Landscape Ecology 18: 303–314, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

303

Research article

Temporal variability of connectivity in agricultural landscapes: dofarming activities help?

Jacques Baudry1,∗, Françoise Burel2, Stephanie Aviron2, Manuel Martin2, Annie Ouin2,3,Guillaume Pain1,4 & Claudine Thenail11INRA – SAD Armorique, CS 84215, 65 Rue de Saint Brieuc, 35042 Rennes Cedex, France2UMR 6553 Ecobio, CNRS – Universite de Rennes 1, Campus de Beaulieu, 35042 Rennes Cedex, France3INP-ENSAT Avenue de l’Agrobiopole, B.P. 107, Auzeville Tolosane 31326, Castanet, Tolosan Cedex, France4Ecole Superieure d’Agriculture, 55 rue Rabelais, BP 748, 49007 Angers Cedex 01, France∗Corresponding author: (Tel.: 00 33 (0)223485621; Fax: 00 33 (0)223485620; E-mail: jbaudry@roazhon.inra.fr)

Key words: connectivity, farming system, landscape structure, simulations, temporal variability

Abstract

In landscapes where natural habitats have been severely fragmented by intensive farming, survival of many speciesdepends on connectivity among habitat patches. Spatio-temporal structure of agricultural landscapes dependson interactions between the physical environment and farming systems, within a socio-economic and historicalbackground. The question is how incentives in agricultural policies may influence connectivity? May they be usedto manage the land for biodiversity conservation? We used simulations based on property field maps to compareconnectivity on the same landscape during seven years of crop succession for two dairy farming systems, one beingrepresentative of conventional systems of western France, the second one representative of systems undergoingintensification of production. Connectivity is a measure of landscape structure and species characteristics basedon individual area requirements and dispersal distance. Models used are based on weighed distances, consideringdifferential viscosity for different land uses. The results show that, for a given farming system, physical and fieldpatterns constraints are such that landscape connectivity remains the same over years, while it is significantlydifferent between the two farming systems. This is consistent with the recent input of policies to promote environ-mentally friendly farming systems, and confirms that policies must encounter the landscape level. The analysis alsodemonstrates that the localisation of forest patches, resulting from long term land cover changes, plays a centralrole in connectivity and overrides changes in agricultural land uses.

Introduction

The main characteristic of temperate agricultural land-scapes is the expansion of farmland at the expenseof forest. In western Europe this process has beentaking place for centuries (Duby and Wallon 1975).For plants and animal living in these fragmented andheterogeneous landscapes, movement is a key processfor survival (Wiens et al. 1993). For mobile species,daily movements are for food search and predatoravoidance. For all species, individuals or propagulesdisperse among local populations or to colonise new

habitats, at a time scale of a year or more. Beforethe period of land use intensification grew from the1950s on, patches and linear elements of semi-naturalhabitats facilitated the movements of some species.Since then, these habitats have decreased dramaticallyin intensively farmed regions (Leonard and Cobham1977; Agger and Brandt 1988; Meeus 1990). Nev-ertheless, numerous forest species continue to thrivein these environments (Burel 1996). Landscape con-nectivity, defined in this paper as ‘the degree to whichthe landscape facilitates or impedes movement among

304

resource patches’ (Taylor et al. 1993) is an importantcontrol of the populations of these species (Petit andBurel 1998). Thence, we consider connectivity as anestimate of population survival in landscapes. Land-scape managers, conservation and integrated pest con-trol planners tend to enhance connectivity to maintainor restore biodiversity at the landscape level.

The measure of connectivity is a current debate inecological literature, mainly between landscape ecol-ogists and metapopulation biologists (Moilanen andHanski 2001; Tischendorf and Fahrig 2001). The de-bate mainly involves the patch centred approach ofthe population biologists versus a landscape orientedone for landscape ecologists, and a neutral view ofnon habitat areas versus a view of a heterogeneousmatrix. Even if those ideas simply reflect differencesin the scale of the studies and the precision of ex-pected results, several authors have developed the ideathat ‘connectivity’ of a landscape depends not onlyon the distance between habitat patches, but also onthe presence of corridors and stepping stones and onthe resistance of the surrounding matrix (Fahrig andMerriam 1994; Moilanen and Hanski 1998; Pain et al.2000; Ricketts 2001). In agricultural landscapes themosaic of crops and uncultivated patches, woodlots,heath land, wetlands and hedgerows, influences indi-vidual movements for carabid beetles (Martin et al.2001), butterflies (Ricketts 2001), badgers (Schip-pers et al. 1996), and damselflies (Pither and Taylor1998). Interactions among individual movements ofa species and a given land use depend on its pheno-logical state (Ouin et al. 2000), available resourcesand/or refuge effect (Henein et al., 1998). Milan dela Peña et al. (2003) find in Brittany, France, thatin landscapes showing similar organization of wood-lands and hedgerows, communities of carabids differaccording to farming systems. In landscapes with fewwoods remaining, carabid communities in dairy farms,with a high proportion of maize, differ markedly fromthe ones in landscapes where pig production, charac-terized by a high proportion of cereals, is dominant.The first ones have communities characteristic of stilldense hedgerow network landscapes. A crop suchas maize, may, in some instance be a substitute forwoodland.

