1

Habitat preferences of small mammals in farmed landscapes and agri-1

environmental schemes 2

3

4

Carlos RODRÍGUEZ1 and Salvador J. PERIS2 5

6

1. Department of Applied Biology. Estación Biológica de Doñana. CSIC. 7

Avda María Luisa s/n. Pabellón del Perú. 8

41013 Sevilla, Spain. 9

10

2. Departament of Animal Biology. Universidad de Salamanca. 11

Campus Miguel de Unamuno, edificio de Farmacia. 12

37071 Salamanca, Spain, 13

14

Running title: Land uses and Mediterranean small mammals 15

16

Correpondence: Dr Carlos Rodriguez 17

Current address: 18

UFZ-Centre for Environmental Research 19

Department of Ecological Modelling 20

PF 500135 21

D-04301. Leipzig, Germany 22

Tel: ++49 (0) 341 235.3245; Fax: ++49 (0) 341 235-3500 23

E-mail: carlos.rodriguez@ufz.de 24

2

Abstract -- The small mammal community in 21 localities of North-western Spain was 1

evaluated in the light of land use composition. The two geomorphologic categories 2

characterizing the study area, the agricultural/grazing use of the land, and the main crop types 3

of each sampling locality were used as potential predictors of the relative abundance of five 4

common small mammal species. The Common vole showed a weak relationship with land 5

uses, probably due to the recent colonisation process this species experienced in the study 6

area. The relative abundance of the Algerian mouse and the Lusitanian pine vole was best 7

explained by models built at the broadest regional scale, being the former more abundant in 8

the more arid, eastern area, while the latter was more abundant in the more humid western 9

part. The Greater white-toothed shrew showed a positive relationship with grassland coverage, 10

and the Wood mouse benefited from increasing proportions of fallow lands within the 11

grazing landscape. These two species are then expected to respond positively to those agri-12

environmental schemes including the increase of fallows and grassy vegetation within the 13

arable landscape as prescriptions. However, further efforts are needed to predict, at least 14

qualitatively the response of other small mammals species to the changing farmed landscape. 15

This is especially true for two endemic species occurring at this area: the Cabrera vole and 16

the Lusitanian pine vole, and for which this kind of information is almost absent. 17

18

Keywords: agri-environment schemes; biodiversity; habitat preferences; Mediterranean; small 19

mammals. 20

3

INTRODUCTION 1

2

Changes in land uses have been identified as one of the most important global threats for 3

biodiversity (Sala et al. 2000). In Western Europe, the agricultural subsidies of the Common 4

Agricultural Policy (CAP) that encouraged agricultural production, have demonstrated a 5

deleterious effect on both ranges and numbers of many populations of farmland organisms 6

(Love et al. 2000; Donald et al. 2001; Thorbek and Bilde 2004). For this reason, it is 7

considered that modern agriculture constitutes a major anthropogenic threat to biodiversity, 8

comparable to climate change in its ability to affect vast areas (Donald et al 2002). Recently, 9

however, the EU decoupled subsidies from agricultural production and made agri-10

environment schemes, which can be used to benefit biodiversity, compulsory in all EU 11

countries. Although these schemes are the most important policy instruments to protect 12

biodiversity in agricultural landscapes (EEA 2004), their effectiveness has demonstrated to be 13

marginal to moderate (Kleijn et al. 2006). The lack of clear and unambiguous biodiversity 14

objectives at the start of the scheme, and a low scientific basis are some of the reasons argued 15

to explain this low effectiveness (Kleijn and Sutherland 2003; Kleijn et al 2006). In addition, 16

the scientific knowledge required to appropriately design agri-environmental schemes is 17

clearly skewed towards avian populations, for which ecologists are currently able to predict, 18

at least qualitatively, the potential effect of different features belonging to agriculture 19

intensification (Ormerod et al. 2003). 20

However, for some organisms and geographical areas, the lack of information hinders 21

the development of appropriate conservation schemes. Such is the case of small mammals in 22

Mediterranean environments and their associated high levels of endemicity and rarity 23

(Baquero and Tellería 2001; Krystufek and Griffiths 2002). Few investigations have 24

addressed the response of small mammal species to changing farm landscapes (but see Torre 25

