Historical dynamics in ecosystem service bundlesDelphine Renarda,1, Jeanine M. Rhemtullab, and Elena M. Bennettc

aDepartment of Geography and Natural Resource Sciences, McGill University, Montreal, QC, Canada H3A 2T5; bDepartment of Forest and ConservationSciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4; and cDepartment of Natural Resource Sciences and McGill School of Environment,McGill University, Ste. Anne de Bellevue, QC, Canada H9X 3V9

Edited by Monica G. Turner, University of Wisconsin-Madison, Madison, WI, and approved September 16, 2015 (received for review February 6, 2015)

Managing multiple ecosystem services (ES), including addressingtrade-offs between services and preventing ecological surprises, isamong the most pressing areas for sustainability research. Thesechallenges require ES research to go beyond the currently commonapproach of snapshot studies limited to one or two services at asingle point in time. We used a spatiotemporal approach to examinechanges in nine ES and their relationships from 1971 to 2006 across131 municipalities in a mixed-use landscape in Quebec, Canada. Weshow how an approach that incorporates time and space canimprove our understanding of ES dynamics. We found an increasein the provision of most services through time; however, provisionof ES was not uniformly enhanced at all locations. Instead, eachmunicipality specialized in providing a bundle (set of positivelycorrelated ES) dominated by just a few services. The trajectory ofbundle formation was related to changes in agricultural policy andglobal trends; local biophysical and socioeconomic characteristicsexplained the bundles’ increasing spatial clustering. Relationshipsbetween services varied through time, with some provisioningand cultural services shifting from a trade-off or no relationshipin 1971 to an apparent synergistic relationship by 2006. By imple-menting a spatiotemporal perspective on multiple services, weprovide clear evidence of the dynamic nature of ES interactionsand contribute to identifying processes and drivers behind thesechanging relationships. Our study raises questions about usingsnapshots of ES provision at a single point in time to build ourunderstanding of ES relationships in complex and dynamic social-ecological systems.

ecosystem services | historical ecology | bundles |ecosystem service interactions | spatiotemporal analysis

Managing multiple ecosystem services (ES) simultaneously,including addressing trade-offs between services and pre-

venting ecological surprises, is among the most pressing concernsof sustainability research (1–3). However, most ES research todate cannot truly address these critical challenges because it hasfocused primarily on quantifying and mapping the delivery ofonly a few services at a single point in time (4). In this study, weanalyze nine ES at five-year intervals from 1971 to 2006 to showhow a spatiotemporal approach can enhance our understandingof ES dynamics.The adoption of a historical perspective has made important

contributions in other areas of ecology (5–8). For example, timehas been revealed to be as important as space for understandingpatterns of species richness and distribution (6, 9). Historicalecology has shed light on the persistent effects of human activityon landscapes (10) and ecosystem function (11–13). This fieldhas also provided the temporal perspective needed to under-stand the underlying causes and rates of change in ecosystems ascontext for the future, including the likelihood of unexpectedregime shifts (14–16), and the potential for conservation, resto-ration, and management of ecosystems (17–19).However, historical analyses have been largely absent from ES

research thus far. The few studies presenting a historical ap-proach mostly compare two snapshots in time (20) or quantify onlya limited number of ES (21, 22), and rarely investigate interactionsamong multiple services through time (but see (23)). Theseshortcomings are of particular concern given that the demand formost services is increasing, making interactions such as synergies

and trade-offs among ES more important to take into account. Abetter understanding of how multiple ES interact, how trade-offsand synergies emerge, and how interactions may shift throughtime as conditions change or respond to new drivers can helpmeet the challenge of managing multiple ES.In this study, we examined how multiple ES and the relat-

ionships among them have changed over time and across space.We extended the spatially explicit ES-bundle approach of Raudsepp-Hearne et al. (24) with data spanning 35 y. We defined an ES bundleas a mix of positively correlated ES provided together in thesame place and at the same time, even though they may not havecausative relationships. Using primary data compiled from di-verse sources, we quantified the delivery of nine ES (Table 1;four provisioning, two regulating, and three cultural services) at5-y intervals from 1971 to 2006 and across 131 municipalities inthe Montérégie, a suburbanizing and heavily agricultural regionin southern Quebec in Canada (11,853 km2). Our multitemporaland spatially explicit approach provided an opportunity to (i)assess the extent of temporal and spatial variation in nine ESindividually; (ii) identify ES bundles, examine their spatiotem-poral dynamics and how they relate to environmental and so-cioeconomic characteristics of the study region; and (iii) determinechanges in the relationships (trade-offs and synergies) betweenmultiple ES through time.