Nevertheless connectivity is a measure of land-scape structure and determines animal or plant move-ment or dispersal at the landscape level. Connectivityinvolves spatial heterogeneity of the land as well asindividual species area requirement and dispersal dis-tance, this is what Vos and colleagues develop as

Ecological Scaled Landscape Indices (Vos et al. 2001).Agricultural landscapes are dynamic at several tempo-ral levels. Within years, growth and harvest of cropschange the mosaic of resources. Among years, cropsuccession in a given farming system changes the spa-tial arrangement of the mosaic, with no or few effectson the shape and size of the fields (Baudry and Papy2001). On longer temporal scales, changes in farmingsystems induce more durable changes that affect thesize and the shape of cropping areas and of natural orextensively farmed areas (Baudry et al. 2000; Thenail2002).

Due to the growing interest in sustaining biodi-versity in agricultural areas, and for conservation andintegrated pest management (Altieri 1980), it is nowimportant to do more than consider habitats in a bi-nary world that reduces a landscape to two basiccategories, suitable habitats and uninhabitable ma-trix (Levins 1970; Gilpin and Hanski 1991; Hanskiand Gilpin 1997). Rather, the heterogeneity of thewhole matrix and its variability through time must beincluded.

In this paper we considered the effects of short timecrop successions and long time changes in farmingsystems on the measure of connectivity. The impor-tance of field patterns and the spatial differentiation oflandscapes due to farming activities have been demon-strated in many instances. At a regional level, Simpsonet al. (1994) show the differentiation of Ohio land-scapes according to the physical environment. Fernan-dez Ales et al. (1992) describe a similar process insouthern Spain. This can also be shown at a globalscale (Turner II and Meyer 1994) or at a landscapescale (Thenail 2002). Our simulations were based onreal landscapes and farming systems, and we tookinto consideration the constraints of the physical en-vironment, and of topology and farm property on theorganisation of the mosaic. Our hypothesis was that indifferent crop mosaics, connectivity of the landscapevaries, and that variability is higher between farmingsystems than within farming systems. Then, farmingsystems can be one of the utensils of the tool box usedfor the ecological management of the land.

We tested the effects of two dairy farm types, con-sidering seven years of crop succession for each. Thetwo farm types differ in land use: the first has morepermanent grassland and hedgerows, the second moremaize. They can be considered as two levels of intensi-fication (Green 1989). Flamm and Turner (1994) havetested the importance of using fields versus indepen-dent pixels in land use simulations. They conclude that

305

Figure 1. Field pattern.

Table 1. Rules of land use allocation in farms of Farm type 1. For maize, cereals, sown grasslandand permanent grassland, the table gives the percent of each crop allocated to the group of fields.

Distance Hydromorphy Surface Maize Cereals Sown grass. Perm. grass.

Low Low <1 ha 0 0 100 0

(<0.5 Km) ≥1 ha 20 10 70 0

Medium < 1 ha 0 0 100 0

≥ 1 ha 10 0 90 0

High < 1 ha 0 0 50 50

≥ 1 ha 10 0 90 0

Medium Low < 1 ha 10 10 50 30

(0,5; 1 Km) ≥ 1 ha 25 25 50 0

Medium < 1 ha 0 0 50 50

≥ 1 ha 45 20 35 0

High < 1 ha 0 0 0 100

≥ 1 ha 50 10 40 0

High Low < 1 ha 0 0 0 100

(≥1 Km) ≥ 1 ha 50 50 0 0

Medium < 1 ha 0 0 0 100

≥ 1 ha 60 40 0 0

High < 1 ha 0 0 0 0

≥ 1 ha 35 5 25 35

306

Table 2. Rules of land use allocation in farms of Farm type 2. For maize, cereals, sown grassland and permanentgrassland, the table gives the percent of each crop allocated to the group of fields.