4

et al. 1996, Cagnin et al 1998). With the main aim of increasing our knowledge on this topic, 1

we investigated how land use influences small mammal diversity and the relative abundance 2

of five common small mammal species in a Mediterranean landscape. Rather than focusing 3

on microhabitat preferences, our approach intended to find those variables best explaining the 4

habitat preferences of these species at two different scales from the landscape perspective. A 5

broader, regional scale that account for geomorphologic features and latitude/longitude 6

gradients to which small mammals may respond (Moreno and Barbosa 1992). And a more 7

detailed scale, accounting for the composition of crop types around each sampling locality 8

that would indicate the preferences of these species for particular crop types or land uses (e.g. 9

Olson and Brewer 2003). 10

11 Study area 12

The study was conducted in the South of the Province of Zamora, North-western Spain (fig. 13

1). The area belongs to the Northern Plateau and ranges in altitude from 350 to 500 m. Mean 14

daily temperatures in July, the warmest month of the year, ranges from 16-18ºC in the 15

Northwest to 22-24ºC in the Southwest. Most of the area has a mean July temperature of 20-16

22ºC. Mean daily temperatures in January, the coldest month of the year, ranges from 2-3ºC 17

in the West to 6-9ºC in the East. Because of the Atlantic influence, the rainfall pattern follows 18

a West-East gradient with mean annual values of 600-800 mm in the West and values below 19

450 mm in the East. In agreement with this pattern, eastern localities are dominated by dry 20

cereal crops and vineyards together with irrigated crops (mainly corn and beet), while in the 21

western localities crops alternate with patches of oak forests (Quercus ilex, Q. faginea, and Q. 22

pyrenaica), and Mediterranean dehesas (savannah-like grasslands). Accordingly, agriculture 23

intensification, although moderate is higher in the eastern than in the western part, where a 24

low input grazing system still exists. 25

5

MATERIAL AND METHODS 1

2

Sampling 3

We analyzed Barn Owl (Tyto alba) pellets as a way of studying the relative abundance of 4

small mammals species in the study area. There, the barn owl is a common species, and it is 5

well distributed among different environments (Martin and del Moral 2003). Because of the 6

relatively low home range and euryphagic habits of the species (Taylor 1994), it is considered 7

that barn owl pellets accurately reflect the relative abundance of small mammals in the field 8

(Clark and Bunck 1991; Taylor 1994). In addition, previous works have explicitly compared 9

this method against live-trapping in Mediterranean environments, and both rendered very 10

similar results (Luiselli and Capizzi 1996). Nonetheless, the method may be inappropriate to 11

evaluate the relative proportion of rare species. For this reason, and despite its higher value 12

from the conservation point of view, we only focused on the five most common small 13

mammal species in the study area to analyze their relationships with land uses composition. 14

These species were: the Algerian mouse Mus spretus Lataste, 1883, the Wood mouse 15

Apodemus sylvaticus Linnaeus, 1758, the Greater white-toothed shrew Crocidura russula 16

Hermann, 1780, the Common vole Microtus arvalis asturianus Miller, 1908, and the 17

Lusitanian pine vole Microtus lusitanicus Gerbe, 1879. The whole set of small mammal 18

species recorded in the study area was only used to calculate the Simpson diversity index. We 19

used this index because it performs well with heterogeneous and modest sample sizes (Lande 20

1996), being the Kemp’ transformation of the index the most appropriate for our data (see 21

Magurran 2003). 22

Pellets were collected during 1993 and 1994 at 21 barn owl resting places (after 23

removing sampling sites with less than 25 prey items). We used dichotomic guides (Dueñas 24

6

and Peris 1985; Gosàlbez 1987; Paz and Benzal 1990) and zoological collections to identified 1

prey-items at the species level. 2

3

Land use information 4

According to geomorphologic criteria, the western part of the study area was constituted by 5

an erosive plain (hereafter EP), while a sedimentary plain (hereafter SP) characterized the 6

eastern part. Thus, each of the sampling localities could be classified as belonging to any of 7

these categories. We also used agrarian statistics from the Spanish Ministry of Agriculture to 8

obtain the relative coverage of land uses around sampling localities. Each of them showed a 9

particular combination of herbaceous crops, woody crops, fallow land, threshing land (plots 10

formerly used to separate the grain from their chaff and straw by means of the thresher), 11

forests, natural grasslands, meadows, unproductive land, rivers and lakes. The relative 12

proportion of these crop types with respect to the total surface was obtained on a county basis. 13

