SEARCH

SEARCH BY CITATION

Keywords:

  • climate;
  • extrapolation;
  • foliar physiognomy;
  • inference space;
  • proxies;
  • stomatal frequency;
  • the Ginkgo paradox;
  • uniformitarianism

Abstract

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

Contents

 Summary29
I.Introduction30
II.Key concepts in the uncertainty of proxy evidence30
III.Uncertainties in major foliar physiognomic proxies of MAT31
IV.Stomatal density and stomatal index34
V.Steps forward38
VI.Synthesis40
 Acknowledgements40
 References40

Summary

This review uses proxies of past temperature and atmospheric CO2 composition based on fossil leaves to illustrate the uncertainties in biologically based proxies of past environments. Most leaf-based proxies are geographically local or genetically restricted and therefore can be confounded by evolution, extinction, changes in local environment or immigration of species. Stomatal frequency proxies illustrate how genetically restricted proxies can be particularly vulnerable to evolutionary change. High predictive power in the modern world resulting from the use of a very narrow calibration cannot be confidently extrapolated into the past (the Ginkgo paradox). Many foliar physiognomic proxies of climate are geographically local and use traits that are more or less fixed for individual species. Such proxies can therefore be confounded by floristic turnover and biome shifts in the region of calibration. Uncertainty in proxies tends to be greater for more ancient fossils. I present a set of questions that should be considered before using a proxy. Good proxies should be relatively protected from environmental and genetic change, particularly through having high information content and being founded on biomechanical or biochemical principles. Some current and potential developments are discussed, including those that involve more mechanistically sound proxies and better use of multivariate approaches.


I. Introduction

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

Because fossils do not provide direct evidence of past environments (Parrish, 2001), they can only provide proxy (surrogate) evidence. However, fossil-based proxies are critically important to our understanding of the origins and evolution of our environment, and provide much of the evidence for past climates (Parrish, 2001) and atmospheric composition (Ehleringer et al., 2005).

Any measurable characteristic of fossil material that can be assumed to reflect climatic, atmospheric and other environmental parameters has the potential to be used as a proxy (Parrish, 2001). Quantitative proxies make numerical estimates of specific aspects of past environments (such as mean annual temperature, MAT) from fossils. Such proxies typically work by quantifying the relationship between current or recent records of the target parameter and features of the living organism, such as morphology, anatomy or chemistry. These traits are then measured on fossils, and the relationship is used to estimate the values of the target parameters for the time and place of the deposition of the fossils.

This review provides a critical framework for an understanding of the uncertainties of quantitative fossil-based proxies of past environments. I consider in detail the use of foliar physiognomy (i.e. gross morphology of fossil leaves; see references cited by Peppe et al., 2011) and the relative frequency of stomata on leaves (Royer, 2001) to estimate MAT and past levels of atmospheric CO2, respectively. These proxies were chosen because they illustrate key principles, are major tools for inferring past environments and are under active research. As a means of understanding the principles involved, the discussion of specific proxies concentrates on the limits of proxies when used in isolation. The logic of this is that an understanding of the uncertainties of components of an inference aids in the understanding of the overall uncertainty. I also discuss how contextual information (including independent, corroborative evidence) can add strength to these proxies. Furthermore, each of the proxies should be considered as a work in progress, and I consider some possibilities for improved methods.

II. Key concepts in the uncertainty of proxy evidence

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

1. Uncertainty in palaeoproxies, inference space and the correlational nature of proxies

Quantitative estimates of past environments made from proxies can be considered as statistical predictions. Prediction involves the creation of a model based on a calibration dataset that is a representative sample of observations from a more general pool (technically known as the population), and the use of this model to predict values for other members of that population (Sokal & Rohlf, 1995). For analytical reasons (in particular, to allow the quantification of uncertainty), the calibration dataset is usually randomly sampled from the population, apart from unbiased external factors imposed on the sampling (fixed effects) (Sokal & Rohlf, 1995). The model is valid across the range of samples present in the population (Sokal & Rohlf, 1995). This range is called the ‘inference space’ of the model.

Some examples can illustrate the inference spaces of proxies, and how they may be constrained in terms of physical and biotic environment, genetics and time. The inference space of a proxy calibrated using a single species sampled across a certain range of environments (e.g. many stomatal frequency proxies; see Section IV) is the genotypic range of the sample, but only across the environmental range sampled. Similarly, proxies calibrated using multiple species (for example, foliar physiognomic proxies; Section III) are valid across the genetic, phylogenetic and ecological range spanned by that sample of species.

As one cannot observe the past directly, the reconstruction of past environments depends on uniformitarian assumptions (Gould, 1965). When using leaf-based proxies to estimate past environments, it is assumed that the fossils are consistent with the inference space of the model. This is, in effect, assuming that the fossils fall within the inference space of the proxy. This makes the implicit assumptions that the leaf–environment relationship has not changed and the fossils are unbiased representatives of the source plants (or that one can compensate for any biases). Because these assumptions may be violated, the application of the proxy to fossils must add uncertainty. Wolfe (1993) assumed that leaf traits are adaptations, and argued that this uniformitarian assumption is acceptable provided that there is a sufficiently strong association between the leaf traits and environment. However, the following discussion argues that the stability of the leaf–environment relationship is made more complex by the correlational nature of proxies and the effect of genetic change (evolution, extinction and movement of species).

The leaf-based proxies focused on in this review are calibrated using correlations between the distribution of the relevant leaf trait(s) in living or recent fossil floras and associated values of the target parameter (Wolfe, 1993). However, when the plants do not respond directly to the target parameter, but instead to a correlated aspect of environment, the proxy must assume that this correlation has not changed. Thus, in the modern world, seasonal temperatures may be correlated with MAT, so that a proxy that depends on a plant’s response to seasonal temperature may also predict MAT. However, changes in seasonality will alter the correlation between seasonal temperatures and MAT, leading to errors in the proxy (Jordan, 1997a,b). An understanding of the mechanism underlying the leaf–environment relationship is therefore fundamentally important for the identification of which environmental characteristic the leaves respond to directly.

There is also the potential that the relationship between the leaf trait and the environment has changed through evolutionary processes. To understand the likelihood of this having happened, one first needs to establish some concepts relating to the control of plant development. The leaf-based proxies depend on the response of the relevant leaf traits to changes in environment through phenotypic plasticity (responses without genetic change) and/or changes in genotypic composition. For proxies employing multiple species (such as the foliar physiognomic approaches; Section IV), the changes in genotypic composition may involve changes in species’ composition and genotypes within species. Genotypic composition at a site can change through evolutionary adaptation, or through immigration or extinction of genotypes. Furthermore, plastic responses are typically under genetic control, so that different species and, indeed, different genotypes within species can show markedly different plastic responses to environment (Scheiner, 1993; Agrawal, 2001). This means that changes in genotypic composition can affect proxies that involve plastic responses to environment.

2. Evidence indicating potential changes in the leaf–environment relationship

Validation tests can be used to identify the potential for biases in proxies resulting from changes in the leaf–environment relationships. Thus, proxies calibrated using a subset of the modern world (different regions, environments or species/genotypes) can be validated by testing how well they estimate for other subsets of the modern world, or whether they are internally consistent. Such tests include looking for regional variation in the relevant leaf–environment relationship, and whether the relationship varies according to the environmental factor that induces the variation in the underlying trait (see the discussion on foliar physiognomy, Section III.2). The presence of variation in the leaf–environment relationships among species or among genotypes within species can be evidence for important genetic effects on proxies (as discussed for stomatal frequency proxies, Section IV.2). Experimental manipulation provides evidence of other environmental effects on the leaf–environment relationships, and common garden experiments can be used to segregate genetic from plastic responses (Falconer & Mackay, 1996). The use of proxies to validate other proxies is logically different and is considered in Section V.1.

3. From living plant to fossil collection – taphonomic, diagenetic and sampling filters

The leaves in a fossil flora do not form a random sample of the leaves in the source vegetation (Spicer, 1989). The movement of leaves from the living plant to the site of deposition (taphonomic processes), the alteration of leaves after deposition (diagenetic or preservational processes), and collection and sample preparation can affect the species present and which leaves of a given species are present in a fossil flora (Spicer, 1989). These changes in composition can result in biases in the leaf–environment relationships (e.g. Greenwood, 1992; Wolfe, 1995).

III. Uncertainties in major foliar physiognomic proxies of MAT

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

The foliar physiognomic methods discussed here (leaf margin analysis, CLAMP (Climate–Leaf Analysis Multivariate Program) and digital leaf physiognomy; Wolfe, 1979, 1993; Royer et al., 2005) estimate MAT from average scores or proportions of leaf morphological traits for the woody dicot species. These approaches have been applied only from the Cretaceous to the Neogene, as they rely on angiosperms. Other foliar physiognomic proxies of various climatic or other environmental parameters (e.g. Christophel & Greenwood, 1989; Wilf et al., 1998; Spicer et al., 2003, 2004) are not considered.

Leaf margin analysis is based on the proportion of species with entire leaf margins present within sites (Fig. 1a). The concept originated with Bailey & Sinnott (1915), and was converted into a quantitative proxy using calibration sets from the Northern Hemisphere (Wolfe, 1979; Wilf, 1997; Traiser et al., 2005; Adams et al., 2008; Su et al., 2010). Different temperature–leaf margin relationships have been established for parts of the Southern Hemisphere (Kennedy et al., 2002; Kowalski, 2002; Greenwood et al., 2004; Hinojosa et al., 2011).

image

Figure 1. Proxies for estimating past climates based on the leaves of woody dicot species. Each graph shows values for a wide range of sites, and a regression that has been used to estimate mean annual temperature (MAT) from fossils. (a) Percentage of species (per site) with entire margined leaves from sites in east Asia vs MAT at that site, adapted from Wolfe (1979), courtesy of the U.S. Geological Survey. (b) Predictive relationship for MAT from Wolfe’s (1995) CLAMP (Climate–Leaf Analysis Multivariate Program). The x-axis is the score along a vector in multidimensional space identified by canonical correspondence analysis to have the strongest correlation with MAT based on site means of a suite of characters describing leaf size, shape and margin type.

