Introduction

Multiannual variation is one of several typesof species morphological variability, one that isdirectly related to ecophenotypic and evolution-ary responses to changing environments. Themorphology of small mammal populations canchange quickly because generation length is

short, usually one year, and individual lifespansare often only a year or two. Morphological dif-ferences among samples of animals in the samecohort, caught in the same season of differentyears may show responses to climatic changesfrom year to year and decade to decade. Theseresponses may be physiological (altered growthpatterns due to changes in nutrition or metabo-lism, for example) or evolutionary (changes in

[193]

Acta Theriologica 55 (3): 193–202, 2010.

PL ISSN 0001-7051 doi: 10.4098/j.at.0001-7051.106.2009

Climate and morphological change on decadal scales:

Multiannual variation in the common shrew Sorex araneus

in northeast Russia

Eugene A. POROSHIN, P. David POLLY and Jan M. WÓJCIK

Poroshin E. A., Polly P. D. and Wójcik J. M. 2010. Climate and morphologicalchange on decadal scales: Multiannual variation in the common shrew Sorexaraneus in northeast Russia. Acta Theriologica 55: 193–202.

Multiannual variation is one of several types of species morphologicalvariability, one that is directly related to ecophenotypic and evolutionaryresponses to changing environments. The morphology of small mammal popu-lations can change quickly because generation length is short, usually oneyear, and individual lifespans are often only a year or two. We studied theresponse of skull and mandible morphology in the common shrew Sorex araneusLinnaeus, 1758 to nine climate factors related to snow cover, temperature andprecipitation at a study site near Syktyvkar, Russia through the period 1976to 2003. We found that these multivariate phenotypes changed significantlyfrom year to year, though there were no clear directional trends in the change.The phenotype itself was closely associated with the range of annual tem-perature and winter precipitation. Changes in summer temperatures andprecipitation seem to drive change in size-related phenotypes, whereas changesin snow cover and winter temperature seem to drive change in shape.

Institute of Biology of Komi Scientific Center Ural Division, Russian Academy of Science, Russia,e-mail: poroshin2002@yandex.ru (EAP); Department of Geological Sciences, Indiana University,1001 E 10th Street, Bloomington, Indiana, USA (PDP); Mammal Research Institute PolishAcademy of Sciences, 17-230 Bia³owie¿a, Poland (JMW)

Key words: Sorex araneus, climate change, geometric morphometrics, phenotypicevolution

the population mean due to differential repro-duction caused by the changes in environment).The environmental factors that affect morphol-ogy may be climatic (temperature, humidity,depth of snow cover, precipitation, or insolation,for example) or biotic (population density, foodavailability, or the density of predators or para-sites, for example) (Hutchinson 1957, Vasilev et

al. 2000). Establishing which factors have hadthe biggest influence on morphological featurescan be difficult. A particularly interesting ques-tion is whether climate on a decadal scale can re-sult in morphological changes, and whetherthose changes fluctuate about a mean value orwhether they accumulate gradually in a direc-tional manner.

Investigations of morphological change onthese annual and decadal scales are less com-mon than they should be because obtaining sta-tistically significant samples with which todocument the very small changes in populationmean from year to year can be difficult: trappingmust be carried out in the same place and thesame season year after year (for examples ofmulti-annual studies of morphology in smallmammals see Andersen and Wiig 1982, Koontzet al. 2001, Wójcik et al. 2006, Wolf et al. 2009).We investigated the impact of environmentalchanges on morphological shape in the commonshrew Sorex araneus Linnaeus, 1758, using sam-

ples from the Vichegda River in Russia that werecollected intermittently between 1976 and 2003.

