Use of pollen data to investigate past climates: spatial and ecological sources of uncertainty Mary Edwards and Heather Binney School of Geography, University of Southampton 11 th IMSC th July 2010

OUTLINE 1. Using spatial arrays of pollen data to reconstruct past climate i) directly from pollen ii) indirectly via biomes 2. Issues of uncertainty linked with spatial properties of the data i) pollen-vegetation relationships ii) spatial interpolation

Bigelow et al 2003 JGR-A thinning out with time…

i) direct climate reconstruction: estimating a function based on a modern training set Linear relationship between climate variable and pollen taxonomic composition (here CCA axis values) Fréchette et al 2008 Quaternary Science Reviews Age/depth

Modern analogue technique - assumes a that if fossil pollen assemblage is similar to a modern assemblage, both derive from similar vegetation and reflect a similar climate. Similarity between fossil and modern assemblages is based on a dissimilarity metric such as the squared chord distance (SCD). One or more ‘best’ analogues with scores that pass an acceptability threshold are used to produce a climate estimate

Anderson et al 1989 J Biogeography

Advantage: establish direct pollen-climate relationship; systematically biased pollen values in relation to vegetation no problem—unless relationship changes with time. Disadvantage: require modern training set based on taxonomy that constrains application to where modern analogues are available, therefore often only reliable from early Holocene forward. Noise in the data calibration partly due to different site types in modern datasets. CO 2 levels affect pollen productivity—may enhance pre- Holocene no-analogue effect if taxa variably affected

Plant-functional type (PFT)/BIOME approach - plants’ evolved relationships with critical climate variables - related to form, physiology and phenology - free from taxonomic constraints and no-analogue problem Pollen types  PFTs (Prentice et al 1996 Climate Dynamics) PFTs calibrated to key climate thresholds/variables Reconstructions based on PFT’s (eg Peyron et al 1998 Quaternary Research) Pollen types  PFTs PFTs  BIOMES -Comparison of pollen-based biomes with output from BIOME4 vegetation model driven by palaeoclimate simulation (eg Kaplan et al 2003 JGR-A, -Inverse use of vegetation model to produce climate estimates Wu et al 2007 Climate Dynamics)

A is affinity score of species i for biome k; summed for all species, j. δ ij is the entry (0 or 1) in the taxon-biome matrix p jk are pollen percentage values θ is a threshold pollen percentage √ 70 = 8.36 (Betula) and √ 30 = 5.47 (Picea) (no threshold) A(shrub tundra) is 8.36 A(taiga) is For the purposes of classification/comparison, the biome is assigned to taiga

Biomized modern pollen TESTING THE BIOMIZATION METHOD Observed modern vegetation (Kaplan et al 2003) BIOME 4 model simulation of modern vegetation (Kaplan et al 2003)

Example of data-model comparison (c) LMDH and (d) UGAMP 21- ka simulations (f) pollen data Kaplan et al 2003 JGR.

Wu et al 2007 clim dyn – 6 ka and 21 ka

6 ka MTCO MTWA (anomalies)

Uncertainty resides - in the way that plants produce pollen (biomass vs pollen abundance) - in the way that pollen is transported in the atmosphere and subsequently deposited And it depends on the spatial properties of the collection locality and the surrounding landscape Abundance in vegetation Pollen abundance Uncertainty – spatial concerns Influences methods where pollen is related to vegetation then climate

Proportion of a species in the regional vegetation is a function of α (pollen productivity) and dispersal-deposition characteristics (C). In larger lakes (radius > 600 m) regional vegetation is consistently recorded, even though there is a heterogeneous vegetation mosaic. Small lakes/mires record vegetation near the site—variably noisy as a regional signal. Sugita 2007 Holocene.

Land cover around a large lake in Sweden estimated with pollen biases taken into account (right) - affects biome affinity scores and possibly biome assignment Hellman 2008

We used a transect approach, comparing all biome calls with all vegetation pixels within a sector along the transect. The bias for forest in tundra in the pollen data relates to relative over- representation of tree pollen (particularly Pinus)

For the treeline we used a ‘simplified’ equation to correct for bias in the pollen signal V i,k is the estimate of the vegetation proportion occupied by species i based on pollen data at site k n ik is the pollen count of species i at site k  is an estimate of pollen productivity (against a standard) C reflects the aerodynamic properties of the pollen and is a function of fall speed  and C were further simplified into 3 and 5 categories, respectively

Both the shape of the curve and the 50% point change The bias is not removed by these factors, but the match is better using  or  with C, but not C alone

Spatial interpolation: Williams et al 2004 Ecol Monogr Davis et al 2003 – did they think of everything? - GIS-based 4-D grid (includes elevation and time) - 4-D smoothing spline Supplement information in space with information in time Quality and spatial coverage of information at each site?

Conclusion 1. Spatial uncertainty in pollen data derives from the biology of pollen production and dispersal and from the discrete and dispersed nature of pollen sites 2. Flexible methods of assessing past climate such as PFT/biomization approaches (i.e. not constrained to modern calibration sets) are affected by both these issues 3. Given the limited availability of pollen data and the high level of effort to retrieve new information—developing biologically informed statistical approaches to improve usefulness of existing data is critical