Download figure to PowerPoint

CLAMP (Wolfe, 1993, 1995) makes estimates of MAT and other climatic and environmental parameters from multivariate analyses of leaf size, shape and margin type. CLAMP is based on a suite of variables representing the presence/absence of categories of the size, margins and shape of leaves (see http://clamp.ibcas.ac.cn/). These data have mostly been analysed using ordination followed by regression of MAT on the resulting axes (Fig. 1b), with some attempts at the multiple regression of raw data (Greenwood & Wing, 1995; Gregory-Wodzicki, 2000; Teodoridis et al., 2011). The principal datasets are largely made up of sites in the USA, Canada, China and Japan (http://clamp.ibcas.ac.cn/). Regions outside this geographical range have been sampled using the same protocols, but these data have not been incorporated into the main datasets and models.

Digital leaf physiognomy (Huff et al., 2003; Royer et al., 2005; Peppe et al., 2011), like CLAMP, estimates MAT and other climatic parameters from multivariate analyses of leaf form. It employs digitally measured, continuous variables and analyses them using multiple regression (Huff et al., 2003; Royer et al., 2005; Peppe et al., 2011). The most recent dataset uses many CLAMP sites, but includes other sites that give a more global representation than CLAMP (Peppe et al., 2011).

1. Underlying traits and control of foliar physiognomic traits

Leaf margin analysis, CLAMP and digital foliar physiognomy are all strongly empirical. This is because leaf margin type dominates the estimates of MAT from all of these methods (Wolfe, 1995; Wilf, 1997; Peppe et al., 2011), and no current explanation for the incidence of leaf teeth implies a direct relationship with MAT. Royer & Wilf (2006) argued that leaf teeth may be sites of elevated photosynthesis during leaf expansion, so that teeth may be favoured in cold climates where rapid expansion during spring is essential. Wolfe (1993) argued that teeth may increase transpiration, thus helping to maintain sap flow in expanding leaves. In addition, leaf teeth release root pressure through guttation from hydathode tissue inside leaf teeth (Feild et al., 2005).

Leaf teeth and other aspects of foliar physiognomy are under strong genotypic control. Potts & Jordan (1994) showed strong quantitative genetic control of leaf shape and size characteristics in a eucalypt. Although Royer et al. (2009b) presented evidence that temperature change induced a plastic response in leaf margin characters in Acer rubrum, the response was only c. 15% of that expected from the temperature differences (allowing for up to c. 85% genetic control of these characters). Indeed, the key trait of the presence/absence of toothed leaf margins appears to be more or less fixed for given genotypes. Thus, species and even major groups of species often either have toothed leaf margins or not, regardless of climate. For instance, all species of Myrtaceae have entire margined leaves, even though they occur across a range of MAT of 23°C or more (Kubitzki, 2007). More generally, the phylogenetic composition of a flora strongly influences the incidence of species with toothed leaves even within regions (Little et al., 2011). The phylogenetic effect may be even greater between broad regions, as some lineages are unique to, or more common in, major regions. As a result, the observed leaf margin–climate relationship appears to be largely a consequence of community assembly processes bringing together the balance of species that creates the relationship.

2. Genetic and environmental impacts on the relationship

Large regional effects show that foliar physiognomy fails the validation test of comparing regional relationships. Temperate floras of different continents have markedly different leaf–climate relationships (Stranks & England, 1997; Gregory-Wodzicki, 2000; Greenwood et al., 2004; Aizen & Ezcurra, 2008; Steart et al., 2010; Hinojosa et al., 2011), resulting in differences in predicted MAT of as much as 5°C or more (Jordan, 1997b). Even within broad regions, relationships can vary (Adams et al., 2008), and responses to MAT arising from altitude can differ from those arising from latitude (Halloy & Mark, 1996; Jordan, 1997b).

Variation in current environments may contribute to regional differences in the leaf–climate relationship. For instance, Southern Hemisphere temperate floras contain fewer deciduous species than floras at comparable northern latitudes (Axelrod, 1966; McGlone et al., 2004), and two of these southern regions (South Africa and Australia) are famous for the predominance of sclerophyllous plants with long-lived, evergreen leaves. These differences may be a result of thermal equability and typically low soil nutrient levels favouring long-lived, evergreen leaves (Turner, 1994; Wright et al., 2004a) in the Southern Hemisphere. Given that cool-climate deciduous species have a greater incidence of leaf teeth for a given climate than do evergreen leaves, these environmentally driven effects on morphology could have induced major differences in leaf–climate relationships between northern and southern floras (Jordan, 1997b; Peppe et al., 2011).

The second potential cause for regional variation in the leaf–climate relationship is a historical genetic signal (Jordan, 1997b). Such signals include phylogenetic effects, in which regional variation in the leaf–climate relationship is attributed to differences in historically determined lineage composition (Greenwood et al., 2004; Little et al., 2011). Thus, the entire margined family, Myrtaceae, dominates many Australian nonarid habitats (Groves, 1999). However, biases resulting from a strong phylogenetic influence on leaf–climate relationships and marked regional differences in lineage composition may be damped to some degree by the way in which foliar physiognomic approaches employ averages across many lineages. In addition, the phylogenetic differences in leaf form may be at least partly associated with differences in habitat through ecological lineage sorting, as discussed by Westoby et al. (1995). Thus, even in the extreme example given above, Myrtaceae are rare or absent from very cold environments, therefore limiting the bias induced by their entire margined leaves on estimates of palaeotemperature.

It has been argued that problems of regional variation in the leaf–climate relationship can be avoided by the use of geographically local physiognomic models (Stranks & England, 1997; Kowalski, 2002; Spicer, 2007; Hinojosa et al., 2011). Indeed, some models are implicitly geographically local – for example, the CLAMP dataset is strongly focused on the northern temperate zone (Wolfe, 1993, 1995; http://clamp.ibcas.ac.cn/). However, I next argue that the utility of local models is limited because they are inconsistent with the assumption that the leaf–climate relationship has remained constant. This limitation becomes progressively greater with the greater age of fossils, regardless of whether the regional variation in the leaf–climate relationship is a result of historical genetic effects or regional differences in environment.

The historical genetic contribution to interhemispheric variation in the leaf–climate relationship has, at times, been explained by putative Gondwanan origins of the Southern Hemisphere floras, compared with the more Laurasian heritage for the northern floras (e.g. Hinojosa et al., 2006). If this was the main factor, then regional models could arguably be extended back to Gondwanan times. However, as noted by Jordan (1997b) and Peppe et al. (2011), this view does not allow for compelling evidence of more recent and profound changes in the phylogenetic composition of temperate floras worldwide in response to climate change, landscape evolution and immigration of species from other regions (Momohara, 1994; Graham, 1999; Lee et al., 2001; Tiffney & Manchester, 2001; Hill, 2004; Hinojosa et al., 2006; Svenning & Skov, 2007; Sniderman & Jordan, 2011).

If geographical variation in the modern leaf–climate relationship is a result of regional differences in current environment, regional leaf–climate relationships cannot have remained constant through time. For example, if thermal equability or soil nutrients influence foliar physiognomy, it is perilous to extend Northern Hemisphere-specific models to the pre-Quaternary, when the Northern Hemisphere had more equable climates (Wing & Greenwood, 1993) and possibly poorer soils before the soil renewing effects of Pleistocene glaciation. Given that prevailing leaf physiognomic models are largely Northern Hemisphere local models, Quaternary climates may have induced fundamental biases. Analogous problems apply across all regions.

3. Analytical, taphonomic and other biases

Multivariate proxies can incorporate aspects of leaf morphology that compensate for biases in univariate relationships. However, Peppe et al. (2010) showed that the use of categorical variables can result in significant systematic errors in CLAMP-based estimates. In addition, the correspondence analysis methodologies employed by CLAMP can distort relationships between dependent and independent variables (Minchin, 1987), which has the potential to bias the results. The alternative approach (multiple regression) can be biased if the relationships between leaf and environmental traits are not linear (as occurs within the CLAMP dataset; Wolfe, 1995).

Taphonomic effects on foliar physiognomic proxies are relatively large, but difficult to quantify (Greenwood, 1992; Spicer et al., 2005; Dilcher et al., 2009). Greenwood (1992) argued that taphonomic processes alone may have added an uncertainty of c.± 1°C to physiognomic temperature estimates using leaf size, although it is less clear how strong the effects would be on other traits. Some broad principles have become apparent. Fossil assemblages are biased towards riparian species, certain taxonomic groups over others (Tegelaar et al., 1991; Briggs, 1999) and, possibly, some morphotypes over others. Post-depositional processes may also be important, but are poorly studied for leaves. However, shrinkage caused by drying and heating (Cleal & Shute, 2007) may affect some important physiognomic features, such as leaf dimensions, size of teeth and numbers of teeth per length of leaf margin, but will have little or no effect on dimensionless measures of leaf shape, such as ratios and the presence/absence of toothed margins.

4. Overall uncertainties

Large uncertainties are associated with current leaf proxies of past climates. Even assuming no biases, globally calibrated leaf physiognomic proxies for temperature have standard errors of c. 4°C (Peppe et al., 2011). This broad uncertainty must widen when the application of the proxy to the past is considered. Phylogenetic, habitat-related, taphonomic, diagenetic and sampling effects can all introduce biases of several degrees and add uncertainty to the proxies (Burnham et al., 2001; Kowalski & Dilcher, 2003; Greenwood, 2005; Royer et al., 2009a,b; Little et al., 2011).

The degree to which phylogenetic and environmental impacts on the leaf–climate relationships affect estimates of past MAT can be expected to be time related. If regional differences in the leaf–climate relationship are mainly phylogenetic, Neogene estimates from geographically local models will be biased by Quaternary floristic restructuring. The biases will be even greater for the Palaeogene and Cretaceous fossils. If regional differences are mainly environmental, the environmental changes over the same periods will also induce biases. This means that, although geographically local models show smaller standard errors within their strict inference spaces than the global model mentioned above, such local models will become progressively less useful for pre-Quaternary periods.

