Milan Djilas
email: djilas@lirmm.fr

Christine Azevedo-Coste

David Guiraud

Jérôme Bourien

Ken Yoshida

Wavelet-Based Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded With Thin-Film Intrafascicular Electrodes

The continuous complex wavelet transform offers a convenient framework for neural spike sorting. Results show that wavelet-based neural spike detection outperforms simple threshold detection, especially with signals with low signal to noise ratio. Classification of action potentials using their signatures in wavelet space performed as well as a classifier based upon principal components analysis, and better than a classifier based upon template matching. Applied on experimental intrafascicular recordings of muscle spindle afferent nerve response to passive muscle stretch, the spike sorting algorithm manages to isolate afferent activity of units having a linear relationship between neural firing rate and muscle length, an important step towards a model-based estimator of muscle length.

Introduction

Closed-loop control of neural prosthetics is seen as a means to compensate for many of the non-linearities related to muscle activation, and the unexpected perturbations one might encounter when using a Functional Electrical Stimulation system. With the advent of advanced implanted neuroprosthetic interfaces, natural sensors, such as the muscle spindle afferent nerve, are being explored as an alternative source for feedback information. In a previous study, a simple feedback controller was implemented to follow a desired joint angle trajectory in the presence of externally applied disturbances [START_REF] Yoshida | Closed-Loop Control of Ankle Position Using Muscle Afferent Feedback with Functional Neuromuscular Stimulation[END_REF]. Muscle length was estimated using aggregate unspecific muscle afferent activity and a lookup table. In another study a simple linear model was proposed to express the relationship between length and unit neural firing rate [START_REF] Azevedo | Towards a Model-Based Estimator of Muscle Length and Force Using Muscle Afferent Signals For Real-Time FES Control[END_REF], [START_REF] Djilas | Interpretation of ENG signal for FES closed-loop control[END_REF]. In order to improve the estimation of muscle length, separation of units in the multi-unit ENG is necessary.

Action potentials are usually detected by looking for signal activity rising above a preset noise gate [START_REF] Schmidt | Computer separation of multi-unit neuroelectric data: A review[END_REF], [START_REF] Lewicki | A review of methods for spike sorting: The detection and classification of neural action potentials[END_REF]. The detected spikes are classified using different methods. Principal components analysis (PCA) and template matching are often used [START_REF] Schmidt | Computer separation of multi-unit neuroelectric data: A review[END_REF], [START_REF] Lewicki | A review of methods for spike sorting: The detection and classification of neural action potentials[END_REF] because of their relative simplicity which enables fast, real-time implementation. In a recent study it was demonstrated that using a continuous complex wavelet based approach to action potential detection in the auditory nerve outperforms a matched filter approach [START_REF] Bourien | Comparison of three spike detectors dedicated to single unit action potentials of the auditory nerve[END_REF]. In this study we expand this detection algorithm so it covers a range of temporal scales of action potentials. Additionally, we introduce a classifier of the detected action potentials. The spike sorting algorithm is evaluated on both synthesized data and experimentally recorded muscle spindle afferent response to imposed muscle stretches. The algorithm performance is compared to methods using PCA and template matching.

Methods

Acute animal experiments

Acute experiments were conducted on 10 anesthetized New Zealand white rabbits. One leg of the rabbit was anchored at knee and ankle joints to a fixed mechanical frame The Achilles tendon was attached to the arm of a servocontrolled motor using a ligature. Pulling on the ligature stretched the Medial Gastrocnemius (MG) muscle and resulted in ankle dorsiflexion. Releasing the tension on the ligature resulted in the muscle shortening to its original length and movement of the ankle in the plantar direction.

A thin-film longitudinal intrafascicular electrode (tfLIFE) was implanted in the MG nerve in the rabbit's hind limb. The electrode enabled simultaneous multichannel monitoring of the ENG from the fascicle in which the structure was implanted. In order to have purely muscle afferent activity in the ENG recordings, the sciatic nerve was crushed proximal to the implantation site. Signals from the structure were amplified and filtered before recording them to a multichannel digital tape recorder.

The MG muscle was presented with sinusoidal stretches 4 mm in peak-to-peak amplitude and 250 mHz in frequency. The force, relative position and the 4 neural signals from the tfLIFE were simultaneously recorded.

Generating synthetic signals

In order to be able to evaluate the performance of the spike sorting algorithm, knowledge of the exact timing and class of each action potential in the ENG signal is needed.

Because of uncertainty about this information in experimental data, artificial signals based upon recorded action potentials were synthesized. Around 20 action potentials with different waveforms were visually identified and extracted from the recorded experimental data. From the set of extracted waveforms, 5 action potentials with distinctly different shapes were chosen to represent 5 neural spikes originating from different axons, i.e. different spike classes. The waveforms were normalized and used to synthesize spike trains. Spike train firing rates were randomly chosen from within the range found in literature [START_REF] Matthews | The sensitivity of muscle spindle afferents to small sinusoidal changes of length[END_REF]. With the exception of burst firing, muscle spindle afferents can fire with a rate up to about 75 Hz. Spike amplitudes were scaled by integer values ranging from 3 to 6 standard deviations of the background noise level, which corresponds to values found by visually inspecting the data recorded using tfLIFE. These 4 scaling factors represented different SNR levels for which the analysis was performed. Signals were synthesized by adding the spike trains onto experimentally recorded background noise. Signals with different number of units firing simultaneously were synthesized, starting from signals having only 2 units firing, up to signals with 10 classes. Spikes having the same waveform and amplitude were considered to be from the same axon (belonging to the same class). A total of 900 signals were generated: 100 signals with 2 units active, another 100 with 3 units, and so on until having 100 signals with 10 units firing simultaneously.

Neural spike sorting 2.3.1 Spike detection

Neural spike detection was based on computing the continuous wavelet transform (CWT) of the ENG using the complex Gaussian wavelet. This wavelet was chosen because its real and imaginary parts resemble the typical action potential shapes found in experimental data. As the output wavelet coefficients are a measure of the degree of similarity of patterns in the signal to the wavelet waveform, the detection algorithm consisted of searching for wavelet coefficients above a preset threshold. Detection was evaluated on a range of thresholds.

Inspection of extracted action potentials waveforms showed that the durations of neural spikes is in a range between a few hundred microseconds up to 1 ms. A multiscale CWT analysis was performed on extracted action potential waveforms in order to find the scales at which the CWT coefficients were maximal. Only the range of scales for which the magnitude of the coefficients were larger than 95% of the magnitude of the maximal coefficient where used in the detection algorithm.

Spike classification

The multi-scale complex CWT also offers a framework for classifying the detected neural spikes. Action potentials differ in their shape and amplitude and it is necessary to choose a feature set and a distance metric with which they would be distinguishable. The multi-scale CWT offers a redundant representation of the spike's shape in time-scale space. Exploratory data analysis on the extracted neural spike waveforms indicated that the CWT coefficients computed using the same set or scale factors as in the detection phase could be suitable as classification features. Classification was performed by creating feature vectors for the detected spikes and then clustering the data using the Euclidean distance metric.

Results

Evaluation on synthetic signals

Performances of the wavelet-based detector and the detector using simple amplitude thresholding are shown on Figure 1 in the form of receiver operating characteristics (ROC curves). These curves are graphical representations of detector sensitivity vs specificity using a range of detection thresholds. On the whole range of SNR levels the wavelet-based detector outperforms the detector based on amplitude thresholding, i.e. for any given specificity, the corresponding sensitivity is greater for the wavelet-based detector. The performance gap becomes especially prominent with low SNR.

Classification results are shown in the form of classification error rates which are ratios of the number of misclassified spikes to the total number of spikes being classified. The wavelet-based classification is compared to two other methods of classification: principal components analysis and template matching. Results are shown starting from the case when only two different spike classes are present in the signal up until the case where 10 units are simultaneously firing (Figure 2). Classification based on template matching produced the highest classification error rates, while wavelet-and PCA-based approaches showed similar results.

Evaluation on experimental data

The spike sorting technique was applied on experimentally recorded muscle spindle afferent nerve activity. Only periods of plantar flexion were analyzed, since the units are silent during the dorsiflexion period. The detection threshold was chosen to be seven times the standard deviation of the background noise level (in wavelet space). Throughout all the trials this threshold value corresponded to the point on the ROC curves where the specificity starts to rapidly deteriorate and there is little improvement in sensitivity. The detected units were classified into 10 clusters. This approximately matches the number of units being picked up by one recording site of the tfLIFE when the muscle is maximally stretched (preliminary results of recent unpublished work). The analysis was performed on data from 3 rabbits. Preliminary results show that from the 10 classes, up to 2 or 3 spike classes show a linear relationship between their computed neural firing rate and instantaneous muscle length. Results from one rabbit are shown on Figure 3. The left plot shows the relationship between the aggregate firing rate of all detected spikes. The relationship is clearly not linear in the region where the normalized muscle length is close to 1. The right plot shows the same, but this time the firing rate was computed using only the activity of two classes having a linear relationship between unit firing rate and muscle length. A linear regression analysis performed on both shows that the fit on the right plot is obviously better.

