Canadian Journal of Zoology
Phenotypic plasticity under desert environment constraints: mandible variation in the dwarf fat-tailed jerboa, Pygeretmus pumilio (Rodentia: Dipodidae)
Journal: Canadian Journal of Zoology
Manuscript ID cjz-2019-0029.R1
Manuscript Type: Article
Date Submitted by the 17-Apr-2019 Author:
Complete List of Authors: Krystufek, Boris; Slovenian Museum of Natural History Janzekovic, Franc; Faculty of Natural Sciences and Mathematics, University of Maribor Shenbrot, DraftGeorgy; Ben-Gurion University of the Negev, Mitrani Department of Desert Ecology Ivajnsic, Danijel; Faculty of Natural Sciences and Mathematics, University of Maribor Klenovsek, Tina; Faculty of Natural Sciences and Mathematics, University of Maribor
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Bergmann’s rule, desert ecology, ecomorphology, geometric Keyword: morphometrics, dwarf fat-tailed jerboa, Pygeretmus pumilio, resource availability
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Phenotypic plasticity under desert environment constraints: mandible variation in the
dwarf fat-tailed jerboa, Pygeretmus pumilio (Rodentia: Dipodidae)
B. Kryštufek, F. Janžekovič, G. Shenbrot, D. Ivajnšič, and T. Klenovšek
B. Kryštufek. Slovenian Museum of Natural History, Prešernova 20, 1000 Ljubljana,
Slovenia, email: [email protected]
F. Janžekovič, D. Ivajnšič, and T. Klenovšek. Faculty of Natural Sciences and
Mathematics, University of Maribor, Koroška 160, 2000 Maribor, Slovenia, emails:
[email protected]; [email protected]; [email protected]
G. Shenbrot. Mitrani Department ofDraft Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel, email:
Correspondence:
T. Klenovšek
Address: Faculty of Natural Sciences and Mathematics, Koroška 160, 2000 Maribor,
Slovenia.
Telephone: +386 41 808 366
Fax: +386 2 25 18 180
E-mail: [email protected]
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Phenotypic plasticity under desert environment constraints: mandible variation in the dwarf fat-tailed jerboa, Pygeretmus pumilio (Rodentia: Dipodidae)
B. Kryštufek, F. Janžekovič, G. Shenbrot, D. Ivajnšič, and T. Klenovšek
Abstract: Arid areas have a comparatively narrow range of habitat types, with restricted variation in environmental parameters, leaving narrow boundaries for phenotypic variation to correlate with ecological variables. To test this presumption, we explored variation in size and shape of the mandible in the dwarf fat-tailed jerboa Pygeretmus pumilio (Kerr, 1792) under the constraints of a rigorous desert environment. Size varied significantly and predictably with geographic position and demonstrated a strong, non-linear longitudinal pattern.
Moreover, size was associated withDraft several other climatic variables, but not with soil properties or with proxies for primary productivity. Our results suggest that, for rodents exposed to rapid and extreme changes, larger size may have multiple advantages, i.e. in maintaining euthermia during cold nights and efficient water metabolism under aridity stress; in accumulating fat reserves for hibernation; and digging deeper burrows, better protected from surface extremes. Shape varied clinally along the longitudinal transect, and the pattern was affected more by temperature than by precipitation. We conclude that the success of dwarf fat-tailed jerboa in occupying an extensive geographic range relies on their ability to meet environmental heterogeneity through cohesive and diverse responses, including physiology, behaviour, life-history traits, and morphological plasticity.
Keywords: Bergmann’s rule, desert ecology, ecomorphology, geometric morphometrics, dwarf fat-tailed jerboa, Pygeretmus pumilio, resource availability.
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Introduction
Interest in ecotypic morphometric variation of endothermic vertebrates goes back to
the mid-19th century. Size has been in the focus ever since Bergmann’s (1847) generalization
of the association between body size and latitude. The emerging third universal response to
anthropogenic warming (i.e. reduction in body size alongside global temperature increases) is
adding new significance to an old interest (Millien et al. 2006). Such attention is
understandable, given that the size of an organism affects virtually every aspect of its
existence (Calder 1984). Various hypotheses were proposed in the past to explain the
functional association between the size of an animal and its environment (Millien et al. 2006;
Yom-Tov and Geffen 2011). The traditional reason for the negative relation between body
size and temperature (Bergmann’s rule) invokes the heat conservation gained through a
decreased surface-area-to-volume ratioDraft (heat conservation hypothesis; Bergmann 1847; Gür
2010). While the low ratio of a larger endotherm better conserves energy in a cold
environment, the high ratio in its smaller counterpart dissipates heat more effectively, which
is advantageous in a warm, humid environment (heat dissipation hypothesis; Aldrich and
James 1991). Since energy (fat) reserves increase with body size faster than the metabolic
rate, a larger animal will endure (fast) for longer during periods of resource shortage
(seasonality hypothesis; Gür 2010). A key predictor in determining body size can be food
availability during postnatal growth, when the body size of endotherms is largely determined.
The resource availability hypothesis therefore predicts a positive correlation between body
size and environmental productivity itself, rather than temporal variability of productivity
(Mueller and Diamond 2001; Yom-Tov and Geffen 2011).
After the early interest in size variation, geographic variation in morphological shape
and its association with environmental factors progressed, with the introduction of
multivariate statistics in data analysis and particularly benefited from the revolution in
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geometric morphometrics and analysis of landmarks (Rohlf and Marcus 1993). A number of studies showed that animals were capable of modifying their shape to improve resource use in a changing habitat and to cope with intra-specific competition (Souto-Lima and Millien 2014;
Renaud et al. 2015).
Deserts are environments of extremes in temperature and water availability. Extensive water shortages and a scarcity of vegetation create serious selective pressure and determine the occurrence, adaptation and distribution of desert animals (Laity 2008). Adaptations to extreme desert conditions have been studied at length in various groups, particularly rodents
(for reviews, see MacMillen 1983; Genoways and Brown 1993; Mueller and Diamond 2001).
Where body size is concerned, several authors noted that some mammals are smaller in desert environments than they would be in more mesic situations (the desert rule; McNab 2010).
Furthermore, trends in size are associatedDraft more closely with precipitation and primary productivity than with temperature (Yom-Tov and Geffen 2006; McNab 2010).
The dwarf fat-tailed jerboa Pygeretmus pumilio (Kerr, 1792) is a small (mean body mass = 45 grams), bipedal dipodid (family Dipodidae) from the semideserts and clay deserts of southeastern Europe and Central Asia. The geographic range covers ca. 5350 km in longitude and ca. 1750 km in latitude (Figure 1). It is a nocturnal leaf-eater and a facultative hibernator (Shenbrot et al. 1995). The dwarf fat-tailed jerboa is a promising model for studying ecotypic variation in a desert rodent. It is a ricochet rodent that occupies an extensive desert belt, is abundant, and is polytypic, with five recognized subspecies (Shenbrot et al.,
1995 (2008)).
We analyzed the morphological variability in P. pumilio by studying the mandible, which is a remarkably flexible structure and reacts actively to various interactions and feedbacks by changing shape (Renaud et al. 2015). Despite its obvious simplicity, the mandible evolved as a mosaic of elements and comprises several morphogenetic regions
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(Casanovas-Vilar and van Dam 2013). These regions show dissimilar rates of differentiation,
composition, and function, making the mandible a highly informative structure. The mandible
is the only movable bone of the skull, effectively acting as a lever through articulation
between the condylar process and the temporal bone of the cranium. The main function of the
mandible is to produce gnawing and chewing actions. It is nearly flat and it can be effectively
analyzed in two dimensions, from a lateral point of view (Renaud et al. 2015). This minimizes
error, which is an important issue in studies where any measured differences are naturally
subtle.
