POLLEN METHODS AND STUDIES | BIOME Model of Vegetation Reconstruction

POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction patterns of diversity based on data from over 2600 populations. Forest Ecology and Management 156, 5–26. Petit, R. J., Pineau, E., Demesure, B., Bacilieri, R., Ducousso, A., and Kremer, A. (1997). Chloroplast DNA footprints of postglacial recolonization by oaks. Proceedings of the National Academy of Sciences of the United States of America 94, 9996–10001. Prentice, I. C., Bartlein, P. J., and Webb, T. (1991). Vegetation and climate change in eastern North America since the Last Glacial Maximum. Ecology 72, 2038–2056. Tinner, W., and Lotter, A. F. (2006). Holocene expansions of Fagus silvatica and Abies alba in Central Europe: Where are we after eight decades of debate? Quaternary Science Reviews 25, 526–549. Willis, K. J., Braun, M., Sumegi, P., and Toth, A. (1997). Does soil change cause vegetation change or vice versa? A temporal perspective from Hungary. Ecology 78, 740–750.

BIOME Model of Vegetation Reconstruction M E Edwards, University of Southampton, Southampton, UK ª

2007 Elsevier B.V. All rights reserved.

Introduction: Reconstructing Late-Quaternary Vegetation at Large Spatial Scales When vegetation change is viewed at continental scales, dynamic biogeographic patterns become apparent. Individual taxon ranges shift markedly and whole vegetation formations appear, fragment, or disappear over time. If we assume that large-scale distribution patterns of vegetation are, on millennial timescales, mainly controlled by climate, such changes are likely to reflect variations in key atmospheric circulation features, such as the spatial extent of a monsoon system or the average position of the polar jet and associated storm tracks. These, in turn, are affected, for example, by Milankovitch variations or the presence of a large ice sheet. Features at this scale are those most effectively simulated by general circulation models (GCMs). Given the present need to model climate reliably in relation to increasing levels of atmospheric CO2, it is important to know how well climate models simulate climates different from those of the present day. Climate model performance can be assessed through the comparison of vegetation predicted from a paleoclimate simulation with actual past vegetation as recorded by paleodata. The approach was first developed for the COHMAP (1988) project and evolved into the BIOME approach to vegetation reconstruction (Prentice and Webb, 1998). This article explains the basis of ‘biomization’ (Prentice et al., 1996), which uses a globally

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applicable algorithm to generate maps of vegetation biomes from large arrays of pollen data; it then summarizes the results of the BIOME 6000 project (Prentice and Webb, 1998), which mapped vegetation at 0, 6,000, and 18,000 14C yr BP. Further sections discuss insights gained into Quaternary plant biogeography and illustrate model-data comparisons. Objective Vegetation Mapping It is difficult to make a map of modern global vegetation using a consistent and repeatable procedure, and mapping past vegetation is even more challenging. Disparate interpretations of past vegetation due to differences in regional approaches and/or paleoecological methodology make inter-regional compilations of interpreted data difficult, and attempts to make global maps of paleovegetation have been subjective and hard to replicate. The biome method provides some distinct advantages over other approaches. It uses an objective and repeatable method to group pollen assemblages (the primary data) directly into globally consistent vegetation types (biomes); these reflect the structure and function of constituent taxa rather than their floristic identity. Maps for a given time slice (e.g., 6 kyr BP) are created by assigning a dot, color-coded for a particular biome, to each site that has a record. Such maps have relatively coarse spatial detail, but they are generally preferable to those created by subjective interpolation among data points, especially if the latter mask areas of poor data coverage. Paleoclimate Reconstructions The biome method provides a robust approach to investigating past climate. Reconstructions of past climate from paleodata usually follow the taxon-based transfer-function approach (see Use of Pollen as Climate Proxies and Numerical Analysis Methods), in which numerical relationships are developed between, for example, modern pollen abundances and observed climate variables. The numerical function is applied to fossil pollen assemblages and past climate variables are calculated. This method is constrained by observable modern relationships. In the past, certain taxa occurred in abundances observed nowhere today, and fossil pollen spectra may have no recognizable modern counterpart – so-called ‘noanalog’ assemblages (see Overview, Northeastern North America, Changing Plant Distributions). Once outside the envelope of modern observations, fossil pollen spectra, described in terms of taxon abundance, cannot be reliably related to past climate. The problem becomes acute for times prior to the Holocene, when

2552 POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction

the climate system and global vegetation were very different from today. The biome approach circumvents this problem: plant–climate relationships are described with reference to plant structure and function and are not dependent upon the fossil pollen assemblages having modern counterparts. The vegetation distributions thus obtained can be checked for concordance with those derived from climate-model simulations using the same bioclimatic rules.

warm temperate evergreen sclerophyll forest. In this type of classification, species identity is not paramount. Furthermore, the known bioclimatic tolerances of PFTs theoretically allow, and do indeed predict, novel admixtures – no-analog biomes – under certain climatic conditions. Climate is, of course, not the only factor affecting plant distribution. Differing growth strategies and interspecific competition also play a role in determining which PFTs dominate a region. For example, drought, fire, and regeneration potential all affect the balance of grassland and woodland at the moister borders of savanna and temperate grassland regions. Soil and topography also influence vegetation, occasionally at regional scales, but more often on a local basis that is not resolved in continental-scale reconstructions.

