Linking palaeoenvironmental data and models to understand the past and to predict the future

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    Review TRENDS in Ecology and Evolution Vol.21 No.12provide a sound theoretical basis for the sustainable man-agement of ecosystems and for measures adopted byhuman societies to cope with environmental change. Inrecent years, a research agenda has emerged that focuseson the combined use of palaeoenvironmental data drawnfrom environmental archives, such as lake sediments,peat deposits, floodplain stratigraphy and tree-rings,and dynamic simulation models based on mathematicalexpressions for biological and abiotic processes. Severalrecent developments have led to this synergy.

    First, recent analyses of instrumental andpalaeoenvironmental records show that anthropogenicdisturbance in the form of global warming, disruption ofnutrient cycles, soil erosion, atmospheric pollution, UVradiation, land-cover change, habitat destruction andspecies invasions, has brought about considerable changeto ecosystems and their rates of change over the past 250years [1]. This has increased the need for models that

    to validate complexity theory, but could be the only way oftesting the ability of dynamic models to simulate rarelyoccurring thresholds.

    We highlight here a holistic view of environmentalchange that combines dynamic modelling and palaeoeco-logical methods [6] in a methodological framework wherepalaeoenvironmental reconstruction, hypothesis genera-tion and testing, and model development and validationare linked over a wide range of spatial and temporal scales,and levels of system complexity [7]. The main interactionsbetween modellers and palaeoecologists to date havefocussed on climate per se, especially in terms of usingpalaeoenvironmental records, such as pollen diagrams, asa response to climate forcing [8,9] and, hence, as a palaeo-climate proxy. However, at smaller spatial and temporalscales, particularly during the period of major anthropo-genic activity, models should be able ideally to simulate theinteractions among climate, ecosystems and humanactivities. Therefore, we also summarize the recent devel-opments in simulating the long-term (decadescenturies)

    Corresponding author: Anderson, N.J. (n.j.anderson@lboro.ac.uk).Available online 26 September 2006.

    www.sciencedirect.com 0169-5347/$ see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tree.2006.09.005Linking palaeoenvirand models to undand to predict the fN. John Anderson1, Harald Bugmann2, Jo1Department of Geography, Loughborough University, Loughb2 Forest Ecology, Department of Environmental Sciences, Swiss3Department of Geography, University of Liverpool, Liverpool,4Department of Biology and Environmental Sciences, Universit

    Complex, process-based dynamic models are used toattempt to mimic the intrinsic variability of the naturalenvironment, ecosystem functioning and, ultimately, topredict future change. Palaeoecological data provide themeans for understanding past ecosystem change andare the main source of information for validating long-term model behaviour. As global ecosystems becomeincreasingly stressed by, for example, climate change,human activities and invasive species, there is an evengreater need to learn from the past and to strengthenlinks between models and palaeoecological data. Usingexamples from terrestrial and aquatic ecosystems, wesuggest that better interactions between modellers andpalaeoecologists can help understand the complexity ofpast changes. With increased synergy between the twoapproaches, there will be a better understanding of pastand present environmental change and, hence, animprovement in our ability to predict future changes.

    IntroductionWith the possibility of significant environmental changeoccurring over the coming decades, there is a pressing needto optimise our understanding of ecological change to helpnmental datarstand the pastture

    n A. Dearing3 and Marie-Jose Gaillard4

    ugh, UK, LE11 3TUderal Institute of Technology Zurich, 8092 Zurich, Switzerland, L69 7ZTf Kalmar, SE-39182 Kalmar, Sweden

    produce ecological scenarios over the following years,decades and centuries. Second, the belief that the way inwhich ecosystems respond today might be a legacy of thehistory of the system [2] implies thatmodelling our presentand future will require a starting point in the past. Thus,where relevant timescales are greater than the length oftime over which instrumental data occur, palaeoenviron-mental data usually represent the only means for drivingand testing simulation models. Third, following Deeveys[3] adage of coaxing history to conduct experiments, awealth of information about ecological change can begained frommodelling the past to test post hoc hypotheses.Comparing model outputs for the past with palaeoenvir-onmental data can help untangle the relative roles ofmultiple stressors on an ecosystem, for example, climate,human activity and disease on vegetation change [4], orcan help identify the role of internally generated ecologicalchange, for example in aquatic ecosystems [5]. Fourth, theincreased theoretical awareness of the complexity ofecosystems, particularly the likelihood of sudden unanti-cipated responses, has focused attention on the evidencefor nonlinear change contained in palaeoenvironmentaltime series. Such time series not only provide the means

  • Scandinavia over the past 8000 years, and by Cowlinget al. [14] who focused on vegetation dynamics at two sitesin Scandinavia at a higher temporal resolution over thepast 1500 years.

