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Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176

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Palaeogeography, Palaeoclimatology, Palaeoecology journal homepage: www.elsevier.com/locate/palaeo

Fire dynamics under monsoonal climate in Yunnan, SW China: past, present and future Shufeng Li a,c, Alice C. Hughes d,⁎, Tao Su a, Julie Lebreton Anberrée a, Alexei A. Oskolski e,f, Mei Sun g, David K. Ferguson h, Zhekun Zhou a,b,⁎⁎ a

Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan 666303, China Key Laboratory of Biogeography and Biodiversity, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650204, China c State Key Laboratory of Paleobiology and Stratigraphy, Nanjing Institute of Geology and Paleontology, Chinese Academy of Sciences, Nanjing 210008, China d Centre for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan 666303, China e Department of Botany and Plant Biotechnology, University of Johannesburg, PO Box 524, Auckland Park 2006, Johannesburg, South Africa f Komarov Botanical Institute, Prof. Popov Str. 2, 197376, St. Petersburg, Russia g National Plateau Wetlands Research Center, Southwest Forestry University, Kunming 650224, China h Department of Paleontology, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria b

a r t i c l e

i n f o

Article history: Received 28 June 2016 Received in revised form 18 October 2016 Accepted 20 October 2016 Available online 24 October 2016 Keywords: Charcoal Miocene Wildfire Southwestern China Climate Temperature

a b s t r a c t Climate change is likely to alter wildfire regimes, but the significance of climate-driven factors in regional fire regimes over extended temporal scales is poorly understood. Comparison of the reconstructed fire dynamics from charcoals in sediments with modern active fires may provide clues about the drivers of wildfire activities, and help us validate models of fire activity for both the past and the future. Microscopic charcoals from Miocene sediments in Wenshan Basin (Yunnan, southwestern China), were used to reconstruct fire dynamics in deep time. Palaeoclimatic data were obtained from the previous quantitative reconstruction using pollen samples from a sedimentary sequence. The relationship between palaeoclimatic parameters and the ratio of charcoals during the Miocene was explored, and compared with maximum entropy (Maxent) model results throughout different time periods. Our results indicate that the temperature in the dry season was the main factor controlling the frequency of fire in Wenshan during the Miocene. Maxent modeling results based on the modern active fire dataset of Yunnan Province from NASA Earth observations are consistent with the results from fossil sediment analysis, that dry season temperature is the main driver of fire activity. Our findings suggest a significant fire-temperature relationship under the monsoonal climate since the Neogene. Furthermore, models suggest that the last-glacial maximum (LGM) had lower levels of fire activity than present, which increased during the Holocene prior to reaching present levels. Models also predict increases in fire activity across most of Yunnan in the future. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Fire is an important process in the Earth system, which influences many aspects of the global environment, including the distribution of biomes, biodiversity, biogeochemical cycles and climate (Bond et al., 2005; Bowman et al., 2009; Page et al., 2002; Patra et al., 2005). Changes in the fire regime are not only caused by human activity (Gill and Catling, 2002), but also by climate change (Field et al., 2014; Gillett et al., 2004; Kasischke and Turetsky, 2006; Littell et al., 2009; Westerling et al., 2006). Given the strong links between fire and climate ⁎ Corresponding author. ⁎⁎ Correspondence to: Z.-K. Zhou, Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan 666303, China. E-mail addresses: [email protected] (A.C. Hughes), [email protected] (Z. Zhou).

http://dx.doi.org/10.1016/j.palaeo.2016.10.028 0031-0182/© 2016 Elsevier B.V. All rights reserved.

(Aldersley et al., 2011; Marlon et al., 2008; Swetnam and Betancourt, 1990), it is very likely that climate-driven changes to fire activity will occur in many regions, but fire risk sensitivity to changes in climatic factors like temperature and precipitation remains unquantified (Brown, 2012; Daniau et al., 2012). Despite the dominant role of fire in determining the distribution of species and guilds, the mechanisms driving fire prevalence and activity are still relatively poorly understood, especially over extended timescales. This is problematic, as understanding these changing fire regimes is key to understanding the modern patterns of species distribution. Thus novel approaches are necessary to provide further insights into fire regimes across extended timescales, and the drivers of these patterns (Moritz et al., 2012). Here we explore how such regimes can be studied across long time periods, using a combination of palynological data, charcoal analysis and integrating this with predictive approaches to understand how these regimes have changed over time in a highly

