[Advances in Ecological Research] Ecological Networks in an Agricultural World Volume 49 || Interaction Networks in Agricultural Landscape Mosaics

CHAPTER FIVE

Interaction Networks inAgricultural Landscape MosaicsFrançois Massol*,1, Sandrine Petit†*UMR 5175 CEFE—Centre d’Ecologie Fonctionnelle et Evolutive (CNRS), Montpellier cedex 05, France†UMR 1347 Agroecologie, AgroSup/UB/INRA, Pole Ecologie des Communautes et Durabilite des SystemesAgricoles, 21065 Dijon cedex, France1Corresponding author: e-mail address: [email protected]

Contents

1.

AdvISShttp

Introduction

ances in Ecological Research, Volume 49 # 2013 Elsevier LtdN 0065-2504 All rights reserved.://dx.doi.org/10.1016/B978-0-12-420002-9.00005-6

292
2. Ecological Patterns and Processes in Spatially Structured Ecosystems 295
2.1
Ecological properties of interest: Biodiversity, network topology andfunctioning 295
2.2
Landscape complexity and the spatial heterogeneity of ecological patterns 299 3. The Goals of Agricultural Landscape Mosaics Studies: Management for Crop
Production and Other Ecosystem Services
303 4. Specific Properties of Agricultural Landscape Mosaics: Temporal and Spatial
Heterogeneity
307 5. Metaecosystems and Agricultural Landscape Mosaics 311
5.1
Metaecosystem models 315 5.2 Agricultural mosaics as evolving metaecosystems and prospects for future
advances
320 6. Conclusion 324 Acknowledgements 325 References 325
Abstract

The organisation of human-populated landscapes results from many interacting pro-cesses tied to the historical development of societies and human activities. Agriculture,in particular, has dramatically altered much of the Earth’s surface over many millennia.Landscapemosaics can be used to understand these impacts, which are increasing at anaccelerating rate on a global scale. On a local-to-regional scale, mosaic concepts are alsoof practical interest for designing natural enemy-based pest control strategies as analternative to intensive pesticide use.

Considerable empirical and theoretical work has been conducted into theseapproaches recent years, although it has largely dealt with issues of landscape biodiver-sity at the species population level, rather than considering the role of species interac-tions. When viewed as interaction networks, agricultural landscapes can be used toaddress a more diverse suite of topics, such as those related to network complexity,

291

http://dx.doi.org/10.1016/B978-0-12-420002-9.00005-6

292 François Massol and Sandrine Petit

community demographics, or ecosystem functioning, and how these properties varywith the complexity of the landscape mosaic over time and space.

Ultimately, the functioning of an agricultural landscape determines the productionof ecosystem services, such as crop production and pest control, yet ecological theory-based management options are still scarce. Consequently, there is a significant gap tobe bridged between the current state-of-the-art in theoretical ecology and practicalstrategies for the management of agricultural landscapes. One way to bridge thisgap involves developing metaecosystem models that can account for the movementand internal dynamics of both biotic and abiotic components of ecosystems. Byrecognising the specifics of agricultural landscapes, that is, their heterogeneity over timeand space, the occurrence of frequent perturbations and the importance of nutrientadditions at the landscape scale, metaecosystem models can be proposed to answersome current challenges in agroecology.

1. INTRODUCTION

When viewed from the air, most of the inhabited parts of the Earth

have a patchwork or ‘Harlequin’ appearance (Horn and MacArthur,

1972), highlighting how humans have become the predominant force in

shaping the terrestrial landscape. Agriculture is arguably the most pervasive

and powerful driver through which humans have altered the appearance of

the planet’s surface (Vitousek et al., 1997). The organisation of agricultural

landscapes is a result of an often long history between societies and the envi-

ronments in which they live, reflecting synergies among historical properties

of the land, developments in agricultural technology, and the underlying

environmental heterogeneity of a given area (Navarro and Pereira, 2012).

The study of agricultural landscapes has been a recurring theme within

landscape ecology, a sub-discipline within ecology that focuses on the prop-

erties, patterns and processes of large areas that encompass different ecosys-

tems (Merriam, 1988; Naveh and Lieberman, 1984; Turner, 1989; Urban

et al., 1987). Agricultural practices (Kleyer et al., 2007) are of particular

interest to landscape ecologists because they generate frequent perturbations,

notably through crop planting, ploughing, weeding, irrigation, harvesting,

and, increasing over the last century, the use of agrochemicals, such as pes-

ticides and herbicides.

The concepts of landscape mosaics (Forman, 1995; Watt, 1947), mosaic

cycles (Kleyer et al., 2007; Remmert, 1991) or shifting mosaics (Bormann

and Likens, 1979; May, 1994) describe landscapes that consist of various

habitat patches inhabited by different communities over time, building on

earlier ideas related to succession (Clements, 1916), Margalef’s theory on

293Interaction Networks in Agricultural Landscape Mosaics

transitions between community states (Margalef, 1962, 1963), and more

recent theories about community assembly (Levin et al., 2001; Lockwood

et al., 1997; Morton and Law, 1997) and metacommunities (Holyoak

et al., 2005; Leibold et al., 2004).

Interest in understanding how agricultural landscape mosaics work has

been renewed over the last couple of decades (e.g., Banaszak, 1992; Blitzer

et al., 2012; Duelli, 1997; Hoffmann and Greef, 2003; Jackson et al., 2007;

Swift et al., 2004; Vasseur et al., 2013), primarily for two reasons: (i) to design

pest control strategies based more on naturally occurring biocontrol agents

(predators and parasitoids), rather than having to rely on the use of chemicals

(Bianchi et al., 2006; Griffiths et al., 2008); (ii) to decrease the harm caused by

agricultural practices to wild biodiversity (Jackson et al., 2007) in line with

conservation policies and legislation. For these reasons, structural measures

of biodiversity have focused on a few nodes in the wider food web (mainly,

arthropods and plants) as indicators of ‘good’ agricultural landscape mosaics—

in addition to process-based measures of agricultural productivity. Because

measuring biodiversity is difficult, especially when dealing with poorly

described taxa, many research programmes have tried to correlate measures

of species diversity with metrics of habitat complexity within the landscape,

to generate proxy measures. However, viewing a snapshot of an agricultural

landscape does not tell usmuch about its underlying processes. Proxies derived

from a single snapshot of habitat arrangement to predict future biodiversity can

be unrealistic if rotation cycles are long and applied to very large fields and/or

if the turnover between cultivated and non-cultivated areas is fast, all of which

are common properties of many agroecosystems (Thenail et al., 2009).

Attempts are being made to view agroecosystems from a more holistic per-

spective that considers them as networks of species embedded in a fragmented

landscape mosaic, mirroring recent trends elsewhere in ecology (Hagen et al.,

2012; McLaughlin et al., 2013).

Metapopulation theory (Hanski, 1999; Levins, 1969; Merriam, 1988),

which considers source–sink dynamics of multiple populations in a land-

scape context, can help us understand why the dynamics of habitat complex-

ity are important for understanding the dynamics and persistence of

non-cultivated species within agroecosystems. In a landscape of rotating

crops, with more or less predictable fallow periods, crop pests or their natural

enemies will experience the environment as a stochastically disturbed

metapopulation of shifting patches over time. These species will maintain

a stable metapopulation—at the landscape level—by re-colonising disturbed

patches from a neighbouring, undisturbed patch. In this context, the


asynchrony of perturbations across patches is an important factor in

metapopulation persistence. As demonstrated by metacommunity theory

(Calcagno et al., 2006; Hastings, 1980; Tilman, 1994), competitive ability

is also an important trait because the maintenance of species diversity in such

landscapes relies on the fact that more competitive species are less able to

re-colonise patches because of life-history trade-offs, such as seed size versus

seed quantity (Geritz et al., 1999), thus creating a time window for lesser

competitors to establish and reproduce. Dormancy, or more generally resis-

tance to perturbations (Muller-Landau, 2010), also enables species to coexist

(Lamy et al., 2012), for instance by being able to resist the perturbation

imposed by harvesting crop at a certain stem height (Calcagno et al., 2010).

The interest in agricultural landscape mosaics has generated a substantial

body of empirical and theoretical work, but the underlying questions have

been almost exclusively and narrowly focused on species diversity, coexis-

tence and persistence issues (Benton et al., 2003). From an interaction net-

work perspective (Massol et al., 2011), agricultural landscape mosaics offer

opportunities to address many more questions, such as those related to net-

work complexity, stability or functioning maintained by a given landscape

(Hagen et al, 2012). For instance, in theory mutualistic networks should

react differently to habitat loss based on their degree of nestedness

(Fortuna and Bascompte, 2006), and habitat loss should have complex effects

on the structure of mutualistic networks at the landscape level (Fortuna et al.,

2013), yet the empirical tests of these ideas are notable by their absence.

A better understanding of how species interact within a community, and

how communities function at the landscape scale, would assist in designing

management options that enable the maintenance and use of biodiversity in

agricultural mosaics.

Here, we highlight the potential benefit of placing agricultural landscape

mosaic studies into a wider framework, such as the one laid out by meta-

ecosystem theory. Metaecosystem models (Loreau et al., 2003) consist of

models that account for the spatial and internal dynamics of species (i.e.,

biotic agents) and abiotic compartments (e.g., nutrient flows and stocks).