    Finally, a few studies have addressed the interactionsbetween direct climatic forcing, indirect climatic effects viadisturbance regime (e.g. wildfires), and direct human inter-ventions (i.e. management). For example, Keller et al. [15]used a forest model to evaluate the relative effects ofchanges in climate, forest fires and direct human impactson the decreasing abundance of European silver fir Abiesalba in the southern Alps that has been observed duringthe late Holocene. They found that a combination of directhuman activities (e.g. selective cutting), as well as a changein the wildfire regime must be invoked to explain thedynamics reflected in pollen records, thus indicating thatmodels can be used successfully for disentangling the

    represent the integrated response of ecosystems or evenlandscapes, rather than local-scale variables that are typicallysimulated by dynamic models. In this context, dynamic modelsthat explicitly resolve the key driving variables in a mechanisticmanner have been used successfully to simulate vegetationdynamics during the Holocene [61]. Most of these studies focusedon forest succession and used forest gap models [62]; four modes ofsuch applications can be distinguished: Model validation: until the 1990s, the major emphasis was on

    using pollen data from lake profiles to test the applicability ofmodels under climatic conditions (e.g. [6365]). Thus, palaeoeco-logical data were used in a one-way manner to validateecological models.

    Projecting the future: model applications have long been used toprovide scenario assessments of the future behaviour ofecosystems under changing environmental conditions (e.g.[66,67]). Such an application requires previous model validationefforts, where palaeoecological data have an important role.

    Synthesizing research findings: models can be used to integrateand synthesize data across disciplines and even temporal orspatial scales [68,69]; we are not aware of such applications in thefield of palaeoecology, but believe that such applications holdmuch promise.

    Supporting data interpretation: dynamic models can be used toassist in the interpretation of palaeoecological data by performingscenario runs to test hypotheses about key driving factors for theobserved palaeo-proxies, such as pollen frequencies or macro-fossil densities.

    Review TRENDS in Ecology and Evolution Vol.21 No.12 697ecological change at patch, stand, water body andcatchment and/or landscape scales, providing examplesof dynamic models developed for terrestrial and aquaticecosystem communities, and abiotic processes.

    Terrestrial systemsChanges in vegetation structure, both natural andanthropogenic-driven, have considerable impact on localand regional climate, hydrology and biogeochemicalcycling. Given the impact of catchment changes on lakeand stream chemistry, and the recent emphasis onintegrated ecosystem science, terrestrial palaeoecologyand modelling are increasingly required to address thecomplexities and consequences of these changes.

    Using models to understand vegetation dynamics atstandlandscape scalesDynamic models are models that describe the rates ofchange of variables characterizing a system rather thanthe state of the system itself, often by the use of differenceor differential equations. They have been combined suc-cessfully with palaeoecological data since the early 1980s.Whereas the early focus was on model validation (Box 1),attention has since shifted to the application of models forquantitatively evaluating hypotheses of the causes of vege-tation changes inferred from palaeo-records. This hasproved particularly successful for separating the directeffects of climate on vegetation dynamics from those ofnatural disturbances or anthropogenic impacts. Forexample, models have been used to identify periods ofthe past for which climate reconstructions need additionalattention: Hall and McGlone [10] simulated vegetationdynamics for islands of New Zealand for a period in therecent past [700800 yr before present (BP)] and in the firsthalf of the Holocene (70008000 yr BP). They interpreteddeviations between model outputs and palaeoecologicaldata in the more distant period as an indication thatclimatic conditions must have been significantly differentfrom those that are usually assumed to have prevailedat that time.