Distance Hydromorphy Surface Maize Cereals Sown grass. Perm. grass. Wood. fallow

Low Low < 1 ha 0 0 100 0 0

(<0,5 Km) ≥ 1 ha 20 20 60 0 0

Medium < 1 ha 0 0 100 0 0

≥ 1 ha 20 20 60 0 0

High < 1 ha 0 0 30 50 20

≥ 1 ha 20 0 80 0 0

Medium Low < 1 ha 10 10 50 30 0

(0,5; 1 Km) ≥ 1 ha 50 50 0 0 0

Medium < 1 ha 0 0 50 50 0

≥ 1 ha 60 30 0 0 0

High < 1 ha 0 0 0 75 25

≥ 1 ha 75 25 0 0 0

High Low < 1 ha 50 50 0 0 0

(≥ 1 Km) ≥ 1 ha 50 50 0 0 0

Medium < 1 ha 0 0 0 50 50

≥ 1 ha 60 30 0 0 0

High < 1 ha 0 0 0 0 100

≥ 1 ha 75 0 0 25 0

‘only patch-based model with ownership boundariescaptured the complexity of the spatial pattern of thelandscape’.

Landscape simulations

To simulate a realistic landscape we used the map ofa real landscape (a second stream order watershed)from which we retained field limits, built up areas,roads and streams (Figure 1). This watershed is partof a long-term landscape research project (Baudryet al. 2000). The total area is 1060 ha. The currentfield pattern is the result of environmental constraintsand of technical and socio-economical history. It isthe result of physical heterogeneity and its diversityof shape and size is the result of interactions be-tween nature and society (Meynier 1966; Rackham1986). Taking an actual field pattern for simulationspermitted to incorporate both what forms a basis oflandscape structure and the spatial constraints withinwhich farming systems operate.

The actual land use of this area is a mosaic ofcrops, grasslands, woods and hedgerows resultingfrom the activities of 25 farms, all of which are notincluded in the area, as in this part of France, the

territories of farms are scattered. Most of the farmsare specialized dairy farms, with more long durationthan short duration sown grassland, silage maize, anda small proportion of grain maize. Beside maize silage,the contribution of grass fodder (hay and silage) isimportant as livestock food. Size and economic inten-sity of the farms vary within a gradient going towardmore cash crops in the farming system, as farm areaincreases (Thenail 2002).

For simulation we used seventeen farms, totallyincluded in the watershed, with the same farming sys-tems for a given simulation. Both types of farmingsimulated in this work are dairy farms: type 1 uti-lizes more grassland than type 2 where maize has animportant role. We simulated land cover, categorizedas woodland, permanent grassland and other crops(rotational grassland, maize and cereals), as well ashedgerows over periods of seven years. Each agricul-tural field belongs to a farm, and all the farms haveall their fields in the watershed. This permitted us toutilize within-farm rules of land allocation to simu-late the landscapes. These rules (Tables 1 and 2) arederived from empirical work in the region (Thenail,unpublished). Distance to farmstead, soil hydromor-phy and field size are the main driving factors of landuse. We define as hydromorphic, a field with visible

307

patches of hydromorphic conditions (redoximorphicfeatures at the surface, on over 80% of its area). Loca-tion of farm buildings are according to the distributionof current villages. Fields were allocated to each farmwith simple rules of distance from buildings and sizeof fields. We considered that farm type 2 is a changetoward agricultural intensification of farm type 1.The simulations went as follows:(1) We randomly assigned woods as to cover about12% of the land. This simulated the fact that the pres-ence of woods is due to a wide variety of causes,outside the farming activities. Among the causes areinheritance, leisure etc. For all subsequent simula-tions, this woodland pattern was retained. We ran thesimulations with three different initial wood patterns(A, B, C), so to test the effect of this wood pattern. Ineach simulation, the same area of wood is kept, but as‘wood’ is assigned randomly to polygons of the map,we obtained a different spatial distribution.(2) We ran simulations for farm type 1. We firstsimulated permanent grassland, we considered thatall fields with permanent grassland are delimited byhedgerows, which is the current trend in hedgerownetwork landscapes (Barr and Gillespie 2000) and par-ticularly in this study site (Baudry et al. 2000). Arestricted number of crop field boundaries (one third)were also assigned as hedgerows.(3) We simulated the crops for farm type 1 (7 runs)with the empirical rules in Table 1. We maintainedthe permanent grassland and hedgerow pattern con-stant for the seven runs, which is coherent with currentagricultural use. The fields were divided into classesaccording to their characteristics (distance to farm,surface, hydromorphy) and within each class cropswere allocated to fields randomly in the proportiongiven in Table 1.(4) We also ran ‘random’ simulations using only theproportion of the different annual cover, assigned ran-domly to fields, woodland and permanent grasslandremained identical. To keep the proportion of the cropsconstant, as compared to ‘rule’ simulations, we splitfields into two size categories: less or equal to 1 haand more than 1 ha. Thus hydromorphy and distanceto farmstead played no role.

