dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Marsicek et al. , 2018; Mauri et al. , 2015). With distance-based weights, the choices of the number of analogues is less critical as poor analogues will be given little weight. Errors of MAT reconstructions are commonly estimated from the climatic dispersion of the selected closest modern analogues around the fossil data  (Guiot, 1990). This definition is problematic as it implies errors cannot be calculated for reconstructions based on one analogue only. An alternative option is to use the results of the modern validation of the dataset (Section 5. 2. 3) as a proxy for the mean error of reconstruction (e. g. the root-mean-square error of prediction; RMSEP). None of these options provide an accurate estimation of the true error associated with a climate reconstruction. In addition, these indices do not include many factors that are likely to inflate the uncertainties, in particular the uncertainties related to the analogue distance.
2. 3). As MAT does not require an explicit model of the pollen-climate relationship, these relationships are not required to be either linear or unimodal over the study area. Therefore, the climatic and environmental span of the modern dataset can be much larger than for other techniques. The most recent MAT developments have thus been primarily focused on increasing the size of the modern datasets to include larger climatic and ecological gradients and increase the chance of finding closer analogues. Larger datasets facilitate the application of MAT on extensive networks of fossil pollen datasets (tens to hundreds) and thus they enable reconstructing continental-scale climate maps (e. g. Davis et al. , 2003; Gajewski et al. , 2000; Ladd et al. , 2018; Mauri et al. , 2015; Sawada et al. , 2004)  or time-series (e.
g. Marsicek et al. , 2018; Viau et al. , 2008). However, including more analogues also increases the risk of false positive matches, especially when the calibration dataset encompasses wide spatial areas where the low taxonomic resolution of pollen data can lead to similar pollen compositions to be observed (low SCD values) despite experiencing very different climates. Williams and Shuman (2008)  have proposed that several broadscale North American taxa (e. g. Pinus, Alnus or Tsuga) could be split into eastern and western pollen taxa to circumvent this issue. Their regional splitting allows capturing the specific climate response of each subgroup and reduces the risk of false positives. Such an approach is also useful to other techniques, such as WA/WA-PLS or the pdf techniques, to estimate more local climate responses (e.
The Revised Analogue Techniques (RAM) The Revised Analogue Technique (RAM) is composed of two distinct steps: a pollen data modelling step to interpolate modern samples using a technique called Response Surface  (Bartlein et al. , 1986; Huntley, 1993; Prentice et al. , 1991; Webb et al. , 1993 ) and a MAT-based climate reconstruction step  (Gonzales et al. , 2009; Waelbroeck et al. , 1998). With Response Surface, the climate response of individual pollen taxa is estimated for two or more variables simultaneously. Pollen percentage data are mapped in a two-or higher-dimension climate space of interest (e. g. July temperature and annual precipitation for instance, such as in  Anderson et al. , 1991)  and a data smoother is used to interpolate the values of the pollen percentages as a function of these variables.
g. Rumelhart et al. , 1988). The most recent applications of ANNs for assemblage data come from the marine realm and the study of foraminifera or dinocyst assemblages to estimate past sea-surface temperature  (de Vernal et al. , 2005; Kucera et al. , 2005; Malmgren et al. , 2001; Peyron and de Vernal, 2001). The main reason why ANNs are not more frequently used in palaeoclimatology is related to the complexity of finding the proper structure of the network. Because of its non-linear property, ANNs can fit the training data set very well, integrating the noise inherent to any pollen dataset as climate signals. This overfitting of the data restricts the predictive power of the transfer function outside of the range of the training set. In addition, understanding why an ANN produces an output from a set of input variables is challenging (statistical black box), rendering analysis and causal interpretations of the results difficult and sometimes speculative.
If we call n the number of samples composing the modern dataset, the bootstrap approach consists of randomly sampling n samples with replacement to create a new dataset of the same size. In this case, some samples are selected multiple times, and some are never selected. Unselected samples become part of the test dataset. An alternative approach based on the combination of regression trees is called Boosted regression Trees (BRT;  De'ath, 2007; Elith et al. , 2008). BRT differs from RF in the definition of the random modern datasets. In RF, each sample gets the same probability of being selected, while in BRT the samples that were insufficiently described in the previous tree get a higher probability of being selected. This approach is called 'boosting' and increases the performance of the model over the elements that are least well predicted.
Bayesian approaches Bayesian statistical models differ from the aforementioned models in that they treat the model parameters and the unobserved data as random variables. This allows for the characterisation of uncertainty in both the data and processes and permits the use of prior information about the pollen-climate relationship when available. The Bayesian framework allows for the estimation of a posterior probability distribution of climate variables. The complexity of the Bayesian approach has limited its applicability in the past, but recent software developments have resulted in increased use of Bayesian models and inference.