For each locality, we also considered whether the land was mainly devoted to agricultural or 14

to grazing use. 15

16

Statistical analyses 17

We used Generalised Linear Models or GLMs as a mathematical description of the 18

relationship between the above defined habitat features and the relative abundance of each 19

small mammal species at each locality. A set of GLMs was built using the SAS V.8 20

Genmod procedure setting a binomial distribution of errors and a logit link function. 21

Starting from a null model, we followed a forward stepwise GLM procedure to build a 22

multivariate model that included the set of variables that best explained the relative 23

proportion of each species. We used p<0.05 as a criterion to include/exclude new 24

variables in the models. Because the response variable was defined by the quotient 25

7

between the number of individuals belonging to a given species and the total number of 1

prey items in each sampling point, models were able to correct for different sample sizes 2

(see Jovani and Tella in press). Models built for diversity values used a normal 3

distribution of errors and the identity link. To correct for inter-annual and seasonal 4

fluctuations in the Barn Owl diet (Clark and Bunck 1991) we tested the significance of 5

both year and season. For comparing levels within a factor, the first level was assigned the 6

value 0 and then other levels measured the change from the first level. Interactions were 7

nonsignificant unless stated otherwise. Models were corrected for data overdispersion 8

using the dscale option of the Genmod procedure, which used the square root of deviance 9

divided by the degrees of freedom as scale parameter (SAS institute 2000). F tests instead 10

Chi tests were then used (Crawley 1993). 11

Because of the high number of land use variables characterizing each sampling 12

locality, and to avoid autocorrelation among them, we used Principal Component Analysis 13

(PCA) as a data reduction method. The three main axes resulting from PCA were then 14

used as potential predictors of the relative abundance of each small mammal species. 15

During the statistical procedure, we first modelled the effect of each group of explanatory 16

variables: geomorphologic categories, agricultural/grazing use, and the three main PCA 17

axes, separately. Then, we attempted to build models that included variables from any of 18

the groups, which potentially could explain more variation than the initial models. To 19

evaluate the information content of models together with their degree of complexity, we 20

used the Akaike Information Criterion (AIC; Sakamoto et al. 1986). According to this 21

criterion, from a set of models built for the same response variable, we should select the 22

one showing the smallest value of AIC. 23

PCA was made in Statistica ’99. AIC values were calculated with S-Plus. 24

25

8

RESULTS 1

2

We identified a total of 5644 prey-items at the species level, including 12 small mammal 3

species: Arvicola sapidus (<1%), Apodemus sylvaticus (10%), Crocidura russula (13%), 4

Microtus agrestis (<1%), M. arvalis (23%), M. duodecimcostatus (2%), Mus domesticus 5

(<1%), M. spretus (38%), Rattus norvegicus (<1%), and R. rattus (<1%), including two 6

endemic vole species: Microtus lusitanicus (9%), and M. cabrerae (2%). 7

With the exception of M. arvalis, the relative abundance of the selected small 8

mammal species was independent of either year or season. For this reason, only models 9

for M. arvalis included both year and season in order to correct for the effect of these 10

variables. Mus spretus was more abundant in the SP (eastern area) and in agricultural 11

areas; while the opposite result was found for Crocidura russula, Microtus lusitanicus, 12

and Apodemus sylvaticus (table 1). M. arvalis did not show any pattern at this scale. 13

