Spatial dynamics of wood mouse (
Apodemus sylvaticus
) in an
agricultural landscape under intensive use in the
Mont Saint Michel Bay (France)
Annie Ouin
∗, Gilles Paillat, Alain Butet, Françoise Burel
UMR CNRS 6553 ECOBIO, University of Rennes 1, Ave. Gal. Leclerc, 35042, Rennes Cédex, France
Received 1 October 1998; received in revised form 26 February 1999; accepted 20 August 1999
Abstract
Over the last decades intensification in agricultural use has lead to changes in landscape structure and composition. Seasonal fluctuations of the wood mouse (Apodemus sylvaticus) are well known in cultivated landscapes, but not well explained. The main question relates to the fate of individuals during summer. The hypothesis of a seasonal dispersal of individuals from hedges to crops was investigated in the polder of the Mont Saint Michel Bay (France) at different landscape scales using GIS. Field and landscape scale parameters were analysed in relation to wood mouse abundance in the crops mosaic. The results tend to demonstrate that dilution of the population due to dispersal toward crops could be the main factor explaining the population drops in hedges. Hedgerows act as source habitat in spring whereas field colonisation rate depends on crop quality in summer. The heterogeneity of the crops matrix for the presence of wood mouse has been studied. Variations in vegetation cover of the field plot and its surroundings appear to determine dispersal of mice during summer. Results may have implications in terms of pest management and/or food webs preservation in cultivated landscapes. ©2000 Elsevier Science B.V. All rights reserved.
Keywords: Apodemus sylvaticus; Agricultural landscape; Spatial dynamics; Heterogeneity; GIS; Connectivity
1. Introduction
In Europe, two thirds of the land is devoted to agri-culture, of which a fifth is being used intensively with little account of ecological constraints (Park, 1988; Fry, 1994). European agricultural landscapes are in-creasingly used for other purposes than food produc-tion, for example nature conservation. Biodiversity in common rural landscapes requires not only natural or
∗Corresponding author. Tel.:+33-2-99-28-61-47; fax:+33-2-99-28-14-58.
E-mail address:[email protected] (A. Ouin).
semi-natural areas but also free movement between those areas (Merriam, 1984).
Small mammals are often regarded as pests in most parts of the world (China: Huadi, 1993; Africa: Richards, 1982; Mwanjbabe, 1996; western Europe: Pelz, 1989; Saint Girons and Wodzicki, 1985), or as interesting study models (Merriam, 1988; Wegner and Merriam, 1990). Many studies have focused on uncul-tivated areas such as field boundaries (Montgomery and Dowie, 1993), hedges (Pollard and Relton, 1970) and small woodlands (Middleton and Merriam, 1983; Zhang and Usher, 1991). The land-use pattern (Fitzgibbon, 1997), cover and food availability in arable fields (Montgomery and Dowie, 1993;
stam et al., 1987), harvesting (Tew and Macdonald, 1993) and pesticide use (Tew et al., 1992) have been shown to control small mammals presence in crops.
Apodemus sylvaticus is particularly successful in exploiting farmland mosaics (Green, 1979; Loman, 1991b; Zhang and Usher, 1991; Paillat and Butet, 1997). The annual cycle of wood mice in woodlot and hedge networks includes spring decline and sum-mer collapse of population density (Crawley, 1970; Jamon, 1986; Montgomery, 1989; Butet, 1994). Ac-cording to Watts (1969) and Flowerdew (1974) this trend could reflect a real decline due to a low survival rate of the first litters or to a dilution of the popula-tion through dispersion into temporary habitats dur-ing summer followed by the return of individuals to hedges in early autumn (Bergstedt, 1966; Eldridge, 1971; Loman, 1991a). Both explanations are possible and this study aims at testing the latter. Two main ques-tions were examined: (1) Are crops matrix homoge-neous regarding wood mouse dispersal? and (2) How important are cover, food availability in crops mosaic and the presence of hedges for wood mice dispersal?