Discussion

Results show that the continuous wavelet transform using complex wavelets is the preferred method for neural spike detection. Even though wavelet-based classification does not show improvement in error rates compared to the PCAbased algorithm, the advantage of using the wavelet-based approach is in the fact it is it provides a unique framework for both spike detection and classification, i.e. after computing the complex wavelet coefficients in the detection phase, no additional computation is required in the subsequent classification phase.

Preliminary results show that the spike sorting scheme is capable of isolating the activity of single units having a linear relationship between firing rate and muscle length. The aggregate firing rate from muscle spindle afferents should have some non-linearities where most of the units are being recruited or saturate. There should be a range of length where they behave linearly. From the point where a single unit reaches activation threshold, its activity should have a more linear firing rate to length behavior. This is one possible explanation of the more linear behavior of the firing rate computed using a subset of classes.

On-line estimation of muscle length would require a real-time implementation of the wavelet transform, which has been shown to be feasible with well chosen wavelet families [START_REF] Luo | Real-time condition monitoring by significant and natural frequencies analysis of vibration signal with wavelet filter and autocorrelation enhancement[END_REF].

With each new spike detected, either a new cluster would be formed, either the detected spike would be associated to an existing cluster. In the latter case the cluster centroid would be updated which would also make the algorithm capable of tracking slow changes in spike waveform shape and amplitude (either due to electrode drift, either to electrode fibrous encapsulation leading to a degradation of SNR in the recorded signals).

One of the biggest obstacles in both detecting and classifying neural spikes proves to be the low SNR of the ENG recordings. In parallel to working on the improvement of signal processing methods to minimize the effects of noise, novel recording techniques could also be used to improve signal quality [START_REF] Djilas | Improving the signal-to-noise ratio in recordings with thin-film longitudinal intra-fascicular electrodes using shielding cuffs[END_REF]. It is expected that, when signals are acquired using such techniques, the overall performance of the spike sorting algorithm should improve.

Results from experimental data presented in this paper are preliminary. Final conclusions can not be drawn before processing the data from all 10 rabbit experiments, which is work currently in progress. Comparison with different muscle stretch profiles is also necessary.

Conclusions

The continuous complex wavelet transform offers a convenient framework for both detection and classification of action potentials using intrafascicular electrodes. The neural spike detection outperforms the simple threshold detection, especially with signals with low SNR. Results of classification of units into classes based on action potential waveform signatures in wavelet space indicate that the classifier manages to isolate afferent activity of units having a linear relationship between neural firing rate and muscle length, which is an important step towards a modelbased estimator of muscle length.

Fig. 3 Afferent neural firing rate vs muscle length. The left plot shows the aggregate activity of all detected spikes. On the right, only activity from 2 clusters, having a good linear fit to the data, are used to compute the firing rate. Linear regression analysis was performed on both (full lines). On both plots muscle length was normalized by 4 mm.

Fig 1 .Fig 2 .

 12 Fig 1. ROC curves for four different SNR levels. Performances of a simple threshold detector (empty-circle line) and the waveletbased detector (full-triangle line) are compared

Acknowledgments

Authors would like to thank the staff from the Department of Pathology at Aarhus University Hosptial-Aalborg. Financial support for this work was provided by the EADS corporate foundation.


domain: sde sdu

text:
Scott D Mooney
email: s.mooney@unsw.edu.au

S P Harrison

P J Bartlein

J Stevenson

K C Brownlie

S Buckman

M Cupper

M Black

E Colhoun

D D'costa

10

J Dodson

Haberle S 11

G S Hope

P Kershaw

12

C Kenyon

13

M Mckenzie

N Williams

Late Quaternary fire regimes of Australasia

Keywords: charcoal records, fire, biomass burning, vegetation-fire interactions, human impact on fire, Last Glacial Maximum, Holocene, Dansgaard-Oeschger variability, ENSO variability

We have compiled 223 sedimentary charcoal records from Australasia in order to examine the temporal and spatial variability of fire regimes during the Late Quaternary. While some of these records cover more than a full glacial cycle, here we focus on the last 70,000 years when the number of individual records in the compilation allows more robust conclusions. On orbital time scales, fire in Australasia predominantly reflects climate, with colder periods characterised by less and warmer intervals by more biomass burning. The composite record for the region also shows considerable millennial-scale variability during the last glacial interval (73.5 to 14.7 ka). Within the limits of the dating uncertainties of individual records, the variability shown by the composite charcoal record is more similar to the form, number and timing of Dansgaard-Oeschger cycles as observed in Greenland ice cores than to the variability expressed in the Antarctic ice-core record. The composite charcoal record suggests increased biomass burning in the Australasian region during Greenland Interstadials and reduced burning during Greenland Stadials. Millennial-scale variability is characteristic of the composite record of the subtropical high pressure belt during the past 21 ka, but the tropics show a somewhat simpler pattern of variability with major peaks in biomass burning around 15 ka and 8 ka. There is no distinct change in fire regime corresponding to the arrival of humans in Australia at 50 ± 10 ka and no correlation between archaeological evidence of increased human activity during the past 40 ka and the history of biomass burning. However, changes in biomass burning in the last 200 years may have been exacerbated or influenced by humans.

Introduction

Australia includes some of the most fire-prone landscapes on Earth [START_REF] Williams | The sensitivity of Australian fire danger to climate change[END_REF][START_REF] Bradstock | Flammable Australia: the fire regimes and biodiversity of a continent[END_REF][START_REF] Bond | Fire as a global ‗herbivore': the ecology and evolution of flammable ecosystems[END_REF][START_REF] Russell-Smith | Bushfires ‗down under': patterns and implications of contemporary Australian landscape burning[END_REF]. Fire has major impacts on the native flora and fauna, on landscape stability and on biogeochemical cycling. Characteristics of the land cover and the release of gases and particulates (CO 2 , CO, CH 4 , N 2 O, BVOCs, black carbon) during bushfires affects air quality, atmospheric composition and hence radiation budgets, and thus changes in fire regimes through time could have important feedbacks to climate [START_REF] Ramanathan | Global and regional climate changes due to black carbon[END_REF][START_REF] Bowman | Fire in the earth system[END_REF][START_REF] Arneth | Terrestrial biogeochemical feedbacks in the climate system[END_REF].

Australian vegetation has developed a variety of responses and morphological and reproductive adaptations to fire, including the widespread use of resprouting [START_REF] Purdie | Early stages of regeneration after burning in dry sclerophyll vegetation. I. Regeneration of the understorey by vegetative means[END_REF][START_REF] Gill | Fire in the Australian Biota[END_REF][START_REF] Enright | The ecological significance of canopy seed storage in fire-prone environments: A model for non-resprouting shrubs[END_REF], suggesting that fire has played an important role over evolutionary timescales. Many species require regular fire in order to persist, and this is particularly evident in humid but intermittently drought-prone environments where eucalypts dominate the vegetation. Other taxa, including species typical of the more consistently humid east and northeastern coast, are fire sensitive [START_REF] Gill | Fire in the Australian Biota[END_REF][START_REF] Bradstock | Flammable Australia: the fire regimes and biodiversity of a continent[END_REF]. In the tropical communities of Australasia, fire often depends on drought and fires in the recent past have been linked to human activity and El Niño events (van der Werf, 2008a;[START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF]. Fire is thus a fundamental issue in many Australasian landscapes and influences community composition, the location of boundaries between communities and vegetation dynamics through time.

There are persistent questions about the role of humans in the long-term history of fire and vegetation in the Australasian region. It has been argued that the frequent use of fire by Aboriginal people, to manipulate the availability of resources [START_REF] Jones | Fire-stick farming[END_REF][START_REF] Nicholson | Fire and the Australian Aborigine[END_REF], resulted in vegetation change and other environmental impacts in the late Pleistocene (e.g. Singh et al., 1981;[START_REF] Flannery | The Future Eaters: An Ecological History of the Australasian Lands and People[END_REF][START_REF] Miller | Ecosystem collapse in Pleistocene Australia and a Human role in megafaunal extinction[END_REF]. Ideas about pre-historic fire continue to influence debates concerning natural resource management, with suggestions that Aboriginal-like fire management (i.e. frequent and low intensity fires) could prevent conflagrations in the modern setting (e.g. Select Committee on the Recent Australian Bushfires, 2003).

One of the earliest examinations of long-term changes in fire regimes and their impact on the development of Australian vegetation was provided by [START_REF] Gill | Fire in the Australian Biota[END_REF]. More recently, [START_REF] Kershaw | A history of fire in Australia[END_REF] have summarised the long-term history of fire in Australia, although their discussion of the last 10,000 years focused solely on southeastern Australia and they used a qualitative assessment of ca. 60 sites.

