Pollen Climate Relationships Research Articles

Modern pollen–climate relationships and their application for pollen-based quantitative climate reconstruction of the mid-Holocene on the southern Korean Peninsula

A modern pollen dataset is a prerequisite for reconstructing quantitative paleoclimate and paleovegetation cover using fossil pollen records. Although multiple modern pollen–climate datasets have been established covering a wide range of climate conditions, such datasets are exceedingly rare for the Korean Peninsula (KP). In this study, we acquired a modern pollen dataset from 198 surface soil samples collected on 37 mountains on the southern KP. Redundancy analysis (RDA) and variation partitioning results identified mean annual temperature (Tann) as the most important climate variable shaping pollen assemblages on the southern KP. Using the pollen–climate relationships inferred from the RDA, we applied the Huisman–Olff–Fresco model and determined that arboreal pollen taxa responded sensitively to the climatic gradient, whereas non-arboreal pollen taxa did not. We applied weighted averaging–partial least squares (WA-PLS) and the modern analog technique (MAT) to the pollen dataset, and a comparison of the results showed that MAT performed better than WA-PLS. A transfer function was applied to fossil pollen records from the areas covered by our dataset; the results confirmed that annual precipitation (Pann) and Tann were modulated by different mechanisms, with Pann strongly affected by El Niño–Southern Oscillation-driven typhoons during the Holocene, whereas Tann was mainly influenced by the Tsushima Warm Current from 7500 to 5100 cal yr BP depending on Kuroshio Current inflow intensity, and subsequently followed by the East Asian winter monsoon during 5100–3400 cal yr BP.

Spatial homogenization of soil-surface pollen assemblages improves the reliability of pollen-climate calibration-set

To make a reliable reconstruction of past climate from soil-surface modern pollen, it is necessary to reduce the sources of error. In this paper, pollen percentages of the sub-continental scale modern pollen-climate dataset from China and Mongolia (with 68% soil-surface samples) are homogenized at various spatial scales. A tailored calibration-set is then applied to lake sediment-surface pollen assemblages from north-central China to evaluate their predictive power. Results indicate that spatial homogenization of modern pollen percentages can increase the proportion of inertia explained by climatic variables in CCA and improve the model performance of leave-one-out cross-validation using WA-PLS. Soil-surface pollen assemblages can thus be employed into a calibration-set for reliable climate estimation and they perform better when the calibration-set has been locally homogenized. Small-scale (e.g., radii 2, 5, or 10 km) homogenization reduces the local noise in soil-surface pollen assemblages and improves the cross-validated performance, while broader scale homogenization (more than 20 km radius) blurs the pollen-climate relationship. Lake sediment-surface pollen assemblages from close to the shore could contain pollen grains transported by rivers or from the shore vegetation and thus fail to represent regional climate well like the assemblages from the central part and deep-water area of lake.

A novel procedure for quantitative regional paleoclimatic reconstruction using surface pollen assemblages

Since the inception of pollen analysis, climatic reconstruction using fossil pollen records has been one of its major applications. However, modern pollen-climate relationships have been mainly established using surface soil pollen assemblages (including moss cushions and trap samples), and pollen assemblages from the surface sediments of lakes (and peatlands) have been used less frequently. Previous studies have indicated that surface soil pollen assemblages are derived from a single (local) plant community, with an additional contribution from the regional vegetation, whereas pollen assemblages from the surface sediments of lakes are derived from multiple plant communities within the watershed. This may lead to substantial uncertainties in the application of surface soil pollen assemblages to establishing pollen-climate relationships for use in paleoclimate reconstructions using fossil pollen records from lake sediment cores. Here, we present a novel procedure for quantitative regional paleoclimatic reconstruction using a regional integration method which attempts to resolve mismatches between surface soil pollen assemblages and surface lake sediment pollen assemblages. Principal Co-ordinates Analysis (PCoA) of pollen assemblages from surface lake sediments and surface soils, and regional integrated pollen assemblages indicates that the composition of regional integrated pollen assemblages and surface lake sediment pollen assemblages is very similar. However, the integrated optimal scales of surface soil pollen assemblages are various between different lakes. Based on the regional integrated pollen dataset, reconstructed mean annual temperature and annual precipitation for different lakes in North China are close to modern observations, suggesting that the dataset has great potential for quantitative regional paleoclimatic reconstruction.