S. Li et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176

heterogeneous region in Northern Southeast Asia. We explore how such approaches can be integrated to understand these changing dynamics and drivers of fire in the region, and therefore provide an approach which can easily be adapted for other regions to better understand their fire history. Understanding and interpreting climate-driven factors in regional fire regimes requires a longer-term perspective. Charcoal in sediments provides useful information to reconstruct fire history across extended geological time intervals (Fesenmyer and Christensen, 2010; MacDonald et al., 1991; Millspaugh and Millspaugh and Whitlock, 1995; Whitlock and Larsen, 2001). Many studies focusing on the Quaternary active fires use occurrence of fossil charcoal (Behling et al., 2004; Gavin et al., 2003; Long et al., 1998; Power et al., 2008), but few attempts to investigate its role in the Miocene terrestrial ecosystems (Hoetzel et al., 2013; Keeley and Rundel, 2005; Scheiter et al., 2012). This lack of studies may be due to the generally low (b 5%) charcoal contents of the Neogene coals worldwide (Scott, 2000; Shearer et al., 1995). The Miocene reflects the late Cenozoic cooling, and presents a warmer and more humid climate than today (Bruch et al., 2006; Jacques et al., 2011; Mai, 1995; Micheels et al., 2007; Wolfe, 1994; Xia et al., 2009; Xing et al., 2012). Thus the Miocene provides an analogue for the response of wildfire to the future warmer climate. Modeling analysis provides opportunities for model validation using field gathered data integrated with climatic and environmental data, and further for comparing our predictions of potential fire activity responses to future warming, and for offering unique insights into the responses of fire activity to varying degrees of climate change. By comparing charcoal records from sediments with a modern active fire dataset, and using this to build and test models we can gain new perspectives on environmental factors influencing fire regimes over time. Two key benchmark periods in the past could provide opportunity for investigate the mechanisms of past climate change: the mid-Holocene (6 ka BP), which represents the warmest and wettest period of the Holocene, and the Last Glacial Maximum (LGM, 21 ka BP), which represents an extreme period of the Quaternary with the maximum global ice volume (Braconnot et al., 2000). Modeling results from these two snap-shot can provide a clue to understand how fire activity may have changed across East Asia. Forest fires are a persistent issue in Yunnan Province, southwestern China. The dry season in Yunnan Province (late spring to early summer) leads to high forest fire frequency, which can potentially cause significant environmental and economic damage (Zhao et al., 2009). It has been suggested that the intensity and frequency of extreme events such as floods and droughts are likely to increase in Yunnan in the future due to climatic changes (Ding et al., 2007; Field et al., 2014). Projected increases in drought in this region are likely to enhance the risk of fire (Ding et al., 2007), as has been found in Southeast Asia in studies which have taken place across the last several decades (Giglio et al., 2013). Little is known, however, about the projected magnitude of climate change, and how climate influences fire probability in Yunnan. This paper presents the first wildfire dynamic analysis of the Miocene strata of China by analyzing charcoal from a Miocene outcrop of lacustrine sediments from Wenshan Basin in Yunnan, Southwestern China. We aim to (1) reconstruct the fire dynamics based on the charcoal ratios from the sediments; (2) explore the relationship between the charcoal record and the climatic parameters reconstructed from pollen data to elucidate the major climate parameter(s) contributing to the Miocene wildfire; and (3) apply a modeling technique (Maxent) to predict fire occurrence during the present day, the LGM, the mid-Holocene, and the end of the 21st century using a modern active fire dataset in Yunnan to see if there is consistency in the fire driven factors between Miocene and present. Extending these predictive approaches to explore potential future climate scenarios allows us then to assess the fire risk in Yunnan Province into the future, on the assumption that drivers remain consistent in their relative influences.

