... 31
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4.3 - Multivariate or “traditional” morphometrics ...... 32
4.4 - Outline analysis ...... 36
4.5 - Landmark geometric morphometrics ...... 38
4.6 - General methods used in this study ...... 42
References ...... 44 Morphological variation in red-backed voles (Myodes gapperi) in Québec and western Labrador ...... 59 ABSTRACT ...... 60
INTRODUCTION ...... 61
MATERIAL AND METHODS ...... 64
Specimens ...... 64
Image acquisition ...... 65
Environmental variables ...... 65
Spatial analysis ...... 66
Outline analysis of teeth ...... 67
Statistical analysis of tooth size and shape ...... 68
Geometric morphometric analysis of skulls...... 70
Statistical analysis of skull size and shape: ...... 71
Evaluation of size proxies ...... 72
Software ...... 72
RESULTS ...... 73
Sexual Dimorphism ...... 73
Spatial analysis ...... 73
Relation of tooth size and skull size with other size estimators ...... 73
First upper molar size ...... 74
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First upper molar shape ...... 75
Skull size ...... 77
Skull shape ...... 78
DISCUSSION ...... 80
Tooth ...... 80
Skull ...... 84
ACKNOWLEDGEMENTS ...... 89
REFERENCES ...... 90
TABLES ...... 101
FIGURES ...... 106
Appendix A ...... 114
GENERAL CONCLUSION ...... 116 REFERENCES ...... 117
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Abstract
Environmental heterogeneity has long been associated with morphological variation in small mammals. Studies of ecotypic variation across time and space provide valuable information about the way organisms’ phenotypes respond to changing environmental conditions and the major factors influencing morphological changes, providing insight into the mechanisms that promote adaptation. In this thesis, I quantified morphological variation among 12 populations of the widespread and abundant southern red-backed vole, and examined the degree to which climate, primary productivity and ecozones explained variation in the morphological traits. Teeth and skulls of voles collected in several locations along a 1,000 km latitudinal gradient in Québec and
Western Labrador were studied using geometric morphometric methods, and the relations between morphology and environmental and spatial variables were examined. Most of the spatial variation in morphology was correlated with environmental gradients. The morphological differentiation among populations could be explained by the environmental differences between their habitats, and precipitation and ecozone were the environmental variables mostly correlated with morphological variation.
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Résumé
L'hétérogénéité de l'environnement a depuis longtemps été associée à la variation morphologique chez les petits mammifères. Les études de la variation
écotypique dans le temps et dans l'espace fournissent des informations utiles sur la réponse du phénotype des organismes aux variations des conditions environnementales, sur les principaux facteurs qui influencent les changements morphologiques, et sur les mécanismes qui favorisent l'adaptation. Cette étude examine les réponses morphologiques d'un mammifère généraliste et abondant, le campagnol a dos roux de Gapper, à la variation du climat, de la productivité, et des écozones. Les dents et les crânes de campagnols échantillonnés à plusieurs sites le long d'un gradient de 1000 km de latitude au Québec et a l'Ouest du
Labrador ont été étudiés en utilisant des méthodes de morphométrie géométrique, et les relations entre la morphologie et les variables environnementales et spatiales ont été examinées. La plupart de la variation spatiale de la morphologie est corrélée avec les variations de l'environnement.
Les précipitations et les écozones sont les variables environnementales le plus fortement corrélées avec la variation morphologique, et la différenciation morphologique entre les populations peut être expliquée par la variation de l'environnement entre leurs habitats.
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Contribution of the authors
This thesis consists of two chapters, including one manuscript to be submitted to a journal. The candidate designed the study, collected specimens, analyzed data, and wrote the manuscript. Virginie Millien, the supervisor of the project, collaborated actively in all these steps, and is co-author of the manuscript.
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Acknowledgements
I thank my supervisor Virginie Millien for the opportunity, supervision, and support. I learned a lot during this time I spent in her lab, and I owe much of this to her guidance and patience. I also thank my supervisory committee members
François Joseph Lapointe, Murray Humphries, and Jonathan Davies for all the good advice. Special thanks to my lab mates Jorge, Dan, Anita, Robby, Emilie, and
Ted for all the help, inspired discussions, and fun lunches. My colleague grad students in McGill facilitated much learning with all those cool workshops. Chloe
Makepeace, Jeff Ji, and Xiaolei Liu helped with skull cleaning and photographs, thanks folks. Shusen Wang kindly provided very useful net primary productivity data.
Funding for this project was provided by Northern Scientific Training Program grants and a McGill University Start-up grant to Virginie Millien. NSERC, FQRNT, and McGill University provided fellowships well above the noodle line.
I owe a very special thanks to Anthony Howell and Jorge Camacho, members of the TMF club, who endured harsh conditions and bad black fly bites in our long field trip up north, always with good humor and a positive attitude.
Maira was a great companion in these two years of hard work, making my life colorful and fun, giving me precious advice, love, friendship, and support, besides showing me good music, good books, good films, and good food. Thanks
Brou. My parents, although from a distance and not fully understanding exactly
7 why on Earth I would choose to hunt rodents in the Canadian taiga, were very supportive and grew more and more interested in my work in this time. Reca and
Ju were, as always, great friends and supporters of my work.
I would like to thank all the voles and other small mammals who involuntarily gave their lives for this project. Sorry guys, science needed you. I hope karma doesn’t exist.
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List of tables
Table 1. Populations sampled, total number of adults captured and sample size for each analysis .…………………………………………………………………………………………………………………… 101
Table 2. BIOCLIM variables used to describe the climate of sampling sites. Temperature variables are given in degrees Celsius, precipitation variables are given in millimetres.
Temperature seasonality is the standard deviation of weekly mean temperatures, and precipitation seasonality is the standard deviation of the weekly precipitation estimates expressed as a percentage of the mean of those estimates …………………………………….. 102
Table 3. Proportion of morphological variation explained by different factors. Numbers are adjusted R2 values from a variation partition analysis. Note that negative R2 values are inherent to this analysis and shared fractions ( ) cannot be tested for significance. p values obtained with 10000 permutations: *p<0.05; **p<0.01; ***p<0.001 ……………. 103
Table 4. Permutation p values for Hotelling’s T2 tests on M1 shape. Values marked with an asterisk are significant after Bonferroni correction ………..……………………………………. 104
Table 5. Permutation p values for Hotelling’s T2 tests on skull shape. Values marked with an asterisk are significant after Bonferroni correction …….………………………………………. 105
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List of figures
Figure 1. The 12 vole populations sampled in Québec and Western Labrador. Ecozones are represented in different shades of gray. From south to north, the ecozones sampled are the Mixedwood Plains, the Boreal Shield, and the Taiga Shield .……………………..... 106
Figure 2. Skull in ventral view of a red-back vole. A total of 15 landmarks were used for the analyses of the skull shape; 1: Anterior extremity of the suture between nasals; 2:
Lateralmost point of incisive alveolus; 3: Anterior margin of the incisive foramina; 4:
Posterior margin of the incisive foramina; 5: Suture between premaxilla and maxilla where it intercepts the skull outline on the plan of the photo; 6: Anterior extremity of first upper molar where it intercepts the maxillary; 7: Posterior extremity of third upper molar where it intercepts the maxillary; 8: Posterior point of maximum curvature of the zygomatic arch; 9: Lateralmost point of the suture between presphenoid and basisphenoid; 10: Tip of Eustachian tube; 11: Suture between basisphenoid and basioccipital where it contacts the tympanic bulla; 12: Mastoid apophysis where it intercepts the superior edge of the auditory meatus on the plan of the photo; 13: Tip of paraoccipital process; 14: Most posterior point of occipital condyle; 15: Anterior extremity of foramen magnum. The insert shows the first upper molar (M1) in occlusal view ………………………………………………………………………………………………….………………….….. 107
Figure 3. Cumulative information for an increasing number of harmonics (triangles) and measurement error introduced by each harmonic (diamonds). The dotted line indicates the 10th harmonic threshold used in our study ...………………………………………………...….. 108
Figure 4. Size of first upper molars (square root of occlusal surface area; top) and skulls
(log centroid size; bottom). Each point represents one specimen ……………………………. 109
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Figure 5. First upper molar and skull shape differenciation among populations represented by the first canonical axis (top) and shape variation mostly correlated with environmental (center) and spatial (bottom) variables represented by the first latent variables. Dotted tooth outlines represent low scores and solid tooth outlines represent high scores on each analysis. Black dots represent 15 landmarks on the left side of the skull ventral surface. Grids and landmark vectors represent the deformation of mean skull shape associated with high scores on each analysis, and deformation is multiplied by a factor of three for better visualization ……………………………………………….………….…. 110
Figure 6. First upper molar shape change associated with the environmental gradient as identified by a PLS analysis. Outlines represent the mean shape of the 10 specimens with the highest and lowest scores on the shape axis .………….…………………………………. 111
Figure 7. Skull shape change associated with the environmental gradient as identified by a PLS analysis. Deformation grids and landmark vectors represent the direction of shape variation; shape deformation is multiplied by a factor of 3 for better visualization ….. 112
Figure 8. Skull shape dendrogram (Procrustes distances and UPGMA algorithm).
Numbers at the nodes are bootstrap values (1,000 replicates) expressed as percentage; only values above 50% are shown ……………………………………………………………………..…….. 113
Figure 9: First upper molars with scores 0 (left) and 4 (right) ……………………………….…. 115
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General introduction
The phenotype of organisms is determined, at least in part, by their surrounding environment. In small mammals it appears that climate influences morphology both through its direct effect on thermoregulatory mechanisms and its indirect effect on food type and abundance. The recent revolution in morphometric methods and the increasing availability of environmental data from climate models make this an exciting time to investigate this relationship. The accelerated rates of climate change due to anthropogenic activities and the recognized role of morphological change as an adaptive response to this process make the results from such investigations even more relevant. Québec is an ideal stage for ecotypic variation studies due to its marked environmental gradient, and southern red-backed voles are distributed throughout this gradient. In this thesis, I studied the morphological variation in red-backed vole populations distributed from the mixedwood forest in the south to the taiga in the north of
Québec, and analyzed the relationship between morphology and several climate and climate-related variables. I used a combination of geometric morphometrics and spatial statistics methods to identify spatial patterns of morphological variation in red-backed voles. This allowed the identification of patterns in morphological variation most correlated with spatial and environmental gradients. I further inspected this correlation to identify the most important variables in the morphology-environment relationship, thus providing insight into the possible underlying mechanisms.
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Literature review
1 - Ecotypic variation
Phenotypic variation among populations may be due to three non- exclusive factors: natural selection, phenotypic plasticity, and random genetic variation (Adkison, 1995). The evolution of phenotypic traits occurs through natural selection of phenotypes that have higher fitness in a given environment
(Darwin, 1859). Fitness is defined by a multitude of selective pressures that affect survival and reproductive success of individuals, such as competition for resources and mating partners, predation, parasitism, and physiological challenges, that may vary among populations. As the fittest individuals have more reproductive success, their advantageous heritable characteristics are relatively more represented in the following generations, driving each population closer to the optimal phenotype for its environment (Wright, 1931; Lande, 1976).
A classic example of natural selection is the speciation of finches in the
Galápagos archipelago. The Galápagos islands are home to several species of finches that descend from a single colonizing species and evolved different beak morphologies adapted to different diets (Grant, 1999). A long term study on two species (Geospiza fortis and G. scandens) of these finches has shown that natural selection has strong effects on their morphologies, and that the shape and size of beaks varied considerably within in a 30 year period. A more contemporary example of natural selection is provided by several studies of guppy (Poecilia reticulata) populations in Trinidad (reviewed in Endler, 1995). Many cases of life
13 history, behaviour, morphological, and genetic variation among these guppy populations have been documented. In particular, body size and shape in guppies has been shown to be influenced by predation regimes, canopy structure, and water flow in the sites where populations were sampled (Hendry et al., 2006).
While selection shapes individuals through generations, plasticity can operate within an organism's lifetime. Phenotypic plasticity occurs when more than one phenotype is expressed from the same genotype due to different environmental conditions (Pigliucci, 2001), and is considered to make genetic change less necessary for the adaptation of an organism to its environment
(Wright, 1931). Continuous phenotypic plasticity in response to a gradient in some environmental conditions characterizes a reaction norm (Stearns, 1989).