Nevertheless, [START_REF] Kershaw | A history of fire in Australia[END_REF] indicated that there were differences in the timing of peak Holocene fires associated with different biomes across this region. The interval of least fire across all biomes was during the mid-Holocene (7000-5000 yr BP) and the maximum registration of fire occurred during the early European period. [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF], which is the most recent review of Australian palaeofire regimes but only covers a small number of iconic records, also identifies the mid-Holocene as a time of low fire activity and argues that higher levels of biomass burning are associated with the onset or intensification of the El Niño-Southern Oscillation (ENSO) after ca. 4 ka. [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] conclude that the longer-term record of fire in Australia shows a gradual increase coincident with the purported long-term aridification of the continent [START_REF] Hesse | Late Quaternary climates of the Australian arid zone: a review[END_REF].

There have been several site specific or regional studies examining the interactions between vegetation and fire during the late Quaternary in Australia (e.g. [START_REF] Black | The fire, human and climate nexus in the Sydney Basin, eastern Australia[END_REF]. However, it is only recently, and largely through the efforts of the Global Palaeofire Working Group (GPWG: [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF][START_REF] Power | Fire history and the Global Charcoal Database: a new tool for hypothesis testing and data exploration[END_REF], that sufficient data have become available at a continental scale to make it possible to apply robust statistical techniques to the analysis of past fire regimes. [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF], based on an analysis of 355 charcoal records, showed that fire regimes globally reflected long-term climate changes. This compilation included only 48 records from -Australia‖, (defined as mainland Australia, New Zealand and Pacific Islands west of 180 o E, but not New Guinea which was included in south-east Asia).

This preliminary analysis revealed distinctive patterns in fire regimes over the last 21 ka, with relatively little change during the late glacial and through the transition to the Holocene, and charcoal peaks around 15-16 ka, 11-10 ka and 4.5-2.5 ka [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF]. Here, we present a more extensive synthesis of the charcoal data based on 223 sites from Australasia and analyse these data to determine how fire regimes have changed over centennial to multi-millennial timescales. tropical southeastern Asia, New Guinea, New Zealand and the islands of the western Pacific. Southeastern Asia and New Guinea were included to set the records from tropical Australia in a broader context. The inclusion of sites from the western Pacific (including New Zealand) is partly motivated by the need to address the potential role of changes in the ENSO on Australasian climates [START_REF] Mcglone | El Nino / southern Oscillation climatic variability in Australasian and South American palaeoenvironmental records[END_REF][START_REF] Shulmeister | Pollen evidence from tropical Australia for the onset of an ENSO-dominated climate at c. 4000 BP[END_REF][START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] and partly because this region has a different settlement history from Australia (see e.g. Stevenson and Hope, 2005). We extracted 196 sedimentary charcoal records from this broadly defined Australasian region (20 o N -50 o S and 100 o E to 177 o W) from Version 2 of the Global Charcoal Database (GCD-V2; Daniau et al., in preparation,) compiled by the Global Palaeofire Working Group (GPWG: http://gpwg.org/). These data were supplemented by an additional 27 sites, chosen to increase the number of long records and to improve the spatial coverage (GCD-V2.5).

This GPWG database contains sedimentary charcoal records from both marine and terrestrial sites. It includes descriptive data (metadata) about both the sites and the methods used, and detailed information on site chronology (including information on the number of radiometric dates and, for records extending back beyond the limits of radiocarbon dating, details of correlative tie-points used to erect the chronology). All radiocarbon dates have been calibrated and the age model for each site is expressed in calendar years. Where multiple records (e.g. macro and micro-charcoal records) are available at the same site, all are included in the database.

Since charcoal records are obtained using many different techniques and expressed using a large range of metrics, the data were standardized to facilitate comparisons between sites and through time (see [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF] for a full description). The protocol involved three steps. First, non-influx data (e.g. concentration expressed as particles/cm 3 ; charcoal-to-pollen ratios) were transformed to influx values (i.e. particles/cm 2 /yr), or quantities proportional to influx, by dividing the values by sample deposition times. Second, a Box-Cox transformation was used to homogenize the inter-site variance by transforming individual charcoal records toward normality.

Finally, the data were rescaled using a common base period (0.2-21 ka) to yield zscores, so that all sites have a common mean and variance. Previous experimentation has demonstrated that the choice of the base period does not affect the results significantly [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF][START_REF] Power | Fire history and the Global Charcoal Database: a new tool for hypothesis testing and data exploration[END_REF] and standardization does not alter the overall pattern of variability or -signal‖ in a record. For mapping purposes, the z-scores were divided into five approximately equal groups: z-score >+0.8 (strong positive anomalies compared to the long-term average over the base period), z-score between +0.4 to +0.8 (positive anomalies), z-score between +0.4 to -0.4 (weak positive or negative anomalies), z-score between -0.4 to -0.8 (negative anomalies), and z-score <-0.8 (strong negative anomalies).

In order to summarise the broadest-scale history of changing fire regimes through time, we constructed composite charcoal records for Australasia as a whole and for various sub-regions. Composite curves were then obtained by fitting a locally weighted regression (or -lowess‖ curve) to the pooled transformed and rescaled data (see e.g. [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF], using a fixed window width and a tricube weight function with one ‗‗robustness iteration‖. We used window half-widths of 100, 200, 400, or 2000 years, to emphasise different scales of temporal variability from centennial variability in the past 2000 years through to multi-millennial variability in the longer records. The windows were selected to avoid both over-smoothing, as would result from selecting a large window width, and under-smoothing, leading to a composite curve that was susceptible to the influence of individual data points. These choices do not affect the results and conclusions.

Confidence intervals for each composite curve were generated by a bootstrap resampling (with replacement) of individual sites over 1000 replications. This approach differs from the usual bootstrap method in which individual observations are sampled with replacement, and emphasizes the uncertainty in regression curves that arises from the inclusion or exclusion of the whole record from individual sites. Bootstrap confidence limits for each target point were taken as the 2.5 th and 97.5 th percentiles of the 1000 fitted values for that target point. Our approach is therefore conservative because it permits the identification of -signals‖ that may arise solely from the influence of a particular site: when the bootstrap confidence intervals are wide, this indicates greater uncertainty in the composite curve and greater sensitivity of that curve to the addition/subtraction of an individual record. Minor fluctuations in the composite curve during times when the bootstrap confidence intervals are wide are most probably meaningless. Most of the time, the composite curve lies in the middle of the bootstrap confidence intervals; when one confidence limit deviates from the composite curve more than the other confidence limit, this too indicates that some individual records depart markedly from the typical pattern of the time. Again, the interpretation of minor fluctuations in the composite curve under these circumstances is not likely to be meaningful. We also provide a measure of the number of observations contributing to the composite curve (at each of the target points that define the curve) as an additional measure of the confidence to be placed in the reconstructions. Since this takes into account the number of points at each site that fall within the window width for a particular target point, these curves are not a simple measure of the number of sites.

Results

Characteristics of the data set

The geographic coverage of sites in the dataset is uneven (Figure 1), with few sites from central and western Australia, but with eastern and especially southeastern, Australia well represented. While there are some sites from tropical mainland Australia, most of the tropical records are from Papua New Guinea, with a small number of additional sites scattered throughout southeastern Asia as well as several tropical Pacific Islands (Figure 1a). The coverage of records from New Zealand is reasonable, although it does not approach the known number of records for the island.

The paucity of data from northern, western and central Australia is partially a reflection of absence of suitable sites for the preservation of sedimentary charcoal records [START_REF] Pickett | Pollen-based reconstructions of biome distributions for Australia, South East Asia and the Pacific[END_REF] and partially a function of the comparative difficulty of working in these regions. Nevertheless, the data do provide a reasonably comprehensive coverage of the range of climates and vegetation types found in Australasia (Figure 1a-e).

The temporal coverage of the data set is also uneven (Table 1). There are 18 records that extend back beyond 70 ka (Figure 1c). These sites are not spatially clustered, but rather occur from just north of the equator to the South Island of New Zealand, and therefore experienced very different climate and fire regimes from one another. There are 19 sites that provide a record from the beginning of Marine Isotope Stage (MIS) 3 (here defined as 59.4 ka, following Sanchez Goñi and Harrison, 2010) and 40 sites that are recording by the end of MIS 3 (here defined as 27.8 ka, following Sanchez Goñi and Harrison, 2010). Over half of these records are sampled at an average resolution of more than 1 sample per ka (Table 1). The number of records and their spatial distribution during MIS 3 makes Australasia one of the best-documented regions of the world for this period (see [START_REF] Daniau | Fire regimes during the Last Glacial[END_REF]. The number of charcoal records increases steadily during MIS 2 (27.8-14.7 ka), such that by the end of MIS 2 there are approximately 70 sites and by 10 ka there are about 110 sites recording fire.

Sampling resolution for sites covering the Holocene varies from ca 1 sample per ka to 1 sample per decade: the majority of the short records (i.e. those covering the last 1-2ka) have been sampled at decadal resolution, while the majority of the late Holocene records have centennial resolution.