The main aim of this study was to evaluate the effects of different spatial and
environmental conditions on the size and shape of the mandible of P. pumilio across its wide
geographic range. We hypothesized that variability in mandibular size and shape is adaptive
and correlates with those factors thatDraft are the most limiting under desert conditions. In line
with a resource availability hypothesis, we predicted a positive correlation between size and
proxies for environmental productivity, a relationship already documented in desert mammals
(Yom-Tov and Geffen 2006). Optimal body size maximizes the potential for maintenance of
homeostasis, and changes with varying climatic conditions. We therefore confronted size
variability in P. pumilio with the predictions of three hypotheses. The heat dissipation and
heat conservation hypotheses predict negative correlation between size and temperature, while
the seasonality hypothesis foresees larger size under a more seasonal environment and in
animals that hibernate for longer. The mandible is a morphologically dual structure
responding to different mechanical demands posed by feeding and digging behaviour (Renaud
et al. 2015). In a low-productive desert environment any achievement in processing food
more efficiently and/or shortening the exposure to predation during feeding and burrowing in
open space will add to the fitness of an animal. We therefore hypothesized that habitat
productivity and food availability affect mandible shape. In our expectation, shape change
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correlates with the environmental parameters that determine food availability because varying pressures are exerted on the insertion areas for the masseter muscles. The anterior region of the mandible, used for chisel-tooth digging, is associated with the capacity of bite forces at the tip of the incisors during burrowing. These forces depend on the ratio between the in- and out-lever arm and on the force of the adductor muscles (Becerra et al. 2013). We expected that hard and compact soil will be associated with an increase in muscles force and shortening of the out-lever arm.
Material and Methods
Mandible data
We studied 283 mandibles of P. pumilio (114 males, 155 females, and 14 unknown) from most of its distribution area. Twenty-nineDraft geographically close localities were grouped into 22 sampling sites, hereafter referred to as ‘populations’ (Supplementary Table S11,
Figure 1). Sixteen populations encompassed single-locality individuals. For the remaining six populations, we pooled individuals that were assumed to belong to interbreeding populations in a landscape of topographical and climatic continuity. Samples fewer than five individuals were excluded because their morphometric means can be widely inaccurate for intraspecific comparison (Caumul and Polly 2005). Only adult specimens with fully erupted and at least moderately worn cheek-teeth (categories 2-3 in Figure 282 in Shenbrot et al. (1995) were used. This study was based exclusively on archived museum vouchers; no animals were sacrificed specifically for the purposes of our research. Museum vouchers are deposited in the collections of the Zoological Institute, Russian Academy of Sciences, St. Petersburg (ZIN), and the Zoological Museum of Moscow State University, Moscow, Russia (ZMMU). The complete list of specimens and sample localities can be found in the Appendix A.
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As a facultative hibernator, P. pumilio adjusts its hibernation performance (Shenbrot et
al., 1995 (2008)). On this basis we classified populations into three categories (G.I. Shenbrot,
unpublished): (I) low intensity hibernation – jerboas do not hibernate each winter; when they
do hibernate, hypothermic bouts are short and periods of surface activity long (populations 5–
7, 9); (II) intermediate intensity hibernation – jerboas hibernate regularly but emerge on the
surface for periods of various lengths (populations 1–4, 8, 10–15, 19); (III) high intensity
hibernation – hibernation is not interrupted by surface activity (populations 16–18, 20–22;
Figure 1).
Spatial and environmental data
For each of the 22 populations considered in this study, we extracted geospatial
environmental data (i.e., climatic variables,Draft vegetation density and topsoil properties) using
ArcGIS 9.3 (ESRI, 2010). Climatic variables (BIO; coordinate system WGS84) were taken
for the 1960–1990 period (WorldClim database 1.4 available at http://www.worldclim.org/).
The density of green vegetation, quantified as the Normalized Difference Vegetation Index
(NDVI), was used as a proxy for primary productivity. Data were obtained from
VEGETATION Programme (http://www.spot-vegetation.com; now http://www.vito-
eodata.be; data for 1998-2007, each a ten-day estimate) and averaged for spring, summer and
autumn across all available years. In addition, we obtained 17 variables representing the
physical and chemical properties (PCP) of the topsoil (0-30 cm) for each population by using
the Harmonized World Soil Database v 1.2 (2012) (available at: http://www.fao.org/soils-
portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). Data
were tested for normality and homoscedasticity using the Kolmogorov-Smirnov and Bartlett's
test, respectively. All spatial, BIO, NDVI, and PCP variables used in this study are given in
Supplementary Tables S1, S2 and S31.
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Geometric morphometrics
We examined mandible variation by using landmark-based geometric morphometrics
(GM). It was used because it provides quantitative variables (i.e., size and shape) appropriate for multivariate statistical analyses, as well as depictions of morphological shape variation within a sample; facilitating the linkage between raw data and biological patterns (Zelditch et al. 2012). It also allows the evaluation of allometry, which is one source of shape variation in organisms (Klingenberg 2016). To our knowledge, this is the first GM study of size and shape differences and their correlation with abiotic factors in any ricochet desert rodent.
Each hemi-mandible was placed flat on its labial side and photographed in the lingual view with a Konica Minolta DG-7D digital camera under constant conditions and parallel to the lens plane. Fifteen landmarks (LM)Draft were digitized (Figure 2b) using TpsDig2 software
(Rohlf 2015). A list of landmarks with short descriptions can be found in Appendix B. To minimize measurement error, photographing and digitizing were conducted by the same person. The mandibles of the largest population (N = 73) were digitized twice to test for the digitizing error using Procrustes analysis of variance (Procrustes ANOVA) (Klingenberg and
McIntyre 1998). The digitizing precision was adequate, because the mean squares for individual variation exceeded the digitizing error by 95-fold for size and 13-fold for shape.
Besides repeatability, geometric morphometrics also has a high degree of reproducibility (e.g.
Takács et al. 2016), which was not tested in this study. A Generalized Procrustes Analysis
(GPA) was applied to standardize size and remove differences in landmark configuration due to position and orientation (Rohlf and Slice 1990). Size information was preserved as centroid size (CS), calculated as the square root of the sum of squared distances between each landmark and the centroid of the landmark configuration (Bookstein 1991). Shape information was preserved as Procrustes coordinates. Principal components analysis (PCA)
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was performed, based on the covariance matrix of the Procrustes coordinates to summarize
and explore patterns of variation among specimens in the shape space. Differences in centroid
size were visualized with box and whiskers plots.
Sexual dimorphism
Sexual dimorphism (SD) in size and shape was statistically assessed on the whole
sample, using uni- and multivariate analysis of variance (ANOVA, MANOVA). In
populations with at least 5 specimens of each sex (populations 3, 6, 8, 9; Supplementary Table
S11), sexual dimorphism was also tested within individual populations, as well as for the four
pooled populations and for sex interaction.
Intraspecific variability Draft
Intraspecific variation in mandible size and shape among all populations was assessed
with uni- and multivariate analysis of variance, only as a starting point for further
ecogeographical studies. Additionally, mandible size variation was tested for the effect of
hibernation intensity.
Spatial and environmental patterns in mandible morphology
We assessed the impact of spatial and environmental conditions on mandible
variability. Mandible morphology was represented by mean CS and mean shape variables for
each of the 22 populations.
The effects of explanatory (spatial and environmental) variables on mandible size and
shape were explored using a series regressions along with two block partial least squares
(PLS; e. g. Rohlf and Corti 2000) analysis.