IV. Stomatal density and stomatal index

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

Intense interest in the use of the frequency of stomata on leaves to estimate past atmospheric CO2 commenced with evidence that the number of stomata per unit leaf area (stomatal density, SDe) in a range of woody species increased as the partial pressure of CO2 (pCO2) decreased with altitude (Woodward, 1987). Initial attempts to develop stomatal palaeoproxies of atmospheric CO2 focused on SDe (Beerling & Chaloner, 1992, 1994). However, the numbers of stomata in a developing leaf are fixed well before complete leaf expansion (Schoch et al., 1980; Ticha, 1982), which means that SDe is affected by how much a leaf expands after stomatal fixation. Proxies based on the stomatal index (the ratio of stomata to stomata plus epidermal cells, SI; Salisbury, 1927) avoid this problem and have largely replaced SDe.

Stomatal frequency-based proxies from fossils from a range of extant groups of angiosperms and gymnosperms have been applied from the Holocene (the last c. 12 000 yr) back to the late Palaeozoic c. 300 million yr ago (e.g. Wagner et al., 1999; Retallack, 2001; McElwain et al., 2002; Kouwenberg et al., 2005; Van Hoof et al., 2005, 2006; Kürschner & Kvacek, 2009; Barclay et al., 2010). In addition, some estimates of changes in Mesozoic levels of CO2 have been based on fossils of extinct groups (Haworth et al., 2005; Vording et al., 2009; Bonis et al., 2010).

Most stomatal frequency-based proxies are based on the variation within individual species, and are calibrated in a range of different ways (Royer, 2001). The first is to analyse the temporal trends observed among herbarium specimens of different ages, exploiting the increase in atmospheric CO2 concentration from c. 280 to c. 396 ppm since the advent of the industrial era. The second is by experimental induction of changes in stomatal frequency with elevated or depressed CO2 levels. Given that almost all the plants involved are long lived (usually of the order of 100 yr or more), these two approaches mainly measure plastic responses within species and contain no information on adaptive responses (Royer, 2001). Other approaches incorporate some signal from adaptation by exploiting the altitudinal gradients in CO2 or by using well-dated subfossils that can be related to atmospheric CO2 recorded in ice-cores (Royer, 2001). Nearest living equivalent (NLE) proxies are calibrated using morphologically and putatively ecologically similar species (McElwain, 1998; Haworth et al., 2005; Bonis et al., 2010), and the fossils are scored for deviation in SI from the average in the calibration set.

The proxies are complicated because the calibrations reflect different aspects of atmospheric CO2. Data from ice-cores and herbarium specimens are related to changes in atmospheric CO2 concentration (CO2 as a proportion of the total composition, often expressed in ppm), whereas altitudinal clines reflect changes in pCO2, which is determined by absolute amounts of CO2 and temperature, with little change in concentration. This distinction is not important for most of the principles considered in this review because, for a given altitude, the two measures are equivalent. Where possible, the discussion is mostly expressed in terms of changes in pCO2. However, additional layers of complexity and uncertainty are added by the fact that proxies that estimate pCO2 need to be adjusted for altitude and some proxies that claim to estimate concentration may really be estimating pCO2.

1. Underlying traits and control of stomatal frequency

A relationship between stomatal density and atmospheric CO2 is expected because the main role of stomata is to regulate the permeability of leaves to gases (gas phase conductance), so that the leaves can absorb CO2 for photosynthesis without losing excessive water vapour. Maximum stomatal conductance is determined by the number and size of stomata on the leaf, and plants tend to match this parameter with the maximum demand for CO2 (Wong et al., 1979). Thus, to optimize their resource use and evade costs of having excess stomata, plants could be expected to adjust SDe according to pCO2 (Roth-Nebelsick, 2007). The costs of excess stomata are unclear, but could involve water loss through fully closed stomata, which appear to be more conductive than the general cuticle (e.g. Jordan & Brodribb, 2007), increased risk of fungal penetration (e.g. Manter et al., 2000) or some unknown developmental or maintenance costs. However, as an alternative or adjunct to changes in SDe, maximum conductance can be adjusted by altering stomatal dimensions (Maherali et al., 2002; Franks et al., 2009). The concept underlying the use of SI as a proxy for pCO2 is that plants may change SI as a means of modulating SDe.

2. Evolution, extinction, the Ginkgo paradox and NLE approaches

Although there are strong plastic (developmental) responses in both SDe and SI (Royer, 2001), there is clear evidence of genetic control of the pCO2 response. Thus, in Arabidopsis thaliana, the HIC gene codes for pCO2 responses in both SI and SDe (Gray et al., 2000), and a suite of other genes code for SI and SDe (Dong & Bergmann, 2010). Multiple genes code for pCO2 responses in SI and SDe in poplar and for SI and SDe in oak (Ferris et al., 2002; Gailing et al., 2008). In addition, responses vary markedly among species, not only in the absolute values of SI and SDe for a given pCO2, but also in how these parameters respond to changes in pCO2 (Fig. 2; see also Korner, 1988; Haworth et al., 2010). Some species show significant reverse trends (Atchison et al., 2000), and large differences can occur between closely related species (Fig. 3). Such variation among species suggests (contrary to assumptions) that the evolutionarily optimal relationship between stomatal frequency and environment is not constant, and that evolution has resulted in large differences in this optimum. Furthermore, there are large differences in stomatal function across major plant groups (Brodribb & McAdam, 2011). This makes single species’ proxies increasingly dubious across evolutionary time because such proxies assume that there has been no evolutionary change in the relationship. This is a particularly severe problem for proxies calibrated using experimentally induced responses, because these typically employ only a few individual plants. However, a component of evolutionary adaptation increases the genetic inference space of proxies calibrated using recent fossils (although they are still narrow because they are within species) (Royer, 2001).

image

Figure 2. Variation among species-specific linear regressions of stomatal density (a) and stomatal index (b) against atmospheric CO2 concentration since the early industrial era, based on data from herbarium specimens (crosses, angiosperms; squares, gymnosperms; triangles, ferns). Each point represents the estimated value at 320 ppm CO2 (representing the approximate mid-value for the datasets) vs the standardized slope (slope/estimated value at 320 ppm CO2). Note the high variability in responses among species for both stomatal density and stomatal index, and the presence of relatively large numbers of positive slopes (contrary to the expected relationship). Although relationships between stomatal frequency and levels of atmospheric CO2 are generally nonlinear (Beerling & Royer, 2002), the relationships shown here were approximately linear within the sampled range. Sources: Peñuelas & Matamala (1990); Beerling & Chaloner (1993); Kürschner et al. (1996); He et al. (1998); Rundgren & Beerling (1999); Atchison et al. (2000); Royer et al. (2001); Greenwood et al. (2003); Kouwenberg et al. (2003, 2004); Wagner et al. (2005); Eide & Birks (2006); Miller-Rushing et al. (2009); Gagen et al. (2010); Haworth et al. (2010).

Download figure to PowerPoint

image

Figure 3. The markedly different relationships of stomatal frequency with atmospheric CO2 concentration in Betula nana (crosses; modified from Finsinger & Wagner-Cremer, 2009) and B. pubescens (squares; modified from Eide & Birks, 2006). The two species co-occur, hybridize and are likely to be sister species (Jarvinen et al., 2004), but differ in ploidy level (B. pubescens is tetraploid, B. nana is diploid). (a) Stomatal density. (b) Stomatal index. Similar differences also occur within other genera, including Salix (McElwain et al., 1995; Rundgren & Beerling, 1999), Quercus (He et al., 1998) and Callitris (Haworth et al., 2010), without such differences in ploidy.

Download figure to PowerPoint

The problems resulting from proxies with narrow genetic inference spaces are well illustrated by considering the gymnosperm group, ginkgophytes. SI in this group has been a favoured stomatal proxy for pCO2 from c. 300 million yr ago to recent periods. Estimates have been made from extinct species from the Palaeogene, Mesozoic and even the late Palaeozoic (Retallack, 2001; Quan et al., 2009; Smith et al., 2010), and from recent fossils of the extant species. This proxy is calibrated using the only extant ginkgophyte, Ginkgo biloba, which shows a tight relationship between SI and pCO2 (Royer et al., 2001). However, this seemingly strong calibration may be a result of the narrow genetic and ecological range of Ginkgo biloba (Gong et al., 2008). The estimates using extinct ginkgophytes therefore include extreme extrapolation because the fossils came from a far more geographically widespread and presumably genetically diverse group of plants than G. biloba. Thus, fossil ginkgophytes are found in many parts of the world and are mostly considered to be from extinct species, or even genera (Taylor et al., 2009). In this light, there is every reason to suspect that the SI–pCO2 relationships for extinct ginkgophytes may have differed from that of the extant species. Attempts to focus on fossils that are morphologically similar to G. biloba (e.g. Royer et al., 2001; Bonis et al., 2010; Smith et al., 2010), may reduce, but will certainly not eliminate, the potential for the fossil taxa to show different stomata–pCO2 relationships from the extant species. Thus, palaeoproxies derived from G. biloba appear to be highly vulnerable to the effects of evolution and extinction.

This problem is called the ‘Ginkgo paradox’. Ginkgophytes have been favoured as a proxy because of the rich and ancient fossil record of the group (Taylor et al., 2009) and the strong extant relationship in the calibration set (Royer et al., 2001). However, one likely reason for the strength of calibration (the limited genetic range in G. biloba) is also a reason to expect high uncertainty in pCO2 reconstructions based on fossil ginkgophytes. The significance of this problem should increase with the age of the fossils, through the accumulating effects of extinction and evolution. Thus, when genetic variation in the leaf–environment relationship forces one to restrict the proxy to a given taxon, the taxon that provides the best model in the modern world may be the least appropriate choice, especially for estimates into deep time. Parallel logic can be applied to other models (such as geographically local models) that gain precision by using a narrow inference space.

Because they employ multiple species, NLE models (McElwain, 1998) have the potential to exhibit wider genetic inference spaces than single species’ proxies. However, applications of the NLE approach can have limitations that illustrate the need to critically appraise the nature of the inference space. Thus, Haworth et al. (2005) estimated pCO2 from fossils of an extinct family of Mesozoic conifers using a NLE model calibrated using three members of the extant conifer family, Cupressaceae, and one angiosperm. The angiosperm (Salicornia virginica) should be considered as irrelevant because it is a succulent species of saline, semi-aquatic environments (Kubitzki et al., 1993), which is very likely to be functionally different from a Mesozoic conifer. The calibration therefore depends on the extinct conifer showing the same responses to pCO2 as a morphologically similar, but phylogenetically distinct, group of extant conifers. It does so by assuming that similarity in form and inferred ecology imply similarity in function (McElwain, 1998). However, this assumption is contradicted by the work of Haworth et al. (2010), which showed that the SI–pCO2 relationship of one of the species used in calibration, Callitris rhomboidea, is qualitatively different from that of Callitris oblonga, which is very similar in morphology (Offler, 1984) and occurs in comparable and geographically overlapping habitats (Hill, 1998).