ResultsProvision of ES Increased Through Time and Became More VariableAcross Space. The provision of each service changed significantlyover time [test for the effect of time using space–time interactionanalysis (STI), df = 393, P < 0.01]. The mean provision increasedfor almost every service, by between 20% and 94%, from 1971 to2006 (Fig. 1 and SI Appendix, Fig. S1). Cultural services showedthe greatest magnitude of change through time. Only the mean

Significance

Most approaches to quantifying and mapping ecosystem ser-vices (ES) focus on a single point in time. This static approachcannot provide insight into whether and how the provision ofES changes through time. We examined spatiotemporal ESdynamics by reconstructing the regional provision of nine ESover 35 y. Our approach demonstrated that individual services,ES bundles, and interactions among ES changed across bothtime and space. We also identified trajectories of ES bundlechange and explained how these changes were driven bypolicy, biophysical, and socioeconomic characteristics. Our studydemonstrates the limitations of assuming stationarity in ES andtheir relationships, and emphasizes the importance of taking intoaccount both time and space in the assessment of multiple ES.

Author contributions: D.R., J.M.R., and E.M.B. designed research; D.R. performed re-search; D.R. analyzed data; and D.R., J.M.R., and E.M.B. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: Ecosystem service estimates, socioeconomic and biophysical attributes foreach municipality, for each date, are available on Dryad (dx.doi.org/10.5061/dryad.g4590).1To whom correspondence should be addressed. Email: delphinerenard@hotmail.fr.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1502565112/-/DCSupplemental.

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provision of cattle and flood control declined, by 20% and 30%,respectively. The STI analysis also showed that crop production,flood control, carbon storage and hunting activities were signif-icantly different between municipalities at each time step (testfor the effect of space using STI, df = 520, P < 0.01, Fig. 1).Additionally, the change in the provision of each service throughtime was not the same in all municipalities, as indicated by (i) thehighly significant interactions between space and time for allservices except flood control (STI, P < 0.01), and (ii) the in-creasing variation (i.e., SD) in the provision of each ES amongmunicipalities.

Each ES Bundle Was Dominated by a Few ES. Cluster analysis par-titioned the municipalities into seven groups based on the kindand amount of ES provided through time (Fig. 2). Bundles ofservices varied in the diversity of the ES provided (H, the ef-fective number of ES, ranged from 4.35 to 7.14, Fig. 2). The

bundle with the highest diversity value (bundle type 1, B1, H =7.14) was characterized by multiple provisioning services withlower delivery of regulating and cultural services. The bundlewith the lowest diversity value (B2, H = 4.35) had high delivery ofprovisioning services, but was dominated by crop and cattleproduction, with a high value for flood control. Bundles withintermediate diversity values typically had a few dominant ESand intermediate to low provision of other services. Amongthem, B3 was dominated by crop production and had intermediatevalues for cattle production and flood control. Bundles B4 and B5were characterized by municipalities with intermediate productionof crop and cattle but with other high-producing services (e.g.,regulating services in B4, campsites in B5). Bundle type 6 (B6)characterized municipalities that provided high amounts of carbonstorage and game animals, whereas municipalities in B7 werehighly dominated by recreational activities.