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Mandible size was regressed against spatial (latitude, longitude and altitude) and environmental (BIO, NDVI, PCP, CPC1-3) variables. Additionally, a hierarchical multiple regression with size as the dependent variable and CPC1-3 and NDVI as independent variables was performed. To assess the degree of covariation between size and longitude, we used a polynomial regression and presented the relationship in a biplot. Variation in mandible size was also illustrated by a projection onto the first CPCs with a significant correlation.
Geographical variability of mandible shape was evaluated by simple linear regression of shape on latitude, longitude and altitude. Environmental variability in shape was assessed by three PLS analyses. In each analysis, one block comprised the shape and the other block
BIO, NDVI or PCP variables. PLS uses an RV coefficient (ranging from 0 to 1) as an overall measure of association between the two sets of variables. The significance of the association was tested using a permutation test withDraft 10,000 rounds of randomization. The relationship between mandible shape and climatic variables was visualized using pairs of PLS axes that accounted for the maximum amount of covariance between the two sets. Polynomial regression was used to assess and illustrate a possibly non-linear relationship between shape and longitude, as well as the first pair of PLS axes for the shape and BIO variables. Shape differences were shown by wire-frame graphs based on the thin plate spline algorithm
(Bookstein 1991).
Mandible allometry
We estimated the effect of mandible size on overall variation in mandible shape by multivariate regression of shape variables onto size and estimated the statistical significance of the regression by a permutation test with 10,000 iterations against the null hypothesis of complete independence between shape and size. Our goal was to assess whether allometry influences the ecogeographical patterns of mandible shape variability. Therefore, analyses of
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shape variability versus spatial and environmental variables were repeated with allometry-free
shape variables, i.e. regression residuals.
All statistical analyses were performed using the MorphoJ software (Klingenberg
2011) and Statistica (StatSoft, Inc. 2004).
Results
Ecogeographical heterogeneity of the area
Before testing for the impact of spatial and environmental variables on mandible size
and shape, we evaluated the ecogeographical variability of the area. The 22 populations
covered a longitudinal range of 3675 km and a latitudinal range of 1145 km (Figure 1). The
altitudinal range was 1770 m, but half the populations were at less than 170 m of elevation.
There was a highly significant trend towardsDraft a west-to-east increase in altitude (r = 0.873, df
= 1, 20, F = 50.76, p < 0.0001) that resulted from longitudinal change in topography. Most
BIO variables (15 of 19) correlated significantly with geographical coordinates (see
Supplementary Table S4)1. Temperature variables correlated with both latitude and longitude,
while precipitation variables showed significant correlation primarily with latitude.
Geographic position was a particularly good predictor of the annual mean temperature
(latitude r = -0.82, p < 0.0001; longitude r = -0.71, p < 0.0001), the mean temperature of the
coldest month, and the mean temperature of the coldest quarter (in all cases, R2 > 0.90). The
best fitting precipitation variable to geographic coordinates was the precipitation seasonality,
which correlated negatively with latitude and positively with longitude (R2 = 0.63; see
Supplementary Table S4)1.
Many BIO variables were strongly inter-correlated, and 75 Person’s correlation
coefficients of 171 in total (= 43.9%) were significant at p < 0.05 (see Supplementary Table
S5)1. To avoid redundancy, we reduced the number of 19 z-standardized BIO variables using
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Principal components analysis (PCA) and obtained new climatic variables; which will be referred from now on to as ‘climatic principal components’ (CPCs). Factor loadings, and the amount of explained variance, for the first three CPCs are presented in Table 1. Two of the first three CPCs also displayed significant correlation with geographic coordinates.
Vegetation density was positively associated with latitude (all seasons) and negatively with longitude. Additionally, NDVI in spring showed a weak decrease with altitude (r = 0.46, p =
0.029). Unsurprisingly, all NDVI indices correlated significantly with CPC scores (r > 0.68, p
< 0.001). Topsoil properties, on the other hand, showed no consistent spatial trend (see
Supplementary Table S61).
Sexual dimorphism
Sexual size dimorphism (SSD)Draft in the pooled sample of mandibles was not significant
(t = 1.96, df = 267, p = 0.2668). Tests repeated on populations with a sufficient number of specimens of both sexes (populations 3, 6, 8, 9) showed significant SSD only in population 9
(t = 2.09, df = 67, p = 0.0404). Factorial ANOVA on the four populations (population and sex as factors) yielded no difference in mandible size between males and females, and no interaction between the factors (SSD: F1, 121 = 0.13, p = 0.7191; interaction: F3, 121 = 2.03, p =
0.1136). Similar results were obtained for the shape variables (pooled sample: Wilks’λ = 0.55,
F26, 42 = 1.34, p = 0.1940; individual populations: p > 0.19; pooled populations: Wilks’λ =
0.89, F26, 96 = 0.46, p = 0.9878; sex × populations: Wilks’λ = 0.48, F78, 288 = 1.02, p = 0.4508).
Visual inspection of box-plots for size and PCA plots for shape variation revealed almost a complete overlap between male and female mandibles (not shown). These results, together with the low t-value in the statistically significant test for SSD in population 9, encouraged us to pool sexes together in further analyses.
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Mandible size
Spatial patterns
Mandible size variation among the 22 populations was significant (F21, 261 = 39.31, p <
0.0001). Size variation was significantly associated with longitude (explaining 58% of overall
size variation) but not with latitude or altitude (Table 2). The relationship between CS and
2 longitude was best described by a cubic polynomial regression (Multiple R = 0.77, F3, 18 =
20.31, p < 0.0001). Projection of CS scores against longitude yielded two size-classes of
populations (Figure 1c), the Western, with smaller mandibles (populations 2–13), and the
Eastern, with larger mandibles (populations 15–22). The transition between these two classes
occurred of longitudes approx. 65–80 degrees (Figure 1c), where populations displayed
mandibles of intermediate size. Draft
Environmental patterns
Size in general had a significant negative association with many BIO variables for
temperatures (BIO1, 5, 6, 9, 10, and 11) and a positive one with those for precipitation
(BIO13, 16, 18). The mean diurnal temperature range (BIO2) and mean temperature of the
wettest quarter (BIO8) showed a significant positive correlation with size (Table 2).
Furthermore, CS was significantly associated with two climatic principal components:
negatively with CPC1 and positively with CPC3 (Figure 3; Table 2). In addition to climate,
we tested the correlation of mandible size with primary productivity (Table 2) and topsoil
properties (see Supplementary Table S71), but found no significant correspondence.
Hierarchical multiple regression of CS on the climatic principal components (CPC1-3)
and the NDVI scores for vegetation density for three seasons (spring, summer and autumn)
yielded a significant model, with two climatic predictors: CPC1 and CPC3 (see
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Supplementary Table S8)1. These components explained 64.9% of variance in the original matrix of climatic data. Along the CPC1 axis, populations with large mandibles (average and negative scores along the CPC1) were partly segregated from populations with small and intermediate mandibles (average and positive values along the CPC1; Figure 3). Jerboas with mandibles smaller than average were apparently prospering better in a dry, warm climate
(populations 5-7 and 9) and in regions with pronounced temperature seasonality (populations
2-4, 8, and 10). Populations with large mandibles mainly associated with a wet, cold climate.
The overlap was considerable, hence small (population 14) and large populations (20) were thriving under similar climatic conditions.
One-way ANOVA on CS, with the hibernation intensity class as a factor, yielded highly significant heterogeneity among groups of populations (F2, 19 = 12.08, p = 0.0004).