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2. Locality, materials and methods 2.1. Study area The study area encompasses Yunnan Province (Fig. 1a), the topography is diverse and includes intermontane basins in the south and deeply incised river valleys in the north and west (Fan and Thomas, 2013). The northerly winter monsoon of this region is usually obstructed by high mountains thus generating a warm, dry winter and moderately hot, humid summer monsoon-type climate. The mean annual temperature (MAT) is − 4.3 to 24.7 °C (Fig. 1b; http://www.worldclim.org/), and mean temperature of driest quarter (MTDRYQ) is −9.9 to 20.5 °C (Fig. 1c; http://www.worldclim.org/). There are 6 months of drought per year (November–April) in the strongly seasonal dry monsoon climate. The mean annual precipitation (MAP) is 558–2051 mm (Fig. 1d; http://www.worldclim.org/), and the mean precipitation of the driest quarter (PDRYQ) is 11–87 mm (Fig. 1e; http://www.worldclim.org/). From north to south there is a gradient of increasing temperature and precipitation (Fig. 1b-e; http://www.worldclim.org/). Vegetation in Yunnan is dominated by broad-leaved species in the southwestern portion of the province, while most other areas are dominated by coniferous forests which cover an area of approximately 4.53 million ha and account for 48.6% of the province's forested area (Chen et al., 2014). Fire-adapted taxa are common in this region (Chen et al., 2012; Li et al., 2006, 2008), such as Pinus yunnanensis, P. amandii, Keteleeria evelyniana, and Alnus nepalensis. The warm and dry season and fireadapted vegetation can lead to a high frequency of wildfire in Yunnan. The lacustrine sediment profile presently studied is located at Dayigu village in Wenshan Basin, southeast of Yunnan Province (23°24′13.49″ N; 104°12′29.33″ E, Fig. 1). The profile is 430 m thick and the altitude is 1270 m a.s.l. (Li et al., 2015). The age of these Neogene deposits were previously identified as late Miocene based on lithostratigraphic investigations and palynostratigraphic studies (Li et al., 2015), but was revised later and assigned to Mid-Miocene Climatic Optimum (MMCO; ~15–17 Ma), based on magnetostratigraphic investigations (Lebreton-Anberrée et al., 2016). 2.2. Materials and methods 2.2.1. Charcoals analysis Seventy-two samples with abundant palynomorphs were investigated in this study (Li et al., 2015), and the thickness of sampled intervals varied from 0.5 to 2.5 m (Li et al., 2015). The charcoal particles were extracted by standard processing techniques for palynomorph observation (see Li et al., 2015). The residues were mounted on microscope slides and counted simultaneously with pollen and spore grains under a light microscope (Leica Inc., Bensheim, Germany) at a magnification of 400. We observed N 100 charcoal particles from 10 different well-preserved samples under a scanning electron microscope (SEM, ZEISS/EVO LS10). For each sample, charred particles, pollen and spores were counted row by row simultaneously until at least 300 palynomorph grains were counted. Charcoal particles in slides were classified into two size groups based on Clark and Patterson (1997) and Smol et al. (2002), namely the large charcoal particles (N100 μm) and the small charcoals particles (b100 μm). The percentage of each charcoal group was defined as the number of charcoal particles divided by the sum of the charcoal particles, pollen grains, and spores. 2.2.2. Palaeoclimate analysis The palaeoclimatic parameters for each sample were quantitatively reconstructed using pollen data in sequence by bioclimatic analysis (Li et al., 2015, Table S1). Principal Component Analysis (PCA) was used to extract factors from the climate parameters of the 72 samples to explore climate–charcoal relationships. We calculated Pearson correlation using IBM SPSS Statistics (Version 20) to explore the relationships

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Fig. 1. Map depicts the study area and climatic factors that may affect fire activity in Yunnan Province. The solid star indicates the fossil site in Wenshan Basin, southeastern Yunnan. Abbreviations for the climate parameters: MAT, mean annual temperature; MTDRYQ, mean temperature of driest quarter; MAP, mean annual precipitation; PDRYQ, precipitation of driest quarter. The climatic variables were extracted from the WorldClim database (http://www.worldclim.org, 2.5 arc-minutes resolution).