3. Causes of variation among species and within plants

SI and SDe can be affected by irradiance level, atmospheric moisture, local water availability, nutritional status and leaf economic strategy (Beerling et al., 1992; Atchison et al., 2000; Hovenden & Vander Schoor, 2004, 2006; Lake & Woodward, 2008; Sekiya & Yano, 2008; Casson & Hetherington, 2010). Changes in any of these factors can therefore result in errors in stomata-based individual estimates of past pCO2. Large systematic differences in the stomatal frequency–pCO2 relationship may reflect the evolution of major ecological differences among species (notably the evolution of greater or lesser tolerance to shade, drought or poor soils). However, given the evidence for evolutionary niche conservatism (Losos, 2008), it is reasonable to expect that the development of such large biases will tend to be relatively slow. Furthermore, Beerling (1999) argued that SI was less biased than SDe by external factors.

Stomatal frequency–pCO2 relationships are affected by modification of the dimensions of stomatal apertures. Although such changes could result in large biases in the estimates of pCO2 from stomatal frequency, stomatal aperture dimensions can often be detected from the fossil preparations used to count stomata. As a result, it may be possible to identify some of the biases, and even use them in the development of improved proxies (see Section V.3). However, large differences in the stomatal frequency–pCO2 relationship have been observed between closely related species without comparable differences in stomatal aperture dimensions. For example, two sister species, Betula nana and B. pubescens, have similar stomatal pore lengths (Wagner et al., 2000), but show large differences in the relationships of both SI and SDe to pCO2 (Fig. 3). Whether such discrepancies are widespread is unknown because of a paucity of comparative studies of closely related species, but this discrepancy should create doubt about the evolutionary stability of the relationship between stomatal frequency–pore size and pCO2. This area is clearly ripe for investigation.

4. Environmental inference spaces and the problem of curvilinear relationships

The stomatal proxies for pCO2 are often adversely affected by having narrow environmental inference spaces. Calibrations from herbarium specimens fall within the range from early industrial to recent levels. Calibrations using experimental induction increase this span somewhat. However, there are virtually no data exploring relationships at very high pCO2, with upper values in experimentally induced responses mostly of the order of approximately twice contemporary values. Estimates for periods in which pCO2 was possibly higher than this range (e.g. much of the Palaeogene, Palaeozoic and Mesozoic) are extrapolations. Although the relationship for many species is approximately linear within the range of recent change in pCO2, it becomes increasingly nonlinear at higher levels (Beerling et al., 2009). At high pCO2, it is reasonable to expect that the stomatal frequency–pCO2 relationship will have a positive asymptote because of the need for a minimum number of stomata to maintain a transpiration stream for nutrient supply or to cool the leaf (Upchurch & Mahan, 1988). As noted by Royer (2001), the resulting flat relationship means that currently employed relationships not only have high uncertainties at high pCO2, but can fail to detect such levels – fossils from high-pCO2 environments may generate stomatal frequencies that estimate much lower pCO2 (Fig 4). Beerling et al. (2009) attempted to address this problem using an empirical curve, but, because their relationship curved downwards at the highest calibration point, it also lacked power to estimate high pCO2. Such problems can be significant because the stomatal-based proxies sometimes conflict with estimates of much higher pCO2 from other proxies (Beerling et al., 2009). These issues with curvilinearity also affect proxies employing NLE approaches (McElwain, 1998).

image

Figure 4. A comparison between ‘ true’ and fitted curves for stomatal index vs atmospheric CO2 concentration, showing how the extrapolation of flattening curves can lead to misleadingly low estimates. Note that the estimate of c. 510 ppm CO2 is based on the hypothetical observation from 2000 ppm CO2. The estimated response curve follows the formula for stomatal index in Ginkgo biloba developed by Royer et al. (2001). The ‘true’ response is hypothetical, but approximates Beerling et al.’s (2009) empirical fit for observations up to 500 ppm CO2 and, at higher pCO2, follows an asymptotic curve parallel to the Royer et al. (2001) curve.

Download figure to PowerPoint

The stomatal frequency response at pCO2 below the calibration range (e.g. during glacials) is also poorly known. There are very few experimental investigations of below ambient pCO2 to provide empirical relationships (Hovenden & Schimanski, 2000; Gerhart & Ward, 2010), and the upper values for SI and SDe must presumably be constrained by the available space on the leaf lamina.

5. Other biases

Diagenetic shrinkage of cuticles (see Section III.3) will affect SDe, but will have little impact on SI. A few studies have investigated possible taphonomic effects on SDe and SI (Uhl & Kerp, 2005). Biases towards robust and possibly small leaves may be important.

6. Overall uncertainties

Considerable uncertainty in the stomatal-based proxies relates to evolutionary adaptation and extinction, as shown by their failure to pass the cross-validation test of comparison across species. However, the impacts of evolution and extinction on the proxies are difficult to quantify by the observation of the modern or recent world. Extrapolation to high levels of pCO2, where the stomatal frequency–pCO2 relationship flattens, creates very high uncertainty for any estimates for pre-Neogene periods. Although SI shows better empirical relationships than SDe to pCO2, SI-based proxies are exposed to the same biases as SDe-based proxies.

These uncertainties appear to be large for all periods, except for the Holocene, and become much greater for more ancient periods. The extreme examples of this are the Mesozoic and Palaeozoic estimates from fossils of extinct lineages (Retallack, 2001; Haworth et al., 2005; Bonis et al., 2010).

V. Steps forward

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

Research towards the development of improved proxies should focus on the minimization of the risks involved in extrapolating into the past. I would like to highlight some prospective means of achieving these goals. Many of these involve the incorporation of more information into the models or better mechanistic bases (especially those in which the response has a clear biomechanical or biophysical basis), as well as employing other ways of minimizing phylogenetic/evolutionary impacts on the proxies.

1. Multiple proxy and cross-validation approaches

The use of multiple proxies provides considerable scope and has been applied to the estimation of both past climates (e.g. Yang et al., 2007) and levels of atmospheric CO2 (e.g. Roth-Nebelsick et al., 2004). A logically comparable approach to using multiple proxies is to cross-validate against existing estimates (e.g. Chen et al., 2001 for pCO2), although it is worth noting that estimates from different proxies are contradictory for many periods (Royer et al., 2001). Furthermore, successful cross-validation does not provide evidence that the methods are valid universally, just that the methods have worked under those conditions.

The components of individual proxies act in series, so that, if one step fails, the whole proxy comes into question. By contrast, multiple proxies work in parallel, so that congruent results from multiple proxies increase the chance of a valid answer. To understand the cumulative value of multiple proxies and cross-validation, one can consider the multiplicative way in which uncertainties combine (Sokal & Rohlf, 1995). As a hypothetical example, let us consider two convergent, but independent, proxies which have a 50% (i.e. P = 0.5) chance that the true value of the target parameter falls within a certain range. When these proxies are used together, the probability that the true value will fall in this range only improves to 75% (P = 1 − 0.52). However, the probabilities rapidly become more favourable if the individual proxies have higher certainties (e.g. two 80% values combine to give 96%; i.e. P = 1 − 0.22), or if there are a greater number of convergent proxies. Although it is rarely possible to make such precise calculations, these examples demonstrate that the combination of two poor proxies will lead to a relatively poor combined result.

Multiple proxy approaches also provide an objective means of identifying erroneous estimates, through the presence of contrasting results from different proxies. However, the presence of such anomalies leads to the problem of choosing among alternatives. Workers must avoid favouring the proxy with which they are most familiar. In addition, multiple proxy approaches can generate misleading inferences if several proxies are biased in the same way. For example, natural selection may affect different proxies in similar ways.

An important source of proxies to supplement foliar physiognomic proxies is the widely used taxonomic approach, which includes indicator species and coexistence methods (Mosbrugger & Utescher, 1997). Typically, the taxonomic approach assumes that the relevant palaeoclimate fell within the observed bioclimatic range of the living relatives of the fossil, or at least that deviations from this rule can be identified (Wolfe, 1995). This approach shows limitations that are similar to those affecting foliar physiognomic approaches – the observed and potential ranges of living species may differ because the species’ range has been altered by changes in the abiotic or biotic environment, or by evolution or selective extinction (Jordan, 1997a). It also depends on the accurate identification of the fossils and nearest living relatives. However, the presence of many different taxa in many fossil assemblages means that this approach contains a large amount of potentially useful palaeoclimatic information, because each taxon identified provides a separate source of evidence (Mosbrugger & Utescher, 1997).

Multi-proxy approaches to the estimation of past levels of CO2 can employ nonstomatal proxies (e.g. Pagani et al., 1999, 2005; Hönisch et al., 2009), as well as stomatal frequency from a range of taxa, although attempts to date have involved a small range of species (e.g. Royer et al., 2001; Roth-Nebelsick et al., 2004).

2. Resolving the problem of regional differences in leaf–climate relationships using multivariate approaches

There may be leaf characters that can be incorporated into multivariate analyses to compensate for regional biases and therefore reduce the adverse effects of variation in leaf–climate relationships. One such opportunity is based on the concept that large differences in the degree of scleromorphy and the incidence of deciduousness may be major contributors to the regional differences in leaf–climate relationships (as discussed in Section III.2). Leaf mass per unit area, a widely used measure reflecting scleromorphy and leaf lifespan (Wright et al., 2004b), can be predicted from the area and petiole width of leaves (Royer et al., 2007). This model has a very high power to predict average leaf mass per unit area at the site level (r2 > 0.9) (Royer et al., 2007), and can differentiate between deciduous and evergreen species to the same level (G. J. Jordan, unpublished). Furthermore, it has a clear biomechanical component and appears to apply globally (Royer et al., 2007). The incorporation of petiole characters in multivariate models thus has the potential to improve leaf physiognomic climate models. Although this approach cannot eliminate the potential for evolutionary history confounding the models entirely, it does increase the chance that biogeographical noise will be over-ridden by the signal from convergent evolution.