Material and methods

Data were taken from shrews trapped by crews fromSyktyvkar State University Museum in the years 1976,1981–1986, 1999, and 2000–2003 along the Vichegda Riverin the Komi Republic of Russia, approximately 30 km SE ofSyktyvkar (61.50°N, 51.50°E). Shrews were trapped usingfive metal cone-shaped pitfall traps (20 cm in diameter atthe surface and 50 cm deep). These were dug into shallowtrenches that were 20 cm wide, 20 cm deep, and 50 m long.Cones were spaced 10 m apart and filled with water to ap-proximately 35 cm. Three to seven trap lines were used, de-pending on the year. Only juvenile (sub-adult) shrewscollected in late July and early August were used in ourstudy to avoid the confounding influence of seasonal changesin morphology due to ontogenetic growth, Dehnel’s effect(Dehnel 1949), or seasonal changes in the demographiccomposition of the populations. The determination of whichshrews were juvenile and which had overwintered from theprevious season was made based on: (1) tooth wear, whichis notably greater in overwintered animals; (2) body mass,over-wintered shrews weight about 10–12 g, juvenile about6–8 g; (3) tail hair condition, wintered have a necked tail;and (4) size of the braincase according to Dehnel’s rule. Allold individuals were discarded, leaving a sample of shrewsall in their first year. Male and female specimens werepooled because sexual dimorphism in Sorex araneus skullmorphology is negligible (Rychlik et al. 2006, see analysisbelow). Specimen numbers are reported in Table 1. Gaps ex-ist for some years and some annual samples are small, limi-tations that were unavoidable because of the history ofcollection and of population size. Our statistical tests (de-

194 E. A. Poroshin et al.

Table 1. Sample sizes for the four datasets broken down by year used forstudy of the response of skull and mandible morphology in the commonshrew to climate factors.

Skullmeasure-

ments

Mandiblemeasure-

mentsSkull shape

Mandibleshape

1976 4 13 3 131981 40 34 38 361982 38 37 38 361983 39 40 40 411984 42 42 41 411986 6 7 6 71999 12 10 11 102000 11 10 11 82001 143 136 145 1362003 5 5 5 5

Total 340 334 338 333

scribed below) take these into account and are conservativein that we only concentrate on the patterns that were sta-tistically significant given the limitations of the data.

Twenty seven craniometric measurements were madeon the skull and mandible (Fig. 1a–d). These were trans-formed to their principal components axes to generatescores for further analysis. Measurements were recorded tothe nearest 0.05 mm. Geometric shape was measured with25 two-dimensional landmarks on the ventral cranium and17 for the lateral side of the mandible (Fig. 1e–f). The sam-ples were Procrustes superimposed and projected orthogo-nally into shape tangent space, after which the coordinateswere rotated to the principal components axes of theircovariance matrix to produce scores used in further analy-sis (Rohlf and Slice 1990, Dryden and Mardia 1998) usingthe tpsRelw program (Rohlf 2003). Note that landmark datacontain information about pure shape, whereas the mea-

surement data contain information about size as well asshape.

Climatic change was measured using nine variables: num-ber of days with snow cover, mean thickness of snow cover,maximum thickness of snow cover, mean annual temperature,mean January temperature, mean July temperature, total an-nual precipitation, total January precipitation, and total Julyprecipitation. These data were obtained from the Lun’Weather Station (N 62.233 E 52.483, Altitude: 117m, Synop-tic Index: 23708, http://www.meteo.parma.ru/mst/lun.shtml),which is the nearest monitoring station to the field site.These data were hand-compiled from observation logbooksprovided by the station for the years when shrews werecollected. Additional data from nearby Syktyvkar airportweather station (N 61.641, E 50.838, Altitude: 116 m, Synop-tic Index: 23804, http://www.meteo.parma.ru/mst/sykt.shtml)were used to provide a regional long-term context for the

Climate and morphology in the common shrew 195

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2726

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(a) (b)

(c)

(d)

(e) (f)

Fig. 1. a–d – Twenty-seven orthogonal measurements of the skull of the common shrew used in this study; e – 25 two-dimen-sional landmarks used to measure shape of the ventral cranium; f – 17 two-dimensional landmarks used to measure shape ofthe lateral mandible.

196 E. A. Poroshin et al.

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fluctuations in climate observed at the field site. Note thatwinter climate in the Komi Republic is more extreme thansummer climate and is a priori more likely to have a limitingeffect on organisms than the summer climate. Our data,however, are drawn from populations trapped in the summermonths in order to minimize seasonal non-genetic eco-phenotypic differences and to maximize the contribution oftrue genetic differences due to selection from the previouswinter season.

We tested for sex differences in the cranial and mandib-ular measurements using analysis of variance (ANOVA) onthe large 2001 sample to determine whether the sexes couldbe pooled. Multivariate analysis of variance (MANOVA)was used to test for differences in morphological meansamong years. For the measurement data sets the originalmeasurements were used as the dependent data, for theshape data sets the principal component score shape vari-ables were used (Rohlf 1993). Discriminant function analy-sis was used to determine whether individuals from differentyears could be classified correctly to year using the samedependent variables as for MANOVA. Canonical correlationwas one method used to assess the relationship between cli-mate factors and morphology, also with the same dependentdata. All computations were done in Statistica® v. 6.0(Statsoft 2001).