Method Strengths Weaknesses Pdf techniques • Globally applicable • Good estimation of the uncertainties • Can deal to some extent with non-analogue situations if species- environment relationships are well constrained • Based on the response of plants (instead of pollen) to climate • Use of presence-only data Regression techniques • Useful at the local to regional scale • Can deal to some extent with non-analogue situations if species- environment relationships are well constrained • Often assume pre-defined shape of the pollen-climate relationships (e. g. linear or unimodal). • Struggle with complex (e. g. multimodal) species responses Calibration techniques • No assumptions about the shape of the pollen-climate relationships • Applicable at broad spatial scales • Limited performance in no-analogue situations • Sensitive to spatial autocorrelation (artificial reduction of the uncertainties) • Poor estimation of the uncertainties • High sensitivity to the dissimilarity metric and dissimilarity threshold Machine learning (ANN, BRT, RF) • Good fitting of the calibration data • Potentially applicable to complex species-environment relationships • Stochastic approches • Statistical black boxes • Performance statistics often similar to simpler approaches • Can overfit the data in noisy datasets Bayesian approaches • Explicit and rigorous characterisation of uncertainties • Allow for the formalisation of processes that generate the data (e.
g. use of counts instead of percentages) • Use of a priori knowledge • Specialist expertise required to specify the model and estimate parameters • Specifying appropriate prior probability distributions may be challenging Inverse modelling • Relies on ecologically realistic forward modelling of how climate drives vegetation change • Can model physiological effects of CO 2 atmospheric concentration • No modern calibration dataset needed. • Simulates PFT productivity and not pollen • Low taxonomic resolution • Strongly dependent on available vegetation model specified in a way that minimises their contribution to the inferred posterior result. Finally, the modular structure of a hierarchical Bayesian model also allows for accounting of uncertainty in processes and observations in the individual model components.
Selection of the appropriate technique(s) All the reconstruction techniques have their own set of advantages and limitations (Table  1 ) and many criteria influence the selection of the most appropriate technique(s) to be used on fossil pollen records. In the following section, we present general guidelines that are suggestive of the strategies adopted by the community thus far, but specific applications and/or research questions may require using one specific technique over another. These guidelines must be also combined with the appropriate calibration datasets (Section 5. 2). When possible, we recommend using multiple techniques (e. g. Brewer et al. , 2008; Lotter et al. , 2000; Nolan et al. , 2019; Salonen et al. , 2019)  to examine the effects of technique choice. The first factor to consider is the location of the study site and availability of suitable calibration datasets.
g. a forest replaced by grassland or shrubland vegetation, as has been widely observed in many late Holocene European pollen records), the PFT-based approach probably does not perform better than the pollen-taxa based reconstructions. Detailed testing of this hypothesis with different techniques is needed to better identify when and where it could be most Fig. 10. Map of modern pollen samples available from numerous public databases and studies  (Cao et al. , 2014; Davis et al. , 2020; Flantua et al. , 2015; Fletcher and Thomas, 2007; Gajewski et al. , 2002; Herbert and Harrison, 2016; Montade et al. , 2019b; Montade et al. , 2019b; Whitmore et al. , 2005; Williams et al. , 2018a). The map is based on the 'Equal Earth' map projection to better represent the relative sizes of the different continents  (Šavrič et al.
Transfer function techniques that estimate the pollen-climate relationships from the whole modern dataset (e. g. regression, pdfs and Bayesian techniques) rather than from individual samples (e. g. MAT) are more robust against spatial structure in the training set  (Telford and Birks, 2011b; Telford and Birks, 2005). The influence of spatial autocorrelation can be assessed using an hblock cross validation, even if the test also has limitations (see Section 5. 2. 3).
Design of a modern pollen dataset For approaches relying on modern calibration data, designing a robust modern pollen dataset is critical part of the reconstruction process, as the reconstructed values depend strongly on the empirical relationships between the modern distribution and abundance of taxa and contemporary environmental measurements. Most techniques use modern pollen samples (i. e. modern pollen assemblages measured from soil, mires, moss pollsters or lake sediments, Sections 4. 2, 4. 3 and 4. 5), while others require modern plant presence-only data (i. e. collections of geolocalised observations of plant species, Section 4. 1). We use the generic term modern datasets when we refer to both. Despite these differences, designing an appropriate modern dataset follows similar rules, even if they can be difficult to quantify objectively.