Within this concept there is a hidden assumption of that a ‘spatial interaction

network’ exists (Massol et al., 2011). We will consider the different proper-

ties of spatially structured ecosystems of interest to agroecologists, and how

they relate to landscape complexity.Wewill then link these properties to the

more applied theme of ecosystem services and to the existing management

strategies typically discussed in the agronomical literature. Finally, we will

explore how agricultural landscape mosaics could be investigated through


the application of concepts and models from the metaecosystem framework.

Because the goal of this synthesis is not to explore in detail why and how

agricultural landscape mosaics relate to biotic interaction networks, but to

introduce the broader framework of metaecosystem theory, we refer the

reader interested in biotic interaction networks in agricultural landscapes

to other articles where network terms and concepts are explained at greater

length (Bohan et al., 2013).

2. ECOLOGICAL PATTERNS AND PROCESSES INSPATIALLY STRUCTURED ECOSYSTEMS

2.1. Ecological properties of interest: Biodiversity,network topology and functioning

Most landscape mosaic studies to date have focused on either understanding

patterns in biodiversity per se (e.g., species richness), patterns in ecosystem

services linked to biodiversity, or the productivity of agricultural systems

in variable and heterogeneous landscapes: that is, gauging the effects of hab-

itat complexity on species diversity and ecosystem functions/processes (sensu

de Groot et al., 2002; Wallace, 2007). However, these are only two aspects

of landscape mosaics, while there are at least four other properties that can be

studied in ecosystems (Massol et al., 2011; Fig. 5.1), which are discussed in

the rest of this section.

Biodiversity studies traditionally consider the degree to which species (or

genotypes within a species) can coexist at a given spatial scale over long time-

scales (typically, ecological timescales, i.e., for several hundreds or thousands

of generations; Barot and Gignoux, 2004; Chesson, 2000a, b). Transient

biodiversity (Hastings, 2004), whereby dynamical attractor shifts are evi-

dent, can also be of interest (e.g., Morton and Law, 1997) for addressing

other questions, such as those related to how biodiversity accumulates

between colonisation of a virgin island and its ‘steady-state’ diversity, or

how biodiversity oscillates when it does not converge towards an equilib-

rium. Whittaker (1972) defined local diversity as alpha diversity, while

diversity at the larger spatial scale (across multiple ecosystems in a region)

is considered gamma diversity. Beta diversity is then defined as the difference

between gamma diversity and the average of alpha diversities among con-

sidered locations. There are many indices for quantifying diversity (Jost,

2007), although some are more useful than others, mainly because of statis-

tical properties (Lande, 1996) or their sensitivity to rare/dominant species

(Jost, 2007). A key question is: does landscape complexity promote species

Landscape mosaics

Diversity

Functioning

Networktopology

Demographics

Fluxes of individualsare also fluxes ofmatter and energy

Demographic stabilityalso means persistenceand, thus, coexistence

Diversity and networkconnectance define

ecosystem complexity

Fluxes of matter andenergy flow throughinteraction networks

Figure 5.1 Ecological properties of interest in landscape mosaics (solid squares) andsome of the processes that link them (dashed ellipses).


diversity? Because many ecological processes depend on landscape complex-

ity, this question does not lend itself to a trivial answer.

Functioning patterns refer to the fluxes and stocks resulting from pro-

cesses that movematter among agents within a spatially structured ecosystem

(Massol et al., 2011). These fluxes (Fig. 5.2) can be due to (1) the transfer of

matter from the abiotic compartment to the biotic compartment through

primary production, (2) biotic interactions between individuals (e.g., trans-

fer of matter due to feeding or mutualism), (3) the death and subsequent

recycling of an organism by another organism (i.e., feeding through decom-

position of dead organic matter), or (4) the movement of living individuals,

abiotic matter or detritus between locations (Duffy et al., 2007; Loreau

et al., 2003).

Because all these fluxes are controlled by species traits, functional ecology

has historically focussed on traits as proxies of ecosystem functioning (Violle

et al., 2007). By extension, the distribution of such traits among species from

the same guild or trophic level should determine the functioning of the

system.

Although functioning describes fluxes of matter and energy, these are not

sufficient to characterise the dynamics of species’ abundances within the

Primary producers

Consumers

Detritivores

Detritus

Nutrients

Primary production

Biotic interaction(feeding)

Recycling

Movement

Figure 5.2 Schematic description of possible fluxes in spatially structured ecosystems.Double-arrow blue lines represent spatial fluxes (dispersal, foraging, diffusion, etc. whileother single-arrow lines indicate fluxes due to primary production (green), biotic inter-actions (black), and detritus recycling (red).


ecosystem. Individual growth or reproduction can theoretically represent

the same change in matter/energy stocks, but via very different demographic

processes, which also need to be considered. From an evolutionary ecology

standpoint, the rates at which species move and interact with one another

requires an understanding of demographics, which are described by birth,

death, immigration and emigration rates (Loeuille et al., 2013; Pulliam,

1988; Pulliam and Danielson, 1991). For instance, predator–prey dynamics

influenced by predator movement in response to prey density can be rat-

ionalised when considering the maximisation of individual predator’s fitness,

rather than via a particular ecosystem-level process (Oksanen et al., 1995).

For nutrient and detritus pools, demographics and fluxes cannot be distin-

guished because there is no such thing as ‘nutrient birth’—stock gain of a

certain molecule is always a loss from another molecule stock.

Assuming that individuals, genotypes, or species may be represented as a

network of interacting nodes connected by edges the study of interactions

among individuals, genotypes or species can be achieved by examining the

graph’s topology (Cohen and Newman, 1988; Jordano, 1987; Pimm, 1980).

For the study of ecosystem processes, the edges of the graph can be weighted

by the corresponding rates at which interactions take place (e.g., McCann


et al., 1998), or it can be more qualitative (i.e., based on unweighted edges).

In both cases, a natural algebraic representation for such an object is a matrix

representing the occurrence or the intensity/frequency of interactions

between two nodes. The eigenvalues and eigenvectors of this matrix have

an interpretation in terms of the nodes’ centrality and the speed at which

perturbations spread through the system (Newman, 2004), and ecological

networks often exhibit ‘small world properties’, such that there are many

pathways that allow short-circuiting of the network (Ings et al., 2009;

Memmott, 2009).

When dealing with movement interactions (type 4 under the function-

ing categorisation) with nodes being populations and edges being fluxes

between populations of the same species, the corresponding graph and

matrix represent the rate at which species stocks move from one place to

another (Massol et al., 2011). For example, for species dispersal, the adja-

cency matrix has values of ‘1’ between sites that exchange migrants and

‘0’ on the diagonal, and the total number of connections per site is called

its degree. With diffusive movement, the Laplacian matrix of the graph

(i.e., the matrix defined by the difference between the diagonal matrix of

degrees per site and the adjacency matrix, Chung, 1997) is a natural descrip-

tor of the spatial dynamics of the system (Bohan et al., 2013). Such a network

approach can be used in applied ecology, for example, to parameterise

patch occupancy models to model species dynamics across a watershed

(Berlow et al., 2013).

In the case of biotic interactions (type 2 under our functioning

categorisation), the graph and matrix represent the rates at which species

consume/facilitate/parasitise one another, depending on the type of inter-

actions involved (Ings et al., 2009). It is possible to combine graphs obtained

for different types of interaction (Altermatt and Pearse, 2011; Kefi et al.,

2012; Melian et al., 2009; Pocock et al., 2012), although the vast majority

of studies consider only one type (Hagen et al, 2012; Ings et al., 2009). In the

case of multi-type networks in agricultural landscapes, a representation of

the networks that include all types of interactions may help in understanding

its robustness and resistance to future species loss (Pocock et al., 2012).

The impacts of perturbations on the functioning of steady state ecosys-

tems may be studied mathematically by linearising the ecosystem’s dynamics

and exploring its Jacobian matrix, that is, the matrix of first-order derivatives

of species abundances with respect to other species abundances (May, 1974).

In general, the topology and the rates of interaction in networks of biotic

interactions influence the resilience of the system which, in turn, predicts


its overall stability (Allesina and Tang, 2012; May, 1972, 1974). When com-

bining all four types of interactions, the corresponding graph (with nodes

being populations of different species) represents all flows of energy and

resources between populations of the same or different species. This net-

work integrates flows due to dispersal and flows due to interaction between

different types of agents.

2.2. Landscape complexity and the spatial heterogeneityof ecological patterns

The four properties of interest considered here can all be impacted by land-

scape complexity (Levin, 1998, 2000, 2005). In particular, the effects of

landscape complexity on ecosystem functioning and species diversity have

been the subject of numerous recent studies, especially in agroecosystems

(e.g., Concepcion et al., 2008; Roschewitz et al., 2005; Winqvist

et al., 2011).

Theoretically, landscape complexity could help maintain long-term eco-

logical functioning in highly disturbed agroecosystems (Tscharntke et al.,

2008). In agricultural landscape mosaic studies, habitat complexity and suit-

ability are typically assessed through metrics of patch spatial extent and con-

figuration (Bennett et al., 2006; Fahrig, 2003). Different metrics have been

proposed (Turner et al., 2001), but so far agricultural landscape mosaic stud-

ies have mainly used measures of edge density, habitat diversity (sensu Shan-

non or Simpson) and habitat type coverage per square kilometre (Heikkinen

et al., 2004; Hoffmann and Greef, 2003; McGarigal and McComb, 1995).