Modern pollen and its relationship with vegetation and climate in the Mu Us Desert and surrounding area, northern China: Implications of palaeoclimatic and palaeocological reconstruction

This study establishes the spatial and quantitative relationship between modern pollen, vegetation and climate based on 84 surface pollen samples from a nature reserve and ecological restoration area in the Mu Us Desert and surrounding areas, including seven natural vegetation types. Pollen spectra, discriminant analysis and spatial distribution patterns of the main pollen types are used to explore the relationship between pollen assemblages and vegetation types. Results show that each vegetation type can be characterized by a distinct modern pollen assemblage as follows: (1) coniferous forest: Pinus Diploxylon-type and Picea; (2) deciduous forest: Pinus Diploxylon-type, Quercus, Artemisia and Poaceae; (3) deciduous shrub: Pinus Diploxylon-type, Artemisia and Poaceae; (4) grass meadow: Poaceae and Cyperaceae; (5) grass steppe: Poaceae and Artemisia; (6) desert steppe: Chenopodiaceae, Artemisia and Poaceae; and (7) desert: Chenopodiaceae, Nitraria and Artemisia. The higher percentage and lower concentration of Pinus pollen in the Mu Us Desert can be explained by effective long-distance transport from the mountain forest, while Picea has a limited pollen dispersal ability via wind-borne dispersal. Poaceae pollen is well represented and has a higher percentage in several different types of ecological conditions. Modern pollen–climate relationships are assessed using redundancy analysis and weighted averaging partial least squares regression. Significant relationships exist between pollen assemblages and mean annual temperature (R2 = 0.67; RMSEP 1.7 °C; max. bias 2.5 °C), July precipitation (R2 = 0.58; RMSEP 17.8 mm; max. bias 26.0 mm), and January precipitation (R2 = 0.82; RMSEP 0.9 mm; max. bias 1.8 mm). Furthermore, the climate variables obtained by spatial interpolation of data from meteorological stations are used to cross-check the data reconstructed by pollen-climate transfer functions. Our work provides a more comprehensive understanding of the qualitative and quantitative relationships between modern pollen and vegetation/climate and serves as a basis for the paleovegetation and paleoclimate reconstruction in arid and semiarid regions of northern China and similar areas.

Impacts of the spatial extent of pollen-climate calibration-set on the absolute values, range and trends of reconstructed Holocene precipitation

Pollen-based quantitative reconstructions of past climate variables is a standard palaeoclimatic approach. Despite knowing that the spatial extent of the calibration-set affects the reconstruction result, guidance is lacking as to how to determine a suitable spatial extent of the pollen-climate calibration-set. In this study, past mean annual precipitation (Pann) during the Holocene (since 11.5 cal ka BP) is reconstructed repeatedly for pollen records from Qinghai Lake (36.7°N, 100.5°E; north-east Tibetan Plateau), Gonghai Lake (38.9°N, 112.2°E; north China) and Sihailongwan Lake (42.3°N, 126.6°E; north-east China) using calibration-sets of varying spatial extents extracted from the modern pollen dataset of China and Mongolia (2559 sampling sites and 168 pollen taxa in total). Results indicate that the spatial extent of the calibration-set has a strong impact on model performance, analogue quality and reconstruction diagnostics (absolute value, range, trend, optimum). Generally, these effects are stronger with the modern analogue technique (MAT) than with weighted averaging partial least squares (WA-PLS). With respect to fossil spectra from northern China, the spatial extent of calibration-sets should be restricted to radii between ca. 1000 and 1500 km because small-scale calibration-sets (<800 km radius) will likely fail to include enough spatial variation in the modern pollen assemblages to reflect the temporal range shifts during the Holocene, while too broad a scale calibration-set (>1500 km radius) will include taxa with very different pollen-climate relationships.