The overall quality of a modern dataset depends on various factors, such as 1) the nature of the samples composing the training set itself, 2) the spatiotemporal scale of the study, 3) the number and types of identified taxa, and 4) the location of the target site(s) in the sampled climate space (Fig. 11 ). The following sections provide general guidelines to follow in designing modern datasets and balancing among these different factors. In practice, following all these recommendations is rarely possible and the design of a modern dataset is often a practical compromise between the quality (i. e. the selected data follow all the guidelines) and the quantity (i. e. modern datasets have a finite size and pruning too many samples can lead to a loss of information) of modern data.
Depositional environments of the modern pollen samples For all the techniques using modern pollen samples to establish the link between fossil pollen and climate, the depositional environment of Fig. 11. (A) Conceptual illustration of an ideal relationship between the ecological gradient (the pollen on the x-axis) and the variable to reconstruct (yaxis) in modern datasets. The modern data are homogenously distributed along the gradient of the climate variable to reconstruct. The variable describes a large fraction of the ecological variance (small spread around the trend line), the modern climate of the location where the target pollen record was retrieved is centred on the climate gradient, and the span of the modern dataset is large enough to reconstruct potentially large environmental changes in the past.
Panels (B) to (E) illustrate suboptimal characteristics of modern datasets: (B) Clustered dataset that could create step-wise reconstructions (i. e. jumps from one cluster to the next), (C) weak relationship between the modern ecological gradient and the variable to reconstruct, (D) modern climate of the target site is at the extreme of the covered climate range, creating risks of biases due to past non-analogue climates (i. e. beyond the calibration range) and edge effects and (E) small dataset that only covers a fraction of the potential climate gradient with an effect similar to D (resulting limited range of possible reconstructed values and strong edge effect). such modern samples has been viewed as an important factor, as it defines the spatiotemporal characteristics of the sample. In dryland regions for example, the quantity of preservational environments is limited and most modern pollen grains may be strongly impacted by post-depositional deterioration (e.
This is supported by  Goring et al. (2010)  and  Jantz et al. (2013) , who showed that the use of a modern dataset using samples from different depositional environments generally increases model errors. As such, it is recommended to use samples from the same depositional environment when they are abundant. However, since fossil records are mostly derived from lacustrine sediments or, more infrequently, peat from wetlands (bogs and mires), collecting a large set of lake-or peat-derived modern samples is only achievable in study areas characterised by a high spatial density of lakes and wetlands, such as temperate and boreal regions (e. g. Downing et al. , 2006). In areas with a low density of lakes and wetlands, moss and/or soil-samples (e. g. Gajewski et al. , 2002; Ortega-Rosas et al. , 2008) , marine sediments (e.
In addition, information about the depositional environment is not always available, especially for legacy datasets (i. e. old datasets originally stored and shared on obsolete data formats and hardware infrastructures), which comprise multiple generations of collections, perhaps with differing standards and methodologies. For example, in the latest Eurasian Modern Pollen database (EMPD2), metadata regarding depositional environment for a quarter of all the modern pollen samples (~2000 samples) is unknown  (Davis et al. , 2020). To develop their modern dataset, users often have to make a trade-off between increasing the size of the modern dataset and including sites from different depositional environments. Optimising the number and quality of modern samples in the modern dataset is critical to increase the representation of each climate and vegetation type, and, by extension, extend the range of potential climate values that can be reconstructed from fossil data (Fig.
Span, coverage and size of the modern dataset The accuracy and reliability of any reconstruction depends on the span (i. e. the width of the environmental gradient covered by the samples), the size (i. e. the number of data points), and the coverage of the environmental gradient (i. e. the distribution of the samples along that gradient)  (Juggins and Birks, 2012). The span should range beyond the climatic and environmental values likely to have generated the fossil assemblages to avoid over/underestimation of the extreme values (edge effect). This aspect is critical, as an unfavourable position (Fig. 11D ) and/or span (Fig. 11E ) of the modern dataset will bias the model and artificially restrict the potential range of the reconstructions. When a study site is located near the centre of the climatic range covered by the modern dataset and the distribution of samples along this climate gradient is homogeneous, the possibilities for reconstructing lower or higher climatic values are unrestricted (Fig.
11A ). In contrast, when the site's modern climate is close to the extreme edge of the training set, then only changes in one direction will be accurately reconstructed, while changes in the other direction will be muted or wrongly attributed (Fig. 11D ). The size of the modern dataset (i. e. the number of individual samples) and the distribution of these samples along the studied gradient also have a significant impact on the pollen-climate relationships. While the minimum number of required samples depends on the context of the study (e. g. pollen diversity, time span), aiming for several hundred samples represent a good target for a single site reconstruction without extreme pollen turnover, even if this number will also depend on the methodological choices. For MAT for instance, larger modern datasets sampling large climatic and ecological gradients will improve the quality and quantity of information necessary to reconstruct climate, as this will increase the chances of finding good analogues.