The charcoal records from Australasia were obtained using several different techniques (Table 1). The quantification of charcoal fragments on pollen slides dominates the original records (80.4% of the sites where method was explicitly recorded). However, 44 of the records (19.6% of the sites where method was explicitly recorded) are macroscopic charcoal records obtained by wet sieving.

Macroscopic, sieved, charcoal records are more likely to provide a record of local fires and provide more continuous temporal sequences than charcoal records obtained through other means. Nevertheless, although differences between the macro-and micro-charcoal records at individual sites have been used to infer the spatial scale of fire events [START_REF] Power | Fire history and the Global Charcoal Database: a new tool for hypothesis testing and data exploration[END_REF], both microscopic and macroscopic records produce comparable results in terms of broad-scale regional histories of fire [START_REF] Tinner | Long-distance transport of macroscopic charcoal by an intensive crown fire in the Swiss Alps -implications for fire history reconstruction[END_REF]Conedara et al., 2009).

Temporal trends in biomass burning

There are relatively few sites with a record prior to MIS 4 and the composite curve for the interval prior to 70 ka is too noisy to interpret. We have therefore chosen to focus our analyses on the interval after 70 ka. The composite record from Australasia (Figure 2, purple curve) shows a steady increase in biomass burning during the later part of MIS 4 (73.5-59.4 ka). Biomass burning remains generally high through MIS 3, but decreases at the beginning of MIS 2. Biomass burning levels are generally high through the Holocene. This pattern, of higher levels of biomass burning during MIS 3 than MIS 4, the reduction of biomass burning at the beginning of MIS 2, and the return to higher levels of biomass burning during the Holocene, is consistent with the global pattern of less fire during cold stadial or glacial stages, and increased fire during warmer interstadials and interglacials [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF][START_REF] Daniau | Fire regimes during the Last Glacial[END_REF].

Although there are marked changes in fire activity during MIS 3 (see below), there is no fundamental shift in the composite charcoal record that could be associated with the colonization of Australia by Aboriginal people (50±10 ka: [START_REF] Bird | Populating PEP II: the dispersal of humans and agriculture through Austral-Asia and Oceania[END_REF].

Superimposed on these general trends, the composite record shows considerable millennial-scale variability in biomass burning (Figure 2, black curve). This is most marked in MIS 3, but can also be seen during the later part of MIS 4, with multiple peaks in biomass burning in the 400-year smoothed record between 70 and 20 ka (Figure 2, black curve). Some of these charcoal peaks appear to occur around the time of the Dansgaard-Oeschger (D-O) warming events as described in Greenland ice-core records (Figure 3). Other peaks in the charcoal record are not apparently coincident with D-O warming events, but this may be a function of the considerable uncertainty associated with the chronologies of individual charcoal records that are beyond the limit of radiocarbon dating. Several of the records contributing to the composite curve have been tuned to match assumed limits of the transition between the last interglacial and the glacial (MIS 5/4 boundary) and the MIS 3/2 boundary on the basis of changes in pollen stratigraphy, and this procedure could certainly add uncertainties of several thousand years to the assumed age model. In terms of the number and shape of the peaks, and in terms of the timing during the post-30 ka interval (when radiocarbon dating is likely to be more reliable), the form of the composite charcoal curve is more similar to the pattern of millennial-scale variability shown in the NGRIP record than to the pattern shown by the EPICA deuterium record (Figure 3), which shows a smaller number of lower amplitude -Antarctic Isotope Maxima‖ over the same interval (EPICA Community Members, 2006). Furthermore, this pattern is consistent with a global analysis of charcoal records that span the interval from 80 ka to 10 ka [START_REF] Daniau | Fire regimes during the Last Glacial[END_REF] that show a consistent (across the D-O events) increase in biomass burning accompanying D-O warming events.

There are relatively low levels of biomass burning between ca 24 to 18 ka (i.e. around the time of the Last Glacial Maximum, LGM), but fire increases thereafter. The postglacial increase of biomass burning (Figure 4a) is expressed differently in the Intertropical Convergence Zone belt (ITCZ, 20 N to 20 S, 100 E to 177 W) and the subtropical high-pressure belt (STH, 25-45 S, 100 E to 177 W). In the tropics (Figure 4b), biomass burning increases gradually after the LGM through to the early Holocene, but with two broad peaks of increased fire centered around 15 ka and 8 ka.

There is a sharp decrease in biomass burning culminating at 6.5 ka. There is a rapid recovery after this and the rest of the Holocene appears relatively complacent until the last few hundred years. The records from the STH belt (Figure 4c) show a different pattern, with no marked trends during the deglacial period, but there is a tendency towards increased biomass burning during the first part of the Holocene culminating around 6 ka and a decline in fire during the late Holocene. The STH record also shows centennial-to millennial-scale variability throughout the last 21 ka. Again, the most pronounced changes in STH fire regimes occur during the last few hundred years.

The composite Australasian record of biomass burning over the past two millennia (Figure 5a) is remarkably flat except for the pronounced increase in fire in the past 200 years. [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF] showed that global biomass burning gradually declined over most of the last two millennia in response to long-term cooling culminating in the Little Ice Age. This is seen in the composite record from tropical Australasia (Figure 5b) but not in the record from temperate Australasia (Figure 5c).

There is a pronounced increase in burning after ca 1800 A.D. in both the ITCZ (Figure 5b) and STH regions (Figure 5c), and this increase is also seen in the composite record of the Australian mainland (Figure 5d). The upturn is delayed compared to the timing of the increase in global biomass burning which occurred ca 1750 A.D [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF]. Although European colonization of Australia may have been responsible for the upturn seen on the mainland, and in the composite regional curve, it is more difficult to invoke this explanation for the record from tropical Australasia (Figure 5b) which suggests that any anthropogenic influence was exacerbated by changes in climate and vegetation productivity associated with the post-industrial increase in atmospheric CO 2 concentration. The curves for all of the regions show reduced fire during the last ca. 50 years, and this is despite the widespread use of prescribed burning as a means of fire control in much of Australia.

The observed reduction in biomass burning in Australasia occurs a few decades after the global reduction in biomass burning which [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF] attribute to increased landscape fragmentation and fire suppression. There are a number of reasons why the last fifty or so years of the record might be less tightly constrained, including issues with sampling (Mooney and Tinner, in press), problems with dating and the potential impact of human activities on catchment processes and sedimentation [START_REF] Gale | Event chronostratigraphy: A high-resolution tool for dating the recent past[END_REF]. Nevertheless, there are over 140 sites contributing to the record during the 20 th century and 168 sites recording the last two centuries, and thus the recent downturn in fire appears to be a robust feature of the record. Furthermore, [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF] demonstrate (in their supporting information) that the rapid increase in biomass burning followed by an abrupt decrease cannot be explained by simple sedimentation rate variations at the top of cores.

Spatial patterns in biomass burning since the Last Glacial Maximum

Most of the sites in our data set are concentrated in eastern, particularly southeastern Australia (Figure 1a). Nevertheless, there are spatial patterns in the changes in biomass burning that are worth exploring. Here (Figure 6), we present maps of the average z-scores for key intervals since the LGM to illustrate some of the regional patterns of change: positive z-scores indicate more biomass burning and negative zscores less biomass burning than the long-term average for the base period (21-0.2 ka).

The LGM (Figure 6a) was characterized by low biomass burning in the tropics and over most of Australia. A few sites in southeastern Australia and one site on the South Island of New Zealand show higher-than-average z-scores. There is considerable millennial-scale variability superimposed on the long-term increase in biomass burning after the LGM (Figure 4) and this is reflected in the mapped patterns, with individual sites showing more/less fire in succeeding 1000-yr intervals (not shown).

However, a more coherent spatial pattern is established during the Antarctic Cold Reversal (ACR) (Figure 6b), with low biomass burning in southeastern Australia and New Zealand contrasting with high biomass burning at many sites in the tropics. In contrast, the beginning of the Holocene is marked by high biomass burning in southeastern Australia and low biomass burning in the tropics (Figure 6c).

The most marked spatial feature of the Holocene record is the opposition in the temperate latitudes between sites in southernmost southeastern Australia (including Tasmania) and southeastern NSW (including the interior). In the early Holocene, here illustrated by the interval centered on 8 ka (Figure 6d), the southern region is characterized by high biomass burning and the region further north by low biomass burning. By the mid-Holocene, here illustrated by the interval centered on 5.5 ka (Figure 6e), sites in Tasmania and the southernmost tip of the continent show less biomass burning and many sites further north show increased biomass burning. The pattern has reversed again by the late Holocene (not shown). The pre-industral era (0.2-0.7 ka, Figure 6f) is characterized by high biomass burning along the east coast of Australia, in New Zealand and over much of the tropics. However, Tasmania and the southernmost part of southeastern Australia, and the limited number of sites from western Australia, show less biomass burning than average.