The limitations with multivariate analyses mentioned in Section III.3 can be overcome by alternative methods of analysis, especially when combined with the redefinition of characters. Stranks & England (1997) proposed a more robust methodology than any currently employed. They used resemblance functions, in which palaeoclimatic estimates are based on the multivariate similarity of the fossil assemblage to samples in the calibration set. The use of resemblance functions has several advantages over current approaches. It considers all physiognomic data in an unbiased way and does not assume linearity in leaf–climate relationships. It also facilitates operation at the individual taxon level (i.e. making separate estimates for each species in a flora, and then integrating these data to give overall estimates). This simplifies methods of phylogenetic adjustment, makes the identification and/or exclusion of anomalous taxa possible and facilitates the honest estimation of uncertainty. Resemblance functions have not been taken up by the physiognomic community, perhaps because Stranks & England’s (1997) implementation employed correspondence analysis of CLAMP-type data and generated larger standard errors than those claimed for the approach of Wolfe (1995). However, Jordan (1997b) showed that the latter greatly exaggerated the accuracy of the predictions. Furthermore, there is no need to use correspondence analysis. The availability of continuous leaf characters that show monotonic relationships to environment (Peppe et al., 2011) would allow the use of more powerful and robust similarity measures (such as the Euclidean distance of suitably transformed variables). An alternative approach is to use physiognomic evidence to choose among alternative models. Thus, Teodoridis et al. (2011) proposed a physiognomic rule for choosing between two alternative models in CLAMP. However, this approach is less general than the resemblance function approach and does not overcome the intrinsically local nature of the CLAMP data in its current manifestation.

3. Leaf veins and stomatal modelling

The realization that changes in stomatal aperture dimensions can bias stomatal frequency proxies has led to research into improved proxies (Wynn, 2003). Many aspects of stomatal form can be measured on the cuticle preparations used to determine SI and SDe (the main exception is stomatal depth), which raises the possibility of developing proxies based on the modelling of stomatal responses to CO2. Wynn (2003) proposed a theoretical framework relating stomatal frequency to CO2, Roth-Nebelsick (2007) modeled gas exchange through stomata and Konrad et al. (2008) developed a mechanistic model of the response of stomata to CO2 incorporating stomatal dimensions. Until now, these models have mainly been used to assess the responses of vegetation to changes in atmospheric CO2 concentration (De Boer et al., 2011; Lammertsma et al., 2011). However, it may be possible to elaborate Konrad et al.’s (2008) model into a mechanistic predictive model that can be used to estimate past pCO2.

In addition, an increasing body of evidence shows that the leaf vein density (length of veins per unit lamina area) is closely linked to SDe through co-ordination of the capacity to supply water to the leaves (veins) with the maximum demand for water (stomata) (Brodribb et al., 2007). However, vein density is much less subject to some of the factors that limit the value of SDe as an environmental palaeoproxy. In particular, vein density is much more stable than SDe across genotypes and species, and therefore should be much less vulnerable than SDe to evolutionary changes (Sack & Frole, 2006; Noblin et al., 2008; Boyce et al., 2009). Vein density can also be measured on well-preserved impression fossils, on which it is usually impossible to count stomata. The potential for use as a proxy for levels of atmospheric CO2 is further supported by optimization modelling predicting the effect of CO2 on vein density (Brodribb & Feild, 2010). However, like stomatal characters, vein density responds to a range of parameters that affect assimilation, including light environment (Uhl & Mosbrugger, 1999), and more work is needed before either vein density or stomatal modelling can be used as an effective proxy either in isolation or in combination.

VI. Synthesis

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References

This discussion of leaf-based proxies suggests that, before using a proxy, one should consider a series of inter-related questions (listed in Table 1) on the relationship between the leaves and the target parameter. These questions relate to the genetic and environmental inference spaces of the proxy, the assumptions necessary to make estimates, the likelihood of these assumptions being satisfied, and the consequences of their violation. Stomatal frequency and foliar physiognomic proxies illustrate some key points. The stomatal frequency proxies are mostly based on relatively direct responses to pCO2, but their inference spaces are strongly constrained genetically, so that the proxies are highly susceptible to adaptation and other evolutionary changes. By contrast, the foliar physiognomic proxies may be more buffered against evolutionary changes because they are based on considerably wider genotypic ranges. However, they represent much less direct responses to the relevant environmental parameters, and are therefore strongly empirical. Furthermore, because foliar physiognomic proxies largely depend on community assembly processes, they are vulnerable to phylogenetic effects resulting from assembly. In each case, the genetically and/or environmentally local nature of the proxies makes them vulnerable to environmental and/or evolutionary changes to the leaf–environment relationship. The Ginkgo paradox illustrates how proxies with narrow inference spaces can show lower statistical uncertainties than proxies with wider inference spaces, but can have high true uncertainties because of greater extrapolation.

Table 1.   Key questions that should be considered in developing and using biological proxies, based on the discussion and examples in Sections III and IV
QuestionComments
Which aspect of the environment is the plant responding to directly? Which plant trait responds directly to this environmental characteristic?Understanding the mechanistic basis of the proxy may help answer these questions, which may aid in the determination of the likelihood of biases from changing correlations among traits
What are the environmental and genetic ranges of the calibration data? How much environmental and/or genetic extrapolation is involved in using the proxy?Even if the values of the target parameter from the time and place of fossilization fall within the range of the calibration set, other aspects of the environment may have differed from modern conditions
Does the relationship between the leaf trait and the target parameter vary with other aspects of the environment?Combined with environmental extrapolation, this can result in changes to the leaf–environment relationship, thus biasing the proxy
How much is the relationship between the leaf trait and the target parameter affected by genetic variation?This helps determine the likelihood that the proxy will be affected by extinction, evolution and (for some proxies) immigration. It also encompasses phylogenetic effects
Are there methodological problems with data collection or analysis?Consider this on a case-by-case basis, including problems with extrapolation of potentially nonlinear relationships
What taphonomic, diagenetic and collecting biases can affect the data?These need to be considered on a case-by-case basis

The nature of the uncertainties discussed here means that leaf-based proxies tend to become progressively less reliable as a fossil becomes more ancient (e.g. Uhl, 2006). This is because extinction and biological and landscape evolution have irreversibly altered the biotic and physical environment. The uniformitarian assumption implicit in the application of proxies that ignore extinction and evolution (e.g. geographically local models and single species’ models) is that patterns were the same in the past, whereas the uniformitarian principle is most sound when assuming that the underlying processes have remained unchanged (Gould, 1965). This means that future work should concentrate on proxies with stronger links to biomechanical or biochemical processes, although the stomatal proxies show that even proxies underpinned by a clear mechanism connecting leaves to the target parameter need to be appraised carefully.

Although the magnitude of the increase in uncertainty with time is highly uncertain and idiosyncratic, it may be possible to provide some guidelines to assess whether these processes are likely to have had major impacts on specific proxies. Rules of thumb suggesting robustness in proxies based on plastic responses within single species are as follows: the species involved is the same extant species used in calibrating the proxy; closely related species show similar leaf–environment relationships; and the environmental range of the calibration set is sufficiently broad to be able to assume that the fossil came from an analogous environment. For proxies based on average scores for multiple species, the key test is that the proxy is consistent across different environments and community compositions. Failing this, the proxy is more likely to be valid if there is evidence of similar environment and floristic compositions at the times and places of calibration and fossilization.

The proxies considered here are quantitative. It can be argued that qualitative proxies may be more robust – in that they have the lower aspirations of showing trends and broad patterns rather than providing numerical estimates. However, nothing precludes the use of quantitative methods in a qualitative way – one may be sceptical of the numerical estimate provided, but may accept the presence of a qualitative difference.

All the major issues discussed here apply widely to biologically based palaeoproxies. However, the vulnerability of each proxy needs to be considered separately in the logical framework set out in this review. There appears to be potential to develop more robust proxies in each case by incorporating more information into the proxies. Furthermore, although the uncertainties of individual proxies are often great, congruent estimates from multiple proxies can be useful.