Examining bias in pollen-based quantitative climate reconstructions induced by human impact on vegetation in China

Abstract. Human impact is a well-known confounder in pollen-based quantitative climate reconstructions as most terrestrial ecosystems have been artificially affected to varying degrees. In this paper, we use a human-induced pollen dataset (H-set) and a corresponding natural pollen dataset (N-set) to establish pollen–climate calibration sets for temperate eastern China (TEC). The two calibration sets, taking a weighted averaging partial least squares (WA-PLS) approach, are used to reconstruct past climate variables from a fossil record, which is located at the margin of the East Asian summer monsoon in north-central China and covers the late glacial Holocene from 14.7 ka BP (thousands of years before AD 1950). Ordination results suggest that mean annual precipitation (Pann) is the main explanatory variable of both pollen composition and percentage distributions in both datasets. The Pann reconstructions, based on the two calibration sets, demonstrate consistently similar patterns and general trends, suggesting a relatively strong climate impact on the regional vegetation and pollen spectra. However, our results also indicate that the human impact may obscure climate signals derived from fossil pollen assemblages. In a test with modern climate and pollen data, the Pann influence on pollen distribution decreases in the H-set, while the human influence index (HII) rises. Moreover, the relatively strong human impact reduces woody pollen taxa abundances, particularly in the subhumid forested areas. Consequently, this shifts their model-inferred Pann optima to the arid end of the gradient compared to Pann tolerances in the natural dataset and further produces distinct deviations when the total tree pollen percentages are high (i.e. about 40 % for the Gonghai area) in the fossil sequence. In summary, the calibration set with human impact used in our experiment can produce a reliable general pattern of past climate, but the human impact on vegetation affects the pollen–climate relationship and biases the pollen-based climate reconstruction. The extent of human-induced bias may be rather small for the entire late glacial and early Holocene interval when we use a reference set called natural. Nevertheless, this potential bias should be kept in mind when conducting quantitative reconstructions, especially for the recent 2 or 3 millennia.

A modern pollen–climate dataset from the Darjeeling area, eastern Himalaya: Assessing its potential for past climate reconstruction

Relying on the ability of pollen assemblages to differentiate among elevationally stratified vegetation zones, we assess the potential of a modern pollen-climate dataset from the Darjeeling area, eastern Himalaya, in past climate reconstructions. The dataset includes 73 surface samples from 25 sites collected from a c. 130–3600 m a.s.l. elevation gradient along a horizontal distance of c. 150 km and 124 terrestrial pollen taxa, which are analysed with respect to various climatic and environmental variables such as mean annual temperature (MAT), mean annual precipitation (MAP), mean temperature of coldest quarter (MTCQ), mean temperature of warmest quarter (MTWQ), mean precipitation of driest quarter (MPDQ), mean precipitation of wettest quarter (MPWQ), AET (actual evapotranspiration) and MI (moisture index). To check the reliability of the modern pollen-climate relationships different ordination methods are employed and subsequently tested with Huisman–Olff–Fresco (HOF) models. A series of pollen–climate parameter transfer functions using weighted-averaging regression and calibration partial least squares (WA-PLS) models are developed to reconstruct past climate changes from modern pollen data, and have been cross-validated. Results indicate that three of the environmental variables i.e., MTCQ, MPDQ and MI have strong potential for past climate reconstruction based on the available surface pollen dataset. The potential of the present modern pollen-climate relationship for regional quantitative paleoclimate reconstruction is further tested on a Late Quaternary fossil pollen profile from the Darjeeling foothill region with previously reconstructed and quantified climate. The good agreement with existing data allows for new insights in the hydroclimatic conditions during the Last glacial maxima (LGM) with (winter) temperature being the dominant controlling factor for glacial changes during the LGM in the eastern Himalaya.

Modern pollen–climate relationships in north Xinjiang, northwestern China: Implications for pollen-based reconstruction of Holocene climate