Reaction norms represent the intensity of phenotypic responses to an environmental pressure, and can be represented by a straight or curved line on an environment x phenotype plot (Stearns, 1989). Different genotypes present different reaction norms, and reaction norms are themselves the target of natural selection. Therefore, phenotypic plasticity itself goes through adaptation by means of selection, and evolves as do discrete phenotypic traits (Via & Lande,
1985; Gotthard & Nylin, 1995; Nussey, Wilson & Brommer, 2007). Morphological plasticity in response to different environmental factors has been demonstrated in several experiments. Luning (1992) showed the differential development of defense structures in Daphnia populations in response to the presence or
14 absence of predators. The continuous plastic adaptation of head morphology in a neotropical cichlid in response to different diets was documented by Meyer
(1987). Fish fed on Artemisia nauplii developed head morphology significantly different from that of fish from the same brood fed on flake food and nematodes; when switched to an adult Artemisia diet, both groups displayed similar head morphologies. Peres-Neto & Magnan (2004) showed that the velocity of water flow induces body shape differentiation in brook charr and
Arctic charr. In their experiment fish reared under higher water velocities had smaller pectoral fins and relatively longer heads and bodies than those reared in lower water velocities.
Random variation of gene frequency may also account for differentiation among populations. Even in the absence of selection, mutation, or migration, the frequency of alleles will fluctuate merely by chance in finite populations (Wright,
1931). If populations are isolated, random genetic drift will produce different allelic frequencies and possibly different mean phenotypes (Wright, 1931; Lande,
1976). Genetic drift is important to population differentiation not only in selective neutral populations, but also in populations subjected to selective pressures (Lande, 1976). The interplay between genetic drift and selection is especially important in the case of more than one peak in the adaptive landscape, where the selective disadvantageous space between them (a selective threshold) is crossed through the force of random genetic drift (Lande,
1985). This phenotypic shift is more likely in small isolated populations, since the
15 power of genetic drift is inversely proportional to reproductive population size and gene flow (Wright, 1931). Morphological variation due to genetic drift has been shown in both between modern human populations and between human relatives in the genus Homo. Ackermann & Cheverud (2004) demonstrated that craniofacial morphology in Homo rudolfensis, H. habilis, and H. erectus may have differentiated due to genetic drift, while differentiation in craniofacial morphology of earlier hominins may have arisen due to natural selection. Lynch
(1989) had suggested before that cranial morphology differences between modern human races (negroids, mongoloids, and caucasians) are the product of genetic drift rather than selection.
Studies of morphological variation in small mammals suggest different mechanisms acting on the differentiation of populations, and many indicate a correlation between climate variation and morphological variation. For example, analyzing the skulls of several Arvicanthis species in Africa Fadda & Corti (2001) found that shape variation was related to geo-climatic variables. The authors proposed two alternative explanations for such a pattern: the first is repeated convergent evolution of similar morphology as an adaptation to local climatic conditions, and the second is phenotypic plasticity of the skull due to different rainfall regimes in different regions. Renaud & Michaux (2003) studied the mandible morphology of insular and mainland populations of the European wood mouse (Apodemus sylvaticus) and compared it to the morphology of two species of Japanese field mice (A. argenteus and A. speciosus). They found that
16 mandible shape variation between island populations of A. sylvaticus is random and probably due to genetic drift, while variation of all populations formed a latitudinal gradient due to selection of traits related to the masticatory function.
This latitudinal gradient consisted of a dorso-ventral expansion and more divergent coronoid and angular processes towards the south, a pattern also observed in the two Japanese species (Renaud & Millien 2001). In a later study
Renaud (2005) compared the morphological variation of mandibles and molar teeth of the European wood mouse, and found that molar shape is mostly influenced by gene flow, with more isolated insular populations having the most distinct molar shape. Mandible shape, on the other hand, did not present a major signal of geographical isolation and was instead influenced by habitat type, with forest populations being distinct from populations living in drained marshes and meadows. Renaud suggested that phenotypic plasticity may play an important role in the rapid (post Last Glacial Maximum) adaptation of mandibles to local environmental conditions and distinct diets. Monteiro et al. (2003) observed that skull and mandible shape variation in the punaré rat was associated with a geo-climatic gradient ranging from the xeric caatinga biome to the more humid cerrado in Brazil. Rats from the xeric environment had relatively longer jugal bones and coronoid and angular processes, these structures being associated with masticatory muscles. A difference in diet due to this environmental gradient, with seeds in the punaré rat’s diet being harder in the caatinga, is suggested as the cause for this morphological gradient. Ims (1997)
17 studied the differences in size observed between root voles from northern and southern populations using cross-mating and cross-fostering experiments. He found that in identical laboratory conditions the northern individuals, which in the wild are 51% heavier than the southern individuals, were larger at birth and had higher growth rates. Ims (1997) concluded that the size differences were genetically determined and suggested that this was due to different selective pressures in the northern and southern environments.
In the present time when global climatic change presents a challenge for the survival of many species (Root et al., 2003; Thomas et al., 2004), morphological change has been identified as one of the mechanisms by which species may adapt and persist (Hoffmann & Sgrò, 2011). Thus, the nature of morphological adaptation deserves further studies, especially along climate gradients that may emulate the range of climatic alterations to come in the future. In this context, southern red-backed vole populations in Québec are an excellent system in which to investigate how a species responds to different environmental conditions. Québec presents a marked environmental gradient, mostly driven by a marked climatic gradient, and voles are distributed throughout Québec’s territory with the exception of the extreme north of the province. In this work I intend to identify the most important environmental variables influencing voles’ morphology and their specific effects on the size and shape of skulls and teeth of these small mammals.
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2 - Environmental conditions in Québec
2.1 - CLIMATE Gerardin & McKenney (2001) developed a model of Québec’s climate based on the ANUSPLIN method (Hutchinson, 1987). This model shows that temperature depends on latitude, altitude and distance from large water bodies, and that the general pattern in temperature is a south-north gradient with a decrease in temperature towards higher latitudes. Latitude also has a major influence on precipitation, but the effects of altitude and continentality are stronger for precipitation than for temperature. Altitude generally increases from west to east in Québec, in parallel with continentality. Consequently, there is a general southeast to northwest gradient in total annual precipitation. The length of the growing season presents an opposite trend, with a southwest to northeast gradient, probably influenced by altitude and proximity to large water bodies (Hudson Bay, James Bay, and the Saint Laurence Gulf). The number of growing degree-day seems to be less affected by proximity to water bodies.
The climatic zones defined by an arbitrary division in 51 climatic units in
Gerardin & McKenney (2001) are remarkably coincident with ecoregions defined in earlier studies such as that of Ducruc (1985), supporting the validity of the climatic model and the strong relationship between climate and ecosystems in
Québec.
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2.2 - ECOZONES Ecozone classification is a very broad generalization of biotic and abiotic characteristics of large tracts of land (Wiken, 1986). Québec has five of the fifteen Canadian ecozones represented in its territory: Mixedwood Plains, Boreal
Shield, Taiga Shield, Atlantic Maritime, Southern Arctic, and Northern Arctic
(Wiken, 1986). Some characteristics of the first three are described below based on the works of Wiken (1986) and the Ecological Stratification Working Group
(1995):
Mixedwood Plains:
The climate in this ecoregion is marked by warm summers and cold winters. Mean annual temperatures between 5oC and 8oC, with mean summer temperatures between 16oC and 18oC and mean winter temperatures between -
2.5oC and -7oC. Mean annual precipitation ranges from 720mm to 1000mm, and the weather is highly variable due to the position along one of the major storm tracks of North America. The growing season temperatures vary between 1750 and 2500 growing degree-days per year. The original forest cover varies in composition from mixed coniferous-deciduous stands of white and red pine, eastern hemlock, oak, maple, and birch in the north to deciduous forest in the south. Less than 10% of the original forest remains in this ecoregion, due to heavy human use of the environment.
Boreal Shield:
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The Boreal Shield occupies the largest area of Québec and Canada. The climate is strongly continental with long cold winters and short warm summers, and is generally warmer and wetter on the eastern part of this ecozone, namely in Québec and Labrador. Mean annual temperatures range from -4oC to 5.5oC, with mean summer temperatures ranging between 11oC and 15oC and mean winter temperatures between -20.5oC to -1oC. Mean annual precipitation ranges from 400mm in Saskatchewan to 1000mm in Québec and Labrador, and an even higher precipitation in Newfoundland. The characteristic vegetation of this ecozone is the coniferous forest, with stands of white and black spruce, balsam fir and tamarack. Broadleaf trees are found in the south, and exposed bedrock areas are dominated by lichens, shrubs, and forbs.
Taiga Shield:
The Taiga Shield has short and cool summers and long and very cold winters. Mean annual temperatures in Québec and Labrador range between -1oC and -5oC, mean summer temperatures vary between 6oC and 11oC, and mean winter temperatures are between -11oC and -24.5oC. Mean annual precipitation in Québec and continental Labrador ranges between 500mm and 800mm. The vegetation is a mosaic of wetlands, open forest, shrublands and meadows. Forest areas are typically lichen woodlands with scattered coniferous trees. Open mixedwood areas are also present on upland sites and along water courses.
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2.3 - PRIMARY PRODUCTIVITY According to Liu et al., (2002), “Net primary productivity (NPP) is a quantitative measure of the carbon absorption by plants per unit time and space”. The calculation of net primary productivity involves meteorological data
(including radiation indices), land cover data, plant biomass data, plant physiology data, and soil data (Liu et al., 2002). Net primary productivity is often used as a proxy for resource availability for herbivores in ecological studies (e.g.
McNaughton et al., 1989). The EALCO model (Wang, 2008; Wang et al., 2007,
2009) provides high-resolution net primary productivity data for Canada.
According to this model, the mean annual net primary productivity in Québec has a markedly latitudinal gradient, similar to that of mean temperatures
(Gerardin & McKenney, 2001). Annual net primary productivity values in Québec range from near 0 gC/m2/year in the north of the Ungava peninsula to near 500 gC/m2/year in the valley of the Saint Laurence river.
3 - Southern red-backed vole, Myodes gapperi (Vigors, 1830)
3.1 - ORIGIN AND EVOLUTION The oldest fossils of Myodes (formerly Clethrionomys) are found in
France, indicating a European origin for the genus approximately 2.5 million years ago (Chaline & Graf, 1988). Myodes expanded its distribution eastwards to
Asia during the Pleistocene (Chaline & Graf, 1988), and reached North America through the Bering land bridge (Cook et al., 2004). The earliest colonization of
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North America was by a progenitor of Myodes gapperi, in the early Pleistocene
(Cook et al., 2004). The southern red-backed vole is therefore a species endemic to North America whose ancestors can be traced back to the Palearctic.
The Pleistocene was marked by strong climate fluctuations, with repeated periods of glaciation and deglaciation. This pattern produced the expansion and contraction of biomes, and in colder periods forested habitats would be limited to certain isolated areas. Forest dwelling animals were repeatedly isolated in these forested refugia, which often lead to the divergence of lineages within species (Hewitt, 2000; 2004). Southern red-backed voles are an example of such dynamics in North America (MacPherson, 1965). Using ecological niche modeling, Waltari et al. (2007) hypothesize 3 areas suitable for southern red-backed voles during the last glacial maximum (approximately
20,000 years ago). This is in agreement with the study on post-glacial colonization by Runck & Cook (2005), in which the authors identify three clades of southern red-backed voles that originated in three glacial refugia. One clade is distributed west of the Rocky Mountains, one clade east of the Appalachians, and one central clade between the Rockies and the Appalachians. Runck & Cook
(2005) also identified signs of a recent colonization in most of the northern populations analyzed, emphasizing the very recent formation (i.e. less than
13,000 years) of northern populations of red-backed voles.