Other metrics, based on the dynamics of field fragments, have been devel-

oped (Kleyer et al., 2007), but are seldom used because they require far more

data to be properly assessed. Metrics dedicated to the study one particular

ecosystem component or service (e.g., pollination) have also been developed

and used to assess the effects of landscape complexity (Kennedy et al., 2013;

Lonsdorf et al., 2009).

The crux of the effect of landscape complexity on species diversity

(Benton et al., 2003) relates to the ‘area-heterogeneity trade-off’

(Fig. 5.3) (Allouche et al. (2012). Essentially, species diversity at the land-

scape scale depends on a few processes that, in turn, depend on the relative

spatial extents of the different habitat types: (i) ecological drift (stochasticity

in mortality and recruitment) tends to decrease the number of species that

can potentially coexist when the underlying hospitable area decreases

(Etienne and Alonso, 2005; Hubbell, 2001), (ii) local adaptation/habitat fil-

tering tends to select different species in habitat patches of different habitat

Local diversity (a )Spatial diversity

turnover (b )

Local ecological drift

Competitiveexclusion

Variable competitivehierarchy and habitat filtering

Dispersal

Habitatheterogeneity

Habitat size

Trade-off

Figure 5.3 The area-heterogeneity trade-off, from processes to diversity patterns. Solidarrows represent increases (where the arrow is pointing) and decreases (the origin ofthe arrow) in diversity at both the local scale (a diversity) and as turnover in diversityamong sites (b diversity). The thickness of arrows represents the typical strength ofthe process, but this may vary depending on context (e.g., dispersal may be more orless intense depending on patch connectivity, or ecological drift may increase in smallpopulations). The two characteristics of the area-heterogeneity trade-off, that is, habitatheterogeneity and habitat size, are depicted in dashed boxes on the right. Dashed linesindicate the sign of the effect of these two characteristics on the aforementioned pro-cesses, with habitat size negatively affecting ecological drift (hence, the line ending witha disk) while habitat heterogeneity increases the effect of both variable competitivehierarchies and habitat filtering (hence, the line ending with arrow).


types, and hence increases species diversity with the number of habitat types

(i.e., species sorting), (iii) dispersal limitation (and limited dispersal scale)

constrains the possibility for regionally competitive species to dominate

the landscape (Mouquet and Loreau, 2002). Having too little habitat hetero-

geneity tends to result in dominance by species adapted to the main habitat

types; too much subjects each habitat type to ecological drift, so that each

small patch will be dominated by a single locally adapted species, and thus

the whole landscape will not support many species. Varying competitive

hierarchies and/or the strength of competitive exclusions among habitat

types also contributes to a higher diversity of persisting species (Mouquet

and Loreau, 2002; Mouquet et al., 2013).

The spatial arrangement of the different habitats also affects species diver-

sity (Ries et al., 2004), an observation which has underpinned the Single

Large or Several Small debate (Atmar and Patterson, 1993; Hanski, 1999;

Hanski and Gaggiotti, 2004; Hanski and Gilpin, 1997; Semlitsch and


Bodie, 1998). While the initial point was that ecological drift and immigra-

tion did not scale linearly with patch area, so that species diversity could not

be obtained additively from a sum of small patches, more recent studies have

also highlighted the role of ecotones in increasing species diversity. For

instance, more reticulate edges between two different habitats offer more

opportunity for habitat generalists and specialists to coexist at the regional

scale (Debarre and Lenormand, 2011). The spatial scales of habitat hetero-

geneity, competitive interactions and dispersal should not be neglected

when assessing the biodiversity supported by a given landscape because

nonlinear processes are at work (Snyder and Chesson, 2003, 2004).

The temporal turnover of landscape fragments also plays a major role in

determining biodiversity. In the same way that species coexistence rules are

governed by mechanisms linked to environmental heterogeneity (Chesson,

2000a), these rules also obey principles relating to the temporal variability of

environmental quality (Armstrong and McGehee, 1980; Chesson, 1994;

D’Odorico et al., 2008). Habitat patches with frequently changing environ-

ments tend to favour generalists on evolutionary timescales (Massol, 2013),

which also makes sense from an ecological viewpoint (Chesson, 1994).

Higher rates of catastrophic disturbance tend to favour species with higher

colonisation rates (Hastings, 1980; Nee and May, 1992) rather than those

with greater competitive abilities (Calcagno et al., 2006). To plant ecolo-

gists, disturbances are essential in allowing the persistence of the so-called

ruderal species (Grime, 1974), that is, species that are neither stress-tolerating

nor good competitors, but that can afford to move around and colonise

‘empty spots’.

The temporal turnover of landscape mosaics can be described by their

frequency, spatial extent and size (Kleyer et al., 2007), essentially

characterising the spatial and temporal Fourier spectra of disturbances

(Caswell and Cohen, 1995)—the spatial period characterising spatial extent,

the inverse of the temporal period characterising frequency and the size of

the disturbance being given by the corresponding values of the spectra. This

seemingly pragmatic way of assessing disturbance and environmental hetero-

geneity in landscape mosaics is, however, seldom used as a modelling frame-

work in theoretical studies on the coexistence and dynamics of ecological

communities (Snyder and Chesson, 2003, 2004), although such approaches

are starting to be used for single-species studies (Munoz et al., 2007) in order

to link processes to patterns (Clinchy et al., 2002). The development of

community ecology statistics based on Moran Eigenvector Maps, that is,

the partitioning of community signal according to a series of ‘pseudo-waves’


of decreasing spatial autocorrelation (Dray et al., 2006; Griffith and Peres-

Neto, 2006; Munoz, 2009), shares many attributes of Fourier analyses

(Munoz, 2009) and represents an important advance towards charactering

disturbance effects in metacommunities (Cottenie, 2005; Leibold et al., 2010).

The effect of landscape complexity—both spatially and temporally—on

population demographics can be understood through the notion of sources

and sinks (Dias, 1996; Loreau et al., 2013; Pulliam, 1988; Runge et al.,

2006). A given location is a source or sink for a given species if it is a

net exporter or importer of this species at a certain timescale. Depending

on the conditions required, and the timescale considered, this simple

definition can be extended to encompass different cases, such as conditional

or unconditional sources/sinks (Loreau et al., 2013). At demographic

equilibrium, if birth and death rates vary spatially, locations with a

higher demographic growth rate will be sources while other locations

will be sinks (Pulliam, 1988). Source–sink dynamics have impacts on

the coexistence of competing species (Amarasekare and Nisbet, 2001) and

have also been extended to encompass both biotic and abiotic ecosystem

components (Loreau and Holt, 2004; Loreau et al., 2003, 2013; Polis

et al., 1997).

Assessing the ultimate causes of landscape variability over time and space

is challenging, especially as it can be externally or internally driven (Chesson,

1994). External drivers encompass many different elements such as physical,

geological, hydrological or chemical heterogeneities, climatic and altitudinal

gradients and anthropogenic factors (Vitousek et al., 1997) such as habitat

pollution, exploitation, fragmentation and destruction (Hagen et al.,

2012). Internal drivers refer to ecological processes that affect ecosystem

dynamics (Rietkerk and van de Koppel, 2008). For instance, host–parasitoid

dynamics can create spatio-temporal variability in species abundances

(Hassell et al., 1994) which, in turn, can produce habitat heterogeneity

for other species dependent on either the host or the parasitoid. Preda-

tor–prey dynamics (Gurney and Nisbet, 1978; Taylor, 1990), ecosystem

engineering (Jones et al., 1994), nutrient recycling dynamics (DeAngelis,

1992) or a combination thereof (McKey et al., 2010) are other processes that

may lead to habitat heterogeneity and/or temporal turnover in environmen-

tal quality.

Understanding how agricultural landscape complexity affects the differ-

ent properties of an ecosystem is the key question for agroecologists, and

from here the applied goals related to management scenarios and the

expected ecosystem services that emerge can be described.


3. THE GOALS OF AGRICULTURAL LANDSCAPEMOSAICSSTUDIES: MANAGEMENT FOR CROP PRODUCTION
AND OTHER ECOSYSTEM SERVICES
Understanding how agricultural landscape mosaics work has probably

never been so high on the political and research agenda. These systems have

to meet a growing demand for food and also deliver a number of other eco-

system services (Costanza et al., 1997), as described in the Millennium Eco-

system Assessment (Reid et al., 2005). Among the scientific challenges to

enhancing food security, understanding the role of biodiversity and associ-

ated ecosystem services for food production is key. The detrimental effects of

agricultural intensification on biodiversity have been illustrated by countless

studies (Geiger et al., 2010; Kleijn and Sutherland, 2003; Robinson and

Sutherland, 2002), and it is likely to be important for the provision of

services to agriculture (Firbank et al., 2013; Moonen and Barberi, 2008).

Maintaining or enhancing these services will require an in-depth under-

standing of the functional role of species and interactions networks in

agricultural mosaics (Kremen, 2005).