We then ran the simulations with farm type 2.We used the pattern of woodland, permanent grass-land and hedgerows of farm type 1 with the followingchanges:(5) The decreasing role of permanent grassland ledto the abandonment of small, distant, hydromorphicfields, therefore woodland may increase. This trend

Table 3. Summary of simulations.

Farm type Wood and Rules Random

hedgerow

pattern

1 A 1Arul (7 runs) 1Arand (7 runs)

1 B 1Brul (7 runs) 1Brand (7 runs)

1 C 1Crul (7 runs) 1Crand (7 runs)

2 A 2Arul (7 runs) 2Arand (7 runs)

2 B 2Brul (7 runs) 2Brand (7 runs)

2 C 2Crul (7 runs) 2Crand (7 runs)

1 A random 1Ahr (8 runs)

hedgerows

has been observed in several situations, when crops areintensified in farming systems, use of wet meadows isno longer part of cattle raising (Medley et al. 1995). Incontrast, some permanent grassland, in these hydro-morphic zones, could be plowed and were consideredas fields. Subsequently, surrounding hedgerows wereremoved, only one third are maintained.(6) Other crops were allocated following empiricalrules for farm type 2 (Table 2). Seven simulations wererun, each used as a year.(7)‘ Random’ allocation of crops were also simulated.

For one wood pattern (A) we ran eight alloca-tions of permanent grassland at random to test theeffect of hedgerow network patterns largely associatedto permanent grassland. The different simulations aresummarized in Table 3.

Measure of connectivity

In this paper we considered a habitat-specific forestspecies which moves between suitable forest patcheseither for supplementation or complementation (Dun-ning et al. 1992). Individuals move from patch to patchin continuously varying elements, within spatially ex-plicit landscapes. Patches connected to woodlands canbe reached by a mobile individual moving out of apatch and allowed to wander in the landscape on ayearly basis. We used the concept of Minimal Cumu-lative Resistance of landscapes (Knaapen et al. 1992).Maximum distance from a patch was defined by theenergy cost of movement during this period, and is acombination of distance and viscosity of encounteredpatches.

308

Table 4. Coefficient of resistance affected to the dif-ferent land cover types.

Land cover type Resistance

Hedgerows and wood 1

Roads and small streams 300

Built up areas 500

Rotational and permanent grassland 100

Maize 10

Cereals 100

We measured connectivity the following way:We assigned a resistance value to each land use (Ta-ble 4) to simulate the difficulty of traversing thelandscape when moving out of woods or hedgerows.These values were adapted from previous work on for-est carabids using radio-tracing techniques (Charrieret al. 1997) and dispersal estimated parameters (Pe-tit and Burel 1998). A functional distance to woodedelements was measured as the cost of moving in thedifferent agricultural fields, using the costpush moduleof IDRISITM.

The model species was a low mobility one, and themaximum distance allowed for one run for movementis 150 m along hedgerows, 75 m into maize fields and7 m into grassland and cereals. The clusters of con-nected pixels constitute the accessibility surface of Yu(1996).

At the end of the simulations we had seven repli-cates of maps of landscape connectivity for two typesof farming systems. These seven runs can representseven years of crop succession. As we considered thatthe species cannot stay in fields after harvest, we runthe simulations independently for each year. By over-laying the clusters of the different years we obtainthe frequency of connection for the different areas inthe landscape. From these maps we extracted the areaof the different clusters formed by connected woodsand hedgerows and connected pieces of farmland. Wetested for differences between treatments for the totalarea of connected areas of farmland.

Results

Land cover

Table 5 gives the proportion of wood, maize andhedgerows in the simulated maps. Figure 2 givesexamples of land use maps.

There was no change in woodland from Farm 1 toFarm 2 in these simulations. Hedgerows decreased andmaize increased. Due to the fact that we used fields asunits for simulations, there has been no strict controlof area but within Farm 1 percent of maize stayedbelow the one within Farm 2, and within a wood-land/hedgerow pattern and Farm type, the order wassimilar.

Connectivity

Table 6 gives the measure of connectivity, consideringonly hedgerows as suitable habitat or also consideringmaize as suitable, though not optimal, for the differentsituations. Examples of connectivity are presented inFigure 3.

By construction, connectivity due to hedgerowsdecreases from Farm 1 to Farm 2, by about 25%.When maize is considered as suitable habitat, con-nectivity increases (by construction); a larger area ofmaize, even with less hedgerows, increases connectiv-ity.

Test of differences between simulations. Differencesbetween initial woodland/hedgerow patterns and typesof farming systems were tested by analysis of varianceof the SYSTAT software.