Low intensity hibernators (category I)Draft were the smallest (mean CS ± SD = 20.74 ± 0.451); high intensity hibernators (category III) were the largest (23.09 ± 0.482), and most of those with intermediate intensity of hibernation (category II) were of intermediate size (21.80 ±
0.913) (Figure 1a). Hibernation explains the outlying position along the longitudinal gradient of population 1 in Figure 1c.
Mandible shape
Spatial patterns
Morphological variation in shape among 22 populations was statistically significant
(Wilks’ λ = 0.0009, F546, 3936 = 3.88, p < 0.0001). Linear regression showed that longitude had a highly significant effect on shape variation (R2 = 0.32, p < 0.0001; Figure 4a), while the impact of latitude was not significant (R2 = 0.84, p = 0.091). Regression of shape against
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longitude showed a response that was remarkably similar to longitudinal variation in size. The
western populations 1–13 had negative shape scores, and the eastern populations 14–22 had
positive ones (Figure 4a). The relation between the shape means and longitude was best
2 described by a cubic polynomial regression (Multiple R = 0.87, F3, 18 = 41.27, p < 0.0001).
Shape variation was also associated significantly with altitude (R2 = 0.20, p = 0.0003).
The similarity in patterns of geographic variation in size and shape strongly suggested
a considerable effect of allometry on shape variation. The allometric effect was indeed highly
significant (p < 0.0001), but proportionally low (7.29%). After correcting for allometry, shape
variation among the populations was still significant (Wilks’ λ = 0.0036, F546, 3935 = 2.96, p <
0.0001). Although the correlation between allometry-corrected shape and longitude was
significant, the proportion of variance explained was low (R2 = 0.16, p < 0.0001). Therefore,
more complex models were tested andDraft we found that the best-fitting one was a cubic
polynomial regression (Multiple R2 = 0.73, p < 0.0001; Figure 4c), suggesting a cline of west-
to-east shape change. The association with latitude slightly improved (R2 = 0.09, p = 0.043).
Environmental patterns
The PLS analysis yielded a strong and significant association (RV = 0.467, p =
0.0006) between mandible shape and climatic variables, with the first pair of PLS axes
explaining 72.25% of covariation (r = 0.80, p = 0.0040). In general terms, temperature (e.g.
BIO1, 2, 5–11) had a greater influence on mandible shape variation than precipitation (Table
3). The relation between the population shape means and climatic variation (according to
PLS1) was best described by a cubic polynomial regression (Figure 4b) (Multiple R2 = 0.70,
F3, 18 = 13.96, p < 0.0001). Associations of shape with NDVI or PCP variables were not
significant (NDVI: RV = 0.195, p = 0.1020; PCP: RV = 0.144, p = 0.8020).
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Comparison of the extreme shapes along PLS1 (Figures 4B and 5) showed that populations distributed in warmer regions (populations 1–12; BIO1 in Table 3) had a more robust incisor alveolar region (LM3, 13), a shorter diastema (LM1, 3) a shallower ramus- corpus junction (LM5, 12), a lower ramus (LM7, 12), a lower coronoid (LM6), a wider and longer articular process (LM7–9), and a narrower angular process (LM10–12; W in Figure 5).
These western populations, except for BIO1, were linked to pronounced max./min. T of the warmest/coldest month (BIO5, 6) and mean T of the driest/warmest/coldest quarter (BIO9–
11). Conversely, the opposite shape features were most obvious in populations 16 and 18–22 from colder regions, with a more pronounced mean diurnal T range (BIO2), annual T range
(BIO7), mean T of the wettest quarter (BIO8), P of the wettest month (BIO13) and P of the warmest quarter (BIO18).
Allometry-free shape variabilityDraft correlated with climate. The strength of association was lower for the overall association (RV = 0.382, p = 0.0059) and not significant for the first pair of PLS axes (r = 0.76, p = 0.1072) that explained 46.08% of the total covariation. The relation between the allometry-free shape means and climatic variation (according to PLS1)
2 was still best described by a cubic polynomial regression (Figure 4d) (Multiple R = 0.59, F3,
18 = 8.78, p < 0.0008). Allometry-free shape was also tested for any association with NDVI or
PCP variables, which was not significant. After correction for allometry, the pattern of mandibular shape changed only in scale (graphs not shown), being similar to the one described above but less pronounced. Mandibular shape changes along the species east-west distribution are not therefore the result of size increase along the longitudinal gradient.
Discussion
Environmental setting
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Our study confirmed topographic and climatic heterogeneity within the distribution
area occupied by P. pumilio (Shenbrot et al. 1995). The overall climatic pattern was a cline of
decreasing temperature in the South-West to North-East direction. Precipitation, seasonality
in temperature, and primary productivity increased with latitude, and seasonality in
precipitation declined in the same direction. The lack of a spatial pattern in topsoil properties
is puzzling. It may be, at least partly, due to low resolution in the datasets available for
Central Asian deserts. Therefore, in several cases, our field observations deviated
significantly from data reported in Supplementary Table S31 (G.I. Shenbrot, unpublished).
Size
Geographic position does not directly affect body size (Yom-Tov and Geffen 2011),
but what really matter are the environmentalDraft variables that ultimately affect the phenotype. It
is so because these variables frequently change on a predictable manner along a latitudinal
gradient, but not much with longitude, that the major phenotypical heterogeneity is also
associated with latitude. A pattern of positive association between body size and latitude or,
conversely, its negative relationship with ambient temperature, is typical of many birds and
mammals (e.g. Watt et al. 2010) and is generally known as Bergmann’s rule. This rule was
originally applied to interspecific comparison in endotherms but has received many revisions
over years, being applied at the intraspecific level, in endotherms and ectotherms alike, and in
diverse functional connotations. Frequently, no distinction has been made between the
observation and its explanation, and Bergmann’s rule gradually lost clarity (Blackburn et al.
1999; Watt et al. 2010). According to the positive/negative Bergmann’s rule, we understand a
negative/positive association between size and temperature, a relationship later explained
below and confirmed in P. pumilio.
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We found a strong, non-linear trend in the size of P. pumilio along the longitudinal transect. Furthermore, size was negatively associated with temperature and positively with precipitation; hence, jerboas tended to be smaller in dry, warm climates and larger in wet, cold climates. This polarity was well captured by the CPC1 (Figure 3). Although precipitation was a strong predictor of size, our results showed no correlation between size and normalized vegetation density scores (NDVI) as a proxy for primary productivity. We therefore reject the productivity hypothesis as an explanation for size variation in P. pumilio, concluding that the causes underlying the correlation between precipitation and size, if any, may be elsewhere than in the impact of resource availability. Yom-Tov and Geffen (2006) and McNab (2010) highlighted primary productivity as a major driver of size variability in mammals in a desert environment. Similarly to us, Yom-Tov and Geffen (2006) found a significant correlation between size and precipitation, and interpretedDraft the latter as a proxy for productivity. In our results, precipitation correlated with both primary productivity and size; however, we still found no significant association between productivity and size. Therefore, our study calls for caution in interpreting results constructed on correlation-based analyses since they may not represent the real relationships in nature. Alternatively, it is possible that the satellite sensor readings in a complex mixture of vegetation and bare ground do not accurately characterize vegetation growth in arid areas (Glass et al. 2000). Before drawing a conclusion on functional interactions between primary productivity and size, one would first need to know the relation between true primary productivity and the NDVI scores.
Exclusion of precipitation as a possible explanatory factor exposes temperature as the next putative driver of size variability in P. pumilio. The overall pattern was of negative correlation with mean temperature, but either of the two size extremes can be adaptive in a desert climate. The presence of larger jerboas in the colder eastern environments matches
Bergmann’s (1847) original prediction of the heat conservation hypothesis. Moreover, the
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occurrence of the smallest jerboas in a hot, dry southern Turan desert (populations 5–7, and 9)
is consistent with the heat dissipation hypothesis (Aldrich and James 1991). Maintenance of
homeostasis in a desert, however, is further intricate by water balance under aridity stress. A
major problem for desert rodents is evaporative water loss, which is mass-specific and
decreases with increasing body mass (Hinds and MacMillen 1985). Larger size is therefore
advantageous over smaller size in maintaining the economics of both energy and water use.