Fossil pollen records are widely used to reconstruct past climate. Such reconstructions require that the relationships between pollen assemblages, vegetation, and climate are well understood. These can be studied in present circumstances given we assume that modern vegetation and climate are analogous to past ones. In this study, we analyze pollen–vegetation–climate relationships in the Jungar desert and Altay Mountains, northwestern China, a region for which careful reconstruction of past climate is needed to answer unsolved questions on past climate in an area located at the boundary between two different climate regimes (westerlies and monsoon). We use a dataset of 66 surface pollen samples from forest, meadow, steppe, and desert vegetation and six related climate variables, Tann, TJan, TJul, Pann, PJan, and PJul. Principal components analysis, redundancy analysis, Monte Carlo permutation tests, and variation partitioning are applied to quantify these relationships. We also assess pollen ratios as indices of aridity. We find that (1) Pannis the major climatic factor influencing pollen assemblages, followed by PJul, (2) the two variables are not correlated, and (3) the shared effect of (1) PJanand PJul, (2) PJanand Pann, (3) PJuland Tann, and (4) Tann, TJan, and TJulexplains a larger portion of the variation in pollen data than the individual effect of each variable. Therefore, robust pollen–climate transfer functions can be developed for Pannand PJul, and several climate variables treated in combination. Artemisia/Chenopodiaceae is a strong index of aridity and Artemisia/Gramineae might be a useful index of Pannand PJul.

Pollen-climate relationships in time (9 ka, 6 ka, 0 ka) and space (upland vs. lowland) in eastern continental Asia

Abstract Temporal and spatial stability of the vegetation–climate relationship is a basic ecological assumption for pollen-based quantitative inferences of past climate change and for predicting future vegetation. We explore this assumption for the Holocene in eastern continental Asia (China, Mongolia). Boosted regression trees (BRT) between fossil pollen taxa percentages ( Abies , Artemisia , Betula , Chenopodiaceae, Cyperaceae, Ephedra , Picea , Pinus , Poaceae and Quercus ) and climate model outputs of mean annual precipitation (P ann ) and mean temperature of the warmest month (Mt wa ) for 9 and 6 ka (ka = thousand years before present) were set up and results compared to those obtained from relating modern pollen to modern climate. Overall, our results reveal only slight temporal differences in the pollen–climate relationships. Our analyses suggest that the importance of P ann compared with Mt wa for taxa distribution is higher today than it was at 6 ka and 9 ka. In particular, the relevance of P ann for Picea and Pinus increases and has become the main determinant. This change in the climate–tree pollen relationship parallels a widespread tree pollen decrease in north-central China and the eastern Tibetan Plateau. We assume that this is at least partly related to vegetation–climate disequilibrium originating from human impact. Increased atmospheric CO 2 concentration may have permitted the expansion of moisture-loving herb taxa (Cyperaceae and Poaceae) during the late Holocene into arid/semi-arid areas. We furthermore find that the pollen–climate relationship between north-central China and the eastern Tibetan Plateau is generally similar, but that regional differences are larger than temporal differences. In summary, vegetation–climate relationships in China are generally stable in space and time, and pollen-based climate reconstructions can be applied to the Holocene. Regional differences imply the calibration-set should be restricted spatially.

The bias and signal attenuation present in conventional pollen-based climate reconstructions as assessed by early climate data from Minnesota, USA.

The inference of past temperatures from a sedimentary pollen record depends upon the stationarity of the pollen-climate relationship. However, humans have altered vegetation independent of changes to climate, and consequently modern pollen deposition is a product of landscape disturbance and climate, which is different from the dominance of climate-derived processes in the past. This problem could cause serious signal distortion in pollen-based reconstructions. In the north-central United States, direct human impacts have strongly altered the modern vegetation and hence the pollen rain since Euro-American settlement in the mid-19th century. Using instrumental temperature data from the early 1800s from Fort Snelling (Minnesota), we assessed the signal distortion and bias introduced by using the conventional method of inferring temperature from pollen assemblages in comparison to a calibration set from pre-settlement pollen assemblages and the earliest instrumental climate data. The early post-settlement calibration set provides more accurate reconstructions of the 19th century instrumental record, with less bias, than the modern set does. When both modern and pre-industrial calibration sets are used to reconstruct past temperatures since AD 1116 from pollen counts from a varve-dated record from Lake Mina, Minnesota, the conventional inference method produces significant low-frequency (centennial-scale) signal attenuation and positive bias of 0.8-1.7°C, resulting in an overestimation of Little Ice Age temperature and likely an underestimation of the extent and rate of anthropogenic warming in this region. However, high-frequency (annual-scale) signal attenuation exists with both methods. Hence, we conclude that any past pollen spectra from before Euro-American settlement in this region should be interpreted using a pre-Euro-American settlement pollen set, paired to the earliest instrumental climate records. It remains to be explored how widespread this problem is when conventional pollen-based inference methods are used, and consequently how seriously regional manifestations of global warming have been underestimated with traditional pollen-based techniques.