References

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Key concepts in the uncertainty of proxy evidence
  5. III. Uncertainties in major foliar physiognomic proxies of MAT
  6. IV. Stomatal density and stomatal index
  7. V. Steps forward
  8. VI. Synthesis
  9. Acknowledgements
  10. References
  • Adams JM, Green WA, Zhang Y. 2008. Leaf margins and temperature in the North American flora: recalibrating the paleoclimatic thermometer. Global and Planetary Change 60: 523534.
  • Agrawal AA. 2001. Ecology: phenotypic plasticity in the interactions and evolution of species. Science 294: 321326.
  • Aizen MA, Ezcurra C. 2008. Do leaf margins of the temperate forest flora of southern South America reflect a warmer past? Global Ecology and Biogeography 17: 164174.
  • Atchison JM, Head LM, McCarthy LP. 2000. Stomatal parameters and atmospheric change since 7500 years before present: evidence from Eremophila deserti (Myoporaceae) leaves from the Flinders Ranges region, South Australia. Australian Journal of Botany 48: 223232.
  • Axelrod DI. 1966. Origin of deciduous and evergreen habits in temperate forests. Evolution 20: 115.
  • Bailey IW, Sinnott EW. 1915. A botanical index of Cretaceous and Tertiary climates. Science 41: 831934.
  • Barclay RS, McElwain JC, Sageman BB. 2010. Carbon sequestration activated by a volcanic CO2 pulse during Ocean Anoxic Event 2. Nature Geoscience 3: 205208.
  • Beerling DJ. 1999. Stomatal density and index: theory and application. In: Jones TP, Rowe NP, eds. Fossil plants and spores: modern techniques. London, UK: Geological Society of London.
  • Beerling DJ, Chaloner WG. 1992. Stomatal density as an indicator of atmospheric CO2 concentration. Holocene 2: 7178.
  • Beerling DJ, Chaloner WG. 1993. Stomatal density responses of Egyptian Olea europaea L. leaves to CO2 change since 1327 BC. Annals of Botany 71: 431435.
  • Beerling DJ, Chaloner WG. 1994. Atmospheric CO2 changes since the last glacial maximum: evidence from the stomatal density record of fossil leaves. Review of Palaeobotany and Palynology 81: 1117.
  • Beerling DJ, Chaloner WG, Huntley B, Pearson JA, Tooley MJ, Woodward FI. 1992. Variations in the stomatal density of Salix herbacea L. under the changing atmospheric CO2 concentrations of late- and post-glacial time. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 336: 215224.
  • Beerling DJ, Fox A, Anderson CW. 2009. Quantitative uncertainty analyses of ancient atmospheric CO2 estimates from fossil leaves. American Journal of Science 309: 775787.
  • Beerling DJ, Royer DL. 2002. Reading a CO2 signal from fossil stomata. New Phytologist 153: 387397.
  • Bonis NR, Van Konijnenburg-Van Cittert JHA, Kürschner WM. 2010. Changing CO2 conditions during the end-Triassic inferred from stomatal frequency analysis on Lepidopteris ottonis (Goeppert) Schimper and Ginkgoites taeniatus (Braun) Harris. Palaeogeography, Palaeoclimatology, Palaeoecology 295: 146161.
  • Boyce CK, Brodribb TJ, Feild TS, Zwieniecki MA. 2009. Angiosperm leaf vein evolution was physiologically and environmentally transformative. Proceedings of the Royal Society B: Biological Sciences 276: 17711776.
  • Briggs DEG. 1999. Molecular taphonomy of animal and plant cuticles: selective preservation and diagenesis. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 354: 717.
  • Brodribb TJ, Feild TS. 2010. Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification. Ecology Letters 13: 175183.
  • Brodribb TJ, Feild TS, Jordan GJ. 2007. Leaf maximum photosynthetic rate and venation are linked by hydraulics. Plant Physiology 144: 18901898.
  • Brodribb TJ, McAdam SAM. 2011. Passive origins of stomatal control in vascular plants. Science 331: 582585.
  • Burnham RJ, Pitman NCA, Johnson KR, Wilf P. 2001. Habitat-related error in estimating temperatures from leaf margins in a humid tropical forest. American Journal of Botany 88: 10961102.
  • Casson SA, Hetherington AM. 2010. Environmental regulation of stomatal development. Current Opinion in Plant Biology 13: 9095.
  • Chen LIQ, Li CS, Chaloner WG, Beerling DJ, Sun QIG, Collinson ME, Mitchell PL. 2001. Assessing the potential for the stomatal characters of extant and fossil Ginkgo leaves to signal atmospheric CO2 change. American Journal of Botany 88: 13091315.
  • Christophel DC, Greenwood DR. 1989. Changes in climate and vegetation in Australia during the Tertiary. Review of Palaeobotany & Palynology 58: 95109.
  • Cleal CJ, Shute CH. 2007. The effect of drying on epidermal cell parameters preserved on plant cuticles. Acta Palaeobotanica 47: 315326.
  • De Boer HJ, Lammertsma EI, Wagner-Cremer F, Dilcher DL, Wassen MJ, Dekker SC. 2011. Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2. Proceedings of the National Academy of Sciences, USA 108: 40414046.
  • Dilcher DL, Kowalski EA, Wiemann MC, Hinojosa LF, Lott TA. 2009. A climatic and taxonomic comparison between leaf litter and standing vegetation from a Florida swamp woodland. American Journal of Botany 96: 11081115.
  • Dong J, Bergmann DC. 2010. Stomatal patterning and development. Current Topics in Developmental Biology 91: 267297.
  • Ehleringer JR, Cerling TE, Dearing DM, eds. 2005. A history of atmospheric CO2 and its effects on plants, animals, and ecosystems. New York, NY, USA: Springer.
  • Eide W, Birks HH. 2006. Stomatal frequency of Betula pubescens and Pinus sylvestris shows no proportional relationship with atmospheric CO2 concentration. Nordic Journal of Botany 24: 327339.
  • Falconer DS, Mackay TFC. 1996. Introduction to quantitative genetics, 4thedn. Harlow, UK: Addison Wesley Longman.
  • Feild TS, Sage TL, Czerniak C, Iles WJD. 2005. Hydathodal leaf teeth of Chloranthus japonicus (Chloranthaceae) prevent guttation-induced flooding of the mesophyll. Plant, Cell & Environment 28: 11791190.
  • Ferris R, Long L, Bunn SM, Robinson KM, Bradshaw HD, Rae AM, Taylor G. 2002. Leaf stomatal and epidermal cell development: identification of putative quantitative trait loci in relation to elevated carbon dioxide concentration in poplar. Tree Physiology 22: 633640.
  • Finsinger W, Wagner-Cremer F. 2009. Stomatal-based inference models for reconstruction of atmospheric CO2 concentration: a method assessment using a calibration and validation approach. Holocene 19: 757764.
  • Franks PJ, Drake PL, Beerling DJ. 2009. Plasticity in maximum stomatal conductance constrained by negative correlation between stomatal size and density: an analysis using Eucalyptus globulus. Plant, Cell & Environment 32: 17371748.
  • Gagen M, Finsinger W, Wagner-Cremer F, McCarroll D, Loader NJ, Robertson I, Jalkanen R, Young G, Kirchhefer A. 2010. Evidence of changing intrinsic water-use efficiency under rising atmospheric CO2 concentrations in Boreal Fennoscandia from subfossil leaves and tree ring 13C ratios. Global Change Biology 17: 10641072.
  • Gailing O, Langenfeld-Heyser R, Polle A, Finkeldey R. 2008. Quantitative trait loci affecting stomatal density and growth in a Quercus robur progeny: implications for the adaptation to changing environments. Global Change Biology 14: 19341946.
  • Gerhart LM, Ward JK. 2010. Plant responses to low [CO2] of the past. New Phytologist 188: 674695.
  • Gong W, Chen C, Dobes C, Fu CX, Koch MA. 2008. Phylogeography of a living fossil: Pleistocene glaciations forced Ginkgo biloba L. (Ginkgoaceae) into two refuge areas in China with limited subsequent postglacial expansion. Molecular Phylogenetics and Evolution 48: 10941105.
  • Gould SJ. 1965. Is uniformitarianism necessary? American Journal of Science 263: 223228.
  • Graham A. 1999. Late Cretaceous and Cenozoic history of North American vegetation. Oxford, UK: Oxford University Press.
  • Gray JE, Holroyd GH, Van Der Lee FM, Bahrami AR, Sijmons PC, Woodward FI, Schuch W, Hetherington AM. 2000. The HIC signalling pathway links CO2 perception to stomatal development. Nature 408: 713716.
  • Greenwood DR. 1992. Taphonomic constraints on foliar physiognomic interpretations of late Cretaceous and Tertiary palaeoclimates. Review of Palaeobotany and Palynology 71: 149190.
  • Greenwood DR. 2005. Leaf margin analysis: taphonomic constraints. Palaios 20: 498505.
  • Greenwood DR, Scarr MJ, Christophel DC. 2003. Leaf stomatal frequency in the Australian tropical rainforest tree Neolitsea dealbata (Lauraceae) as a proxy measure of atmospheric pCO2. Palaeogeography, Palaeoclimatology, Palaeoecology 196: 375393.
  • Greenwood DR, Wilf P, Wing SL, Christophel DC. 2004. Paleotemperature estimation using leaf-margin analysis: is Australia different? Palaios 19: 129139.
  • Greenwood DR, Wing SL. 1995. Eocene continental climates and latitudinal temperature gradients. Geology 23: 10441048.
  • Gregory-Wodzicki KM. 2000. Relationships between leaf morphology and climate, Bolivia: implications for estimating paleoclimate from fossil floras. Paleobiology 26: 668688.
  • Groves RH. 1999. Present vegetation types. In: Orchard AE, ed. Flora of Australia Vol. 1, 2nd edn. Canberra, Australia: Australian Biological Resources Study, 369401.
  • Halloy SRP, Mark AF. 1996. Comparative leaf morphology spectra of plant communities in New Zealand, the Andes and the European Alps. Journal of the Royal Society of New Zealand 26: 4178.
  • Haworth M, Heath J, McElwain JC. 2010. Differences in the response sensitivity of stomatal index to atmospheric CO2 among four genera of Cupressaceae conifers. Annals of Botany 105: 411418.
  • Haworth M, Hesselbo SP, McElwain JC, Robinson SA, Brunt JW. 2005. Mid-Cretaceous pCO2 based on stomata of the extinct conifer Pseudofrenelopsis (Cheirolepidiaceae). Geology 33: 749752.
  • He X, Lin Y, Lin J, Hu Y. 1998. Relationship between stomatal density and the changes of atmospheric CO2 concentrations. Chinese Science Bulletin 43: 928930.