The Biome Approach PFTs and Climate The distributions of plant species are partly controlled by bioclimatic factors, for example, their susceptibility to heating, cooling, or frost, and a minimum requirement for growing degree days. Various classification systems for global vegetation are based on this principle, for example, Holdridge’s (1967) life zones. Associated with these bioclimatic responses are morphological and physiological plant traits that equip plants to succeed in certain environments (although this usually means they are less suited to others). For example, in the cold conditions of the far north growing degree days are below the minimum for the growth of trees. All Mediterranean floras are characterized by species that are droughtand fire-tolerant, although there is little overlap in floristic composition among the Mediterranean regions of the world. Thus, global vegetation can be described according to the plant functional types (PFTs) that dominate in various types of climatic conditions, and these PFT assemblages can be classed as formations or biomes, such as arctic tundra or

Modelling Vegetation If bioclimatic requirements and growth rates, regeneration, and competition are taken into account, it is possible to model global vegetation effectively. Prentice et al. (1992) developed a global vegetation model (BIOME) that incorporated these principles. BIOME can be fed data from modern climate observations or a climate model simulation and will produce a vegetation map for those conditions (see Fig. 1). Later, improved versions account for variations in atmospheric CO2 and other parameters that have a major effect on plant growth and distribution (see, e.g., Kaplan et al. (2003)). BIOME is an equilibrium model; for a prescribed climate it generates an optimal vegetation map based on a global grid of points, but it does not incorporate any subsequent interactions of climate and vegetation. More

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Figure 1 A comparison of mapped modern vegetation for the Arctic and sub-Arctic, the vegetation simulated by the BIOME 4 using a modern climate data set as input, and modern biomes based on biomized pollen spectra (Kaplan et al. (2003) and Bigelow et al. (2003).

POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction

recently, dynamic vegetation models such as LPJ (Sitch et al., 2003) have been developed, and interactive vegetation packages are now incorporated into GCMs. In this way, feedback related to vegetation– atmosphere interactions (such as albedo and moisture fluxs) can be modeled. As they are modified and improved, climate models require continual evaluation. Mapped paleodata play a central role as they can be directly compared with simulated biome maps in so-called data-model comparisons (see below). Classifying and Mapping Pollen Data as Biomes – Biomization and the BIOME 6000 Project In a global vegetation reconstruction, biomes should have distinct and realistic ecological qualities, and they must also be distinguishable from other biomes by pollen composition. In order to compare observed past vegetation with simulated past vegetation, biomes should be consistent with those defined in a vegetation model (such as BIOME). While these requirements constrain the number of biomes that can be defined, this is not a problem – too many, and the biomization and resultant maps become unnecessarily complicated. Information may be lost if there are too few biomes; the ideal number depends upon the questions being asked of the data. For example, 18 biomes were used in the BIOME 6000 overview reconstructions for the northern continents and Africa (Prentice et al., 2000), and these included one tundra biome. In contrast, for a more detailed investigation of the Arctic, Bigelow et al. (2003) defined five separate tundra biomes. Pollen-based biomes are defined using the biomization approach (Prentice et al., 1996; Prentice and Webb, 1998). As pollen assemblages are conventionally reported as frequency data (see Pollen Analysis, Principles, Numerical Analysis Methods), the percentage values of pollen taxa are used as the basis of the biomization procedure. An algorithm that relates the taxon abundances in a pollen spectrum to PFTs and PFTs to biomes is then applied to each fossil pollen spectrum from a region of interest. The biome that has the highest resultant score is selected in each case (see Fig. 2 for further details). The biomization of modern pollen samples is validated by comparison with descriptions of modern vegetation. This is not as straightforward as it may seem, as regional vegetation maps are not easily amalgamated, nor are different global vegetation maps mutually consistent (Prentice and Webb, 1998). Biomizations have been validated for several continents, using regional maps and/or vegetation data reported with the modern pollen samples (e.g., Bigelow et al. (2003); Fig. 1). Visual inspection of the maps generally shows good agreement, although