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3.2 - DISTRIBUTION AND PREFERRED HABITATS The current distribution of southern red-backed voles has a “horseshoe shape”, extending transcontinentally through Canada and northern United
States, and stretching southwards through the Appalachians and the Rocky
Mountains. Although sometimes found in open habitats, southern red-backed voles are essentially forest dwelling animals, occurring in deciduous, mixed, and coniferous forests (Merritt, 1981). In a study comparing vole demography in deciduous and coniferous forests, Bondrup-Nielsen (1986) described deciduous forests as more favourable environment for voles. This author reports larger populations, higher recruitment and survival of young, and smaller female home ranges in deciduous forest stands, and ascribes these demographic effects to more debris on the ground, more cover and potentially more food available for voles in the deciduous forest. The importance of ground-level debris is also emphasized by Wywialowsky & Smith (1988), who associate higher capture rate to the presence of fallen logs in a subalpine coniferous forest in the Rocky
Mountains. Miller & Getz (1977) highlight the positive correlation between vole abundance and soil moisture or presence of standing water, agreeing with the high water requirements of voles. Keinath & Hayward (2003) report a strong preference for uncut subalpine coniferous forest over regenerating stands 20 years after clear-cutting on the Rocky Mountains, and attributes this preference to denser canopy cover and the higher availability of coarse wood debris on the forest ground. These authors also suggest that fungal biomass (although not measured) could also be an important factor. Martell (1983) describes the
24 collapse of populations two years after clear-cutting in central-northern Ontario, attributing it to the low survival rates of young individuals. On the other hand,
Yahner (1986) reports a preference for clear-cut habitats in aspen and oak stands in Central Pennsylvania, with total ground cover as the most important variable discriminating vole habitat. This author argues that more ground cover would result in more mesic conditions on the soil and a more humid environment. Two factors appear to be important in southern red-backed voles’ habitat choices: first the presence of vegetation cover and ground-level wood debris, which is probably associated with predator avoidance; and second humidity, which is probably associated with the high water requirements of voles.
3.3 - DIET Southern red-backed voles are generalist omnivores whose diet shifts according to the availability of food items (Merritt, 1981). Although they opportunistically feed on invertebrates, the bulk of their diet is composed by fungi and vegetative parts of plants. Whitaker (1962) reported that on average
20.2 % of the volume of stomach contents of voles in New York was composed by Endogone fungi. In his study of food habits in different aged clear-cuts in the mixed deciduous forest of western Virginia Schloyer (1977) found that fibrous and leafy vegetation comprised 75.9% of stomach contents, fungi comprised
21.9% (most of it Endogone) of stomach contents, and that blackberries, roots, bark, moss, ferns, and invertebrates were also consumed. This author attributes the high amounts of succulent plant material consumed to the high water
25 requirements of southern red-backed voles, since voles living near streams or in humid areas consumed slightly less plant material. Martell (1981) documented the temporal variation in the diet of voles in northern Ontario. This author recorded new green vegetation, seeds, berries, and mushrooms entering vole diet as they became available. Martell (1981) also reports that lichens and fungi comprised on average 80-89% of vole diet in the mixed wood and black spruce stands where he conducted his study, with lichens dominating the diet in early
May and important throughout the summer and mushrooms growing in importance from early summer until they were one of the primary food items in
August and September. Maser & Maser (1988) identified at least 23 different genera of fungi in the diet of voles collected from widely scattered areas in the
United States. A similar result was found in a microscopic analysis of fecal pellets, in which Orrock & Pagels (2002) recorded that 68% of fields examined contained spores of Ascomycetes, Basidiomycetes, and Zygomycetes, and 57% of fields contained plant material. The authors conclude that voles are fungi generalists.
Norrie & Millar (1990) demonstrated the opportunism of voles through lab feeding tests when captive animals ate 78 out of 81 plant and fungi species presented to them, with a high consumption of 16 species. Kasparian & Millar
(2004) demonstrated that captive voles preferred sunflower seeds over a subset of these high consumed plant species.
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3.4 - HOME RANGE Home range and spacing behaviour are different between male and female southern red-backed voles. Females are territorial and show little or no home range overlap, while males have large home ranges and display extensive overlap (Mihok, 1981; Bondrup-Nielsen, 1986). Beer (1961) reports a mean winter home range of 0.1 ha in Minnesota. Blair (1941) reports a mean home range of 0.25 ha in Michigan, and interestingly describes an overlap in the home ranges of a sexually mature and an immature female. Merritt (1981) mentions mean home ranges ranging from 0.01 ha to 0.5 ha in his review. Bondrup-Nielsen
(1986) found female home ranges of 0.26 ha and 0.33 ha in deciduous and coniferous forests respectively. Male and female differences in home range and spacing behaviour are probably associated with the search for different reproductive resources: females compete for space where they can raise their young, while males compete for access to reproductive females (Mihok, 1981).
3.5 - ACTIVITY PATTERN Southern red backed-voles are active throughout the year, maintaining activity in the subnivean space (between the snow layer and the ground) and showing no hibernation or torpor during the winter (Merritt & Merritt 1978). The activity of southern red-backed voles is mostly nocturnal and consists of short activity bouts followed by short resting periods. Herman (1977) observed activity cycles of 2 to 5 hours in subarctic Canada, with most of the activity occurring after sunset in the summer, fall, and early winter. This study also showed a shift in activity time in winter and spring, when voles became slightly more diurnal.
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Activity time in an artificial tunnel system was mostly nocturnal (Baron & Pottier,
1977). In outdoor enclosures voles were mostly nocturnal, with short periods of activity followed by short periods of inactivity, with a circadian rhythm and peaks of activity at dusk and dawn (Stebbins, 1975). Vickery & Bider (1981) noted that voles were significantly more active in rainy nights and moonless nights, attributing this behaviour to predator avoidance.
3.6 - PHYSIOLOGY A high metabolic rate and high water requirements seem to be the major features of the physiology of southern red-backed voles. Merritt & Merritt
(1978) observed that the mean resting metabolic rate of southern red-backed voles was 25% over the value predicted for a mammal of similar size. These authors also observed that resting metabolic rate and body mass were lower in the winter time, and consider this and as an adaptation for lower caloric intake requirement. High water consumption was noted by Getz (1962), who also observed that water intake is more influenced by temperature than by humidity.
In a later study, Getz (1968) recorded that water consumption was 0.315g/g/day, and that the minimum water ration required for maintenance of body mass was
0.248g/g/day. After 48 hours of water deprivation voles lost on average 24.8% of their body mass and the mortality rate was over 50%. This author also reported a high evaporative water loss and low urine concentration, probably due to relatively inefficient kidneys. Despite their high water requirements, voles in captivity were able to maintain their body weight, obtaining water solely through
28 the ingestion of succulent mushrooms for two weeks. Deavers & Hudson (1979) recorded a high minimum water requirement of 0.34g/g/day, a water turnover double of that expected for mammals of this size, and found a relatively low thickness of voles’ kidney medulla. These authors also report 47.6% of water loss through urine and 42% through pulmocutanean evaporation. Based on the high metabolic rates of voles (thus high caloric intake requirement) coupled with a diet of high water content and low protein and salt content Deavers and Hudson
(1979) conclude that voles in the wild probably get more water than they need from their food. McManus (1974) found high water intake in captive voles, with values varying between 0.637g/g/day at 15oC and 0.931g/g/day at 30oC, a basal metabolic rate 95% higher than expected, and a thermal conductance 31.4% higher than expected. This author suggests that a high basal metabolism/conductance rate is an adaptation to low environmental temperatures, since it leads to a high and precisely regulated body temperature.
McManus (1974) also suggests that the energetic cost of high metabolic rates should be low if compared to the food availability for generalist herbivores such as voles. Merritt & Zegers (1991) show that voles have a great ability to increase their non-shivering thermogenesis in response to cold temperatures and that these animals can control the amount of energy invested in thermoregulation and growth, retarding growth and investing more energy in thermogenesis in colder conditions.
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4 - Morphometrics
4.1 - A BRIEF HISTORY OF MORPHOMETRICS The idea that the form of an object could be expressed numerically was already present in ancient civilizations such as the Babylonians the pre-Plato
Greeks (Reyment et al., 1984). Nevertheless, it was only around the end of the
19th century, when Biology went through a transition from a descriptive discipline into a quantitative one, that the study of the forms of living beings had the measurement and comparison of morphological traits established as its basis
(Bookstein, 1998). With the adoption of statistical methods for the description of shape variation in the mid-20th century, the foundations of the field that is currently known as morphometrics were settled (Adams et al., 2004).
During the 1960’s and 1970’s the field evolved with the adoption of a full set of multivariate methods such as Principal Component Analysis (PCA) and
Canonical Variates Analysis (CVA) (Reyment et al., 1984). The morphological variables used in these analyses were measurements of linear distances (e.g. maximum length or maximum width of structures, or distances between pairs of landmarks), or less commonly ratios and angles (e.g. Baker et al., 1978; Mihok &
Fuller, 1981; Snell & Cunnison, 1983). The multivariate statistical methods employed at this time are still the core of more recently developed and more sophisticated morphometric methods. Many studies on allometry were carried out during this period, which generated a great advance in methods for size correction (e.g. Jolicoeur, 1963). Another great advance in the 1970’s was the
30 use of Fourier series in the description of outlines (Kaesler & Waters, 1972;
Delmet & Anstey, 1974).
The suite of methods known today as geometric morphometrics was matured in the 1980’s and 1990’s (Bookstein et al., 1985; Rohlf & Bookstein,
1990; Bookstein, 1991). Analyses that captured and conserved the geometrical information of structures, thus making it possible to visualize morphological variation as a representation of shape deformation in a hypothetical average specimen were appraised as a revolution in the early 1990’s (Rohlf & Marcus,
1993). Indeed the new methods are more powerful in detecting and depicting shape variation, permitting the application of morphometrics to new biologically relevant questions (Barciová 2009; Lawing & Polly 2010).
4.2 - APPLICATIONS OF MORPHOMETRICS According to Rohlf & Marcus (1993) “Morphometric methods are needed whenever one needs to describe and to compare shapes of organisms or of particular structures.” Such need to describe and compare shapes is widespread in the biological sciences. Morphometric methods are currently used in studies of systematics, ecology, genetics, and developmental biology, besides having practical applications in the medical sciences (Rohlf, 1990).
Morphometrics play an important part in evolutionary biology as well.
Morphological analyses of fossils provide precious information on the evolutionary history of several groups of animals (Bookstein et al., 1999; Luo et
31 al., 2001; Bush et al., 2002). Studies on ecotypical variation of extant species reveal how organisms evolve in response to changes in their environment, providing valuable insights into evolutionary processes (Fooden & Albrecht,
1993; Renaud & Millien 2001; Ledevin et al., 2010).
4.3 - MULTIVARIATE OR “TRADITIONAL” MORPHOMETRICS Multivariate morphometrics, also referred to as traditional morphometrics, is the application of multivariate statistic methods to a set of linear measurements, or less commonly angles or ratios (Rohlf & Marcus, 1993).
The variables (i.e. the traits measured) must be carefully selected considering the biological question being tackled, as different sets of variables yield very different results. This is due to the fact that variables selected will be the axes of a multivariate space in which specimens will be points, and it is generally desirable that distances between these points will be a reasonable approximation of morphological divergence (Rohlf, 1990). Below is an overview of the most used multivariate methods in morphometrics.
Principal Components Analysis (PCA) is a method used to summarize the information contained in the data set to a smaller number of variables, the principal components (PCs), in a way that each PC represents part of the total variance in the sample and is independent from the other PCs. Principal components are eigenvectors of a variance-covariance matrix with eigenvalues equal to the variance they explain (Marcus, 1990). In a multivariate space that has the measured variables as axes and specimens as points, the principal
32 components are orthogonal straight lines that each crosses a section of the space in a manner that the distance between points is maximized, the distance from the points to the line is minimized and covariance between lines is zero. In that sense, the first line to be traced (the first principal component) is the axis that explains the maximum proportion of the total variance in the sample. The second line (the second principal component) must be traced perpendicular to the first, and in the direction that explains the maximum proportion of the remaining variance. The third line (third principal component) will be traced perpendicular to the last two, and explain the maximum proportion of the remaining variance and so on. As these lines are perpendicular (orthogonal) to each other, the covariance between them is zero. Once the principal components have been identified they can be considered as the axes for a new coordinate system in the same multivariate space, and scores of points
(specimens) on each of these axes can be extracted. The first principal component (PC1) explains most of the variation and, as it commonly has high loadings on all variables, it is usually considered as a measure of size.
Nevertheless, due to allometry the PC1 also might carry some information about shape (Reyment et al., 1984). The following PCs explain a lesser part of the total variance and are interpreted as variation in shape. Usually only a few PCs are needed to explain most of the variation in a sample. Plots of scores on different
PCs (e.g. PC2 x PC3) often produce clusters of points (specimens), helping identify patterns of within-group shape variation.