The Bundle Provided by Any Given Municipality Changed ThroughTime. The most common types of ES bundles and their spatialdistribution across the landscape changed through time (Fig. 3).Although B2 (crops, cattle and flood control) was the dominantbundle in 1971 and 1976, provided by 41% and 44% of munic-ipalities respectively, bundle types were more evenly distributedby 2006 (Fig. 3 A and B). Despite this increase in evenness,Moran’s I values showed that municipalities providing the samebundle became increasingly spatially clustered over time (in 1971I = 0.11, P = 0.02; in 2006 I = 0.24, P < 0.01, SI Appendix, TableS1), forming a landscape-scale trade-off among ES bundles thatis clearly visible in 2006 (Fig. 3C). This landscape-scale dynamicwas due to changes in the bundle of services each municipalityprovided over time, which primarily followed four different tra-jectories (Fig. 3B). Municipalities providing B2 (crops, cattle andflood control) in 1971 changed to B3 (crop production) or B4(carbon storage and flood control) by 2006, reflecting trajectoriestoward crop production specialization or toward rewilding fol-lowing agricultural abandonment. Sixty-four percent of munici-palities providing the B4 bundle in 1971 continued along therewilding process through forest succession to provide highamounts of carbon stored and game animals (B6) by 2006.Twenty-seven percent of B5 municipalities in 1971 also changedto become B6 municipalities by the end of our study. This changeoccurred primarily in municipalities that were in the direct vi-cinity of the growing B6 cluster. The municipalities providingmainly recreational activities (B7) and animal production (B1) in1971 continued to provide the same bundle of services throughtime. The number of municipalities providing the B1 bundle ofservices increased through time and expanded in spatial distributioninto adjacent municipalities, resulting in the formation of a largecluster of municipalities that provided primarily animal production.

Determinants of ES Dynamics Were Related to the Location ofMunicipalities. At all time steps, the spatial distribution of ES

Table 1. Ecosystem services quantified from 1971 to 2006 acrossthe Montérégie in Quebec, Canada, and covariables used toexplain the spatial variability in the distribution of ES

Ecosystem services Indicators/units

ProvisioningPoultry Poultry*/km2

Cattle Cattle/km2

Pork Pigs/km2

Crops %Regulating

Flood control δ(max − mean)†/5 yCarbon sequestration kg C/km2

CulturalHunting Animals hunted/km2

Campsites Campsites/km2

Outdoor activities Outdoor recreation centers‡/km2

CovariablesPopulation density Inhabitants/km2

Soil capability for agriculture Classes (0-7)Distance from Montreal km

All ES quantified rely on local ecosystems for their supply, including farmanimals that are mostly fed with locally produced feed and outdoorrecreation centers that involve outdoor activities in natural environments.Indicators were chosen based on the availability of primary data that wasmeasured consistently through time and space. More detailed descriptionsof the ES and historical data sources are provided in the SI Appendix.*Includes chickens, ducks, turkeys, and other poultry.†Flood control was quantified using the difference (i.e., δ) between themaximum and the mean number of flooding events over 5 y.‡Outdoor recreation centers are starting points for snowshoeing and skiingin winter or hiking trails in summer.

1971 1981 1991 2001

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1971 1981 1991 2001

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1971 1981 1991 2001

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STI (p = 0.34)S (R2 = 0.18, p < 0.01*)T (R2 = 0.18, p < 0.01*)

STI (R2 = 0.04, p < 0.01*)S (R2 = 0.59 p < 0.01*) T (R2 = 0.09, p < 0.01*)

STI (R2 = 0.01, p < 0.01*) S (R2 = 0.55, p < 0.01*)T (R2 = 0.10, p < 0.01*)

STI (R2 = 0.06, p < 0.01 *)S (p = 0.61)

T (R2 = 0.13, p < 0.01*)

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Fig. 1. Change in the provision of five selected ES through time (mean ± SD across all municipalities). Each ES was standardized to unit variance to allowcomparison among the values. Results of the space–time interaction (STI) analysis performed for each ES are presented below the corresponding plot. Theindependent tests for spatial and temporal structures were abbreviated S and T respectively. We used α = 0.05, and the P value was calculated after 999permutations. A significant STI indicates that the temporal change of a given ES was not the same in all municipalities.