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there is some level of error in biome assignments. In some regions modern vegetation reflects considerable alteration by humans, but even in these areas modern biomizations appear ‘realistic’ in relation to our understanding of potential natural vegetation (Prentice et al., 1996). The BIOME 6000 Project The BIOME 6000 project has produced pollen-based biome maps for much of the globe for 0 and 6,000 14 C yr BP, and there are also maps for 18 14C kyr BP for many regions. The exercise involved hundreds of participants worldwide. Regional experts screened radiocarbon-dated pollen data for taxonomic and chronological reliability, related pollen taxa to one or more PFTs, and assigned each PFT to one or more biomes. Fossil pollen spectra dated to within 500 radiocarbon years of 6 and 1 kyr of 18 14C kyr BP were then biomized and the results presented as maps of past biome distributions. Table 1 summarizes the publications in which biome reconstructions can be found; for a useful overview of the climatological and biogeographic implications of the past biome distributions, see Prentice et al. (2000). Figure 3 presents biome maps for 0, 6,000, and 18,000 14C yr BP. The next section describes some of the prominent biome shifts and mentions probable climatic causes.

Global Vegetation Patterns at 18 and 6 14C kyr BP (18 ka and 6 ka) The Last Glacial Maximum The Last Glacial Maximum (LGM; 18,000 14C yr BP or 18 14C ka) represents a period when insolation patterns (as controlled by Milankovitch cycles) were similar to present, but in almost every other aspect conditions were markedly different: ice sheets were extensive across northern land regions, oceans were cooler, atmospheric dust loadings were higher, and CO2 levels were depressed. All these features contributed to global climates and growth conditions that contrast greatly with those of today. Paleoecological data for the LGM are relatively sparse compared with the data available for the Holocene, but their synthesis nevertheless reveals useful patterns. The northern continents Much of the Arctic region was under ice during the LGM. To the south of the ice and across unglaciated Beringia (northwest Canada, northern Alaska, and northeast Siberia) tundra vegetation dominated (Edwards et al., 2000). Boreal forest was displaced to more southerly latitudes, and its overall extent contracted. In Eurasia particularly, steppe vegetation was far more extensive than today (Tarasov

2554 POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction STEPS IN BIOMIZATION 1. Assemble and check original pollen data, then convert to % 2. Define a set of biomes (regional or global coverage) 3. Define a set of plant functional types (PFTs) 4. Define the biomes by their constituent PFTs 5. Assign all recorded pollen taxa to one or more PFTs TAXON

A simple example with 2 taxa, 3 PFTs, and 2 biomes is shown here PFT

Boreal-arctic deciduous broadleaved shrub: PFT contributes to the shrub-tundra biome

Boreal summergreen deciduous broadleaved tree: PFT contributes to the taiga (boreal evergreen forest) biome

Boreal evergreen needle-leaved tree: PFT contributes to the taiga biome 6. Assign taxa to biomes in a taxon-biome matrix (presence/absence) Taiga biome

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Aik = Σ δij √ {max[0,(pjk – θj)]} j

Where Aik is the affinity of pollen sample k for biome i ; summation is over all taxa j ; δij is the entry (0 or 1) in the taxon-biome matrix; pjk are pollen percentage values; θ is a threshold pollen percentage. Underlying this equation is the selection of taxa (taxon-biome matrix entries), the summing of the square roots of the percent values of taxa in a given biome, and the chance to set the threshold percent >0, that is, to exclude very small pollen values that may be due to long-distance transport of grains from beyond the region of interest. Assuming the threshold is zero for convenience, √ 70 = 8.36 and √ 30 = 5.47; therefore the total affinity score for shrub tundra is 8.36 (from Betula via the shrub PFT) and the total affinity score for taiga is 8.36 + 5.47 = 13.84 (from Betula via the deciduous tree PFT and Picea from the evergreen tree PFT). Figure 2 Steps in biomizing a simple pollen spectrum.

et al., 2000). In the mid-latitudes of Europe, temperate deciduous forests and Mediterranean forests and woodlands were also greatly reduced and in many areas replaced by steppe (Elenga et al., 2000). In China, total site coverage is sparse, but a marked reduction of forest over much of central China is evident in the data (Yu et al., 1998). The predominance of treeless biomes reflects heightened aridity as well as lower temperatures. The lower latitudes and Southern Hemisphere In the tropics and subtropics the data are sparser than for north-temperate regions, but they nevertheless clearly indicate major changes in vegetation cover,

particularly the fragmentation and areal reduction of tropical moist forest in Africa (Prentice et al., 2000). The desert regions of southwest North America were characterized by conifer woodland; this has been shown to be the consequence of winter storms and rain linked to a southward displacement of the jet stream that was in turn due to intense high pressure over the Laurentide Ice Sheet (COHMAP, 1988; Thompson and Anderson, 2000). The extents of most other desert regions were similar to or greater than today’s, reflecting generally greater aridity under cooler global climates. In Australia, the LGM vegetation was characterized by increased dominance of xerophytic PFTs over today, both in the southeast