The diurnal range in temperature (BIO2) heavily contributed heavily to the formation
of CPC3 and correlated positively with mandible size. To the best of our knowledge, no
studies so far have suggested an association between size and diurnal oscillation in
temperature. Rather than searching for a small-scale climatic pattern, studies have invariably
addressed the association between size and long-term temperature means, whether monthly,
quarterly, seasonal, or annual. VariationDraft in temperature between day and night is more
extreme in a desert than in any other terrestrial ecosystem; therefore, the impact of diurnal
temperature range on size is biologically meaningful. Desert endotherms are exposed within
the same day to hyperthermia during intense solar radiation and to hypothermia at night
(Willmer et al. 2004). Any increase in diurnal temperature range pushes mid-day or nightly
temperatures, or both, to further extremes. Notably, in our results, the diurnal variation in
temperature is largely independent of temperature mean and seasonality.
Like many desert rodents, P. pumilio is crepuscular and nocturnal in its activity,
spending the day in a burrow. The nest chamber extends on average 37–51 cm below the
surface (Shenbrot et al. 1995) and is therefore deep enough to maintain favourable
temperatures and humidity at all times (Schwimmer and Haim 2009). The insulation effect is
further enhanced by plugging the entrance with soil during the day (Shenbrot et al. 1995). At
night, P. pumilio can temporarily avoid sudden drops in ambient temperature by retreating to
the burrow and may employ longer episodes of torpor during winter. Regardless of this, the
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jerboa cannot entirely avoid surface activity on chilly nights, which might make heat conservation a more important issue than heat dissipation during hot days, when the animal invariably remains inside its burrow. Given the positive allometric relation between bite force and body mass (Becerra et al. 2013), a larger rodent is also more effective at digging burrows through the action of its incisors, compared to a smaller rodent. Because P. pumilio is a chisel tooth digger (see discussion below), larger individuals may dig faster and/or deeper, or can drill through more compact soil than smaller jerboas. This may be another advantage of larger size under the expanded diurnal temperature range.
Seasonality in precipitation and temperature displayed opposing trends in our results, thus allowing various interpretations regarding the validity of the seasonality hypothesis. On the other hand, and in accordance with the seasonality hypothesis, the obligatory hibernators
(class III) were on average the largest,Draft while the facultative hibernators (class I) were the smallest (Figure 1a).
Shape
Morphological variation in shape showed a cline of west-to-east change. The pattern was remarkably similar to the longitudinal variation in size and it was only minimally altered after correction for allometry. Gradation in shape change was significantly correlated with environmental heterogeneity, although the association was not a simple one. In the east, shape primarily responded to mean diurnal and annual temperature ranges, while in the western end of the study area, shape was associated with the variables of mean temperature and precipitation in the wettest month and warmest quarter. These variables possibly affected the shape of mandible through various pressures on bony tissue by the action of the gnawing muscles. Such pressures usually depend on the diet and the substrate (see Becerra et al. 2013;
Renaud et al. 2015).
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In our study, shape changes affected all three mandibular processes, which act as
insertion areas for the temporal and masseter muscles. These muscles move the mandible (see
Figure 2) and, therefore, produce the gnawing action by the incisors and the grinding action of
the molars (Cox et al. 2012). The actions of each of these groups of muscles are meticulously
documented in sciuromorphous (Casanovas-Vilar and van Dam 2013), myomorphous
(Baverstock et al. 2013), and hysticomorphous rodents (Cox et al. 2012; Becerra et al. 2013).
Much less is known of the Dipodidae, which are a sister group to the myomorphous
Muroidea, although possessing hystricognatous masticatory muscles (Parsons 1894;
Klingener 1964; Gambaryan et al. 1983; Cox et al. 2012). Thanks to the hypetrophied
zygomaticomandibularis, the hystricomorph morphology is more efficient at muscle-to-bite
force transmission during molar chewing than during incisor gnawing (Cox et al. 2012).
Klingener (1964) suspected that in dipodids,Draft the zygomaticomandibularis is associated with
gnawing as well as mastication, and recently Becerra et al. (2013) showed for a subterranean
Ctenomys tuconax Thomas, 1925 (family Octodontidae) with a hystricomorphous
morphology, that the medial and superficial masseters contribute most to bite force during
chisel tooth digging. Furthermore, the temporalis, which is the major masticatory muscle in
Muroidea (Baverstock et al. 2013), is reduced in Dipodidae along with the coronoid process
into which its tendon inserts. The temporalis in dipodids probably pulls the mandible
backwards, rather than upwards as in Muroidea, producing horizontal movement instead of a
vertical crushing action and it is consequently associated with mastication (Klingener 1964).
It is therefore difficult to make a clear distinction in dipodids between parts of the mandible
responding to change in chewing and those reacting to mastication.
As it has been established, P. pumilio, like all ricochet rodents, survives the extremes
of the desert environment in its burrow. Each animal must therefore dig out at least a
permanent burrow with a nest chamber and several temporary shelters scattered across the
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home range (Shenbrot et al. 1995). Unlike many desert ricochet rodents that are claw and chisel tooth diggers (Nikolai and Bramble 1983), jerboas in the subfamily Allactaginae, which includes Pygeretmus, are chisel tooth diggers. They break down compact soil exclusively by vibrating the lower incisors like a jackhammer (Gambaryan et al. 1978). Chisel tooth diggers, independent of taxonomic group, show more developed jaw musculature (Becerra et al. 2013) and differ in mandible shape from their congenerics that lack such adaptations (Kryštufek et al. 2016). We therefore expected to find an association between the soil type and the mandible shape in P. pumilio. This however was not the case. Truly subterranean rodents, like
Ctenomys tuconax (Becerra et al. 2013), Nannospalax ehrenbergi (Nehring, 1898) (Zuri et al.
1999), Fukomys micklemi (Chubb, 1909) (Van Wassenbergh et al. 2017) and fossorial bandicoot rats (Kryštufek et al. 2016), excavate extensive underground galleries and daily use their chisel teeth to dig through the soil.Draft On the other hand, the episodes of burrow excavation in P. pumilio are perhaps too sporadic to exert sufficient pressure on bony tissue to affect mandibular shape. Besides, in deserts with hard compact soil, there is always an abundance of old unused burrows, which can be reconstructed with little effort (G.I. Shenbrot, personal observation). Under such circumstances, larger size alone may compensate for modifications in masticatory muscles by increasing drilling efficiency through the positive allometric relationship between bite force and body mass (Becerra et al. 2013).
Although digging may be superficial in P. pumilio, the animals spend on average 3 hours each night foraging (Shenbrot et al. 1995). Any achievement in processing more food per time unit will reduce the total foraging time in a barren desert landscape where a rodent is exposed to predation (Reichman and Price 1993). Moreover, low net primary productivity is among the defining characteristics of all deserts (Laity 2008), which puts serious pressure on the maintenance of euthermia. Desert rodents survive on a lower daily food intake and maintain a slower basal metabolic rate than species of comparable size in more productive
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habitats (Mueller and Diamond 2001). Under such circumstances, foraging efficiency is
imperative, and any achievement in processing food more efficiently may add to the animal’s
fitness.
The diet of P. pumilio comprises 57–90% green vegetable parts, 1–19% underground
plant parts and 8–27% seeds, depending on season and location (Shenbrot et al. 1995).