33
Canonical Variates Analysis (CVA) or Discriminant Function Analysis (DFA).
CVA is a method intended to describe differences among groups (which can be tested with a MANOVA). While in a PCA the variance-covariance matrix is used to find new variables that maximize within-group variance, in a CVA the ratio of the within-group and the between-group variance-covariance matrices will be used to find new variables that maximize between-group variance (James &
McCulloch, 1990). The new axes (canonical axes) in which specimens will be scored are rescaled so as to minimize within-group variance. This procedure alters the dimensions of the multivariate space in which the points (specimens) are plotted, so that distances between points are distorted in relation to the
Euclidean space. In this newly defined space the appropriate measure of distances between points is the Mahalanobis distance (Marcus, 1990). Scores on a canonical axis should be interpreted as scores on a conjugation of original variables that best discriminate between groups. Differences on canonical variates coefficients might actually represent a very small morphological variation, but nonetheless the one that best discriminates between groups.
Multivariate Analysis of Variance (MANOVA). The MANOVA is used to test for significance in differences among groups with multiple dependent variables.
As a test of hypothesis, the logic is to compare the observed value of a test statistic with the probability distribution of expected values considering the distribution of variation in a population (Zelditch et al., 2004). If only two groups are being compared, Hotelling’s T2 test assesses the hypothesis of equality
34 between sample means. When more than two groups are analyzed, the test statistic must be a function of the within-group and between-group variance- covariance matrices. There are a few test statistics that can be used in a
MANOVA, such as Hotelling-Lawley trace, Wilk’s λ, and Pillai’s trace (Zelditch et al., 2004). There is not a consensus on which of these statics must be used, since they have similar performances. The MANOVA assumes a multivariate normal distribution with homogeneous variance-covariance matrices (Marcus, 1990); as a test of hypothesis, it tests for differences among group means but does not quantify them.
Multivariate regression. This method is employed to relate morphometric variation to possible explanatory factors, such as climate. The procedure consists of building a mathematical model that predicts shape as a function of the explanatory (independent) variable, fitting data to the model and evaluating the fit. This is different from testing if the explanatory variable actually causes the effects observed on shape (Zelditch et al., 2004). A causal interpretation would demand experimental tests. One approach used in linear regression is to regress the full set of dependent variables (the distances measured) against one independent variable, such as temperature. Another option is to extract factors
(using PCA, for example) of shape and regress these factors against one independent variable or a factor of independent variables (e.g. a combination of climatic variables). As a consequence, it may be possible to predict shape variation from other variables.
35
One interesting example of multivariate morphometrics is Snell &
Cunninson’s (1983) work on the relation between geographic variation in vole skulls and climate. The authors measured six distances in 819 skulls from 38 localities, and related the morphological variation to several climatic factors. A series of MANOVAs was used to test for sexual dimorphism before pooling sexes together for analyses. The authors performed a principal components analysis
(PCA) and used the first two principal components in stepwise multivariate regressions against single climatic variables (temperature, rainfall, frost) and combinations of climatic variables (called climatic factors). The PC1 was interpreted as a measure of size, and the PC2 had a high load on only one morphometric variable, so it was considered a measure of variation in this variable (interorbital distance). The results showed an interesting relationship between both size and shape of voles and some single climatic variables.
4.4 - OUTLINE ANALYSIS In outline analysis the shape of a structure is described in terms of its outline. The outline is recorded as a series of (x,y) coordinates (Rohlf, 1990), i.e. points sampled along the outline. These points are fitted with a mathematical function, and the coefficients of the function are used as variables in multivariate analyses (Adams et al., 2004). In eigenshape analysis, for example, the points are converted into a φ (phi) function, which describes the outline in terms of the angles between adjacent points (Lohmann, 1983). A singular value decomposition is then used to summarize patterns of shape variability
36
(MacLeod, 1999). Fourier functions can also be employed in outline analysis
(Younker & Ehrlich, 1977). Elliptic Fourier Analysis (Khul & Giardina, 1982) is an adequate method for describing complex shapes such as those of arvicoline teeth (Navarro et al., 2004). This method consists of fitting a sum of trigonometric functions of decreasing wavelength to the outline. These functions are called harmonics, and each harmonic N consists of four coefficients (An, Bn,
Cn, Dn) that describe an ellipse. The first harmonic is the ellipse that most approximates the outline, and it is used to normalize Fourier coefficients for size and orientation (Ferson et al., 1985). Once normalized, the Fourier coefficients can be used for multivariate analyses, such as PCA and CVA. Usually the first few harmonics are sufficient to describe a complex structure, and excluding the following harmonics decreases digitization error (Rohlf, 1990). The number of harmonics to be used in analyses can be selected based on the amount of shape information they carry and the degree of sampling error associated with them
(Crampton, 1995).
In a study of morphological variation in bank voles, Ledevin et al. (2010) analyzed the outline of the oclusal surface of molar teeth on 145 specimens from
15 sites spread across Europe. The authors sampled 64 equally spaced points along the outline of three different teeth (M1, M3, and m1) and used an Elliptic
Fourier Analysis to identify morphological variation in these structures across different populations. The size of the teeth was estimated by the square root of the outlined area, and size variation was analyzed with an ANOVA followed by
37 pairwise Student’s t-tests. The first 10 harmonics were used in shape analysis, since they contained more than 90% of the morphological information and represented less than 15% of the measurement error. The area of the first harmonic was used to standardize Fourier coefficients for size differences, and the first three coefficients of the first harmonic (A1, B1, and C1) were excluded from the analysis since they correspond to residuals after standardization. The remaining 37 coefficients were used in statistic analyses. A canonical variates analysis (CVA) was used to identify the main axes of differentiation between populations, a Hotelling’s T2 test was used to test for differences between previously identified phylogenetic groups, and a multivariate regression between size and shape was used to test for allometry effects on shape. Morphological variation was visualized using the inverse Fourier method for reconstructing the outlines. A pattern of size variation contrary to that predicted by Bergmann’s rule was found, and distinct lineages could be differentiated by shape analysis. In this fashion, outline shape and size variation permitted valuable inferences on the evolutionary ecology pattern of the bank vole.
4.5 - LANDMARK GEOMETRIC MORPHOMETRICS In landmark geometric morphometrics the first step is to choose a set of landmarks that will properly describe the shape of the structure being studied and that is appropriate for the biological problem considered. Once landmarks are chosen, digital images of the specimens are produced (usually digital photographs taken with a stereomicroscope) and the landmarks are digitized in
38 each specimen using a software such as TPSDig (Rohlf, 2004). The landmarks are registered as coordinates of points in a Cartesian plane, and each specimen is treated as a configuration of landmarks. The next step is to superimpose all configurations in a way that will exclude the effects of position, orientation, and scale, leaving only differences due to true shape variation (Rohlf & Slice, 1990). If size measurement is an objective of the study, centroid size (see calculation bellow) is considered a good proxy for the size of the structure (Bookstein et al.,
1999).
Generalized Procrustes Analysis (formerly known as Generalized Least
Squares) is a set of mathematical techniques that allow such a superimposition of landmark configurations (Bookstein, 1991). The geometric centroid of each configuration is translated to the origin of the Cartesian plane by subtracting the coordinates of the centroid from the coordinates of each landmark. Then the configurations are scaled to a common unit centroid size by dividing each coordinate of each landmark by the centroid size of the configuration. Centroid size is calculated as the square root of the sum of the squared distances of the landmarks from the centroid. Configurations are then rotated so as to minimize the summed squared distances between homologous landmarks (Rohlf & Slice,
1990). Once configurations of landmarks are superimposed they are in no longer in the Euclidean space, but in a curved shape space (Rohlf, 1999). In this space, distances between two landmark configurations (the Procrustes distances) are calculated as the square root of the sum of squared differences between the
39 positions of homologous landmarks. A mean (or consensus) hypothetical configuration can be calculated, and shape variation will be described as
“deformation” of this consensus configuration (Zelditch et al., 2004).
The thin-plate spline, an interpolation function, is used to describe shape variation as a deformation of the consensus configuration (Bookstein, 1991). In this sense variation can be visualized as the deformation of a grid that represents the structure. Total deformation can be decomposed into its uniform (or affine) component and its non-uniform component. The uniform component is that deformation that leaves lines parallel, (i.e. compression/dilatation and shearing).
The non-uniform component is the set of local deformations that alter the alignment of lines in the grid, and it can be further decomposed into a set of orthogonal components, called partial warps. Partial warps describe how strong and localized a deformation is, and can be used as input data for multivariate analyses (Barciová, 2009). For such, partial warps and uniform shape components can be interpreted as axes for a multivariate space tangent to
Kendall’s shape space (Adams et al., 2004). In this tangent space each configuration of landmarks (i.e. each specimen) is represented by one point, and distances between points approximate Procrustes distances. Scores on the axes of the tangent multivariate space, the partial warp scores, can be treated as multivariate data representing shape.
40
A good example of the use of landmark-based geometric morphometric methods for the analysis of ecotypical variation is found in Caldecutt and Adams
(1998). In a study of threespine stickleback skulls, the authors used 23 landmarks to describe the shape of the skulls of four populations in distinct ecological contexts. After superimposition with Procrustes methods, they used the thin- plate spline function to calculate the two uniform and 40 non-uniform (partial warps) components of shape. The number of partial warps obtained in two dimensional data is given by 2p-6, where p is the number of landmarks and 6 is the number of degrees of freedom lost in the process (two for translation, one for rotation, one for scale, and two for the uniform components of shape). The
40 partial warp scores were then used in a PCA in order to identify the major trends of non-uniform shape variation. The first principal components axis, also called first relative warp, represents the direction of maximal non-uniform variation, and shape deformations along this axis were evaluated in relation to ecological parameters. A two-way MANOVA was used to test for differences between sexes and among populations in the uniform and non-uniform components of shape. A CVA was performed using males and females as separate groups to identify patterns of shape similarity among groups. After this, all possible pairwise comparisons were performed on the generalized
Mahalanobis distances between populations based on their F-values. The results of these analyses were interpreted in relation to the feeding ecology of the four
41 populations, providing some interesting insight into the evolution of trophic morphology in teleost fishes.
In recent years geometric morphometric studies of ecotypical variation have greatly benefited from the adoption of a statistical tool that had not been used before in traditional morphometrics, the two-block partial least squares
(two-block PLS). Two-block PLS is a recently developed technique that reduces dimensionality in two blocks of data while searching for the dimensions that represent most of the covariance between blocks (Rohlf & Corti, 2000). This is achieved by a singular value decomposition of the covariance matrix between the two blocks, which yields pairs of vectors (latent variables) that summarize the pattern of covariance. When using a PLS, one can assess how much of the total covariance is explained by each pair of latent variables, and calculate the correlation between the scores on the two blocks of variables (Rohlf & Corti,
2000). This method is extremely useful when trying to identify patterns of morphological variation associated with environmental variation, since the vectors of morphological variation (extracted from the morphological variables block) most correlated with environmental variation (in the environmental variables block) can be identified and the direction of change can be represented in a deformation grid.
4.6 - GENERAL METHODS USED IN THIS STUDY Limitations of different methods have been pointed out in the literature, such as the high correlation between variables in traditional morphometrics or
42 the lack of biological homology in points sampled in outline analysis. Advantages have also been emphasized, especially the ability of outline and landmark geometric morphometric methods to reconstruct shapes, thus facilitating the interpretation of shape variation. In this thesis I used an outline method, the elliptic Fourier analysis, to describe the size and shape of teeth, and a landmark geometric morphometrics approach to describe the size and shape of skulls. I applied a two-block PLS analysis to landmark and outline data; to my knowledge this is the first attempt to apply two-block PLS to outline data.
43
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Morphological variation in red-backed voles (Myodes gapperi) in Québec and western Labrador
Manuscript formatted for submission to the Biological Journal of the Linnean Society
Authors:
Rodrigo B.S. Lima (email: [email protected])
Redpath Museum / Biology Department, McGill University
1205 Dr. Penfield Avenue, Montreal, QC
Canada, H3A 1B1
Virginie Millien (email: [email protected])
Redpath Museum, McGill University
859 Sherbrooke street West, Montreal, QC
Canada, H3A 0C4
59
ABSTRACT
Spatial patterns of morphological variation in small mammals are often associated with environmental factors, and morphological responses to environmental conditions are regarded as important adaptive processes. The southern red-backed vole is a widespread and abundant small mammal in
Canada, occurring in environments as diverse as mixedwood forests and taiga.