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Table 1 Published biome reconstructions for 0, 6, and 21 ka Region/topic

Publication

Overview of the BIOME 6000 project and methods Overview of the northern continents and Africa at 0, 6, and 18 ka Africa and Arabia at 0 and 6 ka Australia, SE Asia, and the Pacific at 0, 6, and 18 ka The Arctic at 0, 6, and 18 ka Beringia at 0, 6, and 18 ka China at 0 and 6 ka China at 6 ka, 18 ka Europe (southern) and Africa at 18 ka Former Soviet Union and Mongolia at 0 and 6 ka Northern Eurasia at 18 ka Japan at 0, 6, and 18 ka North America (west) at 0, 6, and 21 ka North America (east) late-Quaternary

Prentice and Webb (1998) Prentice, Jolly et al. (2000) Jolly et al. (1998) Pickett et al. (2004) Bigelow et al. (2003) Edwards et al. (2000) Yu et al. (1998) Yu et al. (2000) Elegna et al. (2000) Tarasov et al. (1998) Tarasov et al. (2000) Takahara et al. (2000) Thompson and Anderson (2000) Williams et al. (2000)

Two issues of the Journal of Biogeography contain most of the BIOME 6000 vegetation reconstructions for different regions at 0, 6 and 21 ka: Volume 25. No 6 (Nov. 1998) and Volume 27, No. 3 (May 2000).

and the northern tropical region, indicating greater aridity (Pickett et al., 2004). The Mid-Holocene The middle Holocene (6 14C kyr BP or 6 ka) was a period when Northern Hemisphere summer insolation was considerably greater than modern. By this time the northern ice sheets approximated their modern extent, so there was virtually no influence of residual Pleistocene ice on regional climates (eastern Canada is the one exception – see below). As Prentice and Webb (1998) point out, 6 ka does not represent a global climatic ‘optimum’ or even ‘maximum’. The term ‘climatic optimum’ is ecologically meaningless, as different plant species have different climatic requirements; also, the warmest period of the Holocene was not experienced at the same time in all regions. Nevertheless, global averages were most likely 1–2 C warmer than present, and this makes 6 ka a particularly interesting period to study. The site coverage is generally good, and biomized data allow quite detailed comparisons of modern and 6 ka vegetation distributions. The northern continents The Arctic treeline advanced northward by up to a few hundred kilometers in some regions, such as central Siberia, reflecting warming due to heightened summer insolation; in contrast, forest in eastern Canada was displaced to the south of its present limits as a result of residual Laurentide ice over Labrador (Bigelow et al., 2003). In temperate Europe, thermophilous temperate forest expanded north of its current position and also southward into Mediterranean regions (Elenga et al., 2000). Steppe vegetation expanded slightly in North America, compared with current limits, but in China this did not happen; instead, an

enhanced monsoon due to higher summer insolation likely favored the broad distribution of temperate deciduous forest (Yu et al., 2000). In Japan, temperate conifer forest was prominent, possibly favored over today’s broad-leaved evergreen/mixed forests by increased seasonality driven by warmer summers (Takahara et al., 2000). The lower latitudes and Southern Hemisphere Striking differences in vegetation cover between 6 ka and present are observed in the subtropics, notably in Africa, where the biomization suggests much of the modern Sahara was vegetated by grasslands and scrub-woodland (Jolly et al., 1998; Prentice et al., 2000). This feature appears to be related to a far stronger monsoon driven by summer insolation that was greater than present. However, tropical moist forest extent may have been less than present; the enhanced monsoon further north may have been linked to less consistent rains near the equator (Prentice et al., 2000). Relatively small differences between modern and 6 ka vegetation in Australia appear largely to reflect regional variations in moisture availability (Pickett et al., 2004).

Historical Biogeography: Migrations, Refugia, and Novel Biomes BIOME 6000s global paleovegetation maps illustrate the dynamic nature of late-Quaternary biomes. The synthesized data sets can be used to investigate features such as the migration of the constituents of various biomes in response to climate change and the alternative response of in situ survival. Furthermore, in-depth examination of the taxon contributions to various biomes reveals considerable variability in biome composition through time.

2556 POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction

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Figure 3 Biome maps for the northern continents and Africa: LGM and mid-Holocene (from Prentice et al. 2000). Biomes for Australia not shown – see Pickett et al. (2004).