PC analyses grouped the proportion of crop types around each sample point in 14

three main axes. Axis 1 (35.2% of variation) accounted for the relative contribution of 15

herbaceous crops (negative loadings), and grasslands (positive loadings). Axis 2 (18.9% 16

of variation) accounted for the relative contribution of woody crops and fallow lands at 17

positive loadings, and axis 3 (16.5% of variation) accounted for the relative contribution 18

of irrigated crops at negative loadings. Thus, the variation posed by the composition of 19

crop types in the study area could be summarized in the dichotomy between grasslands 20

and herbaceous crops, and the relative abundance of fallow lands, woody crops and 21

irrigated crops (70% of total variation). When using these axes as explanatory variables on 22

the relative abundance of small mammals in each locality, the axis 1 was able to explain 23

the patterns of relative abundance of all study species (table 2). C. russula, and M. 24

lusitanicus showed a strong positive relationship with this axis, and A. sylvaticus also 25

9

showed a positive relationship, though weaker. On the contrary, the relative abundance of 1

M. spretus was negatively associated with axis 1. The axis 2 only contributed to explain 2

the relative abundance of A. sylvaticus (probably due to the positive effect of fallow lands 3

on the relative abundance of the species F = 7.7, p = 0.006). In fact, for this species it was 4

possible to build a multivariate model that included both axis 1, and 2 with positive effects 5

on its relative abundance. According to AIC values, the inclusion of the two variables 6

increased the predictive ability of the model with a very low increase in its complexity. 7

Likewise, the inclusion of axis 2 in models built for the geomorphologic criterion, and the 8

agricultural/grazing use, significatively increased their explanatory ability without 9

increasing their AIC values. In fact, these bivariable models showed lower AIC values 10

than the univariable ones (table 1). It means that Apodemus sylvaticus was more abundant 11

in the western part of the study area, or where grazing use was predominant but, 12

independently, it benefited from the higher relative abundance of fallow land and/or 13

woody crops. We found no significative association between the relative abundance of 14

small mammals and the axes 3. Taking into account both year (F= 5.8, p = 0.003), and 15

season (F= 3.8, p= 0.01), the best model for M. arvalis also included the axis 1 (F= 6.4, p 16

= 0.01), with a negative effect on the relative abundance of the species. 17

Simpson diversity values were higher in the EP area, where grazing was the main 18

use, and it increased with increasing proportion of grassland with respect to herbaceous 19

crops (table 3). 20

21

DISCUSSION 22

23

Selected land-use variables provide information about small mammal habitat preferences 24

at two different scales. On a large scale, the eastern part of the study area was 25

10

characterized by a sedimentary plain and agricultural land use; whereas the western part 1

was constituted by an eroded plain where grazing often was the main use. This explains 2

the slight differences showed by models built for these two factors. As stated above, due 3

to the Atlantic influence, annual rainfall follows a longitudinal gradient resulting in high 4

values of rainfall in the western part, which decline progressively to the East. This could 5

be the main factor explaining the differences found in the relative abundance of Microtus 6

lusitanicus and Mus spretus in the study localities. A more detailed approach, considering 7

the composition of land uses in each of these localities provided a weaker explanation 8

(according to AIC values) than the more general model. 9

M. spretus, requires Mediterranean conditions (Moreno and Barbosa 1992) and 10

several studies have documented its negative relationship with rainfall (Romero 1986; 11

González and Román 1988; Torre et al. 1996; Khidas et al. 2002). The species was also 12

associated with open and cultivated landscapes (González and Román 1988; Peris et al. 13

1999; Khidas et al. 2002); where it feeds on crop seeds and fruits (Khidas et al. 2002). 14

Our results on the selection of the more arid, mainly agricultural eastern area support 15

these previous findings. 16

Microtus lusitanicus is a fossorial, colonial species, which depend on access to 17

suitable dens (Veiga 1986) and has shown a positive relationship with rainfall (Romero 18

1986; Veiga 1986). Thus soil characteristics (stability and humidity) may constitute one of 19

the most important factors for its populations. This could explain the positive association 20

of this species with the western area, where grasslands are not ploughed and values of 21

annual rainfall are higher than in the eastern part. 22

Although both Crocidura russula and Apodemus sylvaticus have showed a positive 23

relationship with rainfall (Romero 1986; González and Román 1988; Torre et al. 1996), 24

our results provide additional information on the factors explaining their relative 25