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bundles across the landscape was related to biophysical and so-cioeconomic attributes of the region (redundancy analysis ap-plied over all time steps, R2 = 0.22, SI Appendix, Fig. S2).Municipalities providing primarily farm animals (B1), and car-bon storage and game animals (B6) were distant from Montreal,had low population density, and soils with the lowest potentialfor crop production. Municipalities that specialized in recrea-tional activities (B7) were located in areas with high populationdensity, where soils also have low agricultural capacity. Areaswhere crops were produced (B2 and B3) were located wheresoils have the best potential for cultivation, at intermediate dis-tances from Montreal, and with intermediate population densi-ties. Municipalities providing B4 (carbon storage and floodcontrol) and B5 (mix of food production and campsites) showedless clear relationships with explanatory variables. This findingaligns with our other results that showed that municipalitiesproviding B4 and B5 changed through time (mostly replaced byB6, Fig. 3B), along with a change in their relationships with theexplanatory variables.

ES Relationships Changed Over Time. At the regional scale, re-lationships among ES also changed through time (Table 2), interms of both the type of relationship and its strength (strong:−0.5 ≤ r ≥ 0.5; moderate: −0.3 ≤ r ≥ 0.3; weak: −0.2 ≤ r ≥ 0.2).All cultural services showed a synergistic relationship with car-bon storage but the strength of this relationship varied throughtime. While the relationship between hunting activity and carbonstorage, already significant in 1971, became stronger throughtime (from 0.39 in 1971 to 0.73 in 2006, P < 0.01), the re-lationship between recreational activities and carbon storagebecame weaker (from a significant correlation coefficient of 0.24in 1971 to a nonsignificant coefficient of 0.10 in 2006). Cropproduction showed consistent and mainly significant trade-offswith every cultural service and with carbon storage, whereas the

relationships between animal production and cultural servicesshifted from trade-off, or no correlation in 1971 and 1976, topositive, significant, relationships by 2006. Relationships in-volving flood control were highly variable through time, showingno clear pattern of correlation with other services.

DiscussionWe provide empirical evidence that the provision of individualES and the relationships between ES are dynamic through timeand space. Our analyses further showed that trajectories ofchange through time were not uniform across the region, butwere instead related to the spatial distribution of environmental,social, and economic characteristics. We also showed that therelationships between services can shift through time. These re-sults indicate that the commonly-held assumption that the pro-vision of ES and their relationships stay the same over time islikely to be incorrect, and show the value of additional informationthat comes with understanding how complex social-ecologicalsystems change through time.The provision of all but three of the ES that we quantified

increased on average from 1971 to 2006. This trend is influencedby the types of ES we quantified in our study. In particular, we

Fig. 3. ES bundle dynamics over time (A and B) and across space (C). (A) Thetable shows the number of municipalities providing each bundle, for each timestep from 1971 to 2006. (B) The web diagram shows the main trajectories ofchange that municipalities followed from one bundle to another between 1971and 2006. The thickness of the lines is proportional to the number of munici-palities in 1971, which follow the trajectory. (C) The maps display the spatialdistribution of each bundle at each time step.

Fig. 2. Ecosystem service bundles and the effective number of ES (H) pro-vided in each bundle. Each petal in the bundles is associated with a symbolcorresponding to an ES listed in Table 1. To facilitate comparison amongservices, ES abundances were normalized by the maximum ES value obtainedfor each bundle. The length of each petal is proportional to the relativeabundances of the other ES within each bundle (petals are comparablewithin bundles).

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found very little data suitable for reconstructing regulating ES,which has almost certainly limited our ability to measure im-portant environmental costs that come with increasing pro-visioning services (25). Our results also showed that the set of ESprovided diversified, with cultural services, represented mainlyby recreational services in our dataset, being increasingly pro-vided across the region. However, the increasing provision ofdiverse services at the regional scale did not mean that eachmunicipality provided a diverse bundle of ES. Instead, mostmunicipalities specialized in a small set of services, resulting inseven unique bundles differentiating through time.Each bundle tended to be fairly specialized in one or a few