Migration versus Refugia Temperate deciduous forest and tropical moist forest were both greatly fragmented during the LGM. Nevertheless, their constituent taxa survived and later assembled to form widespread forest biomes in

the Holocene. For example, the deciduous forest was virtually eliminated from mainland China (Yu et al., 1998). Prentice et al. (2000) argue that while it is possible that the biome was displaced onto continental shelf areas that were subsequently inundated, the

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pollen data show that deciduous tree taxa also survived scattered within other biomes that were more widespread in the LGM, such as broad-leaved evergreen forest. Thus, in situ survival may have been as important as migration. Understanding how and where taxa survived major environmental changes and what ecological and evolutionary processes affected them is of relevance to evolutionary biology and conservation biology (see Qian and Ricklefs (2000) and Harrison et al. (2001)). Species distributions are once again being radically altered, this time by direct human action (such as transformation of ecosystems for agriculture and forestry), and contemporary biomes are threatened with disruption via climate change associated with global warming.

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Temperate Cold deciduous grassland and forest xerophytic shrubland

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Figure 4 Diagram after Kaplan et al. (2003) showing the climate space for northern biomes. Grass-forb tundra occurs in dry climates that are too cool for true steppe.

In certain cases, the biome assignment is sensitive to how a few ‘indicator’ taxa are assigned to PFTs (Prentice et al., 2000). This is the case for steppe and tundra biomes across Eurasia and Beringia in the LGM (Tarasov et al., 2000; Edwards et al., 2000). In Alaska and northwest Canada, the LGM pollen assemblages are assigned to tundra, but those further west and south in Eurasia are assigned to steppe, while sites in between are sensitive to the details of the biomization. Today, boreal forest tends to separate steppe and tundra geographically, and for the most part, steppe and tundra are well differentiated functionally and floristically. However, if we explore the potential climate space (seasonal conditions of temperature and moisture) occupied by these biomes, we find that under conditions too dry, or too cold and dry, for forest (as in the LGM), steppe and drier forms of tundra are adjacent biomes and share similar dominant PFTs (grasses and forbs; Kaplan et al. (2003); Fig. 4). Thus, underlying the instability in the biomization of the steppe–tundra transition in Eurasia is the fact that tundra and steppe are closely related (in terms of PFTs and geographic distribution) under certain configurations of climate. Paleoecologists have long questioned the nature of the treeless LGM vegetation of Beringia, as it is associated with the presence of a diverse Pleistocene megafauna of both herbivores and carnivores and must, therefore, have been productive enough to sustain this extensive faunal ecosystem (see Hopkins et al. (1982)). The dominant pollen taxa, grasses, sedges, and Artemisia (sagebrush, mugwort), have been interpreted as indicating either tundra or steppe

(see above). Neither interpretation is convincing, because debate has focused on the modern properties of tundra and steppe ecosystems. Neither appears a suitable analog for past conditions: most modern tundra types are moist and dominated by woody plants that would not support large populations of specialist grazers, and modern steppe grows under conditions that are far warmer than those of the LGM in Beringia. The refocusing of attention to which PFTs occur in cool, dry climate space, and the possibility that in the LGM Beringian ‘tundra’ was quite unlike contemporary tundra in both PFT composition and ecological properties, offers a promising solution to a long-standing biogeographic conundrum. In their biomization of northern tundra and boreal ecosystems, Bigelow et al. (2003) defined a grassand forb-dominated tundra biome (GFT) to occupy cool, dry climate space. Such vegetation is rare today in the Arctic, but where it does occur it is associated with locally xeric conditions (Yurtsev, 1982). In the biomization, many LGM pollen spectra across Beringia were assigned to this biome. In addition, the BIOME model output based on LGM paleoclimate simulations showed a high level of productivity for GFT, suggesting that it may have been able to support LGM herbivores (Kaplan et al., 2003). The lesson to be learned here is that our conceptions of biomes rely almost entirely on those that exist currently, whereas we should expect a far broader range of biomes to have existed during the highly variable climatic conditions of the Quaternary.

The Arctic Treeline This exercise is usefully illustrated by the status of Arctic treeline (i.e., the northern limit of the boreal forest) at 6 ka. As mentioned above, the biomized

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As mentioned above, a key aim of the BIOME 6000 project is to produce mapped past vegetation at a global scale in order to evaluate the performance of climate models. It is assumed that the biome configurations of 6 and 18 14C kyr BP reflect the climate prevailing at those times (Prentice and Webb, 1998). Reproduction of those configurations by a vegetation model such as BIOME driven by climate-model simulations is most parsimoniously interpreted as reflecting a successful simulation. If simulation and paleodata disagree, both must be revisited to assess where errors lie.