between the reconstructed climatic parameters and the PCA scores, as well as the charcoal ratios. 2.2.3. Modern active fire distribution modeling Maxent (version 3.3.3k; Elith et al., 2011; Phillips et al., 2006) was used to evaluate the relative importance of environmental factors on the fire activities and project fire distribution for each time period examined. We choose Maxent because it has been found to produce robust and accurate projections of the distributions of various entities based on a combination of presence only distribution data and appropriate

environmental data (Hijmans and Graham, 2006; Phillips et al., 2004; Phillips and Dudík, 2008; Stockman et al., 2006). Moreover, several studies have previously demonstrated Maxent is a useful technique to map fire occurrence probability (Moritz et al., 2012; Parisien and Moritz, 2009; Renard et al., 2012). We use the distributions of active fires of Yunnan province from 2001 and 2013 acquired from NASA earth observations (http://neo.sci. gsfc.nasa.gov/) as the input samples. Averaged data calculated by the annual average of monthly data of 14 years were used for modeling (Fig. S1). The environmental datasets of Yunnan province used for

S. Li et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176

Maxent modeling including: (1) fourteen climatic variables extracted from the WorldClim database (http://www.worldclim.org, 2.5 arc-minutes resolution): mean annual temperature (MAT), maximum temperature of warmest month (MTWM), minimum temperature of coldest month (MTCM), mean temperature of wettest quarter (MTWETQ), mean temperature of driest quarter (MTDRYQ), mean temperature of warmest quarter (MTWQ), mean temperature of coldest quarter (MTCQ), mean annual precipitation (MAP), precipitation of wettest month (PWETM), precipitation of driest month (PDRYM), precipitation of wettest quarter (PWETQ), precipitation of driest quarter (PDRYQ) and precipitation of warmest quarter (PWQ), precipitation of coldest quarter (PCQ); (2) Net primary productivity (NPP) data extracted from NASA Earth Observations (NASA MODIS NPP: http://neo.sci. gsfc.nasa.gov/); (3) wind-speed data acquired from a high resolution World Climate Atlas developed by New et al. (2002); and (4) the gridded population data of Yunnan Province downloaded from Global Change Research Data Publisher & Repository (Fu et al., 2014; http:// www.geodoi.ac.cn/). For the detailed description of the methods, please refer to the supplementary Text S1. Additionally we used the same selection of fourteen climatic variables for each time period to model fires for the LGM (20 ka BP: CCSM4 model), the Holocene (6 ka BP: CCSM4) and the future (2070: RCP85, CCSM4). All models were run at 2.5 min resolution data, and used the same fire distribution data, but with the fourteen climatic variables for each time period, in addition to present day conditions for

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wind speed, NPP and population data (to provide a baseline, though we acknowledge that these will change). 3. Results 3.1. General characteristics of the charcoals Charcoals in the Wenshan Basin are well preserved and appear as opaque, planar, angular, and black fragments when observed under a light microscope (Whitlock and Larsen, 2001) (Fig. 2a). The structure of these charcoals is typical for conifer woods (Fig. 2b–e). Small (2– 3 μm in diameter) apertures of bordered pits occur also on the tangential tracheid walls (Fig. 2b). Circular pits on the radial tracheid walls are of 12–16 μm in diameter arranged into uni- and biseriate pattern (Fig. 2c). Piceoid and taxodioid pits of 4–5 μm in diameter are found on cross fields; these pits are arranged in 3–7 pits per ray cell outline, usually in two alternate rows (Fig. 2d, e). This combination of wood characters occurs in the modern genera Picea, Tsuga and Larix (Pinaceae) and in the fossil genus Piceoxylon (Blokhina and Afonin, 2009). 3.2. Zones of the charcoals Based on the relative abundances of pollen and charcoals, the charcoal diagram (Fig. 3) was divided into four zones. The average charcoal ratios in the four zones are shown in Fig. S2.

Fig. 2. Microscope photograph of charcoal particles from Wenshan Basin sediments. (a) Light microscope photograph of charcoal particles. The red arrows indicate the charcoal particles with obvious features. (b–e) SEM microscope photograph of charcoal particles. (b) Tangential section showing apertures of bordered pits on the tracheid walls. (c) Radial section showing bordered pits on the tracheid walls (white arrow). (d, e) Radial section showing cross-fields with casts of tracheid-ray pits: (d) Groups of pits in the ray cell outlines (white arrows); (e) Casts of piceoid (white arrows) and taxodioid (red arrow) pits.