    Other researchers have addressed the problem ofdisentangling direct human impacts (e.g. selectivelogging) from climatic impacts in palaeo-records. Forexample, Heiri et al. [11] evaluated the changes of uppertree-line elevation in the central Swiss Alps during theentireHolocene (Figure 1) using a forest successionmodel.Simulations yielded tree-line fluctuations of approxi-mately 100 m (i.e. between elevations of 2375 and2600 m), confirming results of an earlier palaeobotanicalstudy that had inferred decadalcentennial-scale Holo-cene fluctuations in tree-line altitude as themain driver ofthe observed changes in the pollen profile, rather thanchanges in pollen productivity [12]. The results of bothstudies showed that therehas beena stronghuman impacton tree-line elevation since 4500 yr BP, as climaticforcing alone could not be invoked to interpret the palaeoe-cological findings. Similar analyses about the relativeimportance of climatic versus direct anthropogenic effectson long-term ecosystem dynamics were conducted by

    Bradshaw et al. [13], who evaluated the changes of thesouthern range limit of Norway spruce Picea abies in

    www.sciencedirect.comBox 1. Models of ecological processes and palaeoecological

    research

    Quantitative models range from simple, static relationships thatrepresent a black box view of relationships between systemvariables (e.g. pollen biomass or diatom nutrient transfer functions[37]), to highly sophisticated, dynamic and mechanisticapproaches where the rates of change of the variables of thesystem (rather than their state) are described; such quantitativemodels can therefore be used to study temporal changes in complexphenomena. A model is a deliberate simplification of reality (allmodels are wrong, but some models are useful [60]) and thescientific question posed largely determines the value of a givenmodel. All scientists rely on various forms of models, qualitative orquantitative. Hence, the common divide between modellers andnon-modellers is inappropriate.

    In palaeoecology, a dynamic view of ecological processes isessential, but difficult because the temporal resolution ofpalaeoecological data tends to be relatively low (rarely annual andhardly ever subannual). In addition, the measured variables oftencomplexity inherent in the long-term dynamics ofecosystems.

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    698 Review TRENDS in Ecology and Evolution Vol.21 No.12Geomorphic and landscape-response models

    Figure 1. Alpine tree-line changes. (a) Macrofossil data and (b) simulation resu

    Switzerland, 2350 m a.s.l.). A chironomid-based temperature reconstruction that is i

    composition and tree-line position simulated in this study showed general agreem

    however, palaeobotanical evidence indicated a lowering of the tree-line, whereas th

    changes in temperature alone can account for changes in tree-line elevation at this

    interpretations that there has been strong human influence on Alpine tree-line elevPalaeoenvironmental records exist for flood frequency andmagnitude, soil erosion, sediment yield, fluxes of carbonand nutrients and macroscale changes in river channels ata variety of spatial scales [16]. Learning about thefunctioning of modern systems from studies of these pastgeomorphic records takes many forms [17,18].

    Some attempts have been made to model thesepalaeoenvironmental data in terms of abstract systemsconcepts. For example, Dearing and Zolitschka [19]analysed the magnitudefrequency characteristics of highresolution time series of sediment accumulation in LakeHolzmaar, Germany, and found that the early Holocenefluvial system conformed to a power function model ofself-organized criticality. In this state, most small changesin system behaviour are the result of nonlinear internalprocesses, but these are punctuated by disproportionatelylarge responses to large external forcing. Latercombinations of climate and human impact in thecatchment substantially modified this apparent naturalstate, giving rise to more simple relationships betweenerosion, land use and climate.