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Late Quaternary biomes of eastern North America Wheeras the BIOME 6000 project focused on just two past time slices, in part because these times were the foci for coordinated climate-model experiments, more highly resolved histories of biome distribution during the late Quaternary are feasible. Williams et al. (2004) produced maps of biome distributions from the LGM to present at 1 kyr time slices for eastern North America (Fig. 5; see Northeastern North America). It should be noted that the maps’ fine detail is based on a conservative, quantitative interpolation technique and thus the actual data points are obscured; this format has the advantage of illustrating clearly the continual shifting of biome distributions through time and the marked change in dominant biomes from the Lateglacial to the Holocene, but the details are likely to be inaccurate. Williams et al. (2004) also analyzed the changing composition of given biomes through time. For example, cool mixed forest, characterized by conifers, broadleaved trees and shrubs, and herbs, shows changing frequencies of these types through time. In particular, conifers dominate LGM and Lateglacial records, whereas after ca. 6 ka, broad-leaved taxa are more abundant than conifers. Among the conifers, pine (Pinus) and spruce (Picea) dominate LGM and Lateglacial records, whereas hemlock (Tsuga) dominates in the later Holocene (Fig. 6). Biomizing, by definition, masks changes in taxonomic composition as the PFT–biome relationship drives the method. However, ‘deconstruction’ of biome composition provides interesting ecological insights and raises questions as to the degree to which climate and nonclimatic factors each determine these intra-biome changes.

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2558 POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction

Figure 5 Biome maps for North America at 1 kyr time slices, from Williams et al. (2004). Note that dates are calibrated to calendar years BP. Key to biomes: CCON – cool conifer forest, CDEC – cold deciduous forest, CLMX – cool mixed forest, CWOD – conifer woodland, DESE – desert, MXPA – mixed parkland, SPPA – spruce parkland, STEP – steppe, TAIG – taiga, TDEC – temperate deciduous forest, TUND – tundra, WMMX – warm mixed forest, XERO – xerophytic scrub.

POLLEN METHODS AND STUDIES/BIOME Model of Vegetation Reconstruction

drive such a response in the BIOME model; they also show the tree line displaced northward in Canada, in contrast to the data. This data-model discrepancy can be explained by the last remaining Laurentide ice, which physically prevented the northward advance of vegetation as well as creating regionally cool conditions. This feature was not included in the modeling, and the southward tree line displacement was not simulated.

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The West African Monsoon at 6 14C kyr BP The ‘greening’ of the Sahara is one of the most striking features of vegetation in the mid-Holocene (see above). For some time it has been clear that the primary driver of this feature was the enhanced monsoon related to higher summer insolation in the Northern Hemisphere (e.g., COHMAP (1988)). However, paleoclimate models incorporating the insolation anomaly but no surface feedback consistently fail to simulate the full magnitude of the feature. Model simulations that encompass feedback, for example, the changed vegetation itself and other properties of the land surface such as soil moisture and wetland extent, performed better than those without the feedback, as they generated more precipitation further north into the Sahara (Brostro¨m et al. (1998); Fig. 7). Despite further modeling, there still remains a discrepancy between data and models (de NobletDucoudre et al., 2000).

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Figure 6 Changes in biome composition through time for North American biomes, from Williams et al. (2004). The graphs show long-term means of pollen percentages (for PFTs or taxa) for all sites assigned to the cool mixed forest biome (total number of sites shown by the gray histogram in the uppermost graph).

6-ka tree line is strongly asymmetric latitudinally. A mid-Holocene northward advance might be expected with enhanced summer insolation, particularly in areas of continental climate, such as Siberia. The model simulations adopted in the data-model comparison by Kaplan et al. (2003)

The biome approach was developed to examine global vegetation patterns and as such has limitations as a tool to describe the detailed vegetation history of regions. To ensure global compatibility, taxon–PFT and PFT–biome relationships must be universally agreed, and while the method has proved remarkably successful in general, further refinements are possible. One is the incorporation of macrofossil data, which is currently an underused paleodata resource. In western North America, for example, where arid conditions often preclude pollen preservation, data from packrat middens (see Rodent Middens) have been used to good effect in macrofossil-based biomizations (Thompson and Anderson, 2000). Another approach, particularly promising when the focus is on paleoclimate, is to use plant traits to map vegetation. Traits can be directly selected for their bioclimatic significance, without the need to decide upon one or more PFTs for each taxon (see Barboni et al. (2004) for a demonstration of the utility of this approach).

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40 30 20 10 modern

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Figure 7 Data-model comparison for West Africa at 6 ka, showing improving match of climate simulation and paleodata with the addition of feedback (Bostrom et al., 1998).