Although there are robust, large-scale patterns in changes in biomass burning through time, the changes within any one region through time are complex; adjacent sites can show differences even in the sign of the change at particular times. This may, in part, be a function of the quality of the data and of individual age models. It is also in the nature of wildfires, which are spatially disjunct, influenced by topography and the changing nature of the vegetation itself, and conditioned by timing of previous fires. [START_REF] Smith | Humanenvironment interactions in Australian drylands: exploratory time-series analysis of archaeological records[END_REF] have used a data set of 971 radiocarbon ages from 286 archaeological sites in arid Australia (AustArch1) to illustrate the overall trend in archaeological site incidence by summing the probability density functions of individual radiocarbon ages, which they suggest can be interpreted as a first approximation of human activity and population history through time. [START_REF] Smith | Humanenvironment interactions in Australian drylands: exploratory time-series analysis of archaeological records[END_REF] stress the preliminary nature of this record, but nevertheless the compilation allows us to explore the relationship between an approximate measure of human activity and the charcoal-derived record of fire from Australia (Figure 7). Although there are other compilations of archaeological information from Australia (e.g. [START_REF] Lourandos | Comparing long-term archaeological and environmental trends: North Queensland, arid and semi-arid Australia[END_REF][START_REF] Lourandos | Long-term archaeological and environmental trends: a comparison from late Pleistocene-Holocene Australia[END_REF][START_REF] Turney | ENSO influence on Holocene Aboriginal populations in Queensland, Australia[END_REF], the AustArch1 data set is the most comprehensive and the raw data are available in a format which allows re-analysis.

Fire and humans during the late Pleistocene

There are relatively few archaeological dates, and little structure in the probability density curve, prior to ca. 20 ka. In contrast, there are large changes in fire on millennial timescales between 40-20 ka. The lack of congruence between the archaeological and fire records during this period suggests that the changes in fire activity do not reflect changes in human activity. [START_REF] Smith | Humanenvironment interactions in Australian drylands: exploratory time-series analysis of archaeological records[END_REF] identified six intervals of increased -human activity‖ during the past 20 ka, including a major increase in the late Holocene. Some of the reconstructed peaks in human activity correspond to peaks in fire (e.g. 7-8 ka). However, other peaks in human activity correspond to troughs in biomass burning (e.g. 5-4.5 ka) and there are peaks in biomass burning that have no correspondence with changes in human activity (e.g. at 13 ka and 9.5 ka). Most importantly, the major inferred increase in population during the late Holocene (‗intensification') is not accompanied by an increase in fire. Thus, this comparison does not support the hypothesis that changes in post-glacial fires in Australia were caused by humans.

Discussion and Conclusions

We have presented an analysis of the long-term changes in fire regimes across Australasia based on a comprehensive synthesis of over 200 charcoal records from the region. Despite the supposed importance of fire for Australasian vegetation [START_REF] Bowman | Rainforests and flame forests: the great Australian forest dichotomy[END_REF][START_REF] Bradstock | Flammable Australia: the fire regimes and biodiversity of a continent[END_REF], the long-standing arguments about the role of Aboriginal colonisation on fire regimes (e.g. Singh et al., 1981;[START_REF] Horton | The Pure State of Nature : sacred cows, destructive myths and the environment[END_REF][START_REF] Flannery | The Future Eaters: An Ecological History of the Australasian Lands and People[END_REF][START_REF] Kershaw | A history of fire in Australia[END_REF]Black and Mooney, 2006), and the uncertainty about the impact of climate change on fire regimes and fire-related disasters [START_REF] Chapman | Natural Hazards[END_REF][START_REF] Williams | The sensitivity of Australian fire danger to climate change[END_REF][START_REF] Cary | Importance of a changing climate for fire regimes in Australia[END_REF]Bushfire Co-operative Research Centre, 2006;[START_REF] Russell-Smith | Bushfires ‗down under': patterns and implications of contemporary Australian landscape burning[END_REF], there have been relatively few analyses of the palaeo-record of fire at a continental scale.

Previous reviews (see e.g. Singh et al., 1981;[START_REF] Kershaw | A history of fire in Australia[END_REF][START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] have been based on comparatively few sites and the assumption that iconic records are characteristic of much broader geographic regions. Although there are obvious spatial gaps in the data set used in this analysis, and some uncertainty associated with pooling records with age models of very different quality, we have been able to document robust changes in fire regime through time for which there are physically plausible explanations. The analysis presented here is preliminary in nature: much more could be done to explore the existing data set. Nevertheless, by demonstrating the potential of large-scale syntheses of charcoal data to shed light on the environmental history of the continent we hope to encourage the collection of additional, high-quality data from the Australasian region.

On glacial-interglacial timescales, changes in fire regimes closely follow global temperature: biomass burning is reduced during cold glacial or stadial intervals (MIS 4, MIS 2) and increased during warmer interglacial or interstadial intervals (MIS 3, the Holocene). This finding is consistent with global analyses (e.g. [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF][START_REF] Daniau | Fire regimes during the Last Glacial[END_REF] and presumably reflects the strong control of vegetation productivity on the availability of fuel (Harrison et al., 2010). Although we have confined our detailed analyses to the past 70 ka, we see no evidence for the expression of long-term aridification of Australia (see e.g. [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] in the charcoal records of fire regimes. The length of this interval of increasing aridity cited in the literature is somewhat vague, but we see no evidence for a long-term trend in biomass burning superimposed on the glacial-interglacial pattern.

The charcoal records show considerable millennial-scale variability during the glacial.

Given the limited radiometric age control on these records, and the widespread use of correlation to assumed stage/event boundaries, it is unclear whether individual peaks in charcoal correlate with specific D-O warming events registered in the Greenland ice core and reflected in marine records from the North Atlantic. Nevertheless, the shape of the charcoal curves is more reminiscent of the temperature changes in Greenland than those recorded in Antarctica (Figure 3) and, in conjunction with the number of events registered and the approximate temporal correlations of these events, suggests that Australasian fire regimes have co-varied with D-O cycles: with increased fire during warming events and Greenland Interstadials and reduced fire during Greenland Stadials. Millennial-scale variability has been identified in several individual records from Australasia (see e.g. [START_REF] Sikes | Glacial-interglacial sea surface temperature changes across the subtropical front east of New Zealand based on alkenone unsaturation ratios and foraminiferal assemblages[END_REF][START_REF] Turney | Millennial and orbital variations of El Nino/Southern Oscillation and high-latitude climate in the last glacial period[END_REF]Vandergoes et al., 2005;Kershaw et al., 2007), although with the caveat that the dating was insufficient to determine whether this variability was in or out of phase with the D-O cycles. However, [START_REF] Muller | Possible evidence for wet Heinrich phases in tropical NE Australia: the Lynch's Crater deposit[END_REF] have stated explicitly that warmer intervals of the D-O cycles are associated with dry conditions at Lynch's Crater. EPICA Community Members (2006) have demonstrated that the temperature records from Greenland and Antarctica are out of phase, and this bi-polar temperature seesaw is attributed to the shutdown or slowdown of the thermohaline circulation (see [START_REF] Kageyama | Modelling glacial climatic millennial-scale variability related to changes in the Atlantic Meridional Overturning Circulation: a review[END_REF]. However, the bi-polar seesaw hypothesis does not imply that the opposition in the direction of the change in temperature shown by the ice-core records is a pan-hemispheric phenomenon. In fact, decomposition of climate variability during the deglaciation [START_REF] Shakun | A global perspective on Last Glacial Maximum to Holocene climate change[END_REF], based on 104 highresolution palaeoclimate records, has shown that approximately 60% of the climate signal is common between the two hemispheres, while only 11% of the signal is associated with the bi-polar see-saw. [START_REF] Shakun | A global perspective on Last Glacial Maximum to Holocene climate change[END_REF] attributed the hemispheric synchroneity in climate to the influence of CO 2 , although presumably this argument would apply to all of the greenhouse gases. The greenhouse gas synchronisation of hemispheric climate changes likely operated during the whole of the glacial, where D-O variability was accompanied by changes in CO 2 of the order of 15-20 ppm and CH 4 values of the order of about 100 ppm, or about 20% and 50% of their glacial-interglacial range, respectively [START_REF] Ahn | Atmospheric CO 2 and climate on millennial time scales during the last glacial period[END_REF]. There are other mechanisms that could extend a northern hemisphere climate signal into the southern hemisphere, through changes in atmospheric circulation that accompany THC shutdown, as shown by freshwater-forcing experiments [START_REF] Stouffer | Investigating the causes of the response of the thermohaline circulation to past and future climate changes[END_REF][START_REF] Muller | Possible evidence for wet Heinrich phases in tropical NE Australia: the Lynch's Crater deposit[END_REF][START_REF] Kageyama | Glacial climate sensitivity to different states of the Atlantic Meridional Overturning Circulation: results from the IPSL model[END_REF][START_REF] Kageyama | Modelling glacial climatic millennial-scale variability related to changes in the Atlantic Meridional Overturning Circulation: a review[END_REF]. Thus, the observation that Australasian fire regimes display some coherence with millennial-scale northernhemisphere climate variability is mechanistically credible [START_REF] Clark | The role of the thermohaline circulation in abrupt climate change[END_REF][START_REF] Muller | Possible evidence for wet Heinrich phases in tropical NE Australia: the Lynch's Crater deposit[END_REF][START_REF] Daniau | Fire regimes during the Last Glacial[END_REF]. Furthermore, Southern Hemisphere vegetation records show millennial-scale variability (see e.g. [START_REF] Hessler | Millennial-scale changes in vegetation records from tropical Africa and South America during the last glacial[END_REF][START_REF] Harrison | Global patterns of vegetation response to millennial-scale variability during the last glacial: A synthesis[END_REF], although the vegetation response appears to be more muted than shown in European records [START_REF] Fletcher | Millennial-scale variability during the last glacial in vegetation records from Europe[END_REF] it is nevertheless in phase with D-O events [START_REF] Harrison | Global patterns of vegetation response to millennial-scale variability during the last glacial: A synthesis[END_REF].