Correlation between morphological and climate changewas measured with the method of differences (eg McKinneyand Oyen 1989). Our morphological and climate data aresampled from time-series, so ordinary regression and corre-lation methods cannot be used because temporal correla-tions are introduced by chance. Furthermore, we wish totest whether morphology changed in response to climatechange, not whether a particular morphology was associ-ated with a particular constellation of climate readings. Toobtain the correlation between change in morphology, wecalculated the differences between the means of all pairwisecombinations of years for both morphology and climate; thecoefficient of correlation (r) and the coefficient of determina-tion (R2) were calculated from these differences as a mea-sure of association between morphological and climatechange. Because our data were not sampled evenly throughtime, we calculated the significance of this correlation byrandomization of the variables with respect to each other byyear and recalculated R and R

2. Randomization was re-peated 1000 times and the resulting distribution of R andR

2 were used to test the observed values for departure fromrandomness. This randomization approach corrects for auto-correlation and the uneven sampling in our data (Manly 1991).

Results

Differences between sexes

Based on the 2001 sample, there were no sig-nificant sex differences in the cranial and man-dibular measurements. Of the 27 cranial andmandibular measurements, only two had signif-icant differences between sexes: measurement10, breadth of the rostrum at the infraorbital ca-nal (df model = 1, df residual = 125, F = 7.64, p =–0.007), and measurement 25, depth of the man-dible (df model = 1, df residual = 125, F = 4.75, p

= 0.03). Despite the statistical significance inthese two variables, the variance associatedwith sex differences was small (5.8% for rostrumbreadth, 3.7% for mandible depth, and only 0.8%for the entire data set). We have pooled sexes forthe remainder of the analyses.

Morphological variation between years

Morphological changes from year to yearwere small but significant in all four data sets,with the variation among individuals in a givenyear exceeding the variation among years (Fig. 2).Even though changes were small, they were sig-nificant in MANOVA tests (Table 2). The changesfrom year-to-year were distinctive enough thatindividuals can be assigned to the correct yearwith reasonable accuracy using discriminantfunction analysis (DFA). Percentages of correctclassification were: skull measurements, 57.0%(p = 0.043); mandible measurements, 48.7% (p =

0.029); skull shape, 74.0% (p = 0.015); and man-dible shape, 66.1% (p = 0.022). Scatter plots ofdiscriminant canonical axes for the means of theannual samples are shown in Fig. 3.

Climate and morphology in the common shrew 197

Table 2. Results of multivariate analysis of variance (MANOVA) tests for statistically sig-nificant differences of skull and mandible measurements and shapes in the commonshrew between years. Results for all four data sets are reported. df – degrees of freedom.F is the F-test statistics, p is the probability that the F value is significant.

Data set df model df error F p

Skull measurements 10 329 15.06 0.00Mandible measurements 153 2483 1.91 0.00Skull shape 432 2512.4 4.82 0.00Mandible shape 288 2557.5 4 � 107 0.00

Climate variation

All of the climate variables changed substan-tially through the study period, most of themwith major excursions during particular years(Fig. 4). The number of days of snow cover gener-ally decreased from 1976 to 2003, with a spikedecrease in 1983 and 1984. The comparativedata from Syktyvkar suggest that the overall de-crease is an illusion due to the lack of samplesfrom the late 1980s, but the spike decrease inthe early 80s is real. Except for mean annualtemperature (which appears to have decreasedslightly over the study period), temperatures didnot have an obvious overall trend, but therewere spike decreases in January and July tem-peratures in 1983, perhaps related to the de-crease in snow that year. Precipitation did notshow an obvious trend either, but 1984 had avery dry January and a very wet July; total an-nual precipitation in 1999 was unusually high.

Climate and morphology

Canonical analysis revealed that morphologyand climate were correlated (Table 3). The mostinfluential climatic factors using this method ofassessment were mean January temperature,mean July temperature and January precipita-tion, a correlation that is driven by the large ex-cursions in these factors in the 1980s (Table 4).The position of group means in the discriminantmorphospace of the first and second canonicalaxes are shown in Fig. 3.