A modern pollen–climate dataset from China and Mongolia: Assessing its potential for climate reconstruction

A modern pollen dataset from China and Mongolia (18–52°N, 74–132°E) is investigated for its potential use in climate reconstructions. The dataset includes 2559 samples, 229 terrestrial pollen taxa and four climatic variables — mean annual precipitation (Pann): 35–2091mm, mean annual temperature (Tann): –12.1–25.8°C, mean temperature in the coldest month (Mtco): –33.8–21.7°C, and mean temperature in the warmest month (Mtwa): 0.3–29.8°C. Modern pollen–climate relationships are assessed using canonical correspondence analysis (CCA), Huisman–Olff–Fresco (HOF) models, the modern analogue technique (MAT), and weighted averaging partial least squares (WA-PLS). Results indicate that Pann is the most important climatic determinant of pollen distribution and the most promising climate variable for reconstructions, as assessed by the coefficient of determination between observed and predicted environmental values (r2) and root mean square error of prediction (RMSEP). Mtco and Mtwa may be reconstructed too, but with caution. Samples from different depositional environments influence the performance of cross-validation differently, with samples from lake sediment-surfaces and moss polsters having the best fit with the lowest RMSEP. The better model performances of MAT are most probably caused by spatial autocorrelation. Accordingly, the WA-PLS models of this dataset are deemed most suitable for reconstructing past climate quantitatively because of their more reliable predictive power.

A modern pollen–climate calibration set from central‐western Mongolia and its application to a late glacial–Holocene record

AbstractAimFossil pollen spectra from lake sediments in central and western Mongolia have been used to interpret past climatic variations, but hitherto no suitable modern pollen–climate calibration set has been available to infer past climate changes quantitatively. We established such a modern pollen dataset and used it to develop a transfer function model that we applied to a fossil pollen record in order to investigate: (1) whether there was a significant moisture response to the Younger Dryas event in north‐western Mongolia; and (2) whether the early Holocene was characterized by dry or wet climatic conditions.LocationCentral and western Mongolia.MethodsWe analysed pollen data from surface sediments from 90 lakes. A transfer function for mean annual precipitation (Pann) was developed with weighted averaging partial least squares regression (WA‐PLS) and applied to a fossil pollen record from Lake Bayan Nuur (49.98° N, 93.95° E, 932 m a.s.l.). Statistical approaches were used to investigate the modern pollen–climate relationships and assess model performance and reconstruction output.ResultsRedundancy analysis shows that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Spatial autocorrelation and significance tests of environmental variables show that the WA‐PLS model for Pann is the most valid function for our dataset, and possesses the lowest root mean squared error of prediction.Main conclusionsPrecipitation is the most important predictor of pollen and vegetation distributions in our study area. Our quantitative climate reconstruction indicates a dry Younger Dryas, a relatively dry early Holocene, a wet mid‐Holocene and a dry late Holocene.

Pollen–climate transfer functions intended for temperate eastern Asia

Pollen data of 646 surface samples from northern China and Mongolia and climatic data from the relevant meteorological stations were collected in this study to develop more reliable pollen–climate transfer functions for temperate eastern Asia. Canonical correspondence analysis (CCA) was used to examine the pollen–climate relationships, and mean summer precipitation (MSP) and mean January temperature (MJaT) were inferred to be the first and second important factors controlling the spatial distribution of the surface pollen in the study area. The original dataset was screened with CCA for MSP and MJaT separately to detect anomalous samples that show the extreme values. The first screened dataset was established after excluding those anomalous samples, and the initial transfer function was generated using the weighted averaging partial least squares (WAPLS) method. The jackknife test was then applied to the WAPLS model for determining the optimum transfer function and for detecting large-residual samples, and the final transfer function was generated after removing the large-residual samples from the first screened dataset. The final dataset for MSP and MJaT consists of 428 and 419 samples, respectively. The root mean square errors of prediction for both WAPLS models are 34 mm and 2.7 °C, and the coefficients of determination are 0.85 and 0.73. This study suggests that the values of climatic parameters could be better estimated and the reliability of pollen–climate transfer functions would be significantly improved through removing anomalous and large-residual samples from the original dataset with mathematical methods.