The pre-40 ka composite biomass-burning curve for Australasia is based on 25 individual records and this value rises to 38 sites by 30 ka. There is clearly a need for more long records, sampling a wider geographic range of regional climates. Charcoal records from marine cores are providing an increasingly detailed view of changes in fire regimes during the glacial (see e.g. [START_REF] Beaufort | Biomass burning and oceanic primary production estimates in the Sulu Sea area over the last 380,000 yr and the East Asian monsoon dynamics[END_REF]Thevenon et al., 2004;[START_REF] Daniau | Dansgaard-Oeschger climatic variability revealed by fire emissions in southwestern Iberia[END_REF][START_REF] Daniau | Last glacial fire regime variability in western France inferred from microcharcoal preserved in core MD04-2845, Bay of Biscay[END_REF] with the added advantage that these records can be linked, through isotope stratigraphy, directly to changes in sea-surface temperatures and to changes recorded in ice cores. Marine records could be more widely exploited for charcoal analysis in the Australasian region.

Low biomass burning was characteristic of the LGM. Vegetation reconstructions show an expansion of xerophytic vegetation in southern Australia and of tropical deciduous broadleaf forest and woodland in the tropics (both mainland Australia, Papua and the islands of southeastern Asia) at the LGM [START_REF] Pickett | Pollen-based reconstructions of biome distributions for Australia, South East Asia and the Pacific[END_REF].

Although the number of records included in this synthesis is limited, particularly for northern Australia, the interpretation is consistent with earlier reconstructions (see e.g. [START_REF] Markgraf | Evolution of late Pleistocene and Holocene climates in the circum-South Pacific land areas[END_REF][START_REF] Harrison | Climates of Australia and New Guinea since 18,000 yrs BP[END_REF] showing expansion of droughttolerant vegetation during the LGM. Although superficially these changes could be interpreted as an expansion of more fire-prone vegetation, they have been interpreted as indicating colder and drier conditions. Indeed, [START_REF] Williams | Glacial and deglacial climatic patterns in Australia and surrounding regions from 35,000 to 10,000 years ago reconstructed from terrestrial and near-shore proxy data[END_REF] argue for a reduction in precipitation of ca 30-50% in the tropics and a significant reduction in winter precipitation across the temperate zone, accompanied by reductions in temperature of ca 3-8°C. The observed reduction in biomass burning must reflect a significant reduction in fuel availability under cold, dry conditions, sufficient to offset the increase in aridity and also the stronger winds that have been adduced from dune evidence (e.g. [START_REF] Hesse | Late Quaternary climates of the Australian arid zone: a review[END_REF].

Previous syntheses of palaeoenvironmental data from Australia have identified the late glacial as an interval of aridity, more pronounced than during the LGM and certainly than during the Holocene (see e.g. [START_REF] Harrison | Climates of Australia and New Guinea since 18,000 yrs BP[END_REF][START_REF] Kershaw | Late Quaternary Milankovitchscale climatic change and variability and its impact on monsoonal Australasia[END_REF][START_REF] Williams | Glacial and deglacial climatic patterns in Australia and surrounding regions from 35,000 to 10,000 years ago reconstructed from terrestrial and near-shore proxy data[END_REF]. The charcoal records show considerable spatial and temporal variability during the deglaciation. There is no evidence for dry conditions persisting for several thousand years, as postulated by earlier syntheses. However, the charcoal records do appear to show a reduction in biomass burning during the ACR.

The ACR has been identified in palaeoenvironmental records from southeastern Australia and New Zealand (see e.g. [START_REF] Barrows | Absence of cooling in New Zealand and the adjacent ocean during the Younger Dryas Chronozone[END_REF][START_REF] Calvo | Antarctic deglacial pattern in a 30 kyr record of sea surface temperature offshore South Australia[END_REF][START_REF] Williams | Glacial and deglacial climatic patterns in Australia and surrounding regions from 35,000 to 10,000 years ago reconstructed from terrestrial and near-shore proxy data[END_REF], although the recognition that this climate event could have had an impact on fire regimes at a continental scale is new.

Vegetation reconstructions (see e.g. [START_REF] Harrison | Climates of Australia and New Guinea since 18,000 yrs BP[END_REF][START_REF] Pickett | Pollen-based reconstructions of biome distributions for Australia, South East Asia and the Pacific[END_REF] indicate that the mid-Holocene vegetation patterns of Australia were very similar to those typical of the pre-industrial (and pre-European) era. Despite the lack of vegetation change, [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] indicate that the interval between 7-5 ka (which they call the -climatic optimum‖) was one of reduced biomass burning in southeastern Australia. They do not specify which records this assertion is based on, and their conclusion is certainly not supported by the records presented here: most sites in southeastern Australia show considerably higher-than-average levels of fire at 6 ka, although sites in the interior (and the limited number of sites in western and northern Australia) show less-than-average biomass burning.

We have identified a tendency for sites in the southernmost part of southeastern Australia to show an opposite pattern of change in biomass burning during the Holocene from those further north and along the east coast. This spatial differentiation was also identified by [START_REF] Pickett | Pollen-based reconstructions of biome distributions for Australia, South East Asia and the Pacific[END_REF] in their reconstruction of mid-Holocene vegetation shifts. They argued that sites in the far south show a shift towards more moisture-stressed vegetation in the mid-Holocene, while sites in the Snowy Mountains, on the Southern Tablelands and east of the Great Dividing Range have more moisture-demanding vegetation in the mid-Holocene than today. These shifts in vegetation are consistent with the reconstructed shifts in biomass burning, with the former region characterised by more fire and the latter by less fire during the mid-Holocene. [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] have argued that the pronounced increased in biomass burning after ca. 4 ka shown in the charcoal record from Lake Euramoo (Haberle, 2005), and assumed to represent a regional signal, reflects an increases in climate variability associated with ENSO. There is considerable evidence for an increase in ENSO strength and/or variability after the mid-Holocene (see e.g. [START_REF] Rodbell | An ~15,000-Year Record of El Niño-Driven Alluviation in Southwestern Ecuador[END_REF][START_REF] Tudhope | Variability in the El Niño-Southern Oscillation through a glacial-interglacial cycle[END_REF][START_REF] Andrus | Otolith 18 O record of mid-Holocene sea surface temperatures in Peru[END_REF][START_REF] Koutavas | El Niño-Like Pattern in Ice Age Tropical Pacific Sea Surface Temperature[END_REF] but the onset of increased variability has been variously placed at 6.5 ka [START_REF] Black | Implications of a 14,200 year contiguous fire record for understanding human-climate relationships at Goochs Swamp, New South Wales, Australia[END_REF], 5 ka [START_REF] Mcglone | El Nino / southern Oscillation climatic variability in Australasian and South American palaeoenvironmental records[END_REF]), 4 ka [START_REF] Shulmeister | Pollen evidence from tropical Australia for the onset of an ENSO-dominated climate at c. 4000 BP[END_REF] and 3 ka [START_REF] Gagan | Post-glacial evolution of the Indo-Pacific Warm Pool and El Niño-Southern oscillation[END_REF] and reanalysis of the Lake Pallcacocha record [START_REF] Rodbell | An ~15,000-Year Record of El Niño-Driven Alluviation in Southwestern Ecuador[END_REF] suggests that variations on the ENSO time scale persisted throughout the Holocene [START_REF] Rodó | El Nino-Southern oscillation: absent in the early Holocene?[END_REF]. Neither the composite nor regional charcoal records (Figure 4) show a clear relationship between changes in ENSO (e.g. [START_REF] Moy | Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch[END_REF] and patterns of biomass burning. This suggests the need for a re-evaluation of the relationship between biomass burning and changes in ENSO, over both late Holocene and longer timescales, taking into account that the sign of the relationship between biomass burning and ENSO may be different depending on the nature of the vegetation.