  • Hill KD. 1998. Pinophyta. In: McCarthy PM, ed. Flora of Australia, Vol. 48. Canberra, Australia: Australian Biological Resources Study, 545596.
  • Hill RS. 2004. Origins of the southeastern Australian vegetation. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 359: 15371549.
  • Hinojosa LF, Armesto JJ, Villagrán C. 2006. Are Chilean coastal forests pre-Pleistocene relicts? Evidence from foliar physiognomy, palaeoclimate, and phytogeography. Journal of Biogeography 33: 331341.
  • Hinojosa LF, Perez F, Gaxiola A, Sandoval I. 2011. Historical and phylogenetic constraints on the incidence of entire leaf margins: insights from a new South American model. Global Ecology and Biogeography 20: 380390.
  • Hönisch B, Hemming NG, Archer D, Siddall M, McManus JF. 2009. Atmospheric carbon dioxide concentration across the mid-Pleistocene transition. Science 324: 15511554.
  • Hovenden MJ, Schimanski LJ. 2000. Genotypic differences in growth and stomatal morphology of Southern Beech, Nothofagus cunninghamii, exposed to depleted CO2 concentrations. Functional Plant Biology 27: 281287.
  • Hovenden MJ, Vander Schoor JK. 2004. Nature vs nurture in the leaf morphology of Southern beech, Nothofagus cunninghamii (Nothofagaceae). New Phytologist 161: 585594.
  • Hovenden MJ, Vander Schoor JK. 2006. The response of leaf morphology to irradiance depends on altitude of origin in Nothofagus cunninghamii. New Phytologist 169: 291297.
  • Huff PM, Wilf P, Azumah EJ. 2003. Digital future for paleoclimate estimation from fossil leaves? Preliminary results. Palaios 18: 266274.
  • Jarvinen P, Palme A, Morales LO, Lannenpaa M, Keinanen M, Sopanen T, Lascoux M. 2004. Phylogenetic relationships of Betula species (Betulaceae) based on nuclear ADH and chloroplast matK sequences. American Journal of Botany 91: 18341845.
  • Jordan GJ. 1997a. Eocene continental climates and latitudinal temperature gradients: comment. Geology 23: 1054.
  • Jordan GJ. 1997b. Uncertainty in palaeoclimatic reconstructions based on leaf physiognomy. Australian Journal of Botany 45: 527547.
  • Jordan GJ, Brodribb TJ. 2007. Incontinence in aging leaves: deteriorating water relations with leaf age in Agastachys odorata (Proteaceae), a shrub with very long-lived leaves. Functional Plant Biology 34: 918924.
  • Kennedy EM, Spicer RA, Rees PM. 2002. Quantitative palaeoclimate estimates from Late Cretaceous and Paleocene leaf floras in the northwest of the South Island, New Zealand. Palaeogeography, Palaeoclimatology, Palaeoecology 184: 321345.
  • Konrad W, Roth-Nebelsick A, Grein M. 2008. Modelling of stomatal density response to atmospheric CO2. Journal of Theoretical Biology 253: 638658.
  • Korner C. 1988. Does global increase of CO2 alter stomatal density? Flora 181: 253257.
  • Kouwenberg LLR, Kürschner WM, Visscher H. 2004. Changes in stomatal frequency and size during elongation of Tsuga heterophylla needles. Annals of Botany 94: 561569.
  • Kouwenberg LLR, McElwain JC, Kürschner WM, Wagner F, Beerling DJ, Mayle FE, Visscher H. 2003. Stomatal frequency adjustment of four conifer species to historical changes in atmospheric CO2. American Journal of Botany 90: 610619.
  • Kouwenberg L, Wagner R, Kürschner W, Visscher H. 2005. Atmospheric CO2 fluctuations during the last millennium reconstructed by stomatal frequency analysis of Tsuga heterophylla needles. Geology 33: 3336.
  • Kowalski EA. 2002. Mean annual temperature estimation based on leaf morphology: a test from tropical South America. Palaeogeography, Palaeoclimatology, Palaeoecology 188: 141165.
  • Kowalski EA, Dilcher DL. 2003. Warmer paleotemperatures for terrestrial ecosystems. Proceedings of the National Academy of Sciences, USA 100: 167170.
  • Kubitzki K. 2007. The families and genera of vascular plants, Vol. 9. Flowering plants. Eudicots: Berberidopsidales, Buxales, Crossosomatales, Fabales P. P., Geraniales, Gunnerales, Myrtales P. P., Proteales, Saxifragales, Vitales, Zygophyllales, Clusiaceae Alliance, Passifloraceae Alliance, Dilleniaceae, Huaceae, Picramniaceae, Sabiaceae. Berlin, Germany: Springer.
  • Kubitzki K, Rohwer JG, Bittrich V. 1993. Families and genera of vascular plants, Vol. 2. Flowering plants. Dicotyledons: Magnoliid, Hamamelid, and Caryophyllid Families. New York, NY, USA: Springer Verlag.
  • Kürschner WM, Kvacek Z. 2009. Oligocene–Miocene CO2 fluctuations, climatic and palaeofloristic trends inferred from fossil plant assemblages in central Europe. Bulletin of Geosciences 84: 189202.
  • Kürschner WM, Van Der Burgh J, Visscher H, Dilcher DL. 1996. Oak leaves as biosensors of late Neogene and early Pleistocene paleoatmospheric CO2 concentrations. Marine Micropaleontology 27: 299312.
  • Lake JA, Woodward FI. 2008. Response of stomatal numbers to CO2 and humidity: control by transpiration rate and abscisic acid. New Phytologist 179: 397404.
  • Lammertsma EI, De Boer HJ, Dekker SC, Dilcher DL, Lotter AF, Wagner-Cremer F. 2011. Global CO2 rise leads to reduced maximum stomatal conductance in Florida vegetation. Proceedings of the National Academy of Sciences, USA 108: 40354040.
  • Lee DE, Lee WG, Mortimer N. 2001. Where and why have all the flowers gone? Depletion and turnover in the New Zealand Cenozoic angiosperm flora in relation to palaeogeography and climate. Australian Journal of Botany 49: 341356.
  • Little SA, Kembel SW, Wilf P. 2011. Paleotemperature proxies from leaf fossils reinterpreted in light of evolutionary history. PLoS ONE 12: e15161.
  • Losos JB. 2008. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecology Letters 11: 9951003.
  • Maherali H, Reid CD, Polley HW, Johnson HB, Jackson RB. 2002. Stomatal acclimation over a subambient to elevated CO2 gradient in a C3/C4 grassland. Plant, Cell & Environment 25: 557566.
  • Manter DK, Bond BJ, Kavanagh KL, Rosso PH, Filip GM. 2000. Pseudothecia of Swiss needle cast fungus, Phaeocryptopus gaeumannii, physically block stomata of Douglas fir, reducing CO2 assimilation. New Phytologist 148: 481491.
  • McElwain JC. 1998. Do fossil plants signal palaeoatmospheric CO2 concentration in the geological past? Philosophical Transactions of the Royal Society of London, B: Biological Sciences 353: 8396.
  • McElwain JC, Mayle FE, Beerling DJ. 2002. Stomatal evidence for a decline in atmospheric CO2 concentration during the younger Dryas stadial: a comparison with Antarctic ice core records. Journal of Quaternary Science 17: 2129.
  • McElwain JC, Mitchell FJ, Jones MB. 1995. Relationship of stomatal density and index of Salix cinerea to atmospheric carbon dioxide concentrations in the Holocene. Holocene 5: 216219.
  • McGlone MS, Duncan RJ, Hall GMJ, Allen RB. 2004. Winter leaf loss in the New Zealand woody flora. New Zealand Journal of Botany 42: 119.
  • Miller-Rushing AJ, Primack RB, Templer PH, Rathbone S, Mukunda S. 2009. Long-term relationships among atmospheric CO2, stomata, and intrinsic water use efficiency in individual trees. American Journal of Botany 96: 17791786.
  • Minchin PR. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Vegetation 69: 89107.
  • Momohara A. 1994. Floral and paleoenvironmental history from the late Pliocene to middle Pleistocene in and around central Japan. Palaeogeography, Palaeoclimatology, Palaeoecology 108: 281293.
  • Mosbrugger V, Utescher T. 1997. The coexistence approach – a method for quantitative reconstructions of Tertiary terrestrial palaeoclimate data using plant fossils. Palaeogeography, Palaeoclimatology, Palaeoecology 134: 6186.
  • Noblin X, Mahadevan L, Coomaraswamy IA, Weitz DA, Holbrook NM, Zwieniecki MA. 2008. Optimal vein density in artificial and real leaves. Proceedings of the National Academy of Sciences, USA 105: 91409144.
  • Offler CE. 1984. Extant and fossil Coniferales of Australia and New Guinea. Part 1: A study of the external morphology of the vegetative shoots of the extant species. Palaeontographica Abteilung B 193: 18120.
  • Pagani M, Arthur MA, Freeman KH. 1999. Miocene evolution of atmospheric carbon dioxide. Paleoceanography 14: 273292.
  • Pagani M, Zachos JC, Freeman KH, Tipple B, Bohaty S. 2005. Atmospheric science: marked decline in atmospheric carbon dioxide concentrations during the Paleogene. Science 309: 600603.
  • Parrish JT. 2001. Interpreting pre-Quaternary climate from the geologic record. New York, NY, USA: Columbia University Press.
  • Peñuelas J, Matamala R. 1990. Changes in N and S leaf content, stomatal density and specific leaf area of 14 plant species during the last three centuries of CO2 increase. Journal of Experimental Botany 41: 11191124.
  • Peppe DJ, Royer DL, Cariglino B, Oliver SY, Newman S, Leight E, Enikolopov G, Fernandez-Burgos M, Herrera F, Adams JM et al. 2011. Sensitivity of leaf size and shape to climate: global patterns and paleoclimatic applications. New Phytologist 190: 724739.
  • Peppe DJ, Royer DL, Wilf P, Kowalski EA. 2010. Quantification of large uncertainties in fossil leaf paleoaltimetry. Tectonics 29: article TC3015..
  • Potts BM, Jordan GJ. 1994. Genetic variation in the juvenile leaf morphology of Eucalyptus globulus Labill. ssp. globulus. Forest Genetics 1: 8195.
  • Quan C, Sun C, Sun Y, Sun G. 2009. High resolution estimates of paleo-CO2 levels through the Campanian (Late Cretaceous) based on Ginkgo cuticles. Cretaceous Research 30: 424428.
  • Retallack GJ. 2001. A 300-million-year record of atmospheric carbon dioxide from fossil plant cuticles. Nature 411: 287290.
  • Roth-Nebelsick A. 2007. Computer-based studies of diffusion through stomata of different architecture. Annals of Botany 100: 2332.
  • Roth-Nebelsick A, Utescher T, Mosbrugger V, Diester-Haass L, Walther H. 2004. Changes in atmospheric CO2 concentrations and climate from the Late Eocene to Early Miocene: palaeobotanical reconstruction based on fossil floras from Saxony, Germany. Palaeogeography, Palaeoclimatology, Palaeoecology 205: 4367.