Dietary categories differ in consistency and impose differential pressures on the mandible.
Our study showed most robust mandibles in the warmer western regions. These western
jerboas, with a higher proportion of seeds in the diet (Shenbrot et al. 1995), have enlarged
insertions for all branches of the adductor muscles, and particularly for the anterior medial
masseter (LM3), which is highly specialized in dipodids (Gambaryan et al. 1983). The
diastema is also short in western animals, therefore reducing the out-lever arm of the
zygomaticomandibularis, what might increaseDraft its mechanical advantage (Becerra et al. 2013).
The coronoid process is reduced in the west in comparison to the east, suggesting a smaller
temporal muscle. As established earlier, the masticatory importance of temporalis is minimal
in dipodids (Klingener 1964); hence, the difference in this muscle between the extreme
mandible shapes may have little functional meaning. We conclude that differences in
mandible shape are functionally relevant and constitute an adaptive response to local food
resources.
Arid areas include a comparatively narrow range of habitat types, with restricted
variation in environmental parameters. This should leave narrow boundaries for phenotypic
variation to correlate with ecological variables (Baumgardner and Kennedy 1993). Under
strong pressure from stabilizing selection for the normative (intermediate) phenotype (Ahrens
and Ribera 2009), one would expect only minor phenotypical deviation among conspecific
populations. Such were the conclusions drawn by studies of size variation in Nearctic
kangaroo rats (Dipodomys), which show little general pattern of correlation between
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morphometric variation and abiotic factors (Baumgardner and Kennedy 1993; Best 1993).
Our study yielded significant heterogeneity in jaw size and shape among populations of P. pumilio across its vast distributional range. Patterns in variation suggested strong functional associations with the physiological demands of the local environment and complex interrelationships among variables that interacted (see Best 1981). For a rodent exposed to rapid, extreme environmental changes, larger size may have multiple advantages, i.e. in maintaining euthermia during cold nights and efficient water metabolism under arid conditions; in accumulating fat reserves for hibernation; and digging deeper burrows more effectively insulated from surface extremes. The pattern underlying size may be complex and context-dependent, due to possible impact of competitors (Olson et al. 2009), and interpopulation differences in reproductive and annual activity patterns, which are considerable in P. pumulio (Shenbrot etDraft al. 1995). In addition to size heterogeneity, our study also indicated an adaptive response to local food resources, which affected all mandibular modules. The success of P. pumilio in occupying an extensive geographic range could rely on their capacity to face environmental heterogeneity through cohesive and diverse responses, including physiology, behaviour, life-history traits (Shenbrot et al. 1995) and morphological plasticity.
The results presented in this study are based on correlations and, therefore, do not unambiguously define functional relationships. Clearly, comparative studies on other desert rodents are needed to see the generality of patterns retrieved in P. pumilio and the validity of the suggested functional associations. Several well-controlled studies were performed in the past on Nearctic kangaroo rats (Baumgardner and Kennedy 1993; Best 1993). Since they used traditional morphometrics and a limited number of climatic variables, their results are not always comparable to ours. Opportunities for comparative studies, however, are plentiful.
Rodents of very different phylogenetic background are abundant and diverse in desert
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environments worldwide and offer excellent models for answering questions about finely
tuned morphological evolution in response to habitat heterogeneity and climate change (see
Koontz et al. 2001).
Acknowledgements
Nataliya Abramson, Alexandra Davydova (Zoological Institute, Russian Academy of
Sciences, St. Petersburg) and Vladimir Lebedev (Zoological Museum of Moscow State
University, Moscow) enabled access to specimens, and Elena Potapova helped with
information on masticatory muscles. Michelle Gay Gadpaille improved the language. Funding
for this research was provided through the Slovenian Research Agency (Grant P1-0255 for
B.K. and Grant P1-0403 for T.K. and F.J.). Draft
1Supplementary tables are available with the article through the journal Web site at 25 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 26 of 46
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Table 1. Factor loadings from the principal components analysis of 19 bioclimatic variables
(BIO). Bottom lines report eigenvalues of the first three climatic principal component (CPCs) and the proportion of variance explained, respectively.
Bioclimatic variables Definitions CPC1 CPC2 CPC3
BIO1 annual mean T 0.94 -0.28 -0.07
BIO2 mean diurnal T range -0.10 0.52 0.66
BIO3 isothermality 0.48 0.13 0.81
BIO4 T seasonality (CV) -0.72 0.38 -0.49
BIO5 max. T of warmest month 0.89 -0.16 -0.31
BIO6 min. T of coldest month 0.91 -0.40 -0.03
BIO7 T annual range -0.70 0.52 -0.24
BIO8 mean T of wettest quarter -0.22 -0.29 0.72 BIO9 mean T of driest quarterDraft0.82 -0.06 -0.08 BIO10 mean T of warmest quarter 0.91 -0.20 -0.30
BIO11 mean T of coldest quarter 0.93 -0.33 0.06
BIO12 annual P -0.38 -0.91 0.04
BIO13 P of wettest month -0.38 -0.68 0.56
BIO14 P of driest month -0.62 -0.66 -0.32
BIO15 P seasonality (CV) 0.28 0.40 0.82
BIO16 P of wettest quarter -0.27 -0.79 0.51
BIO17 P of driest quarter -0.63 -0.68 -0.30
BIO18 P of warmest quarter -0.82 -0.41 0.30
BIO19 P of coldest quarter 0.28 -0.83 -0.16
Eigenvalue 8.17 5.03 3.68
Variance (%) 42.99 26.49 19.38
Note: Loadings > 0.60 are in bold. T – temperature; P – precipitation.
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Table 2. Pearson correlation coefficients (r) of dwarf fat-tailed jerboa P. pumilio mandible
centroid size with spatial (latitude, longitude, altitude) or environmental variables (BIO1-19,
CPC1-3 and NDVI).
Variable Definition r p
Latitude Latitude 0.02 0.927
Longitude Longitude 0.55 0.024
Altitude Altitude 0.45 0.072
BIO1 annual mean T -0.566 0.0060
BIO2 mean diurnal T range 0.429 0.0466
BIO3 isothermality 0.125 0.5788
BIO4 T seasonality (CV) 0.218 0.3305
BIO5 max. T of warmest month -0.664 0.0008 BIO6 min. T of coldest month Draft -0.575 0.0051 BIO7 T annual range 0.349 0.1119
BIO8 mean T of wettest quarter 0.511 0.0150
BIO9 mean T of driest quarter -0.517 0.0137
BIO10 mean T of warmest quarter -0.668 0.0007
BIO11 mean T of coldest quarter -0.528 0.0116
BIO12 annual P 0.399 0.0659
BIO13 P of wettest month 0.591 0.0038
BIO14 P of driest month 0.341 0.1208
BIO15 P seasonality (CV) 0.115 0.6111
BIO16 P of wettest quarter 0.529 0.0113
BIO17 P of driest quarter 0.337 0.1251
BIO18 P of warmest quarter 0.732 0.0001
BIO19 P of coldest quarter -0.218 0.3298
CPC1 Climatic principal component 1 -0.642 0.0013
CPC2 Climatic principal component 2 -0.103 0.6493
CPC3 Climatic principal component 3 0.488 0.0213
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NDVI - spring Normalized Difference Vegetation Index for spring -0.25 0.272
NDVI - summer Normalized Difference Vegetation Index for summer 0.40 0.067
NDVI - autumn Normalized Difference Vegetation Index for autumn 0.25 0.270
Note: Significant associations are in bold (p < 0.05).