services. Even the bundle with the highest diversity (B1) wasprimarily composed of provisioning services of various types,with low amounts of regulating and cultural services. The spe-cialized nature of the bundles provided by municipalities in ourstudy region, historically and today, is consistent with mostbundles of ES identified in similar and other mixed-used land-scapes (23, 24, 26). Although the provision of multifunctionalbundles has proven to be possible at broader scales (26, 27), andis both socially desirable (28) and a target for enhancing eco-system stability and human well-being (29), our results suggestthat achieving multifunctional bundles, at least at small scales,has been difficult in the past. One reason could be that the di-versification of bundle types, along with their specialization atthe municipality scale, has produced economic benefits for largeand specialized agricultural units (30). Reconstructing the tra-jectory of bundle changes through time can contribute to un-derstanding how and why the provision of ES follows differenttrajectories, and identifying factors which might encouragegreater multifunctionality in human-dominated systems.

Our analyses also revealed that the different types of ESbundles have been increasingly structured in space in largeclusters of contiguous municipalities. This spatial clustering hasled to the recent emergence of a landscape-scale trade-off be-tween provisioning (B1 and B3), regulating (B4 and B6), andcultural services (B7). Based on our analyses, we suggest that twounderlying processes are responsible for the formation of such atrade-off: (i) the bundles provided through time by any givenmunicipality tend to become more specialized; and (ii) the lo-cation of municipalities dominated this trend, leading to theprovision of particular ES where biophysical conditions are mostsuitable or where people desired these services.Changes in the bundle of services provided by municipalities

might also be explained by changes in the agricultural productioncontext at provincial and international scales. Encouraged by anincrease in the market value of corn, combined with the limitedprospects for development in some agricultural sectors, Quebecadopted its first grain self-sufficiency policy in 1972 (31). Theoperationalization of this policy, supported by subsidies and ad-vances in technology, encouraged the production of cash crops atthe expense of the dairy industry and hayfields that had beendominant since the 1850s in our study region (31). The effects ofthese provincial policy changes were well reflected in two con-trasting trajectories of ES bundles we identified between 1971 and2006, particularly in the agricultural specialization (change fromB2 to B3) and the rewilding after field abandonment (change fromB2 to B4, and from B4 to B6 with forest succession).In parallel with provincial changes, a global trend toward in-

tensification of pork production emerged and strengthenedduring the 1970s (32), leading to the spatial expansion of the bundlespecialized in farm animal production (B1). A similar trend to-ward specialization of agricultural production has been described

Table 2. Pairwise correlations between ES through time

White indicates positive correlations that we defined as synergies (i.e., r > 0.1); dark gray indicates negative correlations that wedefined as trade-offs (i.e., r < −0.1) and the star subscript indicates a significant relationship (P < 0.05); light gray reflects a relationship withno correlation (−0.1 < r > 0.1), always nonsignificant.

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in the United-States (33), in Europe (34), and elsewhere (35).Such specialization has often been paired with the developmentof capital- and technology-intensive agriculture, spurred by theeconomic efficiencies of operating at large scales and increaseddemands from international markets (36).Local environmental and social characteristics helped de-

termine where changes in ES bundles happened across the re-gion. For example, specialization in crop production happenedprimarily in municipalities with the best agricultural soils, whereasregulating services became predominant (B4 and B6) where soilswere of limited use for crop production. Whereas farm animalswere kept far from urban centers, the time series of maps of ESbundles through time showed that bundles comprising recrea-tional activities and camping (B5 and B7) got closer to the mainurban center through time.Mapping the current spatial distribution and congruence of ES

has received much attention among the community of ES re-searchers over the last few years. These maps are used to targetlocations for management or conservation (37–39) and also topredict the future patterns of variation in the delivery of ES (40,41). However, our results showed that such maps change throughtime, and that political decisions and local factors interact toshape these changes. Our study thus raises questions about theability of an approach focused on mapping ES provision at onepoint in time to adequately support landscape management de-cisions aimed at long-term goals, which are by necessity rooted inthe temporal dynamics of complex systems.Our analysis of temporal changes in pairwise interactions