Overall, connectivity of farmland differed accord-ing to wood pattern (A, B, C) and rules of landallocation (Figure 4 and Table 7). The differences aresignificant with the different measures (total numberof connected pixels, cluster of 0.25 ha or more or clus-ter of 1.25 ha or more). Results are only given forconnectivity measured with clusters of all connectedpixels. Landscapes organized according to rules offarm 2 were significantly less connected than thoseorganized according to farm 1 (Figure 4), but these dif-ferences were less than between woodland/hedgerowinitial structure. Comparisons of simulations of farm 1vs. farm 2 show that differences were significant foreach woodland/hedgerow pattern (Table 7). We foundno difference between ‘rule’ and ‘random’ crops, nei-ther within farm rules or within woodland/hedgerowpatterns. Using rules only generated differences inconnectivity measured for each run.

Within both farming system rules, differences be-tween woodland/hedgerow patterns were significantin 5 simulations out of 6 (Table 7). This empha-sized the importance of woodland/hedgerow patterns.Differences in connectivity showed that, when sig-nificant, differences between woodland/hedgerow pat-

309

Figure 2. Examples of land cover patterns.

Figure 3. Examples of connectivity patterns.

310

Table 5. % of the different land cover types that varies (when, by construc-tion the value does not change, it is noted ‘id.’)

Farm type Rules/ Simulation Wood Hedgerows Maize

random

1 Rules A 12.3 6.1 19.3

id. Random A id. id. 18

id. Rules B 13.9 5.7 18.7

id. Random B id. id. 17.2

id. Rules C 11.4 6.1 20.6

id. Random C id. id. 18

2 Rules A 12.3 4.7 25.2

id. Random A id. id. 28.1

id. Rules B 13.9 4.3 25.4

id. Random B id. id. 24.5

id. Rules C 11.4 4.6 27.3

id. Random C id. id. 29.8

Table 6. Measure of connectivity in the different landscapes (made withrules) comparing the role of hedgerows and hedgerows + maize (values inkm2)

Simulation Connectivity due to Connectivity due to Increase

hedgerows hedgerows + maize (%)

1A 1.02 1.79 0.75

1B 0.90 1.50 0.66

1C 0.95 1.68 0.77

2A 0.75 (−26.3%) 1.67 1.21

2B 0.70 (−22.9%) 1.41 1.02

2C 0.68 (−28.7%) 1.52 1.24

terns were more important than differences betweenfarm types (Table 7).

A two ways analysis of variance (woodland/hedge-row pattern and Farm type) showed that both variablesexplained differences (for the type of farm F = 21.929and p < 0.001 and for woodland/hedgerow F = 34.819and p < 0.001) and had no interactions (F = 0.577and p = 0.567). This analysis confirmed the higherimportance of woodland/hedgerow patterns.

Effects of initial woodland/hedgerow pattern. Wood-land initial patterns were randomly generated anddid not differ by either mean distance between woodpatches or fractal dimension. Thence, we tested forthe effect of hedgerow patterns, within one wood-land pattern (A). Hedgerows, in our model are closelyrelated to permanent grassland. So we randomly as-

Figure 4. Mean connectivity in the various simulations.

311

Table 7. Results of the tests of differences between simulations: connectedfarmland (average over 7 runs)

Simulation F P % connectivity difference

(average)

1ABCrul/2ABCrul 8.214 0.007∗∗ −7.6

1ABCrand/2ABCrand 1.655 0.296

12ABCrul/12ABCrand 0.069 0.793

1ABCrul/1ABCrand 0.747 0.392

2ABCrul/2ABCrand 0.129 0.721

1Arul/2Arul 5.572 0.033∗ −7.08

1Brul/2Brul 6.374 0.027∗ −5.96

1Crul/2Crul 10.614 0.007∗∗ −9.50

1Arul/1Arand 0.637 0.440

1Brul/1Brand 0.936 0.352

1Crul/1Crand 2.338 0.152

2Arul/2Arand 0.050 0.827

2Brul/2Brand 0.170 0.687

2Crul/2Crand 0.530 0.481

1Arul/1Brul 37.065 0.000∗∗ −16.3

1Arul/1Crul 3.677 0.079 −6.20

1Brul/1Crul 24.055 0.000∗∗ 12.07

2Arul/2Brul 37.222 0.000∗∗ −15.29

2Arul/2Crul 11.268 0.006∗∗ −8.64

2Brul/2Crul 5.337 0.039∗ 7.85

signed permanent grassland, then crops according tofarm 1 rules in 7 simulations and tested the differencein connectivity between considering all non woodedelements as hostile (viscosity = 100) and connectiv-ity as measure above; so the differences in hedgerownetwork patterns and their relations to wood patchescan be assessed. We found a relationship between bothmeasures (Figure 5): the higher the connectivity withhedgerows, the higher the connectivity consideringboth hedgerows and crops.