Draft
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Table 3. PLS coefficients (PLS1 Shape) obtained from Partial Least Squares analysis of
bioclimatic variables and mandible shape of the dwarf fat-tailed jerboa P. pumilio.
Variable Definition PLS1 Shape
BIO1 annual mean T 0.291
BIO2 mean diurnal T range -0.299
BIO3 isothermality -0.109
BIO4 T seasonality (CV) -0.132
BIO5 max. T of warmest month 0.325
BIO6 min. T of coldest month 0.313
BIO7 T annual range -0.225
BIO8 mean T of wettest quarter -0.283
BIO9 mean T of driest quarter 0.229 BIO10 mean T of warmest quarter Draft0.336 BIO11 mean T of coldest quarter 0.279
BIO12 annual P -0.096
BIO13 P of wettest month -0.212
BIO14 P of driest month -0.061
BIO15 P seasonality (CV) -0.127
BIO16 P of wettest quarter -0.179
BIO17 P of driest quarter -0.073
BIO18 P of warmest quarter -0.276
BIO19 P of coldest quarter 0.179
Singular value 0.032
Variance (%) 72.254
Note: PLS coefficients > 0.02 are in bold.
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Figure 1. Distribution of sampled populations of dwarf fat-tailed jerboa P. pumilio. The grey area outlines the range of the species (modified from Shenbrot et al. 1995). Pie diagrams show variation in mandible centroid size (CS), with the black area being proportional to CS value. The white and black circles represent populations with the smallest and the largest mandibles, respectively. Numbers refer to populations (for identities see Supplementary Table
S11). The white lines delimit populations according to their intensity and length of hibernation from I (lowest intensity) to III (highest intensity; see text for further details). Variation in CS among these groups of populations is summarized as a box and whiskers plot (a) depicting group mean, standard error (box) and standard deviation (whiskers). P. pumilio from
Kazakhstan (b; photo courtesy of Nedko Nedyalkov); plot of centroid size against longitude for 22 populations (dots) with a polynomial best-fit curve (c).
Draft
Figure 2. Mandible of dwarf fat-tailed jerboa P. pumilio in labial (a) and lingual (b) views.
Inset a shows insertion areas of mandibular muscles (nomenclature follows Druzinsky et al.
2011): Mt – temporal muscle; Mz – masseter zygomaticomandibularis; Mpz – masseter posterior zygomaticomandibularis; Mp – profunda masseter; Ms – superficial masseter. Inset b shows segments of mandible and landmarks (see Appendix B for a list of landmarks) used in this study. Abbreviations: mar – molar alveolar region; dia – diastema; iar – incisor alveolar region; anf – angular foramen; anp – angular process; arp – articular process; cp – coronoid process. Scale bar = 5mm.
Figure 3. Plot of 22 populations of dwarf fat-tailed jerboa P. pumilio on climatic principal components 1 and 3 (CPC1 and 3), derived from principal components analysis of 19 climatic variables (BIO1-19). Pie diagrams show variation in mandible centroid size (CS; see references in Figure 1). The character vector diagram illustrates the relative contribution of
1Supplementary tables are available with the article through the journal Web site at 36 https://mc06.manuscriptcentral.com/cjz-pubs Page 37 of 46 Canadian Journal of Zoology
the original BIO variables (only numbers shown) to the CPCs. See Table 2 for BIO identities.
Main climatic characteristics at the extremes of the CPC axes are indicated in bold upper case
letters. Precipitation and temperature are abbreviated as P and T, respectively.
Figure 4. Relationship between mandible shape and longitude (a, c) or climate (b, d) for 22
populations of dwarf fat-tailed jerboa P. pumilio with polynomial best-fit curves. Dots show
population means, and population numbers refer to those shown in Figure 1 and listed in
Supplementary Table S11. Top row: plots of shape variables (Procrustes coordinates in inset
a, and PLS1 scores in inset b) against longitude (a) and PLS1 BIO scores (b). Bottom row:
plots of allometry-free shape variables (regression residuals in inset c and PLS1 scores from
regression residuals in inset d) against longitude (c) and PLS1 BIO scores (d). Draft
Figure 5. Pairwise shape differences among populations of dwarf fat-tailed jerboa P. pumilio
from the extremes of PLS1 BIO score axis (Figure 4), which also followed the longitudinal
gradient. The shape with a high positive PLS1Shape score (0.1) is from the western (W),
warm, and dry end of the geographical range, and the shape with a high negative PLS1Shape
score (-0.1) is from the cold eastern part (E).
1Supplementary tables are available with the article through the journal Web site at 37 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 38 of 46
Distribution of sampled populations of dwarf fat-tailed jerboa P. pumilio. The grey area outlines the range of the species (modified from Shenbrot et al., 1995 (2008)). Pie diagrams show variation in mandible centroid size (CS), with the black area being proportional to CS value. The white and black circles represent populations with the smallest and the largest mandibles, respectively. Numbers refer to populations (for identities see Supplementary Table S11). TheDraft white lines delimit populations according to their intensity and length of hibernation from I (lowest intensity) to III (highest intensity; see text for further details). Variation in CS among these groups of populations is summarized as a box and whiskers plot (a) depicting group mean, standard error (box) and standard deviation (whiskers). P. pumilio from Kazakhstan (b; photo courtesy of Nedko Nedyalkov); plot of centroid size against longitude for 22 populations (dots) with a polynomial best-fit curve (c).
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Figure 2. Mandible of dwarf fat-tailed jerboa P. pumilio in labial (a) and lingual (b) views. Inset a shows insertion areas of mandibular muscles (nomenclature follows Druzinsky et al. 2011): Mt – temporal muscle; Mz – masseter zygomaticomandibularis; Mpz – masseter posterior zygomaticomandibularis; Mp – profunda masseter; Ms – superficial masseter. Inset b shows segments of mandible and landmarks (see Appendix B for a list of landmarks) used in this study. Abbreviations: mar – molar alveolar region; dia – diastema; iar – incisor alveolar region; anf – angular foramen; anp – angular process; arp – articular process; cp – coronoid process. Scale bar = 5mm.
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Draft
Plot of 22 populations of dwarf fat-tailed jerboa P. pumilio on climatic principal components 1 and 3 (CPC1 and 3), derived from principal components analysis of 19 climatic variables (BIO1-19). Pie diagrams show variation in mandible centroid size (CS; see references in Figure 1). The character vector diagram illustrates the relative contribution of the original BIO variables (only numbers shown) to the CPCs. See Table 2 for BIO identities. Main climatic characteristics at the extremes of the CPC axes are indicated in bold upper case letters. Precipitation and temperature are abbreviated as P and T, respectively.
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Relationship between mandible shape and longitude (a, c) or climate (b, d) for 22 populations of dwarf fat- tailed jerboa P. pumilio with polynomial best-fit curves. Dots show population means, and population numbers refer to those shown in Figure 1 and listed in Supplementary Table S11. Top row: plots of shape variables (Procrustes coordinates in inset a, and PLS1 scores in inset b) against longitude (a) and PLS1 BIO scores (b). Bottom row: plots of allometry-free shape variables (regression residuals in inset c and PLS1 scores from regression residuals in inset d) against longitude (c) and PLS1 BIO scores (d).
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Appendix
Phenotypic plasticity under desert environment constraints: mandible variation in the
dwarf fat-tailed jerboa, Pygeretmus pumilio (Rodentia: Dipodidae)
B. Kryštufek, F. Janžekovič, G. Shenbrot, D. Ivajnšič, and T. Klenovšek
Draft
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Appendix A: Collection acronyms and a complete list of specimens.