2. Materials and methods
2.1. Site
The study site was situated in the polders of the Mont Saint Michel Bay (48◦36’N, 1◦32’W, western
France). Ninety percent of this area was under inten-sive agriculture and the semi-natural habitats (hedges, grassy linear habitats) were distributed over a dike net-work. The main crops were wheat (Triticum aestivum L.), maize (Zea mays L.), peas (Pisum sativum L.) and carrots (Daucus carotta L.). A total of 28 plots were used in different crops: 8 fields in wheat, 8 in maize,
Table 1
The three indices used to explain wood mouse abundance: shelter, height, cover and seed availabilitya
Index Shelter Height Cover (shelter×height) Seed availability
1 Bare ground Bare ground Low cover (S×H=1–4) No seed available
2 0–20% covered 0–15 cm Medium cover (S×H=6–9) Seed planted
3 20–40% covered 15–30 cm High cover (S×H=12–16) Seed on the plant
4 > 40% covered >30 cm – –
aShelter: the percentage of the ground covered by the vegetation from a human viewpoint (simulating a predator viewpoint); Height: height of the crops; both are the mean of ten measurement; seed availability: qualitative index based on crop phenology.
8 in peas (for those three crops, 4 fields were chosen with adjacent hedges and 4 without adjacent hedges) and only 4 in carrots because all the fields were sit-uated far from hedges. Two trap lines (100 m long) were placed in tractor wheeling (15 m apart). For plots close to hedges trap lines were placed parallel to the hedges. A standardised method was used consisting of rows of 34 baited (wheat flour and margarine) live traps placed every 3 m. Trapping sessions occurred ac-cording to crop succession and harvest. Traps were armed in the afternoon and checked at dawn. Individu-als trapped were weighed and sexed. Field abundance of mice was estimated by the trap-night index (i.e. the number of individuals caught per 100 traps during one night). Plots were sampled once a month from April to August 1997 for wheat; from May to August for maize; from May to July for peas and carrots. A trap-ping session consisted of 7 consecutive days (4 fields per day).
and (c) average seed availability. Seasonal fluctuations of mice abundance in the matrix were compared with those existing in the hedgerow network from the same study area (Paillat and Butet, 1997) which were in ac-cordance with literature (Crawley, 1970; Jamon, 1986; Montgomery, 1989).
2.2. Data analysis
Capture rates were analysed with ANOVA to test the heterogeneity of the crops mosaic.
2.2.1. Field variables
Simple regression for all crops in May, June and July (August was rejected because of lack of capture in this period) was calculated to extract the best field variable (field cover or seed availability) explaining the variance of mice abundance at field scale.
2.2.2. Landscape variables
Landscape variables were stored in a GIS. Maps for both cover and seed availability were prepared for each month. Values for cover, seed availability and surface
Fig. 1. Crop cover in August in the polders of the Mont Saint Michel Bay. An example of a window 500 m×500 m and calculation to compute landscape cover index of plot 4.
of hedges were extracted around each sampling plots with CHLOE software (INRA SAD Armorique/UMR 6553, 1997) (Fig. 1). For a field plotxof an area ax
and cover value ix (1<ix<16), the value of
land-scape cover around this plot (LCpx) in a window of
area A (500 m×500 m) was calculated as follows (see calculation made for plot 4, Fig. 1):
LCpx= 1
(A−ax)
16 X
i=1 Ci,
whereCi=Ai×i,Ai=area of cover valuei, ifi=ix
Cix = Aix −ax×ix(to exclude cover of the plot
itself).
Fig. 2. Wood mouse trap night index (number of wood mouse trapped per night per 100 traps) with standard error in crops and hedges (from Paillat and Butet, 1997).
3. Results
During the present study 183 individuals were cap-tured. Wood mouse abundance in agricultural matrix peaked in May, while rate of capture in hedges de-creased (Fig. 2). Wood mouse abundance also differed between crops (F4–40=7.99 p<0.01). Two groups of crops were identified according to mice occur-rence: (1) wheat and peas (F2–40=0.21p> 0.05) with 83% of captures; (2) maize and carrots (F2–32=0.21 p> 0.05) with 17% of captures. Mice abundance also varied according to crop phenology (Fig. 3).