  • Royer DL. 2001. Stomatal density and stomatal index as indicators of paleoatmospheric CO2 concentration. Review of Palaeobotany and Palynology 114: 128.
  • Royer DL, Kooyman RM, Little SA, Wilf P. 2009a. Ecology of leaf teeth: a multi-site analysis from an Australian subtropical rainforest. American Journal of Botany 96: 738750.
  • Royer DL, Meyerson LA, Robertson KM, Adams JM. 2009b. Phenotypic plasticity of leaf shape along a temperature gradient in Acer rubrum. PLoS ONE 4: art. e7653.
  • Royer DL, Sack L, Wilf P, Lusk CH, Jordan GJ, Niinemets Ü, Wright IJ, Westoby M, Cariglino B, Coley PD et al. 2007. Fossil leaf economics quantified: calibration, Eocene case study, and implications. Paleobiology 33: 574589.
  • Royer DL, Wilf P. 2006. Why do toothed leaves correlate with cold climates? Gas exchange at leaf margins provides new insights into a classic paleotemperature proxy. International Journal of Plant Sciences 167: 1118.
  • Royer DL, Wilf P, Janesko DA, Kowalski EA, Dilcher DL. 2005. Correlations of climate and plant ecology to leaf size and shape: potential proxies for the fossil record. American Journal of Botany 92: 11411151.
  • Royer DL, Wing SL, Beerling DJ, Jolley DW, Koch PL, Hickey LJ, Berner RA. 2001. Paleobotanical evidence for near present-day levels of atmospheric CO2 during part of the Tertiary. Science 292: 23102313.
  • Rundgren M, Beerling D. 1999. A Holocene CO2 record from the stomatal index of subfossil Salix herbacea L. leaves from northern Sweden. Holocene 9: 509513.
  • Sack L, Frole K. 2006. Leaf structural diversity is related to hydraulic capacity in tropical rain forest trees. Ecology 87: 483491.
  • Salisbury EJ. 1927. On the causes and ecological significance of stomatal frequency, with special reference to the woodland flora. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 216: 165.
  • Scheiner SM. 1993. Genetics and evolution of phenotypic plasticity. Annual Review of Ecology and Systematics 24: 3568.
  • Schoch PG, Zinsou C, Sibi M. 1980. Dependence of the stomatal index on environmental factors during stomatal differentiation in leaves of Vigna sinensis L.: 1. Effect of light intensity. Journal of Experimental Botany 31: 12111216.
  • Sekiya N, Yano K. 2008. Stomatal density of cowpea correlates with carbon isotope discrimination in different phosphorus, water and CO2 environments. New Phytologist 179: 799807.
  • Smith RY, Greenwood DR, Basinger JF. 2010. Estimating paleoatmospheric pCO2 during the Early Eocene Climatic Optimum from stomatal frequency of Ginkgo, Okanagan Highlands, British Columbia, Canada. Palaeogeography, Palaeoclimatology, Palaeoecology 293: 120131.
  • Sniderman JMK, Jordan GJ. 2011. Extent and timing of floristic exchange between Australian and Asian rainforests. Journal of Biogeography 38: doi:10.1111/j.1365-2699.2011.02519.x.
  • Sokal RR, Rohlf FJ. 1995. Biometry, 3rdedn. San Francisco, CA, USA: W. H. Freeman and Company.
  • Spicer RA. 1989. The formation and interpretation of plant fossil assemblages. Advances in Botanical Research 16: 95191.
  • Spicer RA. 2007. Recent and future developments of CLAMP: building on the legacy of Jack A. Wolfe. CFS Courier Forschungsinstitut Senckenberg 258: 109118.
  • Spicer RA, Harris NBW, Widdowson M, Herman AB, Guo S, Valdes PJ, Wolfe JA, Kelley SP. 2003. Constant elevation of southern Tibet over the past 15 million years. Nature 421: 622624.
  • Spicer RA, Herman AB, Kennedy EM. 2004. Foliar physiognomic record of climatic conditions during dormancy: Climate Leaf Analysis Multivariate Program (CLAMP) and the cold month mean temperature. Journal of Geology 112: 685702.
  • Spicer RA, Herman AB, Kennedy EM. 2005. The sensitivity of CLAMP to taphonomic loss of foliar physiognomic characters. Palaios 20: 429438.
  • Steart DC, Spicer RA, Bamford MK. 2010. Is southern Africa different? An investigation of the relationship between leaf physiognomy and climate in southern African mesic vegetation. Review of Palaeobotany and Palynology 162: 607620.
  • Stranks L, England P. 1997. The use of a resemblance function in the measurement of climatic parameters from the physiognomy of woody dicotyledons. Palaeogeography, Palaeoclimatology, Palaeoecology 131: 1528.
  • Su T, Xing YW, Liu YS, Jacques FMB, Chen WY, Huang YJ, Zhou ZK. 2010. Leaf margin analysis: a new equation from humid to mesic forests in China. Palaios 25: 234238.
  • Svenning J-C, Skov F. 2007. Ice age legacies in the geographical distribution of tree species richness in Europe. Global Ecology and Biogeography 16: 234245.
  • Taylor TN, Taylor EL, Krings M. 2009. Paleobotany: the biology and evolution of seed plants. Amsterdam, the Netherlands: Academic Press.
  • Tegelaar EW, Kerp H, Visscher H, Schenck PA, De Leeuw JW. 1991. Bias of the paleobotanical record as a consequence of variations in the chemical composition of higher vascular plant cuticles. Paleobiology 17: 133144.
  • Teodoridis V, Mazouch P, Spicer RA, Uhl D. 2011. Refining CLAMP – investigations towards improving the Climate Leaf Analysis Multivariate Program. Palaeogeography, Palaeoclimatology, Palaeoecology 299: 3948.
  • Ticha I. 1982. Photosynthetic characteristics during ontogenesis of leaves. 7. Stomata density and sizes. Photosynthetica 16: 375471.
  • Tiffney BH, Manchester SR. 2001. The use of geological and paleontological evidence in evaluating plant phylogeographic hypotheses in the Northern Hemisphere Tertiary. International Journal of Plant Sciences 162: S3S17.
  • Traiser C, Klotz S, Uhl D, Mosbrugger V. 2005. Environmental signals from leaves – a physiognomic analysis of European vegetation. New Phytologist 166: 465484.
  • Turner IM. 1994. Sclerophylly: primarily protective? Functional Ecology 8: 669675.
  • Uhl D. 2006. Fossil plants as palaeoenvironmental proxies – some remarks on selected approaches. Acta Palaeobotanica 46: 87100.
  • Uhl D, Kerp H. 2005. Variability of stomatal density and index in the Upper Permian conifer Quadrocladus Modler – a taphonomical case study. Palaeogeography, Palaeoclimatology, Palaeoecology 218: 205215.
  • Uhl D, Mosbrugger V. 1999. Leaf venation density as a climate and environmental proxy: a critical review and new data. Palaeogeography, Palaeoclimatology, Palaeoecology 149: 1526.
  • Upchurch DR, Mahan JR. 1988. Maintenance of constant leaf temperature by plants—II. Experimental observations in cotton. Environmental and Experimental Botany 28: 359366.
  • Van Hoof TB, Kaspers KA, Wagner F, Van De Wal RSW, Kürschner WM, Visscher H. 2005. Atmospheric CO2 during the 13th century AD: reconciliation of data from ice core measurements and stomatal frequency analysis. Tellus, Series B: Chemical and Physical Meteorology 57: 351355.
  • Van Hoof TB, Kürschner WM, Wagner F, Visscher H. 2006. Stomatal index response of Quercus robur and Quercus petraea to the anthropogenic atmospheric CO2 increase. Plant Ecology 183: 237243.
  • Vording B, Krings M, Kerp H. 2009. Reconstruction of late Pennsylvanian CO2 levels based on Odontopteris brardii (Pteridospermopsida, ?Medullosales) cuticles from France and Germany. Neues Jahrbuch fur Geologie und Palaontologie – Abhandlungen 254: 359372.
  • Wagner F, Bohncke SJP, Dilcher DL, Kürschner WM, Van Geel B, Visscher H. 1999. Century-scale shifts in early Holocene atmospheric CO2 concentration. Science 284: 19711973.
  • Wagner F, Dilcher DL, Visscher H. 2005. Stomatal frequency responses in hardwood-swamp vegetation from Florida during a 60-year continuous CO2 increase. American Journal of Botany 92: 690695.
  • Wagner F, Neuvonen S, Kürschner WM, Visscher H. 2000. The influence of hybridization on epidermal properties of birch species and the consequences for palaeoclimatic interpretations. Plant Ecology 148: 6169.
  • Westoby M, Leishman MR, Lord JM. 1995. On misinterpreting the ‘phylogenetic correction’. Journal of Ecology 83: 531534.
  • Wilf P. 1997. When are leaves good thermometers? A new case for leaf margin analysis. Paleobiology 23: 373390.
  • Wilf P, Wing SL, Greenwood DR, Greenwood CL. 1998. Using fossil leaves as paleoprecipitation indicators: an Eocene example. Geology 26: 203206.
  • Wing SL, Greenwood DR. 1993. Fossils and fossil climate: the case for equable continental interiors in the Eocene. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 341: 243252.
  • Wolfe JA. 1979. Temperature parameters of humid to mesic forests of eastern Asia and relation to forests of other regions of the northern hemisphere and Australasia. US Geological Survey Professional Paper 1106: 137.
  • Wolfe JA. 1993. A method of obtaining climatic parameters from leaf assemblages. US Geological Survey Bulletin 2040: 137.
  • Wolfe JA. 1995. Paleoclimatic estimates from Tertiary leaf assemblages. Annual Review of Earth and Planetary Sciences 23: 119142.
  • Wong SC, Cowan IR, Farquhar GD. 1979. Stomatal conductance correlates with photosynthetic capacity. Nature 282: 424426.
  • Woodward FI. 1987. Stomatal numbers are sensitive to increases in CO2 from pre-industrial levels. Nature 327: 617618.
  • Wright IJ, Groom PK, Lamont BB, Poot P, Prior LD, Reich PB, Schulze ED, Veneklaas EJ, Westoby M. 2004a. Leaf trait relationships in Australian plant species. Functional Plant Biology 31: 551558.
  • Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornellssen JHC, Diemer M et al. 2004b. The worldwide leaf economics spectrum. Nature 428: 821827.
  • Wynn JG. 2003. Towards a physically based model of CO2-induced stomatal frequency response. New Phytologist 157: 394398.
  • Yang J, Wang Y-F, Spicer RA, Mosbrugger V, Li C-S, Sun Q-G. 2007. Climatic reconstruction at the Miocene Shanwang basin, China, using leaf margin analysis, CLAMP, coexistence approach, and overlapping distribution analysis. American Journal of Botany 94: 599608.