11

abundance. C. russula showed a strong selection of grasslands, and this was the best 1

model (according to AIC) built for this species. As other insectivorous, it shows high 2

metabolic rates, and thus high prey densities in grasslands (e.g. Bardgett and Cook 1998, 3

Traugott 2002) could explain this positive association. Grasslands could be also selected 4

because they are often separated by stone walls and hedgerows, which are species’ 5

microhabitats (Gosàlbez 1987; Moreno and Balbontin 1998). 6

The consideration of crop types around sampling localities was of great 7

importance to explain the patterns of relative abundance of A. sylvaticus in farmed 8

landscapes. Although it is considered a forest, scrublands species (Boitaqni et al. 1985; 9

Telleria et al. 1991), A. sylvaticus has demonstrated to be able to colonize arable land 10

(Abad 1991; Alcantara and Telleria 1991), and this study suggests that fallow lands favour 11

the occurrence of the species in agricultural areas. As other studies have suggested for 12

rocks and undergrowth cover (Khidas et al. 2002), fallow lands may provide the coverage 13

required for feeding, sheltering and burrowing. In this way, fallows increase the quality of 14

the farmed landscape for this species. 15

These results (the positive selection of grasslands by C. russula, and the 16

importance of fallow lands for A. sylvaticus) may constitute important insights of further 17

consideration by agri-environmental schemes. Since agricultural intensification have 18

produced a polarization of agrarian activities towards agriculture or grazing (Brickle et al. 19

2000), grasslands have almost disappeared from agricultural landscapes (see Donazar et al. 20

1993), and fallow lands have been also reduced in habitats mainly used for livestock 21

raising. According to our results, the presence of grassy vegetation in agricultural 22

landscapes may benefit C. russula, while the intensification of grasslands may constraint 23

the suitability of the farmed landscape for A. sylvaticus. On the contrary, the conservation 24

of fallow lands both in agricultural and grazing landscapes may favour the occurrence of 25

12

this species. These measures are currently implemented in many agri-environmental 1

schemes (e.g. Kleijn et al. 2006), and our results suggest that small mammals would 2

positively respond to these measures. However, further studies should build on our 3

approach, also focusing on other species inhabiting this area, especially the two endemic 4

voles (Microtus cabrerae and M. lusitanicus), for which few knowledge exist on the state 5

of their populations and their relationship with agriculture practices (but some insights are 6

given in Fernandez-Salvador et al. 2005a,b). 7

As stated above, M. arvalis showed a strong relationship with both year and 8

season, and only a negative relationship with grasslands or positive with herbaceous crops 9

was able to explain additional variability in the relative abundance of the species. Palacios 10

et al. (1988) documented the first record of the species in the northern range of the study 11

area, and this distributional range increased markedly southwards year after year 12

(González et al. 1995). This colonisation pattern, per se may explain the weak association 13

between the species’ abundance and land use composition, since suitable places may still 14

be unoccupied and sub-optimal ones may be occupied as a response to the colonisation 15

mechanism. This process has been documented not only in the study area but also in many 16

other places within the Northern Plateau, and it has been hypothesized that the habitat 17

simplification droved by the CAP could be behind this recent colonisation (González et al. 18

1995). 19

Although further studies are needed to ascertain how the small mammal community is 20

responding to the changing farmed landscape, including a higher number of species and land 21

use scenarios, our results indicated that higher diversity values were associated with 22

grasslands in the western part of the study area, where they alternate with crops, small forest 23

patches and scattered trees in an heterogeneous landscape (see Diaz et al. 1997 for a more 24

detailed description). On the other hand, the small mammal community in the eastern 25

13

agricultural area was poorer, probably due to simplification that resulted from increasing the 1

plot size as well as abandoning practices such as crop rotation and winter sowing. Our results 2

support previous findings on the importance of conserving traditional agro-grazing systems in 3

Western Europe (see Donázar et al. 1997). In the study area, the progressive depopulation, 4

and the subsequent abandonment of maintenance activities in these traditional systems may 5

constitute their main threat. Thus, the implementation of adequate agri-environmental 6

schemes that make these systems economically sustainable may constitute the keystone for 7

their conservation. This is extrapolative to other European countries, especially those recently 8

incorporated to the EU, because they still preserve this kind of extensive systems as a 9

consequence of the lower governmental market support to agrarian products (Donald et al. 10