3.1. Field variables
No seeds were available in May and cover explained 56% of wood mouse abundance variation in a field.
Table 2
Results of simple and stepwise (only variables significative at 0.05 is included in the model) linear regression examining the effect of different variables on the abundance of wood mouse.R2 adj (adjusted to the sample size) is given for simple and stepwise regression, it measured the explicative power of the model given by the regressiona
Index May June July
R2adj F p R2adj F p R2adj F p
(a) Simple linear regression
Cover 0.56 9.30 ** 0.44 9.20 ** 0.33 5.87 **
Seed availability No seed available 0.44 18.83 ** 0.13 3.53 NS
b) Stepwise linear regression
0.64 0.55 0.33
Cover 7.5 ** 10.9 ** 5.6 *
Hedges 4.9 *
Land cover 4.9 *
aNS: non significant; *:<0.05; **:<0.01.
Fig. 3. Monthly variation of wood mouse abundance (mean±standard deviation) in studied crops.
In June, cover and seed availability explained 44% of the variation, and both were highly correlated (93% in June). Cover was used for the remainder of the analysis because it was the best predictor of wood mouse abundance (Table 2) in July (33%).
3.2. Landscape variables
vari-Fig. 4. (a) Crop cover dynamics in the polders from May to August, classes are defined as explained in Table 1. (b) Seed availability dynamics in the polders from May to August, classes are defined as explained in Table 1.
ability not explained by field cover. In July, landscape variables did not explain more wood mouse distribu-tion than field cover.
4. Discussion
The present study tends to demonstrate that wood mouse population partly switches from hedges to crops in a farmland mosaic landscape, even if a dilution ef-fect in the matrix leads to underestimate dispersion to-wards crops. Some studies support the hypothesis of a dilution with no relation to environmental variables at field or landscape scale. Leigh Brown (1977) reported some gene frequency changes in the population of a hedge from one autumn to the next. Movement of in-dividuals from hedges to adjacent fields was recorded by Loman (1991a) for at least part of the population. Hedges are clearly important for wintering mice and constitute a source habitat in early spring. Crop ef-fect being excluded, presence of hedges was the best landscape variable explaining wood mouse abundance
in fields during May. Thus, agricultural landscape can provide temporary, suitable habitats inside the crops mosaic. ROMPA (ratio of optimal to marginal patch area) model (Lidicker, 1988) may be used to assess habitats mosaic. In agricultural landscape, ROMPA changes according to seasons and crops rotation and affects spatial dynamics of wood mouse. Mapping of optimal and marginal patches might help to predict presence or absence of mice in a given area.
Small mammals are considered as a key factor in raptors conservation (Butet and Leroux, 1993; Plesnik and Dusik, 1994). The importance of spatial hetero-geneity has been well demonstrated with regards to Lyme disease. Kitron et al. (1992) found a strong cor-relation between tick infestation on white-tailed deer and the occurrence of sandy soil and hardwoods. Ost-feld et al. (1995) concluded that abundance and spatial repartition of ticks depended on the landscape config-uration, patch size and juxtaposition. Thus, a spatial approach may be of interest in terms of integrated pest management.
5. Conclusion
The results discussed in this paper confirm the hy-pothesis of wood mouse dilution in the crops matrix since early spring until summer. Hedgerows act as source habitat and cover provided by crops at field and landscape scale are the main factor explaining wood mouse abundance in crops. Spatial analysis has pro-vide useful landscape data and seems to be a promis-ing tool to approach ecological processes at landscape scale.
Acknowledgements
We wish to thank two anonymous referees for their helpful comments on this paper and English improve-ment of the text. We are very grateful to Sebastien Rodriguez and Yann Rantier for their advice and sup-port for GIS part of this work especially with CHLOE software; Audrey Chanel and Guillaume Ouin for their help during field work and the Mont Saint Michel Bay farmers who allowed us to trap in their fields. This study has been supported by CNRS (SEAH program, UMR “Ecobio”).
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