First upper molars from 12 populations and skulls from 9 populations of southern red-backed voles distributed across approximately 10o of latitude were analyzed by means of geometric morphometric techniques, and their relation to spatial and environmental variables were examined. A strong trend of size increase towards higher latitudes was observed in skulls of voles from the boreal forest and taiga, but the two southernmost populations deviated from this trend.
Environmental variables explained most of the spatial variation in shape detected by a trend surface analysis, and appeared to be important drivers of shape differentiation among populations.
KEYWORDS
Environmental gradient - Myodes - geometric morphometrics - shape - size
60
INTRODUCTION
To be able to persist locally, a species must adapt to a significant degree to its environment. This can be achieved through alterations of its behaviour
(e.g. habitat use) or changes in its phenotype (e.g. morphological changes). The relation between intraspecific morphological variation and environmental factors in mammals has been widely documented (e.g. Renaud & Millien, 2001;
Monteiro, Duarte & Reis, 2003; Piras et al., 2010). Typically, gradients of morphological variation parallel physiogeographic gradients, which led to empirical generalizations in the form of ecogeographical rules, such as
Bergmann’s rule (Mayr, 1956).
Climate is hypothesized to influence the morphology of endotherms either directly, through thermoregulatory driven adaptations (Scholander, 1955;
Mayr, 1956; Brown & Lee 1969), or indirectly, through its influence on food type and abundance (McNab, 1971, 2010; Geist, 1987; Renaud & Michaux, 2003;
Cardini, Jansson & Elton, 2007; Piras et al., 2012). Climate-related morphological variation in endotherms has been observed both at the spatial and the temporal scales (Millien et al. 2006). Examples of temporal variation studies include investigations of variation in external measurements and body mass of heteromyid rodents over eight years (Wolf, Friggens & Salazar-Bravo, 2009), size and shape of skulls and mandibles of shrews over 27 years (Poroshin, Polly &
Wójcik, 2010), external measurements and body mass of shrews over 54 years
(Yom-Tov & Yom-Tov, 2005), and size and shape of teeth of the related fossil
61 rodents Apodemus and Stephanomys over 9 Myrs (Renaud et al., 2005). These studies show the variety of patterns (and sometimes the absence of a clear pattern) that can be observed in different species, anatomical structures, and time-scales, besides the relation between environmental changes and morphological changes. Spatial morphological variation is commonly described in terms of more or less gradual changes in the size and shape of some anatomical structure, such as skulls (Cardini et al., 2007, Martínez & Cola, 2011), teeth
(McGuire, 2010), or mandibles (Renaud & Millien, 2001), across a species’ geographical distribution, and usually a relationship between morphology and the environment is detected.
Distinct morphological structures have distinct functions (Kardong, 2006), and thus may vary in the degree to which they correlate with different environmental factors (Stebbins, 1983). Similarly, the size and the shape of a structure may also respond differently to the same environmental factors (e.g
Monteiro et al., 2003; Cardini et al., 2007; Poroshin et al., 2010). Environmental factors may also differ in their pattern of spatial variation. Hence environment related variation in size and shape of distinct structures is expected to differ.
Identifying which environmental factors are mostly correlated with variation in size and shape of a structure may provide clues about the selective pressures acting on these traits, and contribute to explaining spatial patterns of morphological variation. Finally, when investigating spatially structured patterns, one should take into consideration the fact that the populations studied may not
62 be spatially independent (Legendre & Legendre, 1998). Therefore, it is important to estimate the relative importance of unknown spatially organized processes that may be acting on morphology in conjunction with environmental factors.
Given its wide geographic distribution, its abundance and adaptability to different environments, the southern red-backed vole (Myodes gapperi) provides a good model to investigate the effects of environmental factors on size and shape variation in small mammals. The southern red-backed vole is a small arvicoline rodent widespread in North America, living in environments as diverse as deciduous forests and tundra, although showing a clear preference for forested habitats (Merritt, 1981). In Québec, these voles are distributed throughout the province, except for the north of the Ungava Peninsula and the north-eastern tip of the Labrador Peninsula (Merritt, 1981). The range of red- backed voles in Québec displays marked gradients of temperature and precipitation (Gerardin & McKenney, 2001), a marked gradient of net primary productivity and a succession of ecozones (Ecological Stratification Working
Group, 1995). Southern red-backed voles are opportunistic omnivores; although the bulk of their diet is composed of fungi and vegetative plant parts (Orrock &
Pagels, 2002), their feeding habits change considerably according to the spatial and seasonal availability of food items (Schloyer, 1977; Martell, 1981; Merritt,
1981). These animals remain active throughout the winter, moving mostly in the subnivean space between the ground and the snow sheet (Merritt & Merritt,
1978).
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In this study we tested the hypothesis that morphological variation of red-backed voles follows a latitudinal gradient in Québec. More specifically, we expected that the size of skulls and teeth, as proxies for body size, would conform to Bergmann’s rule, and that environmental factors would be the major drivers of shape differentiation among populations.
MATERIAL AND METHODS
SPECIMENS Four hundred and thirty seven voles were collected from 2006 to 2011 in
Québec and Western Labrador (Table 1). Most of the populations were sampled in the summer (July-August), except for Natashquan River that was sampled in the spring (May), and Plée Bleue and Lake Champlain that were sampled in the fall (October). The 12 populations sampled were distributed across approximately 10o in latitude, or roughly 1,000 km along a south-north gradient
(Fig. 1). Only adult specimens (third upper molar totally erupted) were considered for the analyses. Outliers were identified as specimens outside the quartiles of boxplots for univariate data (size analysis); multivariate outliers were identified as specimens with Mahalanobis distances larger than the critical value from a Chi-squared distribution with p<0.001 (shape analysis). We used 179
(size) and 182 (shape) specimens from 9 populations in the analysis of skulls, and
278 (size) and 265 (shape) specimens from 12 populations in the analysis of teeth (Table 1).
64
IMAGE ACQUISITION Skulls were photographed with a Lumenera Infinity 1 digital camera mounted on a Leica MS5 stereomicroscope. Skulls were placed in a container filled with beads with the ventral surface facing up, and were partially submerged in the beads so that the occlusal surface of the molars was parallel to the lens of the stereomicroscope (Fig. 2). A Leica x0.32 Achromat lens was used for the acquisition of skull images, and a Leica x1.0 Plan lens was used for the acquisition of teeth images. For each specimen the right first upper molar was preferentially used; when it was damaged or absent, the left first upper molar was photographed and the flipped image was used instead.
ENVIRONMENTAL VARIABLES Ten climate variables were considered to characterize the local environment of vole populations (Table 2). Climate variables were extracted from WorldClim (Hijmans et al., 2005) and followed the BIOCLIM arrangement originally proposed by Nix (1986). Net primary productivity (total annual net primary productivity in g C/m2/year) and Ecozones, as defined by the Ecological
Stratification Working Group (1995) were also included in the analysis as environmental variables. Ecozone classification is a broad generalization of biotic and abiotic characteristics of large tracts of land (Wiken, 1986), which is based on the known interrelationships of climate, landform, biota, and soils (Rowe &
Sheard, 1981). The ecozones in which our study populations were included are the Mixedwood Plains, the Boreal Shield, and the Taiga Shield (Ecological
Stratification Working Group, 1995). Net primary productivity (NPP) data was
65 calculated with the EALCO model (Wang, 2008; Wang et al., 2007; 2009), and the mean NPP values from 1999 to 2008 were used as proxies for local productivity.
SPATIAL ANALYSIS We evaluated the correlation between morphology and geographical distance with Mantel tests between M1 size (Euclidean distances), M1 shape
(Mahalanobis distances), skull size (Euclidean distances), and skull shape
(Procrustes distances) and geographical distances between populations.
Significance of Mantel tests was estimated with 10,000 permutations. A Trend
Surface Analysis was performed to take into account the effect of the spatial distribution of populations on morphological variation (Legendre & Legendre,
1998). Latitude and longitude of capture sites were converted into x and y coordinates centred on their means. A third order polynomial function of x and y coordinates was then calculated. To select the significant polynomial terms, we calculated a linear model with size as the dependent variable and the 9 polynomial terms as independent variables. We then selected the significant polynomial terms with an Akaike information criterion obtained by a stepwise selection of the independent variables. The significant polynomial terms for shape analysis were selected using a redundancy analysis (RDA) with shape variables as the dependent variables and all 9 polynomial terms as the independent variables. Significant polynomial terms were then selected using a double criterion forward selection (Blanchet et al., 2008). The significant terms
66 were used in subsequent analyses to account for the spatial structure in the data.
OUTLINE ANALYSIS OF TEETH An outline analysis was performed on first upper molars (M1, Fig. 2).
Outlines were manually traced around the external edge of the occlusal surface along the enamel crest and resampled as 64 equally spaced points. Outline area was calculated and an Elliptic Fourier Transform was performed. The square root of the outline area was used as an estimate of tooth size.
To determine the number of harmonics to use in our analyses, two factors were considered: the cumulative shape information and the measurement error introduced by an increasing number of harmonics (e.g.
Renaud & Millien, 2001; Ledevin et al., 2010a; Fig. 3). Measurement error and cumulative shape information were estimated by 10 repetitions of outline acquisition on one specimen. Measurement error on each harmonic was calculated as the percentage of the mean amplitude represented by the standard deviation of repetitions. The cumulative content information contributed by each harmonic (an estimate of shape information) was calculated by summing the mean amplitudes of all harmonics and then computing the cumulative percent of the total amplitude represented by each harmonic added.
Tooth wear has been recognized as a major source of morphological variation in arvicoline teeth (Guérécheau et al., 2010; Ledevin et al., 2010b),
67 acting as a confounding factor when analyzing covariates of tooth morphology.
Therefore we developed a tooth wear classification method based on the degree of closure of dentinal space isthmuses (Abe, 1973), continuity of the enamel crest, and presence of dentin in the re-entrant angles (Appendix A). Teeth were classified in 5 wear stages and these stages were used in all analyses of tooth shape.
STATISTICAL ANALYSIS OF TOOTH SIZE AND SHAPE Sexual dimorphism was investigated using an ANOVA of size and a permutational (non-parametric) MANOVA (Anderson, 2001) of shape performed on dissimilarity matrices, with sex and population as factors. Size differences among populations identified in the ANOVA were further investigated with
Games-Howell post-hoc mean comparison tests with Bonferroni correction.
Shape differences among populations identified in the permutational MANOVA were investigated with Hotelling’s T2 mean comparison post-hoc tests with
Bonferroni correction. When the number of specimens in a population was too small (i.e. ten or less specimens), the Hotelling’s test was conducted on the sample covariance matrices calculated with James-Stein-type shrinkage estimator (Schäfer & Strimmer, 2005).
Variation partitioning analysis (Legendre & Legendre, 1998) was performed to calculate the unique and shared explanatory power of environmental factors, spatial factors, and tooth wear on M1 morphological variation. For M1 size and shape all climatic variables, NPP and Ecozones were
68 entered as the environmental variables while the trend surface analysis significant polynomial terms were entered as spatial variables. Wear stage was also used in these analyses. A hierarchical partition analysis (Chevan and
Sutherland, 1991) was then used to identify the environmental variables that had the highest independent effect on M1 morphology.
We used a two-block partial least squares analysis to characterize the relation between shape variation, environmental and spatial variation. To detect possible distinct shape variation patterns associated with different sets of factors, we conducted two PLS analyses on M1 shape: the first one using environmental variables, and the second one using spatial variables. We calculated the Pearson product-moment correlation coefficient between scores from the environmental and spatial PLS analyses. To better visualize the shape variation, we reconstructed the mean shape of the ten individuals with the highest and lowest scores on the first latent variables of the environmental and spatial PLS analyses.
We performed a canonical variate analysis (CVA) on shape variables to characterize the patterns of shape differentiation among populations. The
Pearson product-moment correlation coefficients between CVA scores and scores from the environmental and spatial PLS analyses were then calculated to determine if shape differentiation among populations was correlated with shape variation due to environmental and /or spatial factors.
69
Lastly, a UPGMA cluster analysis on Mahalanobis distances between mean population shapes was performed to identify the structure of M1 shape variation among populations.