Several frameworks currently classify processes (or functions) and ser-

vices in ecosystems (Chee, 2004; Costanza et al., 1997; de Groot et al.,

2002; Reid et al., 2005; Wallace, 2007), some of which separate the two,

that is, processes affecting services via provisioning/production, regulation,

support/habitat or cultural and aesthetic benefits (de Groot et al., 2002; Reid

et al., 2005). Other classifications separate the production of goods, regen-

eration processes, stabilising processes, life-fulfilling (aesthetic) functions and

preservation of options (Chee, 2004; Daily, 1999), or use a typology of ser-

vices based on the conditions they create (Raffaelli and White, 2013;

Wallace, 2007). The spatial and temporal scale at which ecosystem services

should be assessed is under debate, though it is often argued that large scales

are more relevant to economic or cultural evaluation (Cardinale et al., 2012).

In agriculture, links to services at the landscape mosaic scale can arise via

abiotic (e.g., water or nutrient flows—Levavasseur et al., 2012; Tiemeyer

et al., 2007) or biotic processes (e.g., propagule flows—Kinlan and

Gaines, 2003; Schmucki et al., 2012) or the population dynamics of natural

enemies (Blitzer et al., 2012; Rand and Louda, 2006). They can also include

potential interactions between abiotic and biotic processes, for example,

flows of nutrients through dispersal of mobile consumers (Polis et al.,

1997; Seagle, 2003) or airborne crop pathogens (Fabiszewski et al., 2010;


Skelsey et al., 2010). Large-scale issues requiring particular attention in agri-

cultural mosaics because of their impact on food security are numerous, and

include the prevention of crop diseases (Meentemeyer et al., 2012), the

durability of crop resistance to pathogens (Fabre et al., 2012), novel trait

confinement (Bagavathiannan et al., 2012), crop pollination (Biesmeijer

et al., 2006; Holzschuh et al., 2007; Kremen et al., 2004; Ricketts et al.,

2008) and the stability and resilience of functional interaction networks

to control crop pests (Tscharntke et al., 2007). Here, we illustrate the link

between biodiversity, processes and services provided to agriculture, with a

focus on the role of off-crop biodiversity in adjacent areas not dedicated to

production (Fig. 5.4). The effect of off-crop biodiversity is mediated by bio-

logically vital functions (plant reproduction and growth), interspecific inter-

actions (pollination, competition, consumption, rhizobial symbiosis),

physical interactions (e.g., trees acting as physical barrier against disease

transmission) and genetic interactions (gene flow or allopolyploidy between

domesticated and wild species). Off-crop biodiversity directly influences

services linked to crop production, including pest regulation through the

Pestregulation

Cropreproduction

Carbonsequestration

Trees andshrubs

Herbaceousplants

Fungi and soilmicrobes

Animals

Biodiversity

Crop relatives

Services

Diseaseprevention

Weedregulation

Nutrientacquisition

Agronomicaloptions

Gen

e flo

w

Dec

ompo

sitio

n +

Fix

atio

n

Com

petit

ion

Landscape fragmentation

Gene flowG

rowth

Com

petition

Herbivory

Pollination

Pre

datio

n

Processes

Figure 5.4 Off-crop biodiversity and its main ecosystem services in agricultural land-scape mosaics. Arrows represent processes (de Groot et al., 2002; Wallace, 2007) linkingecological communities (upper boxes) to ecosystem services (lower ellipses).


spillover of higher predators from semi-natural to agricultural zones (Bianchi

et al., 2006); weed regulation through competition with off-crop plant spe-

cies (Cordeau et al., 2012); herbivory by consumer species that use off-crop

zones as habitat (Menalled et al., 2000) and crop reproduction by pollinators

requiring resources in off-crop habitats (Holzschuh et al., 2011; Kennedy

et al., 2013; Lonsdorf et al., 2009). Off-crop habitats can also increase soil

biodiversity, and hence indirectly improve crop nutrient acquisition

(Hedlund et al., 2004). These elements suggest that understanding the con-

sequences of the spatio-temporal properties of agricultural mosaics (compo-

sition, spatial organisation) on processes and services is of prime importance,

and a prerequisite to design management strategies enhancing the provision

of ecosystem services in agricultural mosaics.

Governments worldwide have invested heavily since the early 1990s in

agri-environmental schemes, to provide financial incentives that compen-

sate farmers who manage their land to conserve biotic or/and abiotic

resources. The aim was to reverse biodiversity loss and mitigate other harm-

ful effect of modern agriculture (Kleijn and Sutherland, 2003;Whittingham,

2007). In Europe, for example, various schemes have been introduced under

the Common Agricultural Policy (EEC Regulation 2078/92) that have led

to national implementations (Keenleyside et al., 2011) as either land-sparing

or land-sharing strategies (Green et al., 2005). The former recognises that

not all biodiversity components can be sustained in agriculture and relies

on non-productive habitats to promote species persistence in agricultural

landscapes. Thus it focuses on the maintenance, restoration or alternative

management of existing off-crop habitats (Albrecht et al., 2007; Critchley

et al., 2004; Griffiths et al., 2008; Pywell et al., 2005a, b; Walker et al.,

2007) or removing small areas from food production and transforming them

into targeted wildlife habitats while yield is boosted elsewhere. Land retire-

ment incentives in the US (Suter et al., 2008), establishment of grassy buffer

strips sown along watercourses (Cordeau et al., 2012) and food areas within

crops for farmland birds (Scholefield et al., 2011) fall into this category.

Land-sharing strategies on the other hand aim to boost within-field biodi-

versity through management of the production area itself (Perfecto and

Vandermeer, 2010; Tscharntke et al., 2012). This strategy promotes ‘low

input’ measures and includes extensive farming in grasslands (Baldock and

Skjemstad, 2000), non-chemical crop management (Chikowo et al.,

2009) and input reduction up to the conversion to organic farming

(Bengtsson et al., 2005; Hole et al., 2005). Both strategies often coexist

within agricultural mosaics.


In the future, managing agricultural mosaics will mean meeting new

expectations, and responding to the current shift in societal focus from spe-

cies conservation to food security issues: that is, agriculture for biodiversity

versus biodiversity for agriculture (Moonen and Barberi, 2008). There are

two important considerations here. First, biodiversity for agriculture refers

to ecosystem functioning, while there are large knowledge gaps in our

understanding of the relationship between biodiversity and the services it

provides to agriculture. The link between biodiversity levels and the services

it provides is often assumed to be positive (Loreau, 2002), but some man-

agement options may promote biodiversity and modify interaction net-

works, without necessarily enhancing services. For instance, species

diversity and food web complexity are increased in organic farms relative

to conventional farms and may increase pest control without affecting the

robustness of the food web (Macfadyen et al., 2009; Winqvist et al.,

2011). Second, this new paradigm implies that management should balance

the delivery of ecosystem services, biodiversity (Macfadyen et al., 2012) and

food production level (Tscharntke et al., 2012), which will require robust

methods to evaluate the unavoidable trade-offs (Foley et al., 2011).

Benefits of agri-environment schemes have not always matched expec-

tations (Kleijn et al., 2006; Whittingham, 2007), a result often attributed to

spatial scale mismatches between managerial and ecological processes

(Cumming and Spiesman, 2006; Pelosi et al., 2010). Schemes are local man-

agement measures and insufficient uptake rate often leads to a patchy distri-

bution that is non-optimal to deliver the expected ecological processes

(Kleijn and Sutherland, 2003). Often, the outcome is the provision of many

small fragmented areas of environmental resource, which is detrimental to

many species (Whittingham, 2007). In addition, the landscape context

where management strategies are implemented modulates biodiversity

(Concepcion et al., 2008; Rundlof and Smith, 2006) via ‘the intermediate

landscape complexity hypothesis’ (Batary et al., 2011; Tscharntke et al.,

2012). This stipulates that gains from agri-environmental schemes will be

low in cleared and in very complex landscapes but maximal in landscapes

of intermediate complexity. Similarly, the landscape context of local mea-

sures can modulate services delivered by biodiversity (Egan and

Mortensen, 2012; Hodgson et al., 2009; Phalan et al., 2011), but general

rules are difficult to perceive at this stage.

The efficiency of local management options could be improved by com-

bining themwith field and landscape scale measures (Gabriel et al., 2010), by

ensuring prerequisite knowledge on regional assets and their spatial


properties is available at appropriate scales (Quijas et al., 2012) and integrated

with local management experiences (Jackson et al., 2012). In addition, dif-

ferent land managers could be encouraged to apply similar management

across multiple farms (Sutherland et al., 2012). Multiple-scale management

requires knowledge of the spatial processes at play and how service provision

is delivered in the landscape.

4. SPECIFIC PROPERTIES OF AGRICULTURALLANDSCAPE MOSAICS: TEMPORAL AND SPATIAL
HETEROGENEITY
The diversity of food and fibre produced is a result of numerous spe-

cific types of crop combined with a vast array of management practices

(Burel and Baudry, 1999), creating a dynamic mosaic of crop and off-crop

habitats that differ in nature and over time.

The heterogeneity of agricultural land mosaics is still striking, even if

field sizes and coverage by monocultures have increased over time

(Benton et al., 2003). Within arable fields, the choice of crop cultivars

(and varietal mixtures in the case of crop associations), the crop sowing strat-

egy (e.g., small vs. large inter-row, separated vs. mixed rows for associations)

and the inherent heterogeneity in nutrient availability or pesticide efficacy

combine to create a spatial complexity that can affect the outcome of eco-

logical processes, such as crop–weed competition (Dieleman et al., 2000).