The method of differences revealed that theonly statistically significant correlations betweenmorphological and climate change were found inthe mandible measurement data set, which wasassociated with maximum snow thickness andannual days of snow cover (Table 5). Precipita-tion and summer temperatures had the most im-portant influence on the mandible measurementdata (which are correlated with body size), but

198 E. A. Poroshin et al.

1976

1981

1982

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DCF 1

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F2

1976

1981

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1983

1984

1986

1999

2000

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2003

DCF 1

DC

F2

(a) (b)

(c) (d)

Fig. 3. Discriminant canonical analysis of morphological variability of the common shrew skulls in different years: (a) skullmeasurement, (b) mandible measurements, (c) skull shape, (d) mandible shape.

Climate and morphology in the common shrew 199

200

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1201975 1980 1985 1990 1995 2000 2005

1975 1980 1985 1990 1995 2000 2005

1975 1980 1985 1990 1995 2000 2005

1975 1980 1985 1990 1995 2000 2005 1975 1980 1985 1990 1995 2000 2005

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Snow days Snow thickness (mean) Snow thickness(max)

Temperature(mean January)

Temperature(mean July)

Temperature(mean annual)

Precipitation (January) Precipitation (July) Precipitation (annual)

cm cm

o o oC C C

mm mm mm

Fig. 4. Plots of the nine climate variables through the period of study. Data for the years from which we have morphologicaldata for common shrews are shown with black dots connected with a broken black line. Data for selected climate variablesfrom nearby Syktyvkar are shown with a solid gray line to provide context on regional climate fluctuations.

Table 3. Results of canonical variants (discriminant function) analysis for differences ofskull and mandible measurements and shapes in the common shrew among years for eachof the four data sets. Canonical R reports the correlation between the first canonicalvariate and the data, the eigenvalue is the amount of variance explained by the first ca-nonical variate, and p is the probability that the canonical variate explains a significantportion of the data.

Data set Canonical R Eigenvalue p

Skull measurements 0.752 0.565 < 0.0000Mandible measurements 0.566 0.32 < 0.0000Skull shape 0.748 0.56 < 0.0000Mandible shape 0.748 0.56 < 0.0000

the duration of snow cover and snow thicknesswere more important (but not significant) for theshape data (which have had the mathematical ef-fects of size removed) (Table 5).

Discussion

In common shrews, we found that changes inmorphology were small but significant in rela-tion to climate. Discriminant canonical analysis

was able to classify samples from different yearswith reasonable accuracy. It is worth mention-ing the differences between the principal compo-nents plots in Fig. 2 and the discriminant functioncanonical variants plots in Figure 3: the PC axesare in natural size or shape units with the axesordered by the amount of overall phenotypicvariation they summarize, whereas the DFA axesare in rescaled units that summarize only thoseparts of the data that best discriminate amongthe groups. The principal components give an

200 E. A. Poroshin et al.

Table 4. Loadings of the climate variables on the first canonical variate for each of thefour data sets used in analysis of the response of skull and mandible morphology in thecommon shrew to climate factors. Loadings with high absolute values have more influencethan do ones with low absolute values (indicated in bold face type).

Climatic factorSkull

measure-ments

Mandiblemeasure-

ments

Skullshape

Mandibleshape

Snow cover, days –2.14 0.64 2.28 –2.53Snow thickness, mean annual 1.68 –0.34 –2.27 2.20Snow thickness, max –0.35 –0.88 0.79 –0.77Temperature, mean January 3.02 –2.87 –3.61 3.44

Temperature, mean July 4.54 –2.36 –3.77 4.00

Temperature, mean annual –3.29 1.09 2.78 –2.94Precipitation, January 3.45 –1.45 –3.57 3.50

Precipitation, July 1.89 0.97 –1.22 1.36Precipitation, annual 0.32 –0.24 0.40 –0.19

Table 5. Results of the first differences test for the association between change in morphology of the com-mon shrew and the change in climate. R

2 reports the proportion of the data explained by a particular cli-mate variable and p is the probability that the correlation is due to chance.