    However,most linkswithmodels are concernedwith thedevelopment, forcing and testing of a steadily increasingnumber of dynamic process-based models [2022]. Two-dimensional models include hydrological models that aredriven by meteorological time series that compute water,soil or sediment discharge based on basic catchment prop-erties, such as size, relative relief and land-cover change.When compared with regional reconstructions of valleyinfilling throughout the Holocene [23] or high-resolutionlake sediment proxies of flooding over recent centuries [2],

    www.sciencedirect.comthey have the potential to unravel climate and human

    om the FORCLIM model ([72]) for the upper tree-line site Gouille Rion (Valais,

    endent of vegetation proxies was used to drive the model. The changes in species

    with palaeobotanical data between 11 000 and 4500 yr BP. In the late Holocene,

    mulation projected continuous forest cover up to an altitude of 2400 m a.s.l. Thus,

    only for the first half of the Holocene, and the results corroborate palaeoecological

    n since at least 4500 yr BP. Modified with permission from [11].impacts on key ecological processes.Progress in linking data and models for dissolved

    inorganic and organic components has been slower.Despite the existence of lake sediment and soil chronose-quence records of Holocene chemical trends (e.g. [24,25]),there have been few attempts to link these with weath-ering models. However, the mineralweathering modelsPROFILE and SAFE [26,27] show promise in simulatingsoil mineral depletion in observed soil chronosequences,river water-quality data and lake sediment data, althoughfurther success will require better parameterization ofmineral:water contact and dissolved organic carbon(DOC) (John Boyle, pers. commun.). DOC has a major rolein lake functioning and its concentrations can reflect bothnatural climate and anthropogenic forcing. Battarbee et al.[28], comparing MAGIC and diatom models, argue thataltered DOC concentrations over the past 100150 years[29] might cause MAGIC to overestimate lake water pHbefore 1850.

    Three-dimensional models are distributed and spatiallyexplicit within a catchment [30]. Some of the most promis-ing models are based on cellular automata where each cellin a grid of interactive cubic cells across a landscapecontains appropriate rules and equations for simulatinga range of processes. The interaction between cells at eachtime step enables continuous feedback and the develop-ment of emergent phenomena, which are key features ofmodels that seek to capture nonlinear environmentalchange [31]. For example, the CAESAR model [32,33]can generate spatial patterns of sediment movement(e.g. alluvial fans or river morphology) and catchment

  • interaction of climate with other processes. For example,Hamilton et al. [44] applied the DYRESIM model ofstratification, combined with future climate scenariosand increased atmospheric nitrogen loading, to a smalllake and showed that climate warming by itself had noeffect on lake productivity, increased nutrient inputs arenecessary to increase productivity. Thus, there is a clearneed for a combination of modelling, palaeolimnology andlong-term monitoring to provide accurate explanations orpredictions of aquatic system changes [6].

    Process-based models of lakes have the advantage thatthey are well-constrained physically, although over longtimescales rapid infilling (and hence shallower waterdepths) can affect their functioning [45]. Estuaries,however, are open-ended, have greater energy inputsand are more dynamic. Although there are linear

    where PL is pollen loading (i.e. pollen deposited on the surface of alake or bog), DWVA is the distance-weighted vegetation abundance(i.e. plants close to the lake or bog contribute more pollen to thedeposition basin than do plants further away), PPE is the pollenproductivity estimate (i.e. plants produce variable quantities ofpollen per unit area depending on the species), and BP is backgroundpollen (i.e. a constant quantity and composition of pollen comingfrom beyond the RSAP; see above) [48].

    One of the most important assumptions of LRA is that, within aregion, there are no significant between-lake differences in pollenassemblages from large sites (lakes or bogs), whereas there aresignificant between-lake differences in pollen assemblages fromsmall sites. Sugita [51,52] has estimated the size of large lakes to be100500 ha, whereas small lakes are

  • 700 Review TRENDS in Ecology and Evolution Vol.21 No.12

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    Review TRENDS in Ecology and Evolution Vol.21 No.12 701time requires an alternative approach. Cugier et al. [46]used a combination of modelling approaches (a model ofnutrient transfer at the catchment scale coupled with a 3Dhydrodynamic and ecological model of the Seine Bight) tomodel the effect of land-use change in the Seine Basin overthe past 200 years. Validated against observational datafor the past 50 years, a range of different nutrient exportscenarios were used to demonstrate how different presentconditions are from the assumed pristine state at the endof the 18th century. These outputs can be compared withpalaeoecological data from estuarine sediments toascertain their accuracy [47].