See also: Diatom Methods: Data Interpretation. Glaciation, Causes: Milankovitch Theory and Paleoclimate. Paleobotany: Paleophytogeography. Paleoclimate: Introduction. Paleoclimate Modeling: Last Glacial Maximum GCMs. Plant Macrofossil Introduction. Plant Macrofossil Methods and Studies: Treeline Studies. Pollen Analysis, Principles. Pollen Methods and Studies: Reconstructing Past Biodiversity Development; Changing Plant Distributions.

References Barboni, D., Harrison, S. P., Bartlein, P. J., et al. (2004). Relationships between plant traits and climate in the Mediterranean region: A pollen data analysis. Journal of Vegetation Science 15, 635–646.

Bigelow, N. H., Brubaker, L. B., Edwards, M. E., et al. (2003). Climate change and arctic ecosystems I: Biome reconstructions of tundra vegetation types at 0, 6, and 18 radiocarbon kyr in the Arctic. Journal of Geophysical Research 108(D19), Art. No. 8170 (doi 10.1029/2002JD002558). Brostro¨m, A., Coe, M., Harrison, S. P., et al. (1998). Land surface feedbacks and paleomonsoons in northern Africa. Geophysical Research Letters 25, 3615–3618. COHMAP Members (1988). Climatic changes of the last 18,000 years: Observations and model simulations. Science 241, 1043–1052. de Noblet-Ducoudre, N., Claussen, R., and Prentice, C. (2000). Mid-Holocene greening of the Sahara: First results of the GAIM 6000 year BP experiment with two asynchronously coupled atmosphere/biome models. Climate Dynamics 16, 643–659. Edwards, M. E., Anderson, P. M., Brubaker, L. B., et al. (2000). Pollen-based biomes for Beringia 18,000, 6000 and 0 14C yr BP. Journal of Biogeography 27, 521–554. Elenga, H., Peyron, O., Bonnefille, R., et al. (2000). Pollen-based biome reconstructions for southern Europe and Africa 18,000 yr BP. Journal of Biogeography 27, 621–634. Harrison, S. P., Yu, G., Takahara, H., and Prentice, I. C. (2001). Diversity of temperate plants in east Asia. Nature 413, 129–130. Holdridge, L. R. (1967). Life Zone Ecology. Tropical Science Center, San Jose. Hopkins, D. M., Matthews, J. V. Jr., Schweger CE, and Young SB (eds.) (1982). Paleoecology of Beringia, pp. 3–28. Academic Press, New York. Jolly, D., Prentice, I. C., Bonnefille, R., et al. (1998). Biome reconstruction from pollen and plant macrofossil data for Africa and the Arabian peninsula at 0 and 6 ka. Journal of Biogeography 25, 1007–1028. Kaplan, J. O., Bigelow, N. H., Bartlein, P. J., et al. (2003). Climate change and arctic ecosystems II: Modeling paleodata-model comparisons, and future projections. Journal of Geophysical Research 108(D19), Art. No. 8171. doi: 10.1029/ 2002JD002559. Pickett, E. J., Harrison, S. P., Hope, G., et al. (2004). Pollen-based reconstructions of biome distributions for Australia, Southeast Asia and the Pacific (SEAPAC region) at 0, 6000 and 18,000 14C yr BP. Journal of Biogeography 31, 1381–1444. Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., and Solomon, A. M. (1992). A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography 19, 117–134. Prentice, I. C., Guiot, J., Huntley, B., Jolly, D., and Cheddadi, R. (1996). Reconstructing biomes from palaeoecological data: A general method and its application to European pollen data at 0 and 6 ka. Climate Dynamics 12, 185–194. Prentice, I. C., and Jolly, D. BIOME 6000 participants (2000). Mid-Holocene and glacial-maximum vegetation geography of the northern continents and Africa. Journal of Biogeography 27, 507–519. Prentice, I. C., and Webb, T., III (1998). BIOME 6000: Reconstructing global mid-Holocene vegetation patterns from palaeoecological records. Journal of Biogeography 25, 997–1005. Qian, H., and Ricklefs, R. E. (2000). Large-scale processes and the Asian bias in species diversity of temperate plants. Nature 407, 180–182. Sitch, S., Smith, B., Prentice, I. C., et al. (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9, 161–185.