Within a given field, different annual crops may be rotated to save nutrients

and prevent diseases (Colbach et al., 1997), resulting in yearly redistributions

over the landscape mosaics. At the farm level, the choice of a cropping sys-

tem within a given field based on rotational principles relates to land use in

the neighbouring field and environmental, socio-economic and agronomic

constraints (Rounsevell et al., 2003). The resulting land use allocation usu-

ally departs from randomness, with aggregations of fields for economic and

logistic reasons (Castellazzi et al., 2007; Thenail et al., 2009). Conversely,

the location of fields used for the multiplication of crop seeds will be con-

strained by minimum isolation distance needed to avoid contamination of

cultivars (Wang et al., 1997). The spatial organisation of farm territories

results from segmentation when geographical areas are differentiated by their

physical environment, climate, as well as social-economic development

(Mignolet et al., 2007). Conversely, organic farms may aggregate in areas

that are less favourable for intensive agriculture (Gabriel et al., 2006).

Figure 5.5 The two dimensions of landscape structural heterogeneity (one shade ofgrey¼one habitat type) (after Fahrig et al., 2011) and its translation into functional het-erogeneity for three organisms (A–C) differing in habitat requirements (black ¼ suitablehabitat; white¼unsuitable habitats).


This combination of processes leads to mosaics of different levels and

types of structural heterogeneity (in the diversity of habitat types and their

relative spatial arrangement) within agricultural landscapes (Fig. 5.5). The

effects of structural heterogeneity on species dynamics are relatively well

understood (Allouche et al., 2012; Amarasekare and Nisbet, 2001;

Mouquet and Loreau, 2002) and its impacts are increasingly being docu-

mented (Alignier et al., 2013; Vasseur et al., 2013). Nonetheless, how species

perceive structural heterogeneity in agricultural mosaics is still poorly under-

stood (Alignier et al., 2013; Vasseur et al., 2013): the concept of functional

heterogeneity (Fahrig et al., 2011) could be particularly useful here,

although it is also still in its infancy in agroecology.

All landscapes change but agricultural land mosaics are particularly

dynamic and changes occur within much shorter timescales compared to

semi-natural conditions. The temporal dimension of agricultural landscapes

has mostly been considered in the context of fragmentation or loss of natural

habitats over many decades (Burel and Baudry, 1990); (Lindborg, 2007).

Landscape monitoring programmes that have developed in some countries

offer finer temporal resolution and reveal important turnover even in


seemingly ‘stable’ habitats, for example, small woodlands scattered within

agricultural mosaics (Petit, 2009). These changes can be difficult to predict

although environmental or topographic factors appear to play a role (Petit

and Firbank, 2006; Verburg et al., 2010). The dynamics of agricultural

mosaics at shorter timescales (within and between seasons) is less studied,

although there is an inherent organisation of recurrent farming practices,

such as the crop management or rotation sequence. There has also been a

loss of temporal heterogeneity during the process of agricultural intensifica-

tion with a simplification of crop rotations, less spread in the timing of man-

agement and fields remaining under similar and agriculturally productive

management for longer (Benton et al., 2003).

The highly dynamic character of agricultural mosaics at different tem-

poral scales has implications for food webs or other interaction networks

(realisations sensu Poisot et al., 2012). Historical contingency can be impor-

tant at large timescales, where organisms may have delayed responses to

habitat fragmentation, or extinction debts (McCarthy et al., 1997; Tilman

et al., 1994). At finer timescales, the snapshot distribution of organisms

reflects species filtering and spatial interactions between neighbouring

habitats and historical local conditions. Moreover, the spatial turnover of

habitats alters species occurrence through time and can obscure the drivers

behind species distributions (Hodgson et al., 2010). This is documented

for standing arable weed communities, which composition reflects past

rotations (Bohan et al., 2011) and historical management (Alignier et al.,

2012). The recurrent changes in the status of habitats within the mosaics

result in frequent intra- and interannual shifts in local community compo-

sition (Holland et al., 2005). This should result in a high temporal variability

in interaction networks in agricultural mosaics, although this aspect is

seldom documented (but see Memmott et al., 2007; Rooney et al., 2008)

and its impact on the provision of ecosystem services such as pest control

remains unclear (Gagic et al., 2011).

Spatial and temporal heterogeneity are interconnected and they can

cause transient changes in population densities, with functional conse-

quences (Tscharntke et al., 2012; Valladares et al., 2012). Theoretically,

the predominance of generalists may reduce the effects of spatial and tem-

poral mismatches, although the consequences of overlaying spatial structure

on temporal mismatches on interaction networks are largely unknown

(Hagen et al., 2012).

Worldwide fertiliser consumption in arable land (NþP2O5þK2O) was

estimated at 170.7 million tons in 2010 (FAO, 2011) and humans annually


release more N and P to terrestrial ecosystems than is released by all natural

sources (Tilman, 2001), yet surprisingly little is known about the extent to

which these subsidies alter species interactions in agricultural landscapes.

Studies conducted in other systems indicate that productivity, disturbance

and ecosystem size affect food chain length, for instance (Takimoto et al.,

2012). External energy inputs in the form of resource subsidies usually lead

to decreased plant species richness, shifts in community composition and

decreased predictability (Murphy and Romanuk, 2012) and alter food

web structure (Haddad et al., 2000; Ostfeld and Keesing, 2000; Polis

et al., 1997). Recent studies also suggest that resource subsidies can enable

some species to become outliers to the general size spectrum relationship

(Hocking et al., 2013), whereas predator–prey dynamics can be decoupled

when predators are heavily subsidised by anthropogenic resources

(Rodewald et al., 2011). Subsidy duration is an important factor and

long-term, repeated introductions of nutrients can alter food web structure

profoundly, although these effects may take years to fully manifest them-

selves (Murphy et al., 2012). Spatial variation in the intensity and frequency

of nutrient pulses within agricultural mosaics may have differential effects on

their interaction networks, and comparison of low-input systems with more

intensive agriculture indeed suggest differences in food web structure

(Macfadyen et al., 2009); (Winqvist et al., 2011) although a full analysis

of the relative role of nutrient inputs, pesticide use and landscape context

on these changes has not yet been attempted.

Agriculture has increased the length of edges and/or boundaries in most

landscapes (Forman, 1995). The latter separates ecosystems or land uses and

represents a strip of activity (high species richness and biomass) where inter-

actions and flows are often concentrated. Boundaries have varying degrees of

softness depending on the level of contrast between the habitats they delin-

eate (Stamps et al., 1987). In agricultural mosaics, edges and/or boundaries

can be prominent features, depending on landscape grain and complexity

and the presence of established linear features in the field margin (ditches,

hedgerows, grass or flower strips etc.). Production areas thus generally have

substantial amounts of interface with off-crop habitats, be it a proper field

margin or adjacent semi-natural habitats such as grassland woodland, or

heathland (Collinge and Palmer, 2002; Duelli et al., 1990; Macfadyen

and Muller, 2013).

Species produced in one habitat frequently end up as food elsewhere and

such spillovers of organisms result in cross-boundary subsidies between hab-

itats (Polis et al., 1997). Movements between habitats may be passive (e.g.,


by winds), or a product of life history (e.g., migration, ontogenetic habitat

switches) or interactions (e.g., interference, phoresy, i.e., the transport of

one species by another) (Polis et al., 1997). Theory predicts that the high

productivity and temporally variable resource abundance of agricultural

mosaics should amplify spillover effects (Ries et al., 2004), at least for habitat

generalists that exploit prey resources within both habitat types. Empirical

studies support this view and suggest that across-habitat spillover often affects

trophic interactions in complex landscape mosaics. Spillover effects often

include semi-natural habitats acting as sources for pest predators that enter

adjacent crops (Landis et al., 2000; Thies and Tscharntke, 1999) and,

although much less well-documented, from managed land to semi-natural

habitats (Blitzer et al., 2012; Rand et al., 2006). Spillover can result from

passive diffusion, or active movements in response to shifts in resource avail-

ability (e.g., after crop harvest) or because organisms use supplementary or

complementary resources in both habitats (Dunning et al., 1992; Rand et al.,

2006). Habitat supplementation has been described for wild bees foraging in

grassland but utilising oilseed rape flowers in adjacent arable crops

(Holzschuh et al., 2011), and complementation is widespread in agricultural

mosaics, as seen in the cyclic recolonisation of crop fields by organisms over-

wintering in field boundaries (Wissinger, 1997; Fig. 5.6). These spillover

effects can enhance species maintenance but can also disrupt existing inter-

action networks, for instance by reducing pollination of grassland plants

when grasslands are located nearby mass-flowering arable crops

(Holzschuh et al., 2011). Further studies are needed to clarify how interac-

tion networks differ in their edge responses and which ratio and types of hab-

itat boundaries within landscape mosaics may enhance service provision.

While the description of patterns in agricultural landscape mosaics can be

resolved using empirical studies, a conceptual framework applicable to the

modelling and understanding of agricultural landscapes and their ecological

properties is also needed, and here we suggest how the metaecosystem

concept may serve this purpose.