    Problems and future requirementsAlthough considerable progress has been made in thelinking of models with palaeoecological data, there is stillroom for improvement. We give two examples of problemsfacing this approach that need to be resolved beforeintegration of modelling and palaeoecology is improved:

    Figure 3. Comparative pH trends at a small Scottish lake. (a) Diatom-inferred lak

    functions (see [28,37])] was compared with pH values derived from the MAGIC mod

    Lough of Glen Head, Galloway, SW Scotland. Both approaches indicate that the lak

    the estimated pH of the lake during the mid-19th century. The differences between

    using this approach. The temporal resolution of the diatom approach (each sample c

    steps) and contemporary measurements of pH (red squares). There is considerable

    reductions in acid deposition is clear (b), which is also apparent in the MAGIC mode

    when different approaches are compared. Data from [28].those reflecting the constraints associated with spatialscale and with temporal control.

    Spatial scales and vegetation modellingAlthough the development of process-based modelsdominates research interactions between the palaeo andmodelling communities, there is a need for developmentsin terms of how proxy data (e.g. pollen) can be bettertranslated into environmental states. For example, despitea century of palaeoenvironmental research that has madeuse of pollen records, the translation of pollen data intoquantitative estimates of vegetation cover remains amajorchallenge [48]. In the example of the Swiss tree-linedynamics (Figure 1), macrofossil data were used as theyare more accurate at the local stand scale than are pollendata. To be relevant for hypothesis and model testing,

    Figure 2. Modelling sediment discharge for northern English rivers. Simulated sedime

    compared with alluviation records for the past 9000 years. (a) shows a palaeoenviro

    vegetation cover (non-arboreal pollen); (b) shows the CAESAR modelled output of se

    alluviation records; and (d) shows the modelled cumulative sediment discharge [from (b

    sediment discharge correlate well with observed frequency record of alluviation (vertic

    maxima, whereas the increasing trend in the magnitude of sediment discharge toward

    www.sciencedirect.comreconstructions of past vegetation and landscapes frompollen data should be as accurate as possible and shouldreflect the vegetation type, (e.g. forest, grassland or crops)and its actual surface area [49]. Progress has been achievedrecently with the development of the LandscapeReconstruction Algorithm (LRA) (Figure 4, Box 2)[50,51], which takes advantage of the development ofmechanistic pollenvegetation relationships (incorporat-ing models of pollen dispersal and deposition) developedduring the 1980s [52] and improved during the 1990s[48,53]. The LRA has been validated for southern Sweden(Figure 4) and shows that it performs significantly betterthan more traditional approaches [54,55].

    The combination of dynamic modelling withpalaeo-vegetation investigations holds much potential atthe regional scale. At the local scale, quantitativereconstructions of land cover and vegetation communitieswill be invaluable in integrated palaeoecological studies of,for example, lake-catchment relationships, as vegetation

    ter pH [triangles and pink squares, estimates derived from two different transfer

    f catchment acidification (circles) and contemporary monitoring data at the Round

    s acidified over the past c. 150 years, although there are clear differences between

    two diatom transfer functions represent the range of values that can be obtained

    rs between five and ten years) is coarser than that of the MAGIC model (annual time

    er- and intra-annual variability in modern pH but the upward trend in response to

    imates. These time series illustrate the problems of temporal scaling that can occurhas a primary influence on these systems. This has clearimplications for the role of vegetation in DOC generationand, hence, impacts on aquatic systems. At regional scales,reconstructions of past land cover will be useful for testingstand and landscape models of past changes in vegetationas well as the interaction between climate and vegetation[11,56].

    Temporal controlEcological systems and their processes are stronglydynamic where, for example, ecological succession, soilnutrient exchange and river flows can vary over a widerange of timescales. In lake ecosystems, for instance,changes in algal population and successional processescan be rapid when compared with terrestrial vegetation(

  • 702 Review TRENDS in Ecology and Evolution Vol.21 No.12capture fine temporal changes through time rather thanthe role of spatial scale on ecosystems. Fortunately, thetemporal scales of existing dynamic models readily reflectthis range of time steps in processes and systems fromhours to centuries.