among ES revealed that interactions are not fixed in time. In ourstudy region, animal production and cultural services (huntingactivities in particular) shifted from a trade-off or no interactionat the start of the study period to an apparent synergistic re-lationship by the end. This change may be explained by changesin animal production methods from traditional breeding in ex-tensive outdoor enclosures to production of animals inside spe-cialized buildings (42). Although changes in farm managementmight have contributed to enhancing spatial compatibility withgame animal movement and hunting activities, this likely led tomore intense trade-offs with other services, such as water quality,erosion control and aesthetic appreciation for which historicaldata were not available.Relationships involving recreational activities also changed

through time. The strength of the synergistic relationship be-tween recreation and carbon storage decreased, whereas thestrength of the trade-off between recreation and crop productiondecreased. These results mirrored changes in the spatial distri-bution of bundles comprising recreational activities and camp-sites (B5 and B7). At the beginning of the time period, thesebundles were provided by the most remote municipalities, whichwere also the most forested. Through time, these bundles ofservices were increasingly provided closer to urban centers, inthe direct vicinity of crop production areas. Although less asso-ciated with forests in these areas, activities associated with out-door recreation centers, such as hiking, snowshoeing, or cross-country skiing, may have been developed in wetlands and riversides.Changes in human demand for more accessible recreational ser-vices, or willingness and interest in recreating in agricultural areas,could explain the relaxation of the trade-off with crop production.With changing trade-offs, the possibility of new synergies canemerge between provisioning and cultural services, notably throughagrotourism activities.Our study brings together the domains of ES science and

historical ecology. We included ES that depend on naturalecosystems for their supply and were relevant to our study re-gion. However, availability and quality of primary data, a chal-lenge for all historical work, exerted a strong filter on the set ofES we could study and the indicators we could use. Ecosystemservices for which long-term primary data are often unavailable(for example, pollination, water purification, aesthetic value, orsense of place) are usually not represented in historical studieslike ours, leading to a reduced recognition of key trade-offs, in

particular with provisioning services. Our ability to further un-derstand how and why the supply of multiple ES and their re-lationships change through time can be much improved bypromoting and designing long-term monitoring programs nowfor future ES historical work.

ConclusionsOur study contributes to a growing line of evidence showing thatignoring time in ecology can be perilous (6, 43–45). Just as to-day’s ecological communities are not considered a reliable rep-resentation of what communities may exist under future conditions(46), our study indicates that we should not expect ES supply toremain constant in time or space. Indeed, our spatiotemporal ap-proach to analyzing multiple ES showed clear evidence of the dy-namic nature of ES, their delivery, and their interactions. We havedemonstrated that temporal dynamics in ES deserve more attentionand hold potential to improve models of ES dynamics.

MethodsQuantification of ES. We quantified nine ES, including provisioning (n = 4),regulating (n = 2), and cultural services (n = 3). Table 1 describes the in-dicators used to estimate each ES and the sources of the data.

Data Accessibility. Ecosystem service estimates, socioeconomic and biophysicalattributes for eachmunicipality, for each date, are available on Dryad (dx.doi.org/10.5061/dryad.g4590). Data used to quantify hunting activities, above-ground carbon storage, and part of pork production are protected by licenseagreements. See SI Appendix for data request procedure.

Temporal and Spatial Scales. Each ES was quantified over 35 y, from 1971 to2006, at 5-y intervals. This time period covered major policy changes in thestudy region (30). We quantified ES through time at the scale of adminis-trative municipalities (n = 176) in the region of Montérégie in southernQuebec, Canada. Municipality area ranged from 27 to 256 km2 with an av-erage of 80 km2. This spatial resolution is not only often used in historicalrecords but is also a scale at which land-use management decisions aremade. Because the boundaries of municipalities changed through time andthe temporal resolution of historical data varied, we standardized the datato the same temporal and spatial scale. Details on the methodology used arepresented in the SI Appendix.