2002). 11

12

ACKNOWLEDGEMENTS 13

14

We are very grateful to Moisés Pescador for his help identifying more difficult skull remains. 15

Antonio Benito, Eduardo Baños, Estrella Rodríguez, M. A. González, and Óscar Barajas 16

helped us with the pellet analysis. Agrarian statistics were provided by the Delegación de 17

Agricultura de Zamora. We thank David Serrano, Jacinto Román, Jane U. Jepsen, and Roger 18

Jovani for their comments on previous versions of the manuscript. C. R. was partially 19

supported during the lab work by a fellowship from the Spanish Ministerio de Educación y 20

Cultura. 21

22

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19

Table 1. 1

Models built for the geomorphologic criterion (GEOMOR), and the grazing/agricultural land 2

use. Multivariable models also including PCA axes are shown. SP = sedimentary plain; EP = 3

erosive plain. 4

5

Species Variables Levels Parameter Estimate SE F d.f. p AIC

Apodemus sylvaticus

GEOMOR

Intercept SP EP

-1.9 -0.6

0

0.2 0.2 0

8.6 34 0.004 192.1

USE

Intercept Agriculture

Grazing

-2 -0.5

0

0.2 0.2 0

5.5 34 0.02 190.7

GEOMOR

Axis 2

Intercept SP EP

-2 -0.6

0 0.3

0.2 0.2 0

0.1

8.6

7.3

33

0.003

0.007

181.8

USE

Axis 2

Intercept Agriculture

Grazing

-1.9 -0.6

0 0.4

0.2 0.2 0

0.1

8.76

10.55

33

0.003

0.001

181

Crocidura russula

GEOMOR

Intercept SP EP

-0.7 -2.1

0

0.090.1 0

266.6 34 <0.0001 147.6

USE

Intercept Agriculture

Grazing

-0.6 -2.1

0

0.090.1 0

251.7 34 <0.0001 144.4

Microtus lusitanicus

GEOMOR Intercept

SP EP

-1.6 -1.6

0

0.2 0.2 0

44.9 34 <0.0001 166.9

USE Intercept

AgricultureGrazing

-1.6 -1.6

0

0.2 0.2 0

42.9 34 <0.0001 169

Mus spretus

GEOMOR Intercept

SP EP

-2.3 2.2 0

0.3 0.3 0

65. 7 34 <0.0001 218.2

USE Intercept

AgricultureGrazing

-2.4 2.2 0

0.3 0.4 0

62.8 34 <0.0001 218.3

20

Table 2. 1

Models on the relationship between the relative abundance of small mammal species and the 2

three main PCA axes. 3

4

Species Explanatory variable Estimate SE F d.f. p AIC

Apodemus sylvaticus Axis 1 0.17 0.08 4.6 34 0.03 192.8

Axis 2 0.33 0.12 7.15 34 0.008 181.1

Axis 1 0.19 0.07 6.58 33 0.01 181.6

Axis 2 0.34 0.1 9.17 0.0025

Crocidura russula Axis1 0.77 0.04 326 34 <0.0001 129.6

Microtus lusitanicus Axis1 0.56 0.09 40 34 <0.0001 177.7

Mus spretus Axis1 -0.62 0.12 34 34 <0.0001 234.2

5

21

Table 3. 1

Models built for the small mammal diversity calculated with the Kemp-transformed Simpson 2

diversity index. 3

4

Explanatory variable Levels Parameter

Estimate SE F d.f. p AIC

GEOMOR Intercept SP EP

1.8 -0.3 0

0.1 0.1 0

4.5 34 0.03 217.9

USE Intercept AgriculturalGrazing

1.8 -0.3 0

0.1 0.1 0

4.4 34 0.04 176.2

Axis 1 0.1 0.05 5 34 0.03 175.5

22

FIGURE LEGENDS 1

2

Figure. 1. Sampling localities in the study area. Gray plots indicate the counties where pellets 3

and land use information were collected. Latitude and longitude range is indicated 4

23

1

20 km20 Km

42º

5º7º

Iberian Peninsula