POLLEN METHODS AND STUDIES/POLLSCAPE Model Takahara, H., Sugita, S., Harrison, S. P., Miyoshi, N., Morita, Y., and Uchiyama, T. (2000). Pollen-based reconstructions of Japanese biomes at 0, 6000 and 18,000 14C yr BP. Journal of Biogeography 27, 665–683. Tarasov, P. E., Webb, T., III, Andreev, A. A., et al. (1998). Presentday and mid-Holocene biomes reconstructed from pollen and plant macrofossil data from the former Soviet Union and Mongolia. Journal of Biogeography 25, 1029–1053. TEMPO Kutzbach, J. E., Bartlein, P. J., Foley, J. A., and Harrison, S. P. Tarasov, P. E., Volkova, V. S., Webb, T., III, et al. (2000). Last Glacial Maximum biomes reconstructed from pollen and plant macrofossil data from northern Eurasia. Journal of Biogeograph 27, 609–620. Thompson, R. S., and Anderson, K. H. (2000). Biomes of western North America at 18,000, 6000 and 0 14C yr BP reconstructed from pollen and packrat midden data. Journal of Biogeography 27, 555–584. Williams, J. W., Shuman, B. N., Webb, T., Bartlein, P. J., and Leduc, P. L. (2004). Late-Quaternary vegetation dynamics in North America: Scaling from taxa to biomes. Ecological Monographs 74, 309–334. Yu, G., Chen, X., Ni, J., et al. (2000). Palaeovegetation of China: A pollen data-based synthesis for the mid-Holocene and Last Glacial Maximum. Journal of Biogeography 27, 635–664. Yu, G., Prentice, I. C., Harrison, S. P., and Sun, X. (1998). Pollenbased biome reconstructions for China at 0 ka and 6 ka. Journal of Biogeography 25, 1055–1069. Yurtsev, B. A. (1982). Relics of the xerophyte vegetation of Beringia in northeastern Asia. In Paleoecology of Beringia (D. M. Hopkins, J. J. V. Matthews, C. E. Schweger and S. B. Young, Eds.), pp. 157–177. Academic Press, New York.

POLLSCAPE Model S Sugita, University of Minnesota, MN, USA ª

2007 Elsevier B.V. All rights reserved.

Introduction POLLSCAPE (Sugita, 1994, 1998) is a simulation framework to explore factors and mechanisms affecting pollen deposition on mires and lakes in heterogeneous landscapes. Differences in pollen productivity and pollen dispersal characteristics among taxa, basin size, spatial patterns of vegetation, and atmospheric conditions such as wind speed and turbulence simultaneously influence pollen dispersal and deposition (Prentice, 1985, 1988; Sugita, 1994). Simulations are a useful tool to understand functional relationships between a combination of these factors and pollen assemblages and to give us plausible scenarios for interpreting fossil pollen data. This article describes contributions of POLLSCAPE to the advances in the theory of pollen analysis and palynology, factors and mechanisms considered in POLLSCAPE, and future directions of this simulation approach.

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Specifying the spatiotemporal scale of the surrounding vegetation is an important step for quantitative reconstruction of past vegetation. Traditionally, many palynologists have been interested in reconstructing past vegetation at a regional to subcontinental scale in order to reconstruct past climate. Pollen records from large- and medium-sized lakes are appropriate for this purpose (Jacobson Jr. and Grimm, 1986; Prentice et al., 1992; Webb III, 1981; Wright et al., 1994). Other ecological questions, which require much finer spatiotemporal resolution, such as species invasion, stand-level vegetation dynamics, and natural and anthropogenic impacts on landscape openness, can be approached by using pollen records from small forest hollows (Andersen, 1970; Bradshaw and Lindbladh, 2005; Davis et al., 1998; Mitchell, 2005). Although these studies emphasized the importance of the spatial scale of vegetation represented by pollen, source area of pollen is still ambiguous and difficult to quantify. Simulation approach has rarely been used in Quaternary palynology, but POLLSCAPE has been particularly useful to develop a new concept and methodology to define the source area of pollen in complex but realistic landscapes.

Pollen Dispersal and Deposition Models in POLLSCAPE POLLSCAPE uses pollen dispersal and deposition models proposed by Prentice (1985, 1988), Sugita (1993), and Sugita et al. (1999). Prentice’s model estimates pollen deposition at a point at the center of a sedimentary basin; thus, it is appropriate for describing the relationships between plant abundance and pollen assemblages on mires and bogs, where pollen grains rarely moved horizontally after deposition. Sugita (1993) modified Prentice’s model to estimate pollen deposition on the entire surface of a basin. Thus, Sugita’s model is more suitable for describing pollen loading and assemblages in lake sediment, because pollen originally deposited over the entire lake surface is redistributed by mixing and focusing before permanent sedimentation (Davis and Brubaker, 1973; Davis et al., 1984). Pollen loading is defined as the number of pollen grains deposited onto the surface of bogs and lake water before sedimentation. Another model proposed by Sugita et al. (1999) – the ring-source model – provides a more accurate description of pollen dispersal and deposition over the entire surface of a lake from the surrounding vegetation than the model described by Sugita (1993).