5. METAECOSYSTEMS AND AGRICULTURAL LANDSCAPEMOSAICS

Agricultural mosaics can be thought of as metaecosystems, and studied

as such (Fig. 5.7) and this concept (Gravel et al., 2010b; Loreau et al., 2003;

Massol et al., 2011) provides a framework for accounting for the spatial and

internal dynamics of species (i.e., biotic agents) and abiotic compartments

Figure 5.6 Cyclic recolonisation among habitats within the agricultural landscapemosaics. The persistence of the organismwill rely on the quantity of winter refugia (fieldmargins, set aside fields) and will need to account for crop rotation such that emigratesfrom fields in the early autumn can provide the immigrants for adjacent fields in thespring. One shade of grey represents one habitat type (after Wissinger, 1997).

Primary producers

Primary consumers

Other consumers

Detritivores

Detritus

Patch 1 Patch 2

Nutrients

Figure 5.7 Conceptual representation of a two-patch metaecosystem (adapted fromGravel et al., 2010a). Solid arrows represent trophic fluxes; dashed arrows represent dis-persal of organisms sensu lato, that is, dispersal or foraging (Massol et al., 2011).



(e.g., nutrient flows and stocks). It is derived from metacommunity theory

(Holyoak et al., 2005; Leibold et al., 2004), which only focuses on compet-

itive interactions.

Identifying metaecosystems as the relevant ‘model’ is useful because it

allows for cross-system comparisons between agricultural and non-

agricultural contexts. Specific properties of agricultural landscapes may have

effects on species diversity, interaction network complexity or ecosystem

functioning that are not seen as often in other landscapes, for example,

due to nutrient subsidies (Hocking et al., 2013). Exploring these interaction

network properties will allow for the identification of patterns that are shared

with, or unique to, agricultural systems, and it opens up new research ques-

tions not yet considered in an agricultural context.

While early metacommunity theory focused on the question of species

coexistence and biodiversity (Leibold et al., 2004), other questions related

to productivity and ecosystem functioning (Loreau et al., 2003, 2004;

Venail et al., 2010), food web complexity and food chain length

(Calcagno et al., 2011; Gravel et al., 2011b; Pillai et al., 2011), or spatial

interaction dynamics and stability have been considered more recently

(Gravel et al., 2011a; Massol et al., 2011). Finally, it provides agricultural

landscape mosaic studies with the possibility of relying on general laws,

principles and constraints, before dealing with the specifics of the system.

Metaecosystem theory is not aimed at understanding specifics of a given

landscape, but rather at understanding generalities that apply to broad

classes of systems that share some similarities. For example, the universality

of mass balance constraints in metaecosystems necessarily implies the

universality of complementary sources and sinks across trophic levels

or functional groups (Loreau et al., 2003, 2004). Mass action in simple

ecosystems implies that spatial fluxes of organisms and nutrients are

necessarily less important, relative to biotic interaction fluxes (trophic,

mutualistic etc.), in high-biomass ecosystems compared with low-biomass

ones (Massol et al., 2011).

Metaecosystems are multiple, spatially distinct ecosystems that interact

with one another through exchanges of organisms, detritus and nutrients

(Loreau et al., 2003). They possess the following properties:

1. A hierarchical network of ecosystems, especially relative to organism dis-

persal and nutrient/detritus diffusion.

2. Intrinsic habitat heterogeneity in process rates among different ecosys-

tems. For instance, some habitats might be more or less prone to nutrient

leaching or detritus recycling through abiotic processes (erosion, etc.).


3. A ‘template ecosystem’ of all potential interactions between all potential

partner pairs. Each local ecosystem is then a subset of this template, given

the local conditions in the habitat patch. In the case of a food web, such

template equals the knowledge of the whole food web at the regional

scale (e.g., Gravel et al., 2011b).

Because the definition of metaecosystems is very general, it encompasses

other more specific models, such as spatial interaction networks and

metacommunities. Spatially structured food web modules (Amarasekare,

2008), for instance, can be understood as a form of simplified meta-

ecosystems. The driving pressure behind developing metaecosystem

models was (i) a need to account for the nutrient balance at the regional

scale (Loreau et al., 2003, 2004) and (ii) a way to investigate the effect

of different movement rates between plants, detritus and nutrients

(Gravel et al., 2010a). There is also a need to place ecosystems in spatially

structured models because previous landscape ecosystemmodels (Running

et al., 1989; Turner and Romme, 1994) quite too coarse in their treatment

of the different abiotic compartments and did not account for the dispersal

of plants and consumers.

Most metaecosystem models are based on a discrete perception of space

where each ecosystem is equated with a single patch (Loreau et al., 2003;

Massol et al., 2011). Some of these are extensions of earlier models (Holt,

1997a) of spatial food webs (Calcagno et al., 2011; Pillai et al., 2011). Other

approaches have focused on the importance of detritus compartments and

their spatial diffusion to understand how detritus sources and sinks feedback

on abiotic nutrient sources and sinks (Gravel et al., 2010a, b). In all these

models, the notion of habitat patch is central because it determines the scale

of all interactions (feeding, recycling etc.) in the model, in contrast with

models incorporating both dispersal and foraging (McCann et al., 2005).

While this can be a drawback (Massol et al., 2011) when studying other less

explicitly structured landscapes (e.g., marine areas), patch-based models are

well suited to agricultural landscapes. As a theory to explain general features

of ecological functioning, metaecosystem theory relies on a few principles

(Loreau et al., 2003; Massol et al., 2011), some of which are just translations

of more general principles, whereas others are guidelines for building eco-

system models (Levins, 1966).

One key principle that affects the functioning of metaecosystems is that

of mass balance (Loreau et al., 2003), whereby inputs must balance outputs

in a sufficiently large system. One important consequence of this principle is

that sources and sinks rarely coincide for plants and their resources (Loreau


et al., 2003, 2013). In agricultural systems where inputs (nutrients, pesticides

etc.) and outputs (harvested crops, forage etc.) are often locally ‘out of

balance’, this principle entails that spatial imports/exports of nutrients or

organisms must somehow balance what human activity does not, for exam-

ple, excess N-fertilising in agricultural fields must leak out to nearby

locations, be stocked in non-harvested plants and/or flow to underground

water systems.

Stoichiometric theories extent the mass balance principle (Massol et al.,

2011), to the elemental level: for example, for C, N, P (Miller et al., 2004;

Sterner and Elser, 2002). Because the building blocks of life (i.e., mitochon-

dria, DNA, RNA, ribosomes etc.) have tightly constrained chemical com-

positions, the fluxes of elements within a metaecosystem will ultimately be

constrained by mass balance as well as by the inability of life to be sustained

beyond certain stoichiometric bounds. The fact that heterotrophic organ-

ism’s stoichiometry is less plastic than those of primary producers (Van de

Waal et al., 2009) and that certain components of life require a given ratio

of P:N elements (Loladze and Elser, 2011) represent two other constraints

that will affect the sources and sinks for a particular element.

The movement of biota within a metaecosystem is governed by how

organisms perceive their environment as well as their fitness, dispersal ability

and the ecosystem processes that emerge from their activity. Biotic flows are

thus more likely from low to high fitness regions: from evolutionary sinks to

sources (Abrams, 1997, 2000; Holt, 1997b; Kawecki, 2004). Such ‘habitat

assessment’ or foraging behaviour can lead to the ‘ideal free distribution’

(Krivan, 2003), or at least to a non-random distribution of individual organ-

isms across the landscape. Such a distribution is bound to alter ecosystem

processes through either trait- or material/energy-based effects (Abrams,

2000; Massol et al., 2011).

5.1. Metaecosystem modelsHere, we present two modelling approaches under the metaecosystem

framework to illustrate how such models might be used to assess the impacts

of landscape mosaic complexity on functioning, species diversity, demo-

graphics or interaction topology. The first is given by Morton and Law

(1997) conceptual model, adapted to spatially structured food chains

(Calcagno et al., 2011), food webs (Gravel et al., 2011b; Pillai et al.,

2011) or metaecosystems (Gravel et al., 2010b). Under this model, each spe-

cies i is modelled through its occupancy pi at the landscape scale (Calcagno


et al., 2006; Hastings, 1980; Holt, 1997a; Levins, 1969; Slatkin, 1974;

Tilman, 1994), following a simple differential equation:

dpi

dt¼ cipi hi� pið Þ� eiþmð Þpi ð5:1Þ

where ci is the average colonisation rate of species i (averaged over all the

patches inhabited by species i), hi is the proportion of patches that is inhab-

itable by species i, ei is the average specific extinction rate of species i (also

averaged over occupied patches) and m is the catastrophe/perturbation rate

(such perturbations being able to wipe a patch clean of all its inhabiting spe-

cies). From a community perspective (Fig. 5.8), the proportion of patches

harbouring species i and j only will decrease as a result of catastrophes wiping

out both species at once, specific extinctions of species i or species j, and col-

onisation by species k into the patch, and will increase via colonisation by

PP

D

PC

SC

D

PP

D

PP PP

PC

SC

PP

D

PC

PP

PC

+ d

etrit

ivor

es (

D)

+ primary producers (PP) + primary consumers (PC) + secondary consumers (SC)

Figure 5.8 Colonisation/extinction dynamics in a metaecosystem (inspired fromCalcagno et al., 2011; Gravel et al., 2010b; Morton and Law, 1997; Pillai et al., 2011).The complete local ecosystem (lower extreme right-hand side rectangle, similar tothe one in Fig. 5.7) and the ‘void’ ecosystem (only comprising nutrients and detritus[in grey boxes], upper extreme left-hand side rectangle) are linked to other states ofthe local ecosystem through colonisations (large solid black arrows), specific extinctions(large dotted black arrows) and whole ecosystem perturbations (solid grey arrows).


species j of patches with only species i (or vice versa) and specific extinction

of species k in patches harbouring species i, j and k.