Cumulative time effects

By overlaying annual cluster within each simulation,we saw the effects of time. As shown in Table 8,the farmland being in a cluster was six times largerwith farm type 2 than 1. For farm type 2, it was alsohigher with simulations according to rules than with‘random’ land allocation. This difference was negli-gible for simulations with farm type 1. This resultdemonstrated the importance of taking time into ac-count. Some hedgerows stay isolated (functional isola-tion) in similar proportions in the different simulations(Figure 3).

Figure 5. Relationship between connectivity due to hedgerows andoverall connectivity.

Discussion

Our simulations demonstrated that rather slightchanges in farming systems (the same crops in dif-ferent proportions and a decrease of hedgerows) canlead to significant differences in landscape ecologi-

312

Table 8. Test of the cumulative effects on connectivity.

Test Sum = 1 Sum = 6 Isolated hedgerows

Rules vs. random F = 0.341, p = 0.572 F = 6.397, p = 0.030∗ F = 2.897, p = 0.12

1rules vs. 1random F = 0.602, p = 0.481 F = 6.149, p = 0.068 F = 0.315, p = 0.604

2rules vs. 2random F = 3.462, p = 0.136 F = 7.696, p = 0.050∗ F = 2.748, p = 0.173

1rules vs. 2rules F = 4.625, p = 0.098 F = 32.780, p = 0.005∗ F = 0.029 , p = 0.873

cal characteristics, notably changes in connectivity,an indicator of potential use of space by animals.Three factors play a role in determining connectivity:(1) land cover distribution, (2) the initial woodland andhedgerow network patterns and (3) farm internal rulesof land allocation.

The explicit program of Landscape Ecology is toconsider the effects of landscape heterogeneity on eco-logical processes as well as to understand the causes ofchanges in this heterogeneity. Up to now, most stud-ies have paid attention to important changes, mainlychanges affecting forest cover. As the time of consid-ering the matrix as neutral or hostile comes to an end(Ricketts 2001), it is necessary to increase the under-standing of the dynamics of this matrix (farmland inthe case of forest as the main habitat). The organiza-tion of landscape patterns may be driven by differentfactors. For instance, forest growth results from socialor historical events (forest attached to castles, sub-sidies for individual reafforestation), while farmingactivities are constrained by field pattern and soil con-ditions, thence afforestation and agricultural land useare, in our simulations, independent. Our simulationsshowed that the way they are combined in space canlead to different landscape ecological characteristics,as the relationships between woodland, hedgerows andmaize field can vary.

Our results showed that a relatively small changein farming modified connectivity in a significant man-ner. Both farming systems considered here, can beclassified as ‘dairy farm’ in the EU farm types (EU-ROSTAT). It was the rules of within farm land useallocation that made the differences, not the propor-tions of the different crops. The importance of farmfunctioning was emphasized by the cumulative ef-fects. Farming systems induce landscape changes attwo time scales. Over a few years time, crop suc-cessions produced year to year change, but overallthere was a stable pattern. In our simulations, forany treatment, the variability between runs was rathersmall. Over a longer period, landscape changes were

determined by changes in the farming systems, andassociated changes in cropping systems. The relativestability of connectivity within the functioning of afarming system can be interpreted as limits within thelandscape/farm systems with a narrow range for itsbehaviour. Relatively small changes in farming sys-tems created significant changes in connectivity, thuschanges in landscape boundary conditions.

The co-operation between scholars of farming sys-tems and landscape ecologists is crucial to decipherlandscape processes, as farming activities are oftenthe most important factor driving the dynamics ofrural landscapes. The concept of micro-regional crop-ping system (the repetitive combination of crops in agiven area (Papy 2001)) may play an important rolein understanding agricultural landscapes. It is the ex-pression of land use by farming systems integratingboth the type of farming systems and the physical andfield pattern constraints. Types of crop successions andcrop management can be inferred from micro-regionalcropping systems. A stronger relationship with farm-ing system scholars would help to model landscapedynamics and look for thresholds in their trends. Itwould also permit one to extrapolate landscape pat-terns and processes at a regional scale. This integrationof causes of landscape changes in landscape modelshas been advocated by Baker (1989) as a condition tounderstand dynamics.

Our simulations demonstrated that the processesthat organize landscape patterns must be taken intoaccount in the analysis of connectivity models. Thesefindings question the way we map landscapes and thetype of information put in the models. The impor-tance of land covers between optimal habitat patches,whether to impede or facilitate movement, is widelyrecognized in empirical and modeling studies (Pe-tit and Burel 1998). Land cover in the ‘matrix’ isusually more unstable than habitat patches of impor-tance in the maintenance of biodiversity; this is whyeither cumulative effects of land uses or land use dy-namics over a number of years must be incorporated

313

in connectivity models. A measure of connectivitybased on dynamic structural patterns and assessmentof potential movement offers the possibility to closelylink biological processes (species behaviour) and land-scape dynamics. It should be suitable to assess theecological outcomes of various landscape scenarios.