Response of arboreal pollen abundance to late-Holocene drought events in the Upper Midwest, USA

The impact of short-term climate anomalies and disturbances on past plant communities can be understood using high temporal or contiguous sedimentary pollen analysis. The response of pollen assemblages to decadal- to multidecadal-scale moisture variability was analyzed for the time intervals 3000–2200 and 1400–500 cal. yr BP. The hydroclimate and vegetation history for Minden Bog in southeastern Michigan were reconstructed using analyses of sedimentary testate amoebae and pollen. Results indicated that moisture anomalies accounted for 3% to 24% of the variation in arboreal pollen abundance for Fagus, Pinus, Quercus, and Tsuga. Betula pollen percentages did not appear to be affected by moisture variability. Given the longevity of these taxa, rapid arboreal pollen assemblage responses to moisture variability at decadal to multidecadal timescales were likely due to climate-induced changes in pollen productivity. Our data suggest that pollen–climate relationships may be counterintuitive at these fine temporal scales. For example, Tsuga pollen percentages generally increased during short-term dry events, in contrast to expected decreases in abundance with drying at millennial timescales based on empirical pollen–climate relationships. Results suggest caution should be used when inferring subdecadal to multidecadal climate variation from highly resolved pollen records. Rather, high-resolution pollen data may more accurately represent superimposed plant responses that are the composite of reproductive output nested within long-term plant community compositional changes in response to climatic variation.

Early to mid‐Holocene climate change at Lago dell'Accesa (central Italy): climate signal or anthropogenic bias?

AbstractDespite the high potential of pollen records for climate reconstruction, pollen–climate relationships may be biased due to past and present human activities on the landscape. We use (i) transfer functions based on modern pollen–climate relationships to infer seasonal temperature and summer precipitation for the period 11 500–4500 cal. a BP and (ii) lake‐level change records based on different sedimentary proxies in multiple cores that are mainly indicative for summer hydrology at Lago dell'Accesa (central Italy). Quantitative reconstructions indicate lowest summer precipitation during two phases (8500–7700 cal. a BP and after 6000 cal. a BP) and a gradual winter temperature increase from 11 500 to ca. 8000 cal. a BP. Lowest summer precipitation was reconstructed during these phases characterised by vegetation shifts from open forests dominated by summergreen oaks (Quercus) to forests dominated by evergreen oaks (Quercus ilex), which are at present most abundant where summer drought is stronger. Similarly, the lake‐level record indicates two long‐lasting low summer precipitation phases (8800–7700 and 6400–4400 cal. a BP) that were interrupted by short‐term high summer precipitation events. Based on the broad agreement between the pollen‐inferred summer precipitation and the low‐frequency lake‐level changes, we suggest that the duration of the high summer precipitation events may have been too short to maintain drought‐sensitive trees, which may have been affected by high mortality rates when summer dry conditions returned. Although past and modern pollen–climate relationships may very likely have been affected by human activities since the Neolithic (i.e. when exploitation of the landscape started), we reject the hypothesis of a significant anthropogenic bias in the pollen‐based climate reconstruction. In addition, we suggest that pollen‐based and lake‐level reconstructions may have different inherent abilities of capturing high‐ and low‐frequency precipitation signals. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Evaluating the indicator value of Tibetan pollen taxa for modern vegetation and climate

Pollen taxa of known indicator value are of great potential in the qualitative interpretation of pollen diagrams. Here we apply several numerical approaches to a lake-sediment based pollen data-set from the eastern and central Tibetan Plateau (112 samples) to assess the indicator value of Tibetan pollen taxa for modern vegetation types and for modern climate. Results from Multi-Response Permutation Procedures indicate that the differences between groups of pollen spectra originating from the same vegetation type (temperate desert, temperate steppe, alpine desert, alpine steppe, high-alpine meadow, subalpine shrub, and patchy forest) are statistically significant. Indicator Species Analyses identify several indicator taxa for most vegetation types. Multivariate regression tree analysis indicates that about 390 mm of annual precipitation is the most critical threshold for the modern pollen spectra. This roughly separates desert and steppe vegetation from high-alpine meadow, subalpine shrub, and patchy forest vegetation. A strong pollen–climate relationship on the Tibetan Plateau is confirmed by the large number of statistically significant pollen taxa–climate (annual precipitation or/and annual temperature) relationships as evaluated by statistical response-modelling, involving generalised linear models.