Equation (1) represents a closed system where colonisation events can

only occur because the focal species occurs in other patches. An alternative

is to assume a mainland–island model (Gravel et al., 2011b; Hanski and

Gyllenberg, 1993), where in an agricultural context the mainland may rep-

resent a semi-natural matrix within which island of fields are embedded—or

vice versa:

dpi

dt¼ ci hi� pið Þ� eiþmð Þpi ð5:2Þ

The only difference between Eqs. (1) and (2) lies in the dependence of

colonisation on the occupancy of the species. In Eq. (2), this means that ci is

not an average of colonisation rates out of patches occupied by species i, but

rather a constant immigration rate from the mainland into the island.

As a model for spatially structured food chains, Eq. (1) has yielded a num-

ber of interesting insights (Calcagno et al., 2011; Holt, 1997a, 2002). The

most straightforward effect of spatial structure is that it limits the maximal

and expected food chain lengths at the landscape level (Calcagno et al.,

2011), dependent on the ratio of specific and perturbation-related extinction

rates to colonisation rates. Another, less intuitive, result is that the distribu-

tion of food chain lengths across the landscape may display very different

shapes depending onwhether predators track their prey amongmultiple pat-

ches and on the dominant type of extinctions (specific vs. catastrophic). For

instance, when predators do not track their prey, the proportion of patches

with at least i trophic levels should decrease linearly with i under catastrophic

extinctions, or quadratically with i under specific extinctions (Calcagno

et al., 2011). Spatially structured food webs have also been studied using sim-

ilar models (Pillai et al., 2011) which have revealed that the complexity and

species diversity of such interaction networks depends on the colonisation to

extinction ratio, with a peak at intermediate ratio values.

Using a mainland–island food web model, Gravel et al. (2011b) have

used the formalism of Eq. (2) to compare its goodness-of-fit with that of

a simpler approach, such as MacArthur and Wilson’s theory of island bioge-

ography (MacArthur and Wilson, 1967). These two models share a main-

land–island formalism, but Gravel et al. (2011b) make use of the available

information on the topology of food web interaction to constrain species

occupancy by the occupancy of their prey species—thereby obtaining a bet-

ter fit. Introducing food web topology changes the shape of the curve


linking occupancy to the colonisation to extinction ratio: introducing tro-

phic dependencies means that the curve shape changes from concave to sig-

moid.When colonisation to extinction ratio is too low, it therefore becomes

even more difficult for species to enter because their prey species cannot

arrive first. The intensity of this effect depends on the diet breadth of the

species considered (Gravel et al., 2011b), and is also likely to depend on

its average ‘trophic level’ (based on metapopulation model results given

in Calcagno et al., 2011).

Another modelling approach based on the metaecosystem framework is

given by the use of generalised Lotka–Volterra equations in a spatially struc-

tured context (Massol et al., 2011). More precisely, the idea in suchmodels is

to keep track of the abundance of all populations from all species at a given

spatial scale. In a food web, interactions between two populations may be

reduced to two categories (Fig. 5.9): feeding links (among populations from

different species) and dispersal links (among populations from the same spe-

cies). If ni denotes the biomass of population i (all populations from all species

are indexed from a unique pool of indices with i�S), such a system can be

written as (modified from Massol et al., 2011):

Primary producers

Detritivores + detritus

Primary consumers

Secondary consumers

Nutrients

Feeding link

Dispersal link

Figure 5.9 Schematic representation of the generalised Lotka–Volterra model (Massolet al., 2011). The complete ecosystem is as simple as in Fig. 5.7 (see legend for graphical-wise interaction model details). Directed solid arrows represent feeding links betweenpopulations of different species while reciprocal dashed arrows stand for dispersal linksbetween populations of the same species.


dni

dt¼ ainiþ

Xj2Ai

bijninjþXj2Mi

dijnj�djini� � ð5:3Þ

where ai are intrinsic growth/birth/mortality terms, bij are interaction terms

representing the effect of population j on population i, and dij quantify dis-persal from population j to population i. Sets of indices Ai and Mi represent

the sets of patches that interact and exchange migrants (respectively) with

population i. By rescaling time and abundance using total system biomass

n (and assuming that n stays constant), the dynamics of pi¼ni/n at the time-

scale of T¼nt are given by (Massol et al., 2011):

dpi

dT¼ ai

npiþ

Xj2Ai

bijpipjþXj2Mi

dijnpj�dji

npi

� �ð5:4Þ

Thus, with increasing ecosystem size, interactions become more impor-

tant than intrinsic rates and migration. By contrast, in small, nutrient- and

energy-poor systems, dispersal and intrinsic growth should be much more

important than interspecific interactions in determining the dynamics of

the system.

The system described by Eqs. (3) or (4) can be simplified using vector

notations and matrix diagonalisation. Let P be the vector of pi, A the diag-

onal matrix containing the ai, B the matrix of the bij and D the matrix of the

dij. LetDD be the diagonal matrix withXj2Mi

dji

!i

on the diagonal (i.e.,DD

is the ‘out-degree’ matrix corresponding to matrix D) and let LD¼DD�Dbe the ‘Laplacian’ of the dispersal matrixD (quotes are needed becauseD, notbeing Hermitian or symmetrical, does not admit a Laplacian matrix stricto

sensu). With ∘ denoting the element-wise (Hadamard) product, Eq. (4) is

equivalent to:

dP

dT¼ 1

nA�LDð ÞPþ BPð Þ∘P ð5:5Þ

Noting (l1, . . .,lS) the eigenvalues of 1nA�LDð Þ, L the corresponding

diagonal matrix, and U the base change matrix such that

1

nA�LDð Þ¼U�1LU ð5:6Þ


Then, noting Q¼UP Eq. (5) can be rewritten as:

dQ

dT¼LQþ BU�1Q

� �∘Q ð5:7Þ

BecauseL is diagonal, Eq. (7) is amenable to the same kind of results that

Bastolla et al. (2005) achieved for spatially unstructured systems. Thus, it is

possible to obtain conditions for the existence of an equilibrium point based

on L and BU�1. Bastolla et al. (2005) have proved that, in the case of com-

petitive systems (not food webs) the vector representing the diagonal of Lhas to be close to the direction given by the first eigenvector of matrix

BU�1. Given that L is of order 1/n while BU�1 is of order 1, equilibrium

feasibility is bound to fail for very large ecosystem size, in a way reminiscent

to the enrichment paradox (Rosenzweig, 1971).

Metaecosystem theory is relatively young, and input from other parts of

ecology still need to be merged more fully with it, and its models are not

rooted specifically in agriculture. However, agricultural landscapes, by their

patchy nature in time and space, seem certain to share many common fea-

tures that make them especially well suited to this particular approach.

5.2. Agricultural mosaics as evolving metaecosystemsand prospects for future advances

The inherent issue with metaecosystem models is that they have hidden

assumptions on the scales of processes acting within and among patches.

A first instance of this issue is that all organisms and nutrients perceive space

with the same grain—movement rates parameterise moment on a trans-

specific scale, which is clearly not the case in nature (e.g., where a predator

population shares several patches as its hunting ground (Massol et al., 2011;

McCann et al., 2005)). When patches are geographically and physically lim-

ited (e.g., fields), some organisms perceive the patch as a constraining unit

for population growth (e.g., harvest mice), while others can live over and

among different ponds (e.g., avian predators). When patches are not phys-

ically explicit, the existence of habitat patches is a model of reality, and the

spatial extent of a patch needs to be linked to the spatial extent of an asso-

ciated metaecosystem process. In metapopulation models, this is usually the

stochastic extinction process and perturbations are assumed to happen inde-

pendently in different patches (Calcagno et al., 2006; Hastings, 1980; Levins,

1969; Tilman, 1994). In metacommunities with local species sorting

(Leibold et al., 2004), the spatial extent of patches must be the spatial scale

at which competition for limiting factors comes into play. Because


metacommunities only consider competitive interactions, there is no intrin-

sic reason why this spatial scale should vary from one species to another.

However, in metaecosystems, many interaction types are considered, so that

(i) the choice of interaction determining the spatial extent of patches is not

trivial and (ii) the spatial scale of important interactions will vary from species

to species. In an agricultural landscape mosaic, which naturally occurs on a

continuous area (i.e., not in discrete habitats like ponds or mountaintops),

the question of properly defining patches is a crucial one. Conceptual

advances will likely feed directly intomodels applied to agricultural contexts.