Lack of data has bedevilled previous interpretations of both the fire history and, more generally, the palaeoenvironmental history of Australasia. Reconstructed changes at individual sites have often been used to draw quasi-continental scale inferences about past climate and environmental changes. The fact that many of the conclusions drawn from such iconic sites are not supported by the large-scale data synthesis of charcoal records presented here argues for an urgent need to re-assess our understanding of the late Quaternary history of Australia.

Chronology has also been a contentious issue for the interpretation of palaeo-fire records from Australasia. Alternative age models have been proposed for several of the longer records included in this synthesis (see for e.g. Lynch's Crater: [START_REF] Kershaw | Climatic change and Aboriginal burning in north-east Australia during the last two glacial/interglacial cycles[END_REF][START_REF] Turney | Redating the onset of burning at Lynch's Crater (North Queensland): implications for human settlement in Australia[END_REF][START_REF] Turney | Millennial and orbital variations of El Nino/Southern Oscillation and high-latitude climate in the last glacial period[END_REF]Kershaw et al., 2007;[START_REF] Muller | Possible evidence for wet Heinrich phases in tropical NE Australia: the Lynch's Crater deposit[END_REF]; Lake George: Singh et al., 1981, Singh andGeissler, 1985;[START_REF] Fitzsimmons | Holocene hydrologic variability in temperate southeastern Australia: An example from Lake George, New South Wales[END_REF]. In the interpretation of individual records, the quality of the age model is of prime importance and reliance on assumed correlations with global events or stratigraphic boundaries (e.g. the MIS boundaries) precludes detailed analysis of the relationships between observed changes in fire and the global controls on regional climate. The strength of the technique used here of compositing multiple records is that it allows a robust assessment of the influence of individual records, and hence of the chronological uncertainties of each record, on the shape of the composite curve.

By focusing on statistically robust features of the composite record, we are able to draw plausible inferences about the controls on these features. Clearly, improvement of the individual age models and increased reliance on radiometric dating would substantially enhance the amount of detail that could be extracted from the composite records. Nevertheless, our analyses show that there is considerable temporal structure in the fire records and it is highly unlikely that this structure could be generated by chance, particularly since the reconstructed changes in biomass burning through time are consistent with current understanding of past climate changes.

The simplest interpretation of the charcoal records is that climate, and climatemodulated changes in vegetation productivity and distribution, control fire regimes on centennial to multi-millennial timescales. The observed changes in biomass burning can be plausibly explained by current understanding of the broadscale changes in climate over the last ca. 70 ka. However, there are many changes in the fire record for which we still lack a robust mechanistic explanation. As previous work has shown (e.g. [START_REF] Power | Changes in fire regime since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data[END_REF], simplistic explanations of fire records in terms of single climate (or environmental) drivers are likely to be wrong. Similar changes in climate can produce changes in biomass burning of opposite sign depending on the state of the vegetation (van der Werf et al., 2008b;Harrison et al., 2010). For example, an increase in precipitation will tend to suppress fires in most forests but could lead to an increase in fire in areas where fuel is limited because it will support more plant growth. Coeval changes in temperature can offset the impact of changes in precipitation on vegetation productivity. Atmospheric CO 2 concentrations influence vegetation productivity and distribution even without a change in climate (Harrison and Prentice, 2003;[START_REF] Prentice | Ecosystem effects of CO 2 concentration: evidence from past climates[END_REF]. Finally, fire regimes are affected by the considerable heterogeneity of local climates. Sites in close geographic proximity may nevertheless have different climates, particularly with respect to rainfall seasonality. [START_REF] Shafer | Understanding the spatial heterogeneity of global environmental change in mountain regions[END_REF], for example, have shown that large-scale changes in atmospheric circulation lead to opposite changes in precipitation between sites in the same region depending on whether they experience a summer or winter-rainfall maximum and this in turn affects the response of the fire regime to these changes in atmospheric circulation [START_REF] Millspaugh | Postglacial fire, vegetation, and climate history of the Yellowstone-Lamar and Central Plateau provinces, Yellowstone National Park[END_REF]. Given all these competing influences, it is unlikely that interpretations of the charcoal record in terms of climate will be unequivocal. While speculations as to the causes of specific changes in fire regimes are interesting as a source of hypotheses, we would argue strongly for the combined use of observational data and modelling (see e.g. [START_REF] Webb | Late Quaternary climate change in eastern North America: a comparison of pollen-derived estimates with climate model results[END_REF]Harrison et al., 2003) to interpret the observed changes in fire regimes through time.

The influence of humans in modifying natural fire regimes has been a major feature of the interpretation of charcoal records from the Australasian region. The apparent coincidence of increases in sedimentary charcoal at iconic sites (Lake George, Lynch's Crater, Darwin Crater) with the arrival of Aboriginal people in Australia, has been attributed to anthropogenic fire (e.g. see [START_REF] Turney | Millennial and orbital variations of El Nino/Southern Oscillation and high-latitude climate in the last glacial period[END_REF]. This has led to circular arguments about the relationship between fire and people, including assertions that people arrived on the continent before the last glacial [START_REF] Jackson | The Tasmanian legacy of man and fire[END_REF]Singh et al., 1981). Changes in vegetation cover driven by anthropogenic modification of fire regimes have been explicitly invoked as a mechanism for causing aridification of Australia over the past 50-60 ka and for megafaunal extinctions [START_REF] Miller | Ecosystem collapse in Pleistocene Australia and a Human role in megafaunal extinction[END_REF]. This causal association of anthropogenic fire with changes in climate, vegetation or fauna has remained seductive, despite several lines of contrary evidence. This evidence includes major changes in Late Quaternary charcoal records and hence fire prior to the arrival of humans (e.g. ca 130 ka: Singh et al., 1981;[START_REF] Dodson | Two fine-resolution Pliocene charcoal records and their bearing on pre-human fire frequency in south-western Australia[END_REF]; the fact that a broadly-synchronous transition to more xerophytic vegetation has been found in New Caledonia, a region which was not settled by humans until ca 3000 years ago (Stevenson and Hope, 2005); and modeling evidence that the purported changes in vegetation cover were insufficient to cause a sustained change in Australasian climate [START_REF] Pitman | The significance of large scale land cover change on the Australian palaeomonsoon[END_REF]. We have found no evidence of a change in fire regimes at a continental scale at the time of Aboriginal colonisation of Australia (50+10 ka).

Changes in Aboriginal socio-economic relationships [START_REF] Lourandos | Change or stability? Hydraulics, hunter gatherers and population in temperate Australia[END_REF][START_REF] Lourandos | Intensification: a late Pleistocene-Holocene archaeological sequence from southwestern Victoria[END_REF] have been invoked as causing changes in fire regimes in the mid-to-late Holocene. [START_REF] Lourandos | Change or stability? Hydraulics, hunter gatherers and population in temperate Australia[END_REF][START_REF] Lourandos | Intensification: a late Pleistocene-Holocene archaeological sequence from southwestern Victoria[END_REF] suggested that the intensification of land use by Aboriginal groups, including a shift from more nomadic to more sedentary populations, was responsible for an increase in fire from ca. 5 ka onwards. [START_REF] Lynch | Using the palaeorecord to evaluate climate and fire interactions in Australia[END_REF] have also argued for an increase in fire associated with people, although they suggest that this occurred somewhat later after ca 3 ka. Again, we see no unequivocal relationship between inferred populations and/or the intensity of Aboriginal occupation of the continent and fire regimes during the past 21 ka, nor do the charcoal records show a marked increase in fire during the late Holocene. The charcoal records support the idea that fire regimes were controlled by changing climate and climate-induced changes in vegetation, though it is possible that these natural changes had an impact on Aboriginal populations through control of resources.

It is possible that our failure to identify a distinctive human fingerprint on fire activity at a continental or regional scale, either at the purported time of Aboriginal settlement of the Australian continent or over the past 21 ka, reflects the use of sedimentary charcoal records or of analytical techniques that emphasize the composite signal of many individual records. It could be argued, for example, that sedimentary charcoal is an unreliable indicator of the small-scale or low intensity fires characteristic of Aboriginal burning, particularly if the material burnt was primarily non-woody.

However, studies from other areas (e.g. [START_REF] Brown | Fire cycles in North American interior grasslands and their relation to prairie drought[END_REF] show that predominantly grassland fires are recorded by sedimentary charcoal. Furthermore, if sedimentary charcoal records fail to capture smaller-scale fires, then the use of individual charcoal records to identify human impact on the landscape is also suspect. In the absence of independent archaeological evidence, the interpretation of local charcoal signals in terms of human impact is at best equivocal as to causation even when accompanied by changes in vegetation.