Expanded response-surfaces: a new method to reconstruct paleoclimates from fossil pollen assemblages that lack modern analogues

Pollen-based paleoclimatic interpretations of late-glacial to early Holocene climates (17–9 ka) in Midwestern North America are hampered by samples that lack modern analogues. Unresolved questions include the magnitude and direction of temperature seasonality (i.e. were these climates more or less seasonal than present) and the temporal changes in precipitation. Central to the no-analogue problem is the truncation of modern pollen-climate relationships for abundant late-glacial taxa such as Fraxinus. Here we present a new method called the expanded response-surface (ERS) method, developed to reconstruct climates from no-analogue pollen assemblages and applied to a high-resolution late-glacial pollen record from Crystal Lake, Illinois to test hypotheses about late-glacial climates. The key assumptions central to the ERS method are: (1) plant species and pollen abundances follow symmetrical unimodal distributions along climatic gradients, (2) taxa with truncated distributions in modern climate space occupy a subset of their fundamental niche, and (3) expansion of truncated distributions by mirroring around the distributional mode recovers the portion of the fundamental niche not realized in the modern climate space. With the ERS method, we expanded modern pollen-climate relationships by mirroring pollen abundances for each taxon around a mode defined with respect to four climate axes (mean winter temperature, mean summer temperature, mean winter precipitation, and mean summer precipitation). The ERS method reconstructed past temperatures and precipitation during the height of no-analogue conditions (14 160–12 370 cal yr BP) for 37% of the Crystal Lake samples where techniques that employed only modern observational data found matches for only 13% of the fossil samples. The total climate space of the expanded taxa set allowed analogue matches under more seasonal-than-present climates with higher-than-present precipitation. The ERS climate reconstructions for the height of no-analogue conditions indicated cooler-than-present summer and winter temperatures, similar-to-present seasonal range in temperatures, higher-than-present winter precipitation, and similar-to-present summer precipitation. These results thus suggest that high moisture availability helped drive the formation of the Midwestern no-analogue communities with high Fraxinus nigra abundances, but do not show higher-than-present temperature seasonality notwithstanding the higher-than-present insolation seasonality at this time. During the no-analogue late-glacial interval, Picea mariana, F. nigra, and Larix stands probably grew on low-lying, poorly drained soils in the Crystal Lake region; whereas Abies, Picea glauca, Quercus, and Ostrya/ Carpinus grew on upland positions with better soil drainage.

Obtaining accurate and precise environmental reconstructions from the modern analog technique and North American surface pollen dataset

Using a recently compiled surface-sample pollen dataset for North America, we test methods to improve the skillfulness (i.e. accuracy and precision) of the modern analog technique (MAT) for continental- to sub-continental-scale paleoenvironmental reconstructions. Interregional floristic differences challenge such reconstructions, because with widening spatial extent each pollen type represents an increasing number of species, each with its unique niche, thus blurring pollen–climate relationships. We conduct a series of cross-validation experiments in which we first explore (1) whether increasing the spatial extent of the pool of potential analogs improves or worsens MAT skill, (2) whether MAT skill is improved by increasing the number of pollen taxa and splitting pollen taxa into regional groups, and (3) the differences in MAT skill among environmental variables. Regional splits are guided by the range boundaries of parent species. Results are analyzed for multiple environmental variables. We then systematically explore options for (a) the value of the no-analog/analog threshold (T), (b) the maximum number of modern analogs (N) allowed for a target fossil sample, and (c) whether the environmental average constructed from the modern analogs should be weighted by their compositional dissimilarity (D) to the target sample. We demonstrate that substantial interregional differences in pollen–climate relationships, particularly between eastern and western North America, degrade MAT precision and accuracy, but these adverse effects can be overcome by expanding the list of taxa used and/or splitting pollen types into regional groups. MAT precision was best when pollen types were regionally split and better when more taxa were used, although increasing the taxon list from 64 to 135 types did not substantially increase performance. Temperature-related variables were reconstructed more precisely than hydrological variables, and there was little difference in MAT skill between climatic and bioclimatic variables (e.g. mean July temperatures versus growing degree days). T scales with the number of taxa analyzed, and there is a tradeoff between skill (best when T is low) and utility (if T is low, few samples can receive environmental inferences). For N, there is a tradeoff between precision and accuracy, such that retaining just the single best analog had the worst precision and best accuracy. Strongly weighting by taxonomic dissimilarity (1/D2) consistently reduced precision, but MAT precision was similar for unweighted and inverse-distance weighted averages. Given the above tradeoffs, we recommend using the 64-taxon list with taxa split by region, 0.20⩽T⩽0.30, 3⩽N⩽7, and either no weighting or an inverse-distance weighting, for North American applications of the MAT.