Data Set Analyzed. The final data set comprised 131 municipalities (44 mu-nicipalities were excluded either because there were no data recorded orbecause data were missing for at least four time steps), eight time steps, andnine ES. Missing data values were filled in with the average value of the givenES, over all of the other time steps, for the municipality of interest. Thismethod has the advantage of limiting the weight of missing data values inmultivariate statistical analysis but can obscure temporal trends. To test this,we compared the results we obtained to the results derived by filling inmissing values using an interpolation method. We obtained very similarresults. More advancedmethods to handlemissing data have been developed(47) but cannot yet be fully used for multivariate analysis. Before analysis,the final data set was transformed using the x’ = sqrt(sqrt(x)) transformationto meet assumptions of normality and standardized to unit variance andzero-mean to cope with the diverse units and ranges of variation of ourdata. All statistical analyses were performed using the software R v3.0.2 (48).

Spatiotemporal Changes in the Provision of Individual ES. For each year, wecalculated the mean and the SD of each of the nine ES, across all municipalities,to examine respectively the temporal trends in the provision of individual ES atthe regional scale and the spatial variation of their provision through time. Tostatistically examine the STI, aswell as the separate temporal and spatial changesin the provision of ES,weused the STI analysis following Legendre et al. (49). Thismethod accounts for the absence of site replication in space–time samplingdesign. The time and space variables are coded using principal coordinates ofneighbor matrices (PCNM) in a two-way analysis of variance (ANOVA) model 5(999 permutations). A significant STI indicates that the spatial distribution of EShas changed through time or that the temporal trends were not the same in allmunicipalities. When STI was significant, we tested for separate temporalvariation site to site and for spatial distribution time to time using a one-factor,ANOVA nested model. When STI was nonsignificant, we tested the significanceof the space and time effects without replications using an ANOVA Model II.We applied the STI analysis to each ES individually.

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Identification and Dynamics of ES Bundles. We identified ES bundles, i.e., mixof positively correlated ES provided together in the same place and at thesame time, using a K-means clustering analysis on the entire time series. Weselected the best partition based on the “simple structure index”. We ana-lyzed the diversity of the set of ES provided in each bundle type using atransformation (H) of the Gini–Simpson’s entropy index (S): H = 1/(1 − S) (50).This transformation provides a measure of the “effective number of ser-vices,” which is the number of equally common services required to obtainthe Gini–Simpson’s index. Using this transformation, diversity measurementshave the same units and the same properties, no matter what was the di-versity or entropy index used originally thus facilitating cross-study com-parisons. We mapped the ES bundles for each year using ArcGIS (51) tovisualize changes in their spatial distribution through time and determinedtheir spatial clustering using Moran’s I (52). Finally, we calculated the per-centage of municipalities changing from one bundle to another throughtime to examine the main temporal trajectories of change that municipali-ties followed between 1971 and 2006.

Drivers of ES Dynamics Through Time and Space.We analyzed the relationshipbetween the provision of ES and socioeconomic (i.e., population density,

distance from the main urban center calculated based on digital boundaryshapefile for 2006) and biophysical attributes (i.e., agricultural land capa-bility, sources of data are detailed in the SI Appendix) of the region using aredundancy analysis (RDA). We controlled the RDA for temporal variation tospecifically assess the link between the spatial distribution of the provisionof ES and the characteristics of the region that did not change through time.The relationship was tested using a permutation test (53).

Change in the Relationships Among Multiple Ecosystem Services Through Time.Weperformed Spearman correlations amongeachpair of ES (n=26pairs) for eachtime step to assess changes in the relationships among services through time.

ACKNOWLEDGMENTS. We thank C. Albert (University Aix-Marseille, UMRCNRS 7263/IRD 237), D. B. McKey (University Montpellier II, UMR CNRS 5175,Institut Universitaire de France), and two anonymous reviewers for theirhelpful comments; and Emily Clark (McGill University) for her language edits.This work was supported by the Natural Sciences and Engineering ResearchCouncil of Canada (NSERC) in the form of a Strategic Projects grant (to E.M.B.and J.M.R.) and Discovery Grants (to E.M.B. and J.M.R.), as well as fundsprovided by the Trottier Institute for Science and Public Policy (TISPP).

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