It is more plausible to argue that the compositing of individual charcoal records at regional or continental scales is more likely to emphasise changes that are congruent with climate or environmental changes that operate at similar spatial scales and less likely to identify human impacts if these changes were time transgressive across a region or highly localized. The timing of Aboriginal settlement of Australia is subject to large uncertainty, and there is no clear agreement about whether settlement was time transgressive or not (see discussion in [START_REF] Bird | Populating PEP II: the dispersal of humans and agriculture through Austral-Asia and Oceania[END_REF]. Furthermore, the debate about Aboriginal use of fire has focused on whether it resulted in widespread transformation of the Australian vegetation (see e.g. [START_REF] Flannery | The Future Eaters: An Ecological History of the Australasian Lands and People[END_REF][START_REF] Horton | The Pure State of Nature : sacred cows, destructive myths and the environment[END_REF][START_REF] Miller | Ecosystem collapse in Pleistocene Australia and a Human role in megafaunal extinction[END_REF]. Thus, while we cannot dismiss the idea that some individual records may contain an overprint of human influence on the local fire regime, the evidence presented here clearly demonstrates that the dominant control of fire activity is climate or climate-modulated changes in vegetation cover.

In conclusion, compilation and analysis of charcoal records from Australasia have allowed us to document changes in fire regimes over the past 70 ka and thus to examine (and resolve) some persistent controversies about the relationship between fire, climate and humans. We cannot, as yet, explain all of the features of the fire record nor have we exhausted the potential of the current database. The ongoing improvement of the database, through e.g. extension of the geographical coverage, higher resolution sampling, and improvement of age models, will allow further questions to be addressed. Community-based regional synthesis of charcoal and vegetation records, particularly if this can be combined with carefully designed model experiments, will continue to yield new insights into the behaviour of fire in response to climate and other environmental factors on palaeo-timescales. (c,d) bioclimate and vegetation space and (e) fire regime space. In (b), a site is counted if it contributes data to a 4000-year wide bin. Bioclimate space is defined here by growing degree days above 0 (GDD0: an index of summer warmth) and the ratio of actual to equilibrium evapotranspiration (AE/PE: an index of effective moisture) calculated using climate data from the CRU historical climatology data set [START_REF] New | A high-resolution data set of surface climate over global land areas[END_REF].

Figure and Table Captions

The biome at each site (c) is based on a modern day simulation with the BIOME4 model [START_REF] Kaplan | Climate change and Arctic ecosystems II: Modeling, palaeodata-model comparisons, and future projections[END_REF] and has been grouped into major vegetation types (megabiomes) following the classification in Harrison and Prentice (2003). The tree cover (d) is derived from remotely-sensed vegetation data [START_REF] De Fries | Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data[END_REF]. Fire regimes (e) are represented by burnt area (annual average total burnt fraction) from the GFED v2.1 data set (van der [START_REF] Van Der Werf | Interannual variability of global biomass burning emissions from 1997 to 2004[END_REF]. Charcoal data are summarized using a lowess curve with a 400-year half-window width (black). For comparison with the charcoal curve, the 20-year sampling resolution NGRIP record (agecal. yr b1950) and EPICA are shown, along with a 400year smoothed curve (bold lines in blue and purple, respectively). The numbered vertical lines mark the Dansgaard-Oeschger warming events. The ice core data were obtained from the World Data Center for Palaeoclimatology hosted by NOAA/NGDC (http://www.ncdc.noaa.gov/paleo/paleo.html).

Figure 1 :

 1 Figure 1: Distribution of sites in (a) geographic space, (b) through time, (c, d)

Figure 2 :

 2 Figure 2: Reconstruction of biomass burning over the period from 70 ka to present for Australasia as a whole (20 o N -50 o S; 100 o E to 177 o W). The curves have been smoothed using a window of 2000 years (purple curve) to emphasize the long-term trends and a window of 400 years (bold black curve) to emphasize the millennialscale variability. The number of observations contributing to the record within each 400-year window is also shown (blue curve). The limit for Marine Isotope Stage (MIS) 1 is defined as 14.7 ka to present, MIS 2 (27.8-14.7 ka), MIS 3 (59.4-27.8 ka), and MIS 4 (73.5-59.4 ka) (Sánchez Goñi and Harrison, 2010).

Figure 3 :

 3 Figure 3: Millennial-scale variability in biomass burning during the glacial (70-20 ka) compared to temperature indices from the NGRIP and EPICA ice-core records.

Figure 4 :

 4 Figure 4: Reconstruction of biomass burning for the last 21 ka for Australasia as a whole (20 o N-50 o S, 100 o E-177 o W), the belt corresponding to the modern Inter-tropical Convergence Zone (ITCZ: 20 o N-20 o S, 100 o E to 177W, broadly the tropical region) and the modern sub-tropical high pressure belt (STH: 25 o S-45 o S, 100 o E to 177 o W,

Figure 5 :

 5 Figure 5: Reconstruction of biomass burning for the last 2 ka for Australasia as a whole (20 o N-50 o S, 100 o E-177 o W), the belt corresponding to the modern Inter-tropical Convergence Zone (ITCZ: 20 o N-20 o S, 100 o E to 177W, broadly the tropical region), the modern sub-tropical high pressure belt (STH: 25 o S-45 o S, 100 o E to 177 o W, broadly

Figure 6 :

 6 Figure 6: Reconstructions of the geography of changes in fire regimes, as expressed

Figure 7 :

 7 Figure 7: Comparison of the composite charcoal curve for sites from the Australian

  Figure 1

Table 1 :

 1 Sites with charcoal records from the Australasian region used in this study.

Table 1 :

 1 Sites with charcoal records from the Australasian region.

	Site Name	Latitude	Longitude	Elevation	Site type	Charcoal methods	Record length	Resolution	References
		(º)	(º)	(m)			(age ka)	(samples/ka)
	Aguai Ramata	-6.5639	145.211	1950	lacustrine	macro, sieved	15.84	32.88	Haberle, 2007
	Aire Crossing	-38.6467 143.4797	180	terrestrial	micro, pollen slide	10.33	3.97	McKenzie and Kershaw, 2004
	Ajax Hill	-46.4192	169.2922	680	mire	macro, sieved	14.21	4.01	McGlone, 2009
	Ajkwa 1	-4.867	136.968	1.3	coastal	micro, pollen slide	6.16	4.38	Ellison, 2005
	Ajkwa 2	-4.867	136.968	1.3	coastal	micro, pollen slide	10.43	5.85	Ellison, 2005
	Ajkwa 3	-4.867	136.968	1.3	coastal	micro, pollen slide	2.96	13.83	Ellison, 2005
	Ajkwa 4	-4.867	136.968	1.3	coastal	micro, pollen slide	5.55	4.86	Ellison, 2005
	Ajkwa 5	-4.867	136.968	1.3	coastal	micro, pollen slide	5.55	7.38	Ellison, 2005
	Allom Lake	-25.2333	153.1667	100	lacustrine	macro, sieved	57.00	2.14	Donders et al., 2006
	Anggi Lake	-1.403	133.902	1945	lacustrine	micro, pollen slide	26.08	0.35	Hope, 2007a
	Anumon Swamp	-20.163	169.828	45	mire	micro, pollen slide	1.87	4.27	Hope, 1996b
	Argan Swamp	-10.05	142.06	3	coastal	macro, sieved	5.52	23.74	Rowe, 2006a
	Aru	-5.917	134.2	1	terrestrial	micro, pollen slide	6.89	2.61	Hope and Aplin, 2005
	Badu 15	-10.06	142.09	20	terrestrial	macro, sieved	4.19	7.63	Rowe, 2006a, b, 2007
	Banda Sea Core SHI-	-5.76667	126.9667	-3163	marine	micro, pollen slide		0.54	van der Kaars et al., 2000
	9014						173.31
	Bar20	-10.1	142.12	18	coastal	macro, sieved	2.83	25.42	Rowe, 2006a,
	Bega Swamp	-36.5167	149.5	1080	mire	micro, pollen slide	12.25	39.92	Polach and Singh, 1980
	Black Swamp	-30.0206	151.4828	1450	mire	micro, pollen slide	10.62	6.21	Dodson et al.,
	Blue Lake Kosciuzko -36.141	148.437	1950	lacustrine	macro, sieved	8.18	60.43	Raine, 1974
	Blundells Flat	-35.19	148.49	762	mire	macro, sieved	2.69	90.70	Hope et al., 2006b
	Bobundara Swamp	-36.25	150.06	75	coastal	micro, pollen slide	8.01	9.99	Hope et al., 2006a
	Boggy Swamp	-29.9611	151.4986	1160	mire	micro, pollen slide	12.49	6.73	Dodson et al.,

This paper is a contribution to the ongoing work of the QUAVIDA working group of the ARC-NZ Network for Vegetation Function, to the UK Natural Environment Research Council (NERC) funded project -Analysis of long-term Climate Change in Australia (ACACIA)‖ and of the Global Palaeofire Working Group, supported by NERC and the US National Science Foundation (NSF). Mike Smith (Australian National Museum, Canberra) and Alan Williams (ANU, Canberra) supplied the archaeological data for Figure 7. We thank Martin Williams for his helpful comments on the manuscript.