GEOMETRIC MORPHOMETRIC ANALYSIS OF SKULLS Fifteen landmarks were digitized on the ventral surface of skulls (Fig. 2).
Landmarks were only digitized on the right side of the skull to avoid a potential bias caused by asymmetry. Aligned coordinates (Procrustes coordinates) were projected to the tangent space and subjected to a relative warp analysis
(Bookstein, 1991; Rohlf, 1996) to identify the main patterns of shape variation in the sample and reduce the number of shape variables. Relative warps (RWs) were used as shape variables in all multivariate statistical analyses except for two-block PLS and CVA for which we used Procrustes coordinates projected to the tangent space as shape variables. Using relative warps has the advantage of not needing the adjustment of degrees of freedom in analyses of variance and regression related methods. Log-transformed centroid size was used as a measure of skull size.
In order to estimate the magnitude of digitizing error present in the landmark data we randomly selected one specimen from each of nine populations, reproduced their images nine times, randomly ordered the resulting 81 images and digitized landmarks in all of them. These 81 configurations were then superimposed and submitted to a Procrustes ANOVA using specimen as a factor.
The proportion of the total sum of squares represented by the factor and the
70 residual were then used to estimate the percentage of variation due to digitization error. Digitization error represented 0.04% of skull size variation and 2.77% of skull shape variation.
STATISTICAL ANALYSIS OF SKULL SIZE AND SHAPE: Sexual dimorphism and differences between populations were investigated using the same procedures described above for teeth. A variation partitioning analysis (Legendre & Legendre, 1998) was performed to calculate the unique and shared explanatory power of environmental factors, spatial factors, and size to skull morphological variation. We did not control for size in the variation partitioning analysis so that we could detect the effect of size variation shared with spatial and environmental variables in skull shape. Instead, allometry was investigated along with the other factors in the variation partitioning analyses. For the skull size analysis, all climatic variables, NPP and
Ecozones were entered as environmental variables while the trend surface analysis significant polynomial terms were entered as spatial variables. For the skull shape analysis, we used environmental variables, spatial variables, and size as explanatory variables. A hierarchical partition analysis (Chevan and
Sutherland, 1991) was then used to identify the environmental variables that had the highest independent effect on skull morphology.
Similarly to what was done for teeth, we used two-block PLS analyses of skull shape covariation with environmental and spatial variables separately. We also performed a CVA to identify the major axes of shape differentiation
71 between populations, and calculated the correlation coefficients of scores on separate PLS and CVA analyses. Patterns of shape variation described by the PLS and CVA analyses were represented as deformation grids.
Lastly, the structure of skull shape variation among populations was investigated by using a UPGMA cluster analysis on Procrustes distances.
EVALUATION OF SIZE PROXIES The 183 specimens for which we had information on both centroid size and M1 area were used to assess whether the square root of M1 occlusal area and skull log centroid size were appropriate proxies for body size. We calculated the Pearson correlation coefficients and associated p-values between these two size estimators, the total body length (distance from the tip of the nose to the tip of the tail) and body mass measured on the specimens.
SOFTWARE Outline tracing and landmark digitizing were performed using TPS Dig
2.16 (Rohlf, 2010). Outline area was calculated using GMTP 2.1 (Taravati, 2010) and the Elliptic Fourier Transform was performed using PAST 2.15 (Hammer et al., 2001). Teeth two-block PLS, CVA, and outline reconstructions were performed using PAST version 2.15 (Hammer et al., 2001). Skulls two-block PLS,
CVA, and construction of deformation grids were performed using MorphoJ version1.05a (Klingenberg, 2011). Games-Howell tests were calculated in SPSS for Windows version 19.0.0 (SPSS Inc., Chicago, IL, USA). All other analyses were performed using R (R Development Core Team, 2012) using the packages vegan
72
(Oksnanen et al., 2012), stats (R Development Core Team, 2012), HDMD
(McFerrin, 2009), Hotelling (Curran, 2011), ecodist (Goslee & Urban, 2007), phangorn (Schliep, 2011), packfor (Dray, Legendre & Blanchet, 2011), and hier.part (Walsh & Mac Nally, 2008).
RESULTS
SEXUAL DIMORPHISM Sexual dimorphism was not detected in size or shape of skulls and teeth
(all p>0.05) Interaction factors of sex*population were not significant as well (all p>0.05). Sexes were thus pooled together for further analyses.
SPATIAL ANALYSIS The significant polynomial terms from the trend surface were x,y,xy,y2,y3,x2y,xy2 for M1 size, y,x2,y2,xy,x2y,x3 for M1 shape, y,xy,y2,y3,x2y for skull size, and y,y2,x2,x3 for skull shape. Mantel tests indicated a significant correlation between the geographical distances between populations and the shape of teeth (Mantel r = 0.19, p<0.05) and skulls (Mantel r = 0.40, p<0.005).
Geographical distance was not significantly correlated with M1 size (Mantel r =
0.12, p=0.14) or skull size (Mantel r = -0.07, p=0.71).
RELATION OF TOOTH SIZE AND SKULL SIZE WITH OTHER SIZE ESTIMATORS The square root of M1 area was positively correlated with total body length (r=0.50, p<0.001) and body mass (r=0.56, p<0.001). The Log skull centroid size was a better size proxy, with the highest correlation coefficients both with total body length (r=0.71, p<0.001) and body mass (r=0.78, p<0.001).
73
FIRST UPPER MOLAR SIZE A variation partitioning analysis revealed that wear is the major factor influencing tooth size in red-backed voles. The independent contribution of wear to M1 size variation was of 20.4%, and its total explanatory power was 33.3%
(Table 3). Spatially structured environment explained 15.69% of M1 size variation; spatial variables independently of environment and wear contributed
3.11% of explanatory power to the model (p<0.001). Considering only the fraction of variance explained by the environment (17.08% of the total variance, p<0.001), the variables that independently represented the highest percentage of explanatory power, as identified by a hierarchical partition analysis, were temperature seasonality (13%, p<0.05), precipitation of the warmest quarter
(10.56%, p<0.05,) and precipitation seasonality (10.28%, p<0.05). Tooth size was negatively correlated with temperature seasonality, precipitation of the warmest quarter, and precipitation seasonality (r = -0.28, r = -0.22, and r = -0.23 respectively, al p<0.001), meaning that teeth were larger where the climate was less seasonal and summers were drier.
An ANOVA on M1 size revealed significant differences between populations (F=8.66, p<0.001). Voles from Natashquan had teeth significantly larger than those from voles in all other populations (post hoc mean comparison tests, all p<0.05) except Mont St-Hilaire, Mont Yamaska, and Schefferville (Fig. 4).
Teeth from Schefferville voles were significantly larger than those from
Ashuanipi (p<0.05) and Chibougamau voles (p<0.05).
74
FIRST UPPER MOLAR SHAPE We retained the first 10 harmonics that represented 92.6% of shape information, while displaying low measurement error (between 0.06% in the 1st harmonic and 2.93% in the 10th harmonic; Fig. 3). We thus used 37 coefficients
(40 minus the first 3 coefficients of the first harmonic) as our shape variables.
Wear was the most important factor influencing M1 shape variation
(Table 3), explaining 15.96% of the total variation (p<0.001). The independent contribution of wear to the model was 9.43% (p<0.001). Spatially structured environment variables explained 12.14% of the total M1 shape variation, 6.6% of which conjointly with wear. The independent explanatory power of spatial variables was low (1.48%), but still significant (p<0.003). Environmental variables did not have any significant independent explanatory power (p=0.07). A hierarchical partition analysis showed that the environmental variables with the highest independent contribution to M1 shape variation were ecozones (11.31%, p<0.05), annual mean temperature (10.64%, p<0.05), and mean temperature of the coldest quarter (10.3%, p<0.05).
A permutational multivariate analysis of variance identified significant differences among populations (F=4.95, p<0.001), and post-hoc tests revealed a number of significant differences among them. Voles from Natashquan River had a mean tooth shape significantly different from that of all other populations except for Rupert River and Lake Dufresne. The mean tooth shape of voles from
Schefferville was significantly different from that of all other populations except
75 for Rupert River, Lake Dufrene, and Esker. Other significant differences were found, but with no clear pattern (Table 4).
The two-block PLS analyses revealed distinct patterns in environment- correlated and space-correlated M1 shape variations (Fig. 5). The first latent variable (LV1) from the environmental PLS represented 96.12% of the total covariation between shape and environment, and there was a significant correlation between LV1 scores on the shape and environment blocks (r=0.53, p<0.0001). The environmental variables with the highest loadings on LV1 were maximum temperature of the warmest month and precipitation of the warmest quarter. These variables had negative loadings, meaning that positive scores on the shape block are associated with colder and drier summers (Fig. 6). LV1 from the spatial PLS represented 84.52% of the total covariation between M1 shape and space, and LV1 scores on the spatial and shape blocks were significantly correlated (r=0.42, p<0.0001). LV2 represented a further 11.18% of shape-space covariation, and LV2 scores on the two blocks of variables were also significantly correlated (r=0.36, p<0.0001). Scores on environment LV1 and space LV1 were highly correlated (r=0.69, p<0.0001). The first axis from a CVA explained 39.11% of M1 shape differences between populations, and CA1 scores were more strongly correlated with environment LV1 (r=0.69, p<0.0001) than spatial LV1
(r=0.40, p<0.0001). The representation of M1 shape variation described by the
PLS analyses and the CVA is illustrated in Figure 5. High scores on CV1 and environment LV1 represented a forward displacement of labial triangles and
76 backward displacement of lingual triangles, giving the impression of an inclination on the “lateral axis” of the tooth; a relative increase in the proportion of the anterior lobe and deeper re-entrant angles were also observed. Increasing scores on the spatial LV1 represented a forward displacement of the central labial triangle and a relative decrease in the proportion of the anterior lobe. The cluster analysis did not reveal any recognizable spatial or environmental pattern in M1 shape variation.
SKULL SIZE A variation partitioning analysis showed that spatially structured environment explained 27.27% of the total variance observed in skull size.
Although the explanatory power of shared fractions such as spatially structured environment was not testable, both fractions constituting it (environment and space) were highly significant (p<0.001). Environmental and spatial variables independently did not have any significant explanatory power (Table 3). The hierarchical partition analysis identified precipitation of the warmest quarter, total annual precipitation, and ecozone as the environmental variables mostly correlated with skull size, representing respectively 14.16%, 13.07%, and 11.54%
(all p<0.05) of the variation explained by the environment. Size was negatively correlated with precipitation of the warmest quarter and total annual precipitation (r = -0.52 and r = -0.45 respectively, both p<0.001). Net primary productivity was one of the least important environmental variables, explaining only 5.73% of the variation attributed to the environment.
77
An ANOVA on log centroid size revealed significant differences in skull size between populations (F=8.998, p<0.001). Post-hoc tests showed that voles from Chibougamau had skulls significantly smaller than those from the three northernmost sites (Ashuanipi, Esker, and Schefferville, all p<0.05; Fig. 4).
SKULL SHAPE Size accounted for most of the variation in skull shape (Table 3), explaining 11.72% of the total skull shape variation (p<0.001), of which 9.4% was due to size independently (p<0.001). Spatially structured environment explained
6.32% of shape variation. Both space and environment independently, although statistically significant, had explanatory power below 1% each (p<0.05 in both cases). A hierarchical partition analysis showed that annual precipitation and precipitation of the warmest quarter were the environmental variables with the highest independent contribution to skull shape variation (15.96% and 14.07% respectively; both p<0.05).
A permutational MANOVA revealed significant differences among populations (F=2.777, p=0.001), and post-hoc tests indicated a number of significant pairwise differences between them (Table 5). Voles from Mont
Yamaska had the most distinctive mean skull shape, differing significantly from all other populations except Mont St. Hilaire and Rupert River. Voles from
Chibougamau had skulls significantly different from voles collected in Lake
Ashuanipi, Esker, and Schefferville, the three northernmost populations.