Modern vegetation–pollen–climate relationships for the Pampa grasslands of Argentina

AbstractAim To analyse the relationships between potential natural vegetation, pollen and climate in order to improve the interpretation of fossil pollen records and provide the background for future quantitative palaeoclimatic reconstructions.Location Pampa grasslands of Argentina, between 33–41° S and 56–67° W.Methods Modern pollen data were obtained from a pollen data base developed by the Grupo de Investigación de Paleoecología y Palinología, Universidad Nacional de Mar del Plata, Argentina (143 surface samples and 17 pollen types). Analysis of pollen and climate data involved multivariate statistics (cluster analysis and principal components analysis), scatter diagrams, Pearson’s correlation and isopoll mapping.Results Vegetation patterns at regional scales (grasslands and xerophytic woodlands) and local scales (edaphic communities) were identified by cluster analysis of pollen surface samples. The main climatic variables that appear to constrain the vegetation distribution and abundance of taxa are mean annual precipitation, annual effective precipitation and summer temperature. Individual pollen types such as Chenopodiaceae, Apiaceae, Cyperaceae, Prosopis, Schinus, Condalia microphylla and other xerophytic taxa are good indicators of moisture regime. Many pollen types are significantly correlated with summer temperature. The modern vegetation–pollen–climate relationships vary in a broadly predictable manner, supporting the contention that fossil pollen assemblages can be related to particular climatic characteristics.Main conclusions An expanded suite of modern analogues facilitated new insights into vegetation–pollen–climate relationships at the regional scale in Pampa grasslands. Relationships between individual pollen types and climate are appraised at a regional scale and new modern analogues are presented. The results provide the basis for improved vegetation and climate reconstruction from fossil records of the study area.

Modern pollen assemblages as climate indicators in southern Europe

ABSTRACTAim and Location Our aim is to develop pollen–climate inference models for southern Europe and to test their performance and inference power by cross‐validation with modern climate data. Surface sediments collected from lakes along a climate gradient from the winter‐cold/summer‐wet Alps to winter‐wet/summer‐dry Sicily were analysed for modern pollen assemblages.Methods For each lake, mean monthly temperatures, seasonal precipitation and site‐specific climate uncertainties have been estimated. Pollen–climate relationships were studied using numerical analyses, and inference models were derived by partial least squares (PLS) and weighted‐averaging PLS (WA‐PLS) regressions for January and July temperatures (T), and for winter, spring and summer precipitation (P). In order to assess whether these variables are also of ecological importance for vegetation in the subregions, we split the data set into an Alpine and a Mediterranean subset.Results Low bootstrap cross‐validated root mean square errors of prediction (RMSEP) for January T (1.7 °C), July T (2.1 °C) and summer P (38 mm), as well as low RMSEPs expressed as a percentage of the gradient length (8–9%), indicate a good inference power. Models revealed excellent to good performance statistics for January T, July T and summer P (r2= 0.8), and for winter and spring P (r2=c. 0.5). We show that the variables with the highest explanatory power differ between the two subregions. These are summer T and P for the Alpine set, and January T, winter P and July T for the Mediterranean set.Main conclusions The study reveals the influence of climatic conditions during the growing season on modern pollen assemblages and indicates the potential of pollen data for long‐term climate reconstructions of parameters such as winter precipitation and temperature, which seem to be the main factors having an influence on the variability of Mediterranean climate. These models may therefore provide important information on past regional climate variability in southern Europe.