Climate variable

Skullmeasurements

Mandiblemeasurements

Skullshape

Mandibleshape

R2

p R2

p R2

p R2

p

Precipitation, annual 0.20 0.14 0.29 0.09 0.07 0.62 0.08 0.79Temperature, mean July 0.19 0.15 0.37 0.04 0.05 0.82 0.08 0.81Snow thickness, max 0.13 0.34 0.35 0.06 0.10 0.45 0.14 0.25Precipitation, January 0.13 0.40 0.14 0.28 0.10 0.36 0.09 0.60Snow cover, days 0.11 0.42 0.05 0.64 0.15 0.14 0.17 0.11Precipitation, July 0.09 0.21 0.51 0.01 0.08 0.61 0.08 0.74Temperature, mean annual 0.04 0.84 0.08 0.47 0.14 0.15 0.13 0.32Snow thickness, mean annual 0.04 0.83 0.06 0.54 0.07 0.66 0.13 0.30Temperature, mean January 0.04 0.86 0.05 0.64 0.07 0.71 0.08 0.83

accurate picture of how much phenotypic simi-larity there is from year to year, the discriminantfunctions emphasize the differences from year toyear. Unsurprisingly, the first DCF separatesthe older samples from the younger ones. In alldata sets the 1981 through 1984 samples werelocated close to one another, indicating that theyshare some aspect of morphology. These yearshad highly variable climates in terms of all thefactors we studied. The association of the sam-ples from 1981 to 1984 is not as obvious in thePC 1 plots in Fig. 2, but that is because the mor-phology they share is only a small component ofthe total morphological variation.

We found that there was no directional trendin morphological change through the years. Thephenotypes showed no indication of directionalchange through the period studied, but insteadthe mean morphology appeared to wanderthrough morphospace. The exception was skullshape, which generally moved from right to leftalong the first principal component of shape.Other studies have also shown that morphologyfluctuates randomly from year to year. Bolshakovet al. (1996) revealed (by discriminant analysis)such a pattern in population of Sorex araneus

from Sakmara River, a tributary of the UralRiver from 1974 to 1978. Related work with sim-ilar results was carried out on the Sakmara for alonger period (1972 to 1991) on Myodes glareolus

(Vasilev et al. 2000). Berry and Jakobson (1975)found a similar random pattern in a populationof Mus musculus.

Temperatures in the warmest and coldestmonths, along with winter precipitation werethe climate factors that had the closest asso-ciation with the phenotypes themselves. Thepattern for which factors were most closelyassociated with change in the phenotypes ismore complicated. For the two measurementdatasets, which include size as an importantcomponent, the change in summer variableshave the highest correlation with morphologicalchange, notably July precipitation, July meantemperature, and annual precipitation; for thetwo shape datasets, change in winter variablesis most important, notably duration of snowcover, maximum snow thickness, and January

precipitation. Because the measurement datasets include size as an important component,these results suggest that the summer environ-ment is an important influence on changes inshrew size over time, whereas it is the winterenvironment that influences change in the shapeof the skull and mandible, perhaps related tofeeding or foraging strategies.

The influence of climate on the morphology ofshrews and other small mammals has beeninvestigated widely, but mostly in terms ofgeographic and altitudinal variation (Wójcik et.

al. 2000, Poroshin et al. 2006, Rychlik et al.

2006, Polly 2007, Kryštufek and Quadracci2008). In a study of the water shrews Neomys

fodiens and N. anomalus, precipitation andminimum annual temperature were found to beclosely associated with morphological variationon a geographic scale (Rychlik et al. 2006). Snowcover was not explicitly studied in the watershrews, but our findings that snow cover andwinter precipitation have on S. araneus morph-ology may be an indication that the same climaticfactors have similar effects on morphologicalchange in these two quite different shrew groups.

It is impossible in a field study to know forcertain whether the differences measured fromyear to year are genetic changes in the popula-tion due to selection and drift or whether theyare non-genetic ecophenotypic changes due todifferences in environment. However, we haveminimized the possible influence of non-geneticvariation as much as possible by analyzingshrews of the same age caught in the same sea-son of each year. The morphological changes inthe common shrew reported here are, thus, as-sumed to be primarily due to the combined ef-fects of selection, migration, and drift ratherthan ecophenotypic variation.

Acknowledgements: We thank E. Shahova from Agrometeo-prognosis of Komi Republic for help acquiring climate data;A. Kozlova for helping us with TPS digitizing; K. Johnson,G. Pavlis, M. Lawing and M. Foote for advice on analyzingtime series; and S. Robeson for advice on measuring tem-perature variability. We also thank K. Schmidt for his help-ful comments on the early draft of this paper. This paperhas been partly supported by a Marie Curie Transfer ofKnowledge Fellowship BIORESC of European Community’sSixth Framework Programme (contract number MTKD-CT-2005-029957).

Climate and morphology in the common shrew 201

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Received 27 November 2009, accepted 14 May 2010.

Associate editor was Krzysztof Schmidt.

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