List of P. pumilio specimens arranged according to 22 population samples. Population label (number), locality, geographic coordinates, altitude: collection and inventory numbers of specimens. ZIN – Zoological Institute of Russian Academy of science, Sankt-Peterburg, Russia ZMMU – Zoological Museum of Moscow University, Moscow, Russia
Population 1, Russia: – Dagestan, Kizlyarskiy uezd (44.2584, 46.8853, -27m): ZIN 15182, 57465, 57466, 57467, 57468. – Kalmykiya/Dagestan border, Lower Kuma River valley, Isheya mosque (44.8092, 46.8824, -26m): ZIN 15183, 15184.
Population 2, Kazakhstan: – Ural'sk (=West Kazakhstan), Khar'kin (48.7333, 51.8167, -7m): ZIN 36643, 36644, 36645. – Ural'sk (=West Kazakhstan), Khar'kin/Kalmykovo (48.8908, 51.7964, -7m): ZIN 36698.
Population 3, Kazakhstan: – Gur'yev (=Atyrau), Zhilaya-Kosa (46.8275, 53.1833, -25m): ZIN 49176, 49177, 49179, 49181, 49183, 49184, 49185, 49187, 49188, 49190, 49191, 49194. – Gur'yev (=Atyrau), Makatskiy r-n, 2 Draftkm NW Baychunas (47.2549, 52.9215, -25m): ZIN 57473.
Population 4, Kazakhstan: – Gur'yev (=Atyrau), Kul'sary (46.9861, 54.0013, -14m): ZIN 49195, 49196, 49198, 49199, 49200, 49201, 49202, 49203, 49204, 49205, 49206, 49207.
Population 5, Turkmenistan: – Balkhan, Akhcha-Kuyma (39.3505, 55.1584, 68m): ZIM 57531, 57533, 57538, 57539, 57540.
Population 6, Turkmenistan: – Balkhan, Kyzyl-Arvat, 15 km N (39.1313, 56.2846, 62m): ZIN 67892, 67893, 67894, 67896, 67898, 67902, 67903, 73970, 73971, 73972, 73974, 73977, 73978, 73979, 73980, 73981, 73982, 73983, 73984, 73986, 73987.
Population 7, Turkmenistan: – Balkhan, Kyzyl-Arvat, 30 km E (39.0067, 56.6164, 43m): ZIN 73990, 73991, 73992, 73993, 73994, 73995, 73996.
Population 8, Kazakhstan: – Aktyubinsk, Chelkar (47.8671, 59.5711, 169m): ZIN 62448, 84050, 84051, 84052, 84053, 84054, 84055, 84056, 84057, 84058, 84059, 84060, 84061, 84062, 84063, 84064, 84065, 84066, 84067, 84068, 84084, 84085, 84087, 84088, 84089, 86692.
Population 9, Uzbekistan: – Karakalpakstan, Takhtakupyr, 100 km E (43.0321, 61.5150, 72m): ZIN 60249, 60250, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60267. – Karakalpakstan, Bozgul' (43.0462, 61.2427, 65m): ZIN 67911, 67915, 67916, 67919, 67923, 67924, 67926, 67933, 67935, 67936, 67937, 67938, 67939, 67940, 67941, 67942, 68095 74011, 74012, 74013, 74014, 74015, 74016, 74017, 74018, 74019, 74021, 74022, 74023, 74024, 74025, 74026, 74027, 74028, 74029, 74030,
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74031, 74032, 74033, 74034, 74035, 74037, 74038, 74039, 74040, 74041, 74043. – Karakalpakstan, Bozgul', 20 km SE (42.8983, 61.4012, 70m): ZIN 74044, 74045, 74048, 74049, 74050, 74051, 74052, 74054, 84082, 84083.
Population 10, Kazakhstan: – Kzyl-Orda, Aral Karakum, Birtomar (46.7487, 63.0807, 100m): ZIN 67875, 67876, 84076. – Kzyl-Orda, Otarbaytyube (46.7615, 62.7176, 59m): ZIN 84069, 84070, 84071, 84072, 84073, 84080.
Population 11, Kazakhstan: – Turgay, Akkum Sands (49.2034, 64.3131, 131m): ZIN 67877, 67878, 67879, 67880, 74059, 74060, 74061.
Population 12, Kazakhstan: – Dzhezkazgan, Zhaltau (48.2563, 66.1753, 401m): ZIN 67885, 67886, 67887, 74066.
Population 13, Kazakhstan: – Dzhezkazgan, Bektauata (47.5013, 74.8305, 669m): ZIN 60268, 60269, 60270, 60271, 60272, 60273, 60274, 74008, 74009, 74010.
Population 14, Kazakhstan: – Taldy-Kurgan, Shubargau (46.8333, 78.5333, 445m): ZMMU 148299, 148300, 148301, 148302, 148303, 148304, 148305, 148306, 148307.
Population 15, Kazakhstan: – Taldy-Kurgan, 40km W Panfilov (43.9868, 79.4358, 516m): ZMMU 143393, 143394, 143395, 143396,Draft 143397, 143398, 143399, 143400, 143401, 143402, 143403, 143404, 143597.
Population 16, Kazakhstan: – Semipalatinsk obl., 35km WSW Semipalatinsk (50.2306, 79.8605, 267m): ZMMU 143428, 143429, 143430, 143431, 143432, 143433, 143434, 143435.
Population 17, Kazakhstan: – Semipalatinsk obl. (47.4646, 78.6362, 538m): ZMMU 148308, 148309, 148310, 148311, 148312, 148315, 148316, 148317, 148318, 148319, 148320, 148321, 148322, 148323, 148324, 148325, 148327.
Population 18, Kazakhstan: – Semipalatinsk obl., 50km N Ayaguz (48.3621, 80.4531, 708m): ZMMU 143417, 143418, 143419, 143420, 143421, 143422, 143423, 143424, 143425, 143426, 143427.
Population 19, Kazakhstan: – Taldy-Kurgan, Alakulski r-n (45.5566, 82.2288, 397m): ZMMU 143411, 143412, 143413, 143414, 143415, 143416, 146869, 146871.
Population 20, Kazakhstan: – Zaisanska kotl., Markakolskij r-n (48.0187, 85.1525, 414m): ZMMU 83008, 83009, 137104, 137105, 137106, 137107.
Population 21, Mongolia: – Dzhungarskaja Gobi, 12km W Nizhni Bulgan (46.1038, 91.3816, 1169m): ZMMU 146305, 146306, 146307, 146308, 146309. – Dzhungarskaja Gobi, 20km SW Nizni Bulgan (45.9746, 91.3515, 1368m): ZMMU 146310, 146311.
Population 22, Mongolia: – Bajan-Hochgorskij, 50km E Bajan-Chagan (44.8507, 99.5210, 1743m): ZMMU 140080, 140086, 140088, 140090, 140093, 140095. – Gobi Altaiski (45.9219, 96.7433, 1617m): ZMMU 140097, 146301.
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Appendix B: List of mandible landmarks.
(1) Upper extreme anterior part of the incisor alveolus.
(2) The deepest point of the diastemal notch.
(3) Anterior extremity of the maxillary toothrow (1st molar alveolus).
(4) Posterior extremity of the maxillary toothrow (3rd molar alveolus).
(5) The deepest point of the ramus cotylae.
(6) Tip of the coronoid process.
(7) The deepest point of the incisura mandibulae.
(8) Anterior tip of the condyle.
(9) Posterior tip of the condyle.
(10) The deepest point of the posterior ramal notch. (11) Posterior extremity of the angularDraft process. (12) The deepest point of mandibular ramus.
(13) The most inferior point of mental protuberance.
(14) Upper extreme anterior part of the incisor alveolus.
(15) The anterior extremity of the angular foramen.
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