78
The two-block PLS analyses revealed similar patterns in the correlation between skull shape variation and space or the environment (Fig. 5). The first latent variable (LV1) of the environment PLS analysis represented 89.18% of the total covariation between shape and the environment (p<0.0001). LV1 scores of the two blocks were highly correlated (r=0.64, p<0.0001). Loadings of environmental variables on LV1 indicated that precipitation of the warmest quarter and annual precipitation contributed the most to the shape variation correlated with environment. Higher scores on the first shape axis were associated with higher precipitation (Fig. 7). LV1 from the spatial two-block PLS analysis represented 74.16% (p<0.0001) of the total covariation between shape and space, and scores on the two blocks were highly correlated (r=0.64, p<0.0001); LV2 represented 18.25% of the covariation, with scores on the two blocks significantly correlated (r=0.39, p=0.0011). Scores on environment LV1 and space LV1 were highly correlated (r=0.99, p<0.0001). The scores from the first axis of a canonical variates analysis, which explained 37.86% of skull shape differences between populations, were highly correlated with environment LV1
(r=0.54, p<0.001) and space LV1 (r=0.56, p<0.001). The shape variation described by CV1 was very similar to that described by environment LV1 and space LV1.
This shape variation consisted mainly of a forward displacement of the tooth row, wider and shorter tympanic bulla, and a wider foramen magnum as precipitation increases (Fig. 7).
79
A cluster analysis revealed two main groups. The first cluster consisted of the two southernmost populations (Mont Yamaska and Mont St. Hilaire), while a second large cluster grouped all other populations (Fig. 8). The three northernmost populations (Schefferville, Esker, and Ashuanipi) were further grouped within this large cluster.
DISCUSSION
TOOTH We found morphological variation in the first upper molars of southern red-backed voles to be mostly correlated with tooth wear. Previous studies have also identified a major effect of wear on tooth size and shape in bank voles
(Guérécheau et al. 2010; Ledevin et al. 2010b). According to these authors, voles trapped in different seasons (e.g. spring and autumn) differ in the degree of wear due to population dynamics. Voles captured in the spring are typically old overwintered individuals with much worn teeth, while in the summer the sample is dominated by voles born within the year with less worn teeth. Indeed, our most distinctive sample (Natashquan) is the only location where voles were trapped in May. Natashquan voles were likely all overwintered individuals so their teeth were very worn out and had the largest mean size. Our work thus confirms the importance of taking into account confounding factors for the study of tooth morphological variation in voles.
Seasonality appeared to be an important component of tooth size variation, together with precipitation. This relationship is highly influenced by
80
Natashquan that, due to its geographical location close to the Atlantic Ocean, has the least seasonal climate among our sampling sites. Voles from Natashquan also had distinctly larger teeth likely due in part to the effects of trapping season and associated tooth wear. The high importance of the space/environment/wear fraction in the variation partitioning analysis seems to support this view.
Alternatively, if seasonality was indeed an important factor for tooth size (and thus body size) variation, it would be expected that tooth size would increase with seasonality, since a larger size increases the individual’s fasting endurance in times of resource shortage (Boyce 1978; Lindstedt & Boyce, 1985; Murphy,
1985). However, we found the opposite trend in our data, with a negative correlation between tooth size and seasonality. When voles from Natashquan were excluded from the analysis, the seasonality variables decreased in importance and their correlation with tooth size became non-significant, while precipitation of the warmest quarter became the most important environmental variable (13.98% of independent explanatory power).
We found that the relation between precipitation and tooth size was opposite to what should be expected: teeth were larger in drier environments.
Precipitation has commonly been associated with plant productivity, and therefore size in herbivorous mammals is expected to be positively correlated with precipitation (Yom-Tov & Geffen, 2006; Cardini et al., 2007; Pergams &
Lawler 2009). Similarly to our study, McGuire (2010) also found a negative correlation between tooth size and precipitation in California voles (Microtus
81 californicus), and suggested that this trend might be related to better water conservation in larger bodied voles. A larger body mass has also been suggested as an adaptation for water conservation in birds (James 1970). Southern red- backed voles have high water requirements (Getz, 1962; Getz, 1968; McManus
1974). Therefore, it is likely that the inverse correlation between tooth size and precipitation is partly due to a water conservation strategy in southern red- backed voles.
Ecozone was the most influential environmental factor for tooth shape, indicating that differences in the local habitat and floral composition have an important effect on tooth shape. The diet of southern red-backed voles is composed of fungi, forbs, roots, bark, berries, ferns, mosses, grasses, lichens, and invertebrates, the proportion of items ingested shifting in different environments according to their availability (Schloyer, 1977; Merritt & Merritt,
1978; Merritt, 1981; Martell, 1981; Norrie & Millar, 1990). The proportions of dietary items available are different in environments as distinct as the mixedwood forest, the boreal forest, and the taiga, and therefore, the diet of an opportunistic generalist must differ substantially in these ecozones. Martell
(1981) found that voles in neighbouring coniferous and mixedwood forest stands in Northern Ontario all used lichens and fungi as their major food source, but voles from the mixedwood stands ate primarily fungi, while those in the coniferous stands ate primarily lichens. Martell attributed this difference to the availability of these food items in the two forest types. Given that the primary
82 function of teeth is to process food, it is likely that tooth shape changes as an adaptation to different food resources. Our analysis also shows an interaction between tooth wear and environmental factors acting on tooth shape. This could provide a clue as to the mechanism through which diet may influence tooth shape, mainly through the effect of more or less abrasive and tough dietary items on tooth shape. A classic example of the effects of abrasive food on tooth morphology is the relation between the evolution of hypsodonty in ungulates and rodents with the adoption of a more abrasive diet. Williams & Kay (2001) demonstrated this relationship on both groups while controlling for phylogenetic dependence, and showed that plant intrinsic abrasiveness and abrasion due to substrate grit are related to cheek teeth crown height. Kay et al. (1999) reported that as the climate becomes drier and colder there is a higher proportion of hypsodont species among sigmodontine rodents in Argentina, arguing that this is due to more abrasive diets.
As the climate becomes colder and drier we found a trend of a greater inclination of the dental triangles (labial triangles leaning forward and lingual triangles leaning backward), deeper re-entrant angles and a larger anterior lobe in the first upper molar. Whereas we found inclination, McGuire (2010) found a climate-related trend on the curvature of first lower molars of California voles, with teeth being more curved in cooler and more humid climates. Although the depicted shape variation patterns differ (as do the climate gradients), both this study and ours indicate the tendency of climate to influence the overall shape of
83 teeth and not only the shape of specific tooth parts. Piras et al. (2010) also observed deeper re-entrant angles in the teeth of Terricola savii in colder and drier environments. The studies by McGuire (2010) and Piras et al. (2010) suggest that the connection between climate and tooth shape is the effect of climate on vegetation and diet, a link that we also invoke here although this has not yet been tested directly.
SKULL Skull size was more strongly correlated with body mass and total length in voles than tooth size, making it a better proxy for vole size. Skull size was heavily influenced by the environment, and all the spatial variation observed in skull size was associated with environmental factors. The negative correlation between size and precipitation, together with the low explanatory power of net primary productivity, indicated that resource availability was not a major driver of size in voles in Québec. This is in disagreement with the resource rule proposed by McNab (2010), and is opposite to what has been observed in
European shrews (Ochocinska & Taylor, 2003), masked shrews (Yom-Tov & Yom-
Tov, 2005), desert rodents (Yom-Tov & Geffen, 2006), and vervet monkeys
(Cardini et al., 2007). Martínez & Cola (2011) observed that skull size of Graomys mice in Argentina was not correlated with precipitation or NDVI (a proxy for primary productivity), but rather with the minimum temperature of the coldest month. Monteiro, Duarte & Reis (2003) did not detect a correlation between environmental factors and the skull size of punaré rats in Brazil, despite the wide
84 range of variation in the primary productivity between their study sites. It seems that although resource availability is an important factor influencing size in mammals, it is not always the main factor behind intraspecific size variation.
A clear Bergmannian pattern of size variation (Rensch, 1938) was observed only in the Northern part of our study area. The seven vole populations living in the boreal forest and in the taiga displayed a pattern of positive correlation between size and latitude, while the two southernmost populations living in the mixedwood forest had skulls larger than the central populations living in the boreal forest. When including the two southern populations, the general trend was of a size increase towards higher latitudes. Overall, precipitation had more influence on skull size than temperature, indicating that the traditional thermoregulatory explanation for Bergmann’s rule cannot be invoked as the main mechanism behind the pattern in our data. Water conservation is a more likely mechanism driving size variation in southern red- backed voles.
Static allometry, in which shape varies with size in individuals of the same species and ontogenic stage (Gould, 1966; Cheverud, 1982), seems to be an important factor in skull shape variation in southern red-backed voles. Size alone had more explanatory power than spatially structured environment, and a large part of skull shape variation explained by spatially structured environment had a size component to it. Accordingly, we attributed the significant difference in skull
85 shape between Chibougamau and the three northernmost populations to the significantly smaller skull size of Chibougamau voles compared to these three populations. On the other hand, the difference in skull shape between voles from Mont Yamaska and all other populations except Mont St. Hilaire and Rupert
River cannot be attributed to allometry, since there was no size difference among individuals from these populations.
Despite their low explanatory power, environmental variables accounted for almost all of the spatial variation observed in skull shape. Skulls are conservative complex structures, and skull shape has been shown to be less responsive than size to environmental variation in African monkeys (Cardini et al., 2007; Cardini & Elton, 2009). The high correlation between scores on the first axes of environmental PLS, spatial PLS, and CVA demonstrates that although accounting for a small portion of total variation, environment is the main factor driving skull shape differentiation between populations. Nevertheless, we cannot at this time identify a direct relationship between precipitation and the position of the tooth row, the elongation of the tympanic bulla or the width of the foramen magnum in voles. Still, interspecific morphological variation of the tympanic bulla along aridity gradients has been observed in rodents (Taylor,
2004; Martínez & Cola, 2011). The position of the tooth row can be associated with differences in diet composition in moister or drier environments, since the anteroposterior position of cheek teeth is related to bite force (Weijs, 1980) and may be influenced by availability of harder or softer food items. These
86 relationships have not been tested though, and further studies are needed to assess the mechanism linking precipitation and skull (and tooth) shape.
The grouping pattern in the cluster analysis could be interpreted in a few different ways. The separation between the two southern populations and the other seven populations could reflect the phylogeography of the species. Most of the Québec region was covered by the Laurentide ice sheet until less than 10 kyr before present (Carlson et al., 2008). Voles colonized this area as the ice sheet retreated and suitable habitats became available, which could have happened differently from a simple south to north expansion. Based on the analysis of mitochondrial DNA markers, Runck & Cook (2005) hypothesized that three refugial vole populations colonized Canada in the post-glacial period. It is likely that the eastern clade identified by these authors was responsible for the colonization of the southern Québec region and expanded its range northwards.
However, from 9 kyr to 8.5 kyr before present, a land corridor between the Great
Lakes and the retreating Laurentide ice sheet was formed (Carlson et al., 2008) through which voles from the central clade could also gain access to central
Québec and colonize central and northern Québec as habitats became available.
This would be in agreement with the fact that skull shape of voles from Mont
Yamaska is significantly different from six of the seven central and northern populations. This hypothesis could be tested through molecular analysis.
Another possibility is that this clustering pattern is habitat driven, and both the separation of the two southern populations and the grouping of the three
87 northernmost populations would reflect morphological adaptations to the mixedwood forest and the taiga. Lastly, the separation of the southern populations could be due to a gap in our latitudinal sampling. Voles from Mont
Yamaska and Mont St. Hilaire are separated from the other populations by approximately 4o of latitude, and it is possible that sampling this gap would reveal intermediate skull shapes.
We conclude that environmental factors are major drivers of morphological variation in southern red-backed voles. The pattern of variation is complex though, and not always in agreement with what has been observed in other species of small mammals. A general trend of size increase towards higher latitudes can be observed, but some populations do not conform to this pattern.
Moreover, food resource availability does not seem to play an important role in vole size variation as would be expected, maybe because water is a more important limiting resource than food for this species in our study area. There is a spatial trend in shape variation that is congruent with the environmental gradient observed, and environmental variables appear to be the most important factor influencing shape differentiation among populations. Lastly, as one would expect, teeth and skulls responded differently to the environmental factors considered. Tooth shape variation was more related to ecozone, while skull shape was more related to precipitation.