The cumulative effects of crop succession are con-sistent with empirical findings on the relationshipsbetween crop successions and field margin flora. Theyare stronger than those between flora and the adja-cent crop et the time of the survey (Le Coeur et al.2002). The close relationships between hedgerows(field boundaries in general), structure and composi-tion, and adjacent land use is established in many in-stances (Barr and Petit 2001; Baudry et al. 2000). Ourresults imply that land management can use a design ofpartially suitable habitat to increase connectivity. Thisalso means that agri-environmental policies cannot berestricted to semi-natural elements, and that ‘normal’activities of farmers can help.

Acknowledgements

We thank the Programme Environnement Vie et So-ciété of the CNRS (Motive) for its financial support.Stimulating discussions with Nicolas Schermann werehelpful.

References

Agger, P. and Brandt, J. 1988. Dynamics of small biotopes in Danishagricultural landscapes. Landscape Ecology 1: 227–240.

Altieri, M.A. 1980. The need for an agroecological approach to pestmanagement. Environmental Management 4: 467–468.

Baker, W.L. 1989. A review of models of landscape change.Landscape Ecology 2: 111–135.

Barr, C. and Petit, D., Eds. 2001. Hedgerows of the World: theirEcological Functions in Different Landscapes. IALE UK, 321 p.

Barr, C.J. and Gillespie, M.K. 2000. Estimating hedgerow lengthand pattern characteristics in Great Britain using CountrysideSurvey data. Journal of Environmental Management 60(1): 23–32.

Baudry, J., Bunce, R.G.H. and Burel, F. 2000. Hedgerow diver-sity: an international perspective on their origin, function, andmanagement. Journal of Environmental Management 60: 7–22.

Baudry, J., Burel, F., Thenail, C. and Le Coeur, D. 2000. A holisticlandscape ecological study of the interactions between farmingactivities and ecological patterns in Brittany, France. Landscapeand Urban Planning 50: 119–128.

Baudry, J. and Papy, F. 2001. The Role of Landscape Heterogene-ity in the Sustainability of Cropping Systems. In: J. Nösberger,H.H. Geiger and P.C. Struik (eds). Crop Science - Progress andProspects. Cab Publishing, Oxon, UK. pp. 243–259.

Burel, F. 1996. Hedgerows and their role in agricultural landscapes.Critical Reviews in Plant Sciences 15(2): 169–190.

Charrier, S., Petit, S. and Burel, F. 1997. Movements of Abaxparallelepipedus (Coleoptera, Carabidae) in woody habitats of ahedgerow network landscape: a radio-tracing study. AgricultureEcosystems and Environment 61: 133–144.

Duby, G. and Wallon, A. (eds). 1975. Histoire de la France rurale- tome 1: la formation des campagnes Françaises. Seuil, Paris,622 pp.

Dunning, J.B., Danielson, B.J. and Pulliam, H.R. 1992. Ecologicalprocesses that affect populations in complex landscapes. Oikos65: 169–175.

Fahrig, L. and Merriam, G. 1994. Conservation of fragmentedpopulations. Conservation Biology 8: 50–59.

Fernandez Ales, R., Martin, A., Ortega, F. and A. E.E. 1992. Recentchanges in landscape structure and function in a Mediterraneanregion of SW Spain (1950–1984). Landscape Ecology 7(1): 3–18.

Flamm, R.O. and Turner, M.G. 1994. Alternative model formula-tions for a stochastic simulation of landscape change. LandscapeEcology 9(1): 37–46.

Gilpin, M. and Hanski, I. (eds). 1991. Metapopulation Dynam-ics: Empirical and Theoretical Investigations. Academic Press,London, UK 336 pp.

Green, B.H. 1989. Agricultural impacts on the rural environment.Journal of Applied Ecology 26: 793–802.

Hanski, I. and Gilpin, M.E. 1997. Metapopulation Biology, Ecol-ogy, Genetics and evolution. Academic press, San Diego, USA.p. 512.

Knaapen, J.P., Scheffer, M. and Harms, B. 1992. Estimating habitatisolation in landscape planning. Landscape and Urban Planning23: 1–16.

Le Coeur, D., Baudry, J., Burel, F. and Thenail, C. 2002. Why andhow we should study field boundaries biodiversity in an agrarianlandscape context. Agriculture Ecosystems and Environment.