In contrast, methods (e. g. regression techniques) and studies focusing on regional pollen climate relationships will often be based on smaller calibration dataset to better capture the regional pollen-climate relationships. In all cases, a homogenous distribution of these samples along the climate gradient is also preferred  (Telford and Birks, 2011b). When the distribution of samples is too clustered in the climate space (Fig. 11B ), the pollen-climate relationships can incorporate such irregularities and the reconstructed values can 'jump' from one climate cluster to the next, especially classification techniques. The techniques that model the modern relationship between pollen and climate can be resilient to small gaps (e. g. regression and Bayesian techniques). However, all techniques will suffer from this when the variable to reconstruct varies more between clusters than within clusters (i.
e. when the clusters are separated by large data gaps). Records of plant species distributions are usually more continuous than distributions of modern pollen samples. Therefore, the techniques based on these data for the calibration step are less sensitive to the homogeneity of the sampling. However, in some areas, sharp transitions in sampling density exist (e. g. across country borders;  Chevalier, 2019; Rutherford et al. , 2003). Some preliminary results suggest a limited impact on the pdfs as long as the full range of climatic conditions where species live remains fully sampled  (Chevalier et al. , 2014). A subsampling of the densest areas can be applied to obtain a more balanced distribution of data and limit the bias of the estimated parameters towards the climate of more data-rich regions.
Testing and validation of reconstruction models and modern datasets Before reconstructing past climate with a model, one should make sure that it has significant predictive power, which can be defined as the capacity of the model to estimate reliable climate values from unknown (i. e. fossil) pollen samples. In most applications, past climates are unknown, and the predictive power of the models is evaluated against modern climate, although in some situations comparisons with reconstructions based on other proxy or climate model data are possible. When comparing inferences with modern climate, data used to develop the climate reconstruction model and the data used to validate it must be different (i. e. independent) to avoid circularity. In the case of MAT for instance, the tested analogue would always be matched against itself.
In this trivial example of circularity, the use of the same data to develop and validate the model biases performance indicators, leading to an improved accuracy and reduced errors. One common solution to circumvent this problem is to use cross-validation, which divides the modern dataset in two parts: one subset to inform the model ('training dataset') and another subset to test it ('test dataset'). The cross-validation must be repeated multiple times to assess the ability of the selected model to characterise all the climate conditions represented in the modern dataset. The assessment of a model's performance should always include looking at the relationship between inferred and observed values and at the distribution of the residuals (i. e. the difference between the reconstructed and the climate values).
The mean offset between the target and the reconstructed values can be measured by the root-mean-square error of prediction (RMSEP), which summarises how far the reconstructed values are from their target on average (i. e. the mean of the squared differences between the reconstructed and target climate values). A model with high precision and accuracy will have an RMSEP close to 0. Another important indicator of model performance is maximum bias, defined as the largest absolute error between the target and the prediction averaged for certain intervals of the climate gradient. A model can be good on average (low mean RMSEP) but fail in some parts of the climate space (high local bias). Calculating the maximum bias per section, for instance by splitting the studied gradient in 10 sections as in  Birks (1998) , can highlight climatic conditions that likely cannot be reconstructed in the past considering the modern calibration dataset  (Lotter et al.
, 1997; ter Braak et al. , 1993). This usually happens near the edges of the sampled climate space (lowest and highest values are generally over-and under-estimated, respectively; see Fig. 11D ) or if the modern climate space is heterogeneously sampled. A common form of cross-validation is the k-fold cross-validation. In this case, the modern dataset is randomly divided into a certain number of groups k of the same size (commonly 5 to 10 groups, see Fig. 12 ). Each time, one group is used to test the model defined by all the other groups (e. g. Barrows and Juggins, 2005; Mauri et al. , 2015). Another commonly used form of cross-validation is called leave-one-out (LOO) cross-validation, in which the test set is composed of only one sample at a time, all the other samples being used to inform the model.
LOO is equivalent to k-fold with as many groups as there are samples in the modern dataset. With LOO, the tested model is the closest to the model that will be used to reconstruct climate from fossil samples (only one sample is removed from the modern dataset). The procedure can, however, require considerable computer resources, especially for models based on hundreds to thousands of modern pollen samples. Bootstrapping cross-validation is also commonly employed to evaluate a model (see definition in section 4. 3. 4). For each bootstrap dataset, some samples will be selected more than once, while the unselected samples will define the test group. The process must be repeated a large number of times to obtain robust statistics  (Juggins and Birks, 2012). Spatial autocorrelation and/or block structure in the modern dataset (Fig.