88
ACKNOWLEDGEMENTS
All field sampling procedures were approved by the McGill Animal Care
Committee (AUP#5420), the Québec government (SEG permits
#2008051501400SF, 2009051501400SF, 2010051501100SF and
2011051501400SF), and the Department of Environment and Conservation of
Newfoundland and Labrador (Scientific Research Permit # IW2011-25). Funding was provided by the Northern Scientific Training Program grants to RL and a
McGill University Start-up grant to VM. RL was funded by NSERC, FQRNT, and
McGill University fellowships. We are indebted to Dr. S. Wang for NPP data, to the MRNF of Québec for some of the specimen collection, and to the Gault
Nature Reserve crew for access to the reserve. A very special thanks goes to A.
Howell and J. Gaitan for help with fieldwork.
89
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TABLES
Table 1. Populations sampled, total number of adults captured and sample size for each analysis.
Sample size Latitude Longitude Population (°N) (°W) Adults M1 size M1 shape Skull size Skull shape Lake Champlain 45.02 -73.25 24 22 22 - - Mont Yamaska 45.46 -72.88 10 10 9 10 10 Mont Saint-Hilaire 45.53 -73.14 4 4 4 3 3 Grande Plée Bleue 46.77 -71.05 34 34 34 - - Chibougamau 49.81 -74.46 36 36 33 33 33 Matagami 49.89 -77.11 18 18 17 17 17 Natashquan River 50.42 -61.70 17 17 17 - - Rupert River 51.36 -77.40 9 9 8 9 9 Lake Dufresne 51.70 -65.68 5 5 5 5 5 Lake Ashuanipi 52.91 -66.19 27 25 24 25 27 Esker 53.73 -66.36 32 32 31 32 32 Schefferville 54.78 -66.78 67 66 61 45 46 Total 283 278 265 179 182
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Table 2. BIOCLIM variables used to describe the climate of sampling sites.
Temperature variables are given in degrees Celsius, precipitation variables are given in millimetres. Temperature seasonality is the standard deviation of weekly mean temperatures, and precipitation seasonality is the standard deviation of the weekly precipitation estimates expressed as a percentage of the mean of those estimates.
Bioclimatic variable Description BIO1 Annual Mean Temperature BIO4 Temperature Seasonality BIO5 Maximum Temperature of Warmest Month BIO6 Minimum Temperature of Coldest Month BIO10 Mean Temperature of Warmest Quarter BIO11 Mean Temperature of Coldest Quarter BIO12 Annual Precipitation BIO15 Precipitation Seasonality BIO18 Precipitation of Warmest Quarter BIO19 Precipitation of Coldest Quarter
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Table 3. Proportion of morphological variation explained by different factors. Numbers are adjusted R2 values from a variation partition analysis. Note that negative R2 values are inherent to this analysis and shared fractions ( ) cannot be tested for significance. p values obtained with 10000 permutations: *p<0.05; **p<0.01; ***p<0.001.
M1 size environment space wear entire 0.1708*** 0.2194*** 0.3330*** pure 0.0095 0.0311** 0.2040*** environment NA 0.1569 0.0976 space 0.1569 NA 0.1246 space wear 0.0932 NA NA Residuals (unexplained) 0.5627 M1 Shape environment space wear entire 0.1235*** 0.1393*** 0.1596*** pure 0.0059 0.0148** 0.0943*** environment NA 0.1214 0.0622 space 0.1214 NA 0.0691 space wear 0.0660 NA NA Residuals (unexplained) 0.7643 Skull size environment space entire 0.2700*** 0.2672*** pure -0.0056 -0.0027 space 0.2727 NA Residuals (unexplained) 0.7356 Skull shape environment space size entire 0.0680*** 0.0680*** 0.1172*** pure 0.0066* 0.0069* 0.0940*** environment NA 0.0632 0.0252 space 0.0632 NA 0.0250
space size 0.0270 NA NA 103 Residuals (unexplained) 0.8332 Table 4. Permutation p values for Hotelling’s T2 tests on M1 shape. Values in bold are significant after Bonferroni correction. Abbreviations for site names are as follows: CHA: Lake Champlain, YAM: Mont Yamaska, MHS: Mont Saint-
Hilaire, PBL: Grande Plée Bleue, CHI: Chibougamau, MAT: Matagami, NAT: Natashquan River, RUP: Ruppert River,
DUF: Lake Dufresne, ASH: Lake Ashuanipi Lake, ESK: Esker and SCH: Schefferville.
YAM MSH PBL CHI MAT NAT RUP DUF ASH ESK SCH CHA 0.569 0.022 0.022 0.019 0.002 <0.0001 0.0001 0.022 0.026 0.011 <0.0001 YAM 0.178 0.009 0.011 0.207 <0.0001 0.022 0.078 0.014 0.005 0.0001 MSH 0.0001 0.004 0.008 <0.0001 0.013 0.195 0.003 0.0007 <0.0001 PBL <0.0001 0.0004 <0.0001 0.036 0.261 <0.0001 0.0001 <0.0001 CHI 0.231 0.0001 0.86 0.170 0.001 0.026 <0.0001 MAT <0.0001 0.226 0.056 0.003 0.001 <0.0001 NAT 0.004 0.002 <0.0001 0.0007 <0.0001 RUP 0.637 0.048 0.122 0.004 DUF 0.653 0.76 0.101 ASH 0.654 0.0005 ESK 0.009
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Table 5. Permutation p values for Hotelling’s T2 tests on skull shape. Values in bold are significant after Bonferroni correction. Abbreviations for site names are as follows: YAM: Mont Yamaska, MHS: Mont Saint-Hilaire, CHI:
Chibougamau, MAT: Matagami, RUP: Ruppert River, DUF: Lake Dufresne, ASH: Lake Ashuanipi Lake, ESK: Esker and
SCH: Schefferville.
MSH CHI MAT RUP DUF ASH ESK SCH
YAM 0.219 <0.0001 0.0003 0.005 0.0006 <0.0001 <0.0001 <0.0001
MSH 0.029 0.061 0.027 0.016 0.001 0.001 0.016 CHI 0.348 0.02 0.003 0.0002 0.0012 <0.0001 MAT 0.038 0.007 0.122 0.033 0.035 RUP 0.005 0.019 0.011 0.543 DUF 0.005 0.043 0.001 ASH 0.049 0.045 ESK 0.007
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FIGURES
Figure 1. The 12 vole populations sampled in Québec and Western Labrador. Ecozones are represented in different shades of gray. From south to north, the ecozones sampled are the Mixedwood Plains, the Boreal Shield, and the Taiga Shield.
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Figure 2. Skull view in ventral view of a red-back vole. A total of 15 landmarks were used for the analyses of the skull shape; 1: Anterior extremity of the suture between nasals; 2: Lateralmost point of incisive alveolus; 3: Anterior margin of the incisive foramina; 4: Posterior margin of the incisive foramina; 5: Suture between premaxilla and maxilla where it intercepts the skull outline on the plan of the photo; 6: Anterior extremity of first upper molar where it intercepts the maxillary; 7: Posterior extremity of third upper molar where it intercepts the maxillary; 8: Posterior point of maximum curvature of the zygomatic arch; 9: Lateralmost point of the suture between presphenoid and basisphenoid; 10: Tip of Eustachian tube; 11: Suture between basisphenoid and basioccipital where it contacts the tympanic bulla; 12: Mastoid apophysis where it intercepts the superior edge of the auditory meatus on the plan of the photo; 13: Tip of paraoccipital process; 14: Most posterior point of occipital condyle; 15: Anterior extremity of foramen magnum. The insert shows the first upper molar (M1) in occlusal view.
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40 % % information 30
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H10 H23 H02 H03 H04 H05 H06 H07 H08 H09 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H24 H25 H26 H27 H28 H29 H30 H01
Figure 3. Cumulative information for an increasing number of harmonics
(triangles) and measurement error introduced by each harmonic (diamonds). The dotted line indicates the 10th harmonic threshold used in our study.
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Figure 4. Size of first upper molars (square root of occlusal surface area; top) and skulls (log centroid size; bottom). Each point represents one specimen.
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Figure 5. First upper molar and skull shape differenciation among populations represented by the first canonical axis (top) and shape variation mostly correlated with environmental (center) and spatial (bottom) variables represented by the first latent variables. Dotted tooth outlines represent low scores and solid tooth outlines represent high scores on each analysis. Grids represent deformation of mean skull shape associated with high scores on each analysis. Grid deformation is multiplied by a factor of three for better visualization.
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Figure 6. First upper molar shape change associated with the environmental gradient as identified by a PLS analysis. Outlines represent the mean shape of the
10 specimens with the highest and lowest scores on the shape axis.
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Figure 7. Skull shape change associated with the environmental gradient as identified by a PLS analysis. Deformation grids represent the direction of shape variation; shape deformation is multiplied by a factor of 3 for better visualization.
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Figure 8. Skull shape dendrogram with Procrustes distances and UPGMA algorithm. The number at the nodes are the bootstrap values (1,000 permutations) expressed as percentage.
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Appendix A
Tooth wear classification scheme
Tooth wear can greatly influence the shape of molars in arvicoline rodents
(Guérécheau et al., 2009; Ledevin et al., 2010b). As a vole grows older and its teeth are worn down, the shape of the complex occlusal surface suffers important alterations (Abe, 1973). The effect of age and therefore wear on molar shape can be of the same magnitude as phylogeographic or population cycle factors (Guérécheau et al., 2009; Fominykh et al., 2010). To control for shape variation due to wear, we classified teeth into five different wear stages. The criteria used were the number of dentinal space isthmuses open, the continuity of the enamel crest on the edges of triangles, and the presence of dentin in the re-entrant angles between triangles. The reasoning for the use of these criteria is that as teeth are worn down the open isthmuses that connect dentin basins decrease in width and ultimately close (Abe, 1973), the enamel at the external edges of triangles is disproportionally worn out leading to a rupture of the enamel crest, and the height of the tooth decreases to a point where the occlusal surface reaches the base of the tooth characterized by the presence of dentin in the re-entrant angles. In this sense, if all isthmuses were open a score of 0 was attributed to the tooth; if two or three isthmuses were open the score was 1; if one or no isthmus was open the score was 2. If the enamel crest was ruptured or much thinned at the external vertices of triangles 1 point was scored. If at least three out of four re-entrances were filled with dentin 1 point
114 was scored. The result was a score from 0 (not worn) to 4 (very worn) for each tooth analyzed.
Figure 9: First upper molars with scores 0 (left) and 4 (right)
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GENERAL CONCLUSION
In this thesis I investigated patterns of morphological variation in teeth and skulls of southern red-backed voles in several populations across Québec. I analyzed the relation between size and shape of these structures and the different environmental conditions experienced by voles using a set of ten climate variables plus the ecozone classification and net primary productivity data. I used techniques in geometric morphometrics (Rohlf & Corti, 2000; Adams,
Rohlf & Slice, 2004) and spatial analysis (Legendre & Legendre, 1998) to identify the spatial patterns of morphological variation in voles and the most important environmental factors influencing it. Morphological variation has been recognized as a mechanism of adaptation to the accelerated rates of climate change occurring nowadays (Millien et al., 2006), thus it is critical to study the effects of spatial climate variation on the morphology of wild populations. The marked environmental gradient in Québec and the wide distribution of voles across the province provide an outstanding opportunity for such type of study.
I found that there is a general trend of size increase towards higher latitudes, conforming to Bergmann’s rule. The size increase towards the north is very strong in populations from the boreal forest and taiga, but southern populations in the mixedwood forest diverge from this pattern. Contrary to what has been proposed in the literature, I found that neither negative correlations with temperature (Rensch, 1938) nor positive correlations with primary productivity (Huston & Wolverton, 2011) are the principal forces driving size
116 variation. Instead, a negative correlation with precipitation is a stronger trend.
This could be explained by the high water requirements of southern red-backed voles and water conservation mechanisms. I also found that environmental variation is the main driver of shape differentiation among red-backed vole populations in Québec. Tooth shape variation was mostly related to ecozones, likely due to different diets in different forest types. Skull shape was less labile with environmental variation than tooth shape, and was mostly related with precipitation. Allometry was a very important factor in skull size variation, and tooth wear was even more important for tooth shape.
Future studies should try to directly assess the mechanisms behind environment-related morphological variation. Is it due to phenotypic plasticity, microevolution, or a combination of both? And what are the ecological and physiological pressures driving this variation? Besides the analysis of wild populations, laboratory manipulations and common garden experiments could help to directly identify the mechanisms acting on morphological adaptation.
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