Leonard, P.L. and Cobham, R.O. 1977. The farming landscape ofEngland and Wales: a changing scene. Landscape Planning 4:205–216.

Levins, R. 1970. Extinctions, Some mathematical questions in biol-ogy. American Mathematical Society, Providence, Rhode Island,pp. 77–107.

Martin, M., Bastardie, F., Richard, D. and Burel, F. 2001. Boundaryeffects on animal movement in heterogeneous landscapes: thecase of Abax ater (Coleoptera, carabidae). Comptes Rendu del’Académie des Sciences Paris, Sciences de la vie 324: 1–7.

Medley, K.E., Okey, B.W., Barrett, G.W., Lucas, M.F. and Renwick,W.H. 1995. Landscape change with agricultural intensificationin a rural watershed, southwestern Ohio, U.S.A. LandscapeEcology 10: 161–176.

Meeus, J.H.A. 1990. The transformation of agricultural landscapesin western Europe. Milieu 6: 225–236.

Meynier, A. 1966. La génèse du parcellaire breton. Norois: 595–609.

Milan de la Peña, N., Butet, A., Delettre, Y., Morand, P. and Bu-rel, F. 2003. Landscape context and carabid beetles (Coleoptera:carabidae) communities of hedgerows in Western France. Agri-culture Ecosystems and Environment 94: 59–72.

Moilanen, A. and Hanski, I. 1998. Metapopulation dynamics: effectof habitat quality and landscape structure. Ecology 79: 2503–2515.

Moilanen, A. and Hanski, I. 2001. On the use of connectivitymeasures in spatial ecology. Oikos 95: 147–151.

Ouin, A., Paillat, G., Butet, A. and Burel, F. 2000. Spatial dynamicsof Apodemus sylvaticus in an intensive agricultural landscape.Agriculture. Ecosystems and Environment 78: 159–165.

314

Pain, G., Baudry, J. and Burel, F. 2000. Landpop: un outil d’étudede la structure spatiale des populations animales fragmentées.Géomatique 10: 89–106.

Papy, F. 2001. Interdépendance des systèmes de culture dansl’exploitation agricole. E. Malézieux, G. Trébuil and M. Jaeger(eds). Modélisation des agro-écosystèmes et aide à la déci-sion. Editions CIRAD-INRA, collection Repères, Montpellier,France, pp. 51–74.

Petit, S. and Burel, F. 1998. Connectivity in fragmented popula-tions: Abax parallelepipedus in a hedgerow network landscape.Compte rendu Académie des Sciences Paris, Sciences de la vie321: 55–61.

Pither, J. and Taylor, P.D. 1998. An experimental assessment oflandscape connectivity. Oikos: 166–174.

Rackham, O. 1986. The History of the Countryside, J.M. Dent andSons Ltd., London, Melbourne, pp. 445.

Ricketts, T.H. 2001. The matrix matters: effective isolation infragmented landscapes. American Naturalist 157: 87–99.

Schippers, P., Verboom, J., Knaapen, J.P. and van Apeldoorn,R.C. 1996. Dispersal and habitat connectivity in complex hetero-geneous landscapes: an analysis with GIS-based random walkmodel. Ecography 19: 97–106.

Simpson, J.W., Boerner, R.E.J., DeMers, M.N. and Berns, L.A.1994. Forty-eight years of landscape change on two continuousOhio landscapes. Landscape Ecology 9(4): 261–270.

Taylor, P.D., Fahrig, L., Henein, K. and Merriam, G. 1993. Con-nectivity is a vital element of landscape structure. Oikos 68:571–573.

Thenail, C. 2002. Relationships between farm characteristics andthe variation of the density of hedgerows at the level of a micro-region of bocage landscape. Study case in Brittany, France.Agricultural System 71: 207–230.

Tischendorf, L. and Fahrig, L. 2001. On the use of connectivitymeasures in spatial ecology, A reply. Oikos 95: 152–155.

Turner II, B.L. and Meyer, W.B. 1994. Global land-use and land-cover change: an overview. W.B. Meyer and B.L. Turner II(eds). Changes in land use and land cover: a global perspective.Cambridge University Press, Cambridge, UK, pp. 3–10.

Vos, C.C., Verboom, J., Opdam, P.F.M. and Ter Braak, C.J.F.2001. Toward ecologically scaled landscape indices. AmericanNaturalist 157(1): 24–41.

Wiens, J.A., Stenseth, N.C., Van Horne, B. and Ims, R.A. 1993.Ecological mechanisms and landscape ecology. Oikos 66: 369–380.

Yu, K. 1996. Security patterns and surface model in landscapeecological planning. Landscape and Urban Planning 36: 1–17.