    However, the sediments that provide themain source ofproxy data for model validation often have considerablycoarser temporal resolution (Figure 3). The possible excep-tions to this are those lake sites with annually laminatedsediments (i.e. sediments where annual or seasonal layersare preserved), but in general there are problems of datingerrors (associated with both 210Pb and 14C), which areoften much greater than the temporal scale of change inthe process or system under investigation. Thus, simulat-ing Holocene changes in, for example, algal abundanceunder different climate scenarios using models such asPROTECH [35]will require sensitivity analyses if they areto be run to match the much coarser temporal scale ofsediment records. Therefore, amajor unresolved issue is todetermine what understanding is lost by not being able tovalidate models at very fine temporal scales.

    Figure 4. Validation of the LRA in southern Sweden. The REVEALS sub-model, used to

    lakes was validated for the Skane and Smaland provinces in southern Sweden [73]. Pl

    assemblages from four large lakes in each region; the example shown here is of Smalan

    plant abundance (%) with vegetation data from two 100-km2 areas in the case of Smala

    statistics. Comparison and statistical tests [73] show that REVEALS performs well in corre

    between-species differences in pollen productivity and in dispersal and deposition prop

    deposition basin [52,71,72]. REVEALS corrects well for the under-representation (i.e. lo

    and for the overrepresentation of Pinus, Betula and Alnus. The sub-model therefore cor

    90% trees in the pollen data and c. 25% herbs and 75% trees in the observed vegetation

    shown in green (dark green, conifers; light green, broad-leaved trees) and herbs are sh

    www.sciencedirect.comConclusionWriting in TREE more than a decade ago, MargaretDavis [57] predicted that palaeoecology and ecologywould continue to draw closer together, increasinglysophisticated theoretical work would illuminate thequantitative links between fossil pollen and vegetation,and modelling would be used more extensively to develophypotheses that can be tested using palaeoecology. Thetrajectories of both predictions were correct, but numer-ous examples of where models and palaeoecology havebeen successfully amalgamated are still lacking, as aremajor improvements in our theoretical understanding oflong-term ecological responses. There is a need to developdynamic simulation models that can explain past changeas well as anticipate future ecosystem behaviour andenvironmental processes in the face of global environ-mental change [58,59]. It is no longer computing powerthat is limiting the development and application of mod-els, but rather creativity in terms of how key problemsand questions are studied using models, coupled with alimited understanding of the relative importance of key

    infer regional vegetation abundance (in % cover) from pollen assemblages in large

    ant abundance was first predicted by REVEALS using a total of 20 modern pollen

    d (a). The model was then validated by comparing the model-predicted (estimated)

    nd (b), compiled from satellite pictures, aerial photos, forest inventories and crop

    cting for the biases characteristic of pollen data. These biases are due to significant

    erties of pollen depending on the type (lake or bog) and characteristics (size) of the

    w pollen productivity) of species such as Picea, Cerealia, grasses and other herbs,

    rects for the biased relationship between herbs and trees [i.e. c. 10% herbs versus

    (27% versus 73%) and the model-predicted vegetation (23% versus 77%)]. Trees are

    own in yellow-orange [orange, herbs (minus cereals); yellow, cereals)].

  • Review TRENDS in Ecology and Evolution Vol.21 No.12 703ecological processes and the restrictions on data that areavailable for their parameterization.

    AcknowledgementsThe authors acknowledge discussions with other colleagues at a recentworkshop held in Buellton, California, for the IGBP-PAGES Focus 5Programme Past Ecological Processes and Human-EnvironmentInteractions. M.J.G. thanks Shinya Sugita, Anna Brostrom and SofieHellman for discussions and access to unpublished data, and the fundingagencies VR (Swedish Research Council) and Nordforsk (the NordicResearch Council). We are grateful to Steve Juggins for assistance withFigure 3 and the anonymous reviewers for comments.

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    2025 August 2007

    11th Congress of The European Society forEvolutionary Biology, Uppsala, Sweden(http://www.eseb.org/)

    Linking palaeoenvironmental data and models to understand the past and to predict the futureIntroductionTerrestrial systemsUsing models to understand vegetation dynamics at stand-landscape scalesGeomorphic and landscape-response models

    Lakes and estuariesProblems and future requirementsSpatial scales and vegetation modellingTemporal control

    ConclusionAcknowledgementsReferences

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