In classical community ecology, it is axiomatic that species coexistence

depends on the number of limiting factors (Meszena et al., 2006). Essentially,

each species must have its own ‘niche’ in the space of limiting factors

(Leibold, 1996; Tilman, 1982). However, from a metacommunity perspec-

tive, the addition of species dispersal makes the picture more complex

because locally outcompeted species can persist thanks to high immigration

from other patches (Mouquet and Loreau, 2002). This so-called mass effect

(Leibold et al., 2004) translates to a source–sink phenomenon already known

for single species metapopulations (Amarasekare andNisbet, 2001; Kawecki,

2004). In ‘limiting factor’ parlance, organism dispersal among patches results

in a shift of the supply point (sensu Tilman, 1982), that is, the concentration

of nutrients in the absence of consumers, along a direction parallel to the

stoichiometry of moving organisms (Gravel et al., in prep.). This can either

favour or disfavour the coexistence of species, depending on dispersal rates.

If we were to extend such a theory to metaecosystems with more than one

biotic trophic level, the various movement rates of inorganic nutrients,

detritus, primary producers, etc. would all influence the coexistence of all

species. In other words, the persistence of a given species in a given location

depends on its ‘local niche’—which is a product of its local limiting factors—

and on its ‘regional niche’—which integrates the ‘fitness’ subsidies obtained

from the movement of all species and abiotic agents from other patches to

the focal patch. For studies focused on agricultural landscapes, widening the

classical scope of limiting factor theory will help understand the coexistence

of crops and weeds in a variety of situations, and might also be of help to

assess the viability of pests’ natural enemies given a certain arrangement of

crop and off-crop habitat patches.

In metapopulation studies, models were initially spatially implicit

(Hanski, 1983, 1992; Levins, 1969), and subsequently explicit (Gilarranz

and Bascompte, 2012; Hanski and Ovaskainen, 2000). Spatially explicit

models for metacommunities (Maser et al., 2007) and metaecosystems are


the next logical steps, once spatially implicit models have been thoroughly

studied and understood (Beeravolu et al., 2009). The transition from spa-

tially implicit to explicit models is needed to infer metaecosystem processes

from empirical data. In the context of agricultural landscape mosaics, spa-

tially explicit metaecosystemmodels will help in predicting beneficial effects

of non-cultivated patches on crops through, for example, pest biocontrol or

nutrient diffusion through soil. The final step in the development of this

modelling framework is to make metaecosystem predictions testable in an

agricultural context, and to translate the findings into management practices.

As metaecosystem theory continues to advance apace, so ecological stoi-

chiometry is being extended to spatially structured contexts. We now need

to understand how it is influenced by spatial structure, for example, through

the existence of biogeochemical hotspots (McIntyre et al., 2008) or by stoi-

chiometric imprinting of dominant organisms (Van de Waal et al., 2009).

Resource ratio theory (Tilman, 1982) has been one of the dominant recur-

ring models to explain the coexistence of species with different ecological

niches, especially among primary producers. In the context of agricultural

landscapes, developing models incorporating ecological stoichiometry will

help understand how limiting nutrients circulate between crop and off-crop

habitat patches, and thus help design landscape mosaic management strate-

gies that ensure adequate availability of limiting nutrients for crops, without

resorting to large amounts of fertilisers.

As highlighted in the early papers on metaecosystems (Loreau et al.,

2003, 2004), the principle of mass balance constrains the location of sources

and sinks across trophic levels. Some unification of the source/sink concepts

has been recently achieved (Loreau et al., 2013), and yet understanding how

certain patches become sources or sinks for certain nutrients or species seems

to be founded on relatively idiosyncratic rules, for example, depending on

the ratios of detritus to nutrient diffusion in the case of short trophic chains

(Gravel et al., 2010a). A general assessment of rules determining source/sink

status of patches for the different trophic levels/functional groups occurring

at the landscape level would be key for future applied sciences programmes

aimed at improving the ecosystem functioning of landscape mosaics. For

example, assessing the source/sink status of non-crop habitat patches with

respect to pest predators or parasitoids would pave the way towards more

sophisticated biocontrol management strategies.

In the vein of source/sink studies, metaecosystem theory offers the

opportunity to study the importance of different ecosystem patches in rela-

tion to a particular property or process. For instance, certain habitat patches


couldhave a largebeneficial anddisproportionateeffectonmetaecosystempro-

ductivity, that is, qualify as keystone ecosystems (Mouquet et al., 2013) or key

habitats (Davidar et al., 2001). Identifying productivity-keystones, stability-

keystones, complexity-keystones, etc.will improveourknowledgeonthe rela-

tionshipbetweendeterminants of ecosystemproperties andalsohelpdetermine

best reserve arrangements for ecosystem functioning as well as species conser-

vation (Forest et al., 2007). In agricultural landscapes, this may be construed as

identifying off-crop patches that contribute exceptionally to ecosystem stabil-

ity, complexity, productivity, and so on, in order to shape management strat-

egies accordingly (e.g., to conserve productivity-keystone habitats).

Whether and how species diversity or ecosystem complexity promotes

stability is one of the oldest debates in ecology (Ives and Carpenter, 2007;

Loreau and de Mazancourt, 2013; May, 1972, 1974; McCann, 2000). Dif-

ferent arguments have been put forth (Kokkoris et al., 2002; Kondoh, 2003),

with some alluding to the links between ecosystem productivity and both

diversity and stability (Moore et al., 1993), while more recent arguments

have focused on the types and configuration of interactions between species

(Allesina et al., 2012; Hughes and Roughgarden, 2000). The role of spatial

structure in mediating stability, while studied at length in simple food webs

(Briggs andHoopes, 2004; Freedman andWaltman, 1977; Jansen and Lloyd,

2000), is still poorly understood in complex ecosystems (Levin, 2000;

Wilson et al., 2003). Metaecosystems offer a comprehensive framework

to explore stability–diversity relationships with multi-type interactions

and nutrient recycling (Gravel et al., 2011a). In particular, the meta-

ecosystem framework makes it possible to reconcile this debate with the

longstanding paradox of enrichment (Rosenzweig, 1971), through consid-

ering both the dynamics of nutrients and energy within ecosystems and their

movement across the landscape (Gravel et al., 2011a; Massol et al., 2011).

Solving such problems will affect the way agricultural landscapes are man-

aged, especially if, as suspected, spatial structure and patch arrangement

prove to be some of the key factors determining ecosystem stability.

Whittaker (1972) proposed a multiplicative (or additive on log-scale)

partitioning of species diversity between alpha (within a given site), beta

(among sites) and gamma (across all sites) diversities. Such a partitioning

has been implemented in various ways based on available information on

species abundances or occupancies ( Jost, 2007; Lande, 1996; Pelissier and

Couteron, 2007; Wagner et al., 2000). A similar partitioning, from both

local and regional vantage points, might lead to efficient measures of ecosys-

tem property dissimilarity, for example, in realised food webs from the


additive partitioning of feeding link occurrences (Poisot et al., 2012).

Partitioning the average within-patch (alpha component) and the

between-patch (beta component) values of ecosystem properties will help

understand the role of connections among ecosystems in mediating certain

metaecosystem properties. Of particular relevance to agricultural landscapes

is the possibility to apply such partitioning to ecosystem productivity and,

more generally, energy and nutrient flows, to understand how functioning

might vary from one patch to another, and to assess whether the mainte-

nance of fast vs. slow functioning habitat patches might help in maintaining

ecosystem stability at large spatial scales (Rooney et al., 2006, 2008).

6. CONCLUSION

Agricultural landscape mosaics are complex ecological objects where

human socioeconomic interests are intimately intertwined with networks of

interacting species, all constrained within a general circulation of energy and

nutrients through trophic links and movements of organisms. Understand-

ing such complex systems is a huge challenge for ecologists (Chave, 2013),

and spatially structured interaction networks are best viewed through a clear

conceptual framework such as metaecosystem theory. As long as its

corresponding models are tuned to the specifics of agricultural landscapes,

be they perturbations, spatial heterogeneity, habitat turnover or the move-

ment of nutrient subsidies across habitats, this framework holds great prom-

ise although it still has some fundamental challenges of its own that it needs

to overcome. Nevertheless, the issues posed by agricultural landscapes are

very close to metaecosystem theory’s core, such as the need to define habitat

patches when species from different functional groups or trophic guilds do

not share the same movement scales (Massol et al., 2011). As met-

acommunity theory has still a long way to go towards achieving an equality

of treatment between theoretical and empirical approaches (Logue et al.,

2011), so too does metaecosystem theory. Metacommunity theory is prov-

ing itself useful for building conceptually orientedmanagement rules, such as

those for the design of nature reserves or the conservation of endangered

species or communities (Holyoak et al., 2005; Loreau, 2010; Loreau

et al., 2004, 2013); in the same way, metaecosystem theory will likely be

pivotal in designing future management strategies that will outperform

and eventually supersede current agri-environmental schemes with respect

to biodiversity conservation, the maintenance of ecosystem functioning and

services, and the need for crop productivity and stability.


ACKNOWLEDGEMENTSWe would like to thank D. A. Bohan and G. Woodward for the invitation to participate to

this special issue. We thank S. Macfadyen and one anonymous reviewer for insightful

comments that helped improve the content of this manuscript. F. M. was funded by the

CNRS; S. P. was funded by the INRA.

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[Advances in Ecological Research] Ecological Networks in an Agricultural World Volume 49 || Interaction Networks in Agricultural Landscape Mosaics