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S. Li et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176

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Fig. 3. The charcoal ratios, palynomorph taxa elements, main climate factors, and principal components analysis (PCA) scores diagram. The palynomorph taxa were grouped by the temperature preferences of their nearest living relatives (Li et al., 2015). These include: mega-mesothermal elements (living in areas with a subtropical climate), mesothermal elements (living in areas with a warm-temperate climate), meso-microthermal elements (living in areas with a cool-temperate climate), microthermal elements (living in areas with a cold climate). PCA2 represents the second PCA axis scores calculated from the 14 climatic parameters reconstructed by pollen analysis for 72 samples of Wenshan Basin. Based on the relative abundances of pollen and charcoals, the charcoal diagram was divided into four zones. Abbreviations for the climate parameters: mean annual temperature (MAT), minimum temperature of coldest month (MTCM), mean temperature of driest quarter (MTDRYQ), mean temperature of coldest quarter (MTCQ).

months resulted in higher occurrence of wildfire in Wenshan Basin during the Miocene. All the precipitation related parameters showed no significant relationships at the 0.01 level with large charcoal particles (Table 1). Although PWQ was correlated with total charcoal particles, and PCQ was

PDRYQ PDRYM MTWM

We visualized the 14 palaeoclimatic parameters in relation to three PCA axes in a three-dimensional diagram (Fig. 4), to understand the palaeoclimate groups and further explore the relationships between the palaeoclimate groups and the charcoal ratios. The first PCA axis (PCA1) represented a combination of temperature and precipitation in a wet or warm season, the second PCA axis (PCA2) indicated the temperature in a cold or dry season, and the third PCA axis (PCA3) depicted the precipitation in the driest season (Fig. 4, Table S2). Five temperature-related variables (MAT, MTCM, MTWETQ, MTDRYQ, MTCQ) and PCA2 correlated (P b 0.01) with all the three types of charcoal particles (Table 1), implying that the temperature was the main factor which determines the frequency of charcoals. More specifically, MTCM, MTDRYQ and MTCQ showed higher R-values for the correlations with charcoal particles than MTWM, MTWETQ and MTWQ (Table 1), indicate that higher temperatures in cold or dry

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Zone I: The average charcoal ratios had medium values compared to the four groups, suggesting a middle level frequency of wildfire. Zone II: The average charcoal ratio was relatively high. Abundances of charcoals in this zone were quite similar to Zone I, but have a slightly lower ratio of large charcoal particles, and a higher level of smaller charcoal particles as well as total charcoal particles than Zone I, suggesting a comparatively higher frequency of wildfire. Zone III: The average ratios of different charcoal particles represented the minimum values of the complete profile, indicating that the frequency of wildfire reached the lowest level among the four zones. Zone IV: The average ratios of charcoal particles reached the maximum of the total profile, indicating that the frequency of wildfire in this period was at the highest level.

0.0

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Fig. 4. The three dimension graph of principal components analysis (PCA) axis scores. The three PCA axis scores (PCA1-PCA3) calculated from the 14 climatic parameters reconstructed by pollen analysis for 72 samples of Wenshan Basin (Li et al., 2015). The blue dot represents a group of combination of temperature and precipitation in a wet or warm season; the yellow dot represents a group of the temperature in a cold or dry season; and the white dot represents the precipitation in the driest season. Abbreviations for the climate parameters: mean annual temperature (MAT), maximum temperature of warmest month (MTWM), minimum temperature of coldest month (MTCM), mean temperature of wettest quarter (MTWETQ), mean temperature of driest quarter (MTDRYQ), mean temperature of warmest quarter (MTWQ), mean temperature of coldest quarter (MTCQ), mean annual precipitation (MAP), precipitation of wettest month (PWETM), precipitation of driest month (PDRYM), precipitation of wettest quarter (PWETQ), precipitation of driest quarter (PDRYQ) and precipitation of warmest quarter (PWQ), precipitation of coldest quarter (PCQ).

S. Li et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176 Table 1 Correlation relationships between charcoal ratios and climate parameters, Principal Component Analysis (PCA) scores as well as pollen taxa elements. **, at the 0.01 level significantly correlated (2-tailed, bold type numbers); *, at the 0.05 level significantly correlated (2-tailed). The palynomorph taxa were grouped by the temperature preferences of their nearest living relatives (Li et al., 2015). PCA axis scores were calculated from the 14 climatic parameters reconstructed by pollen analysis for 72 samples of Wenshan Basin. Abbreviations for the climate parameters are as in Fig. 4.

projected to show a slightly higher prevalence of fire than the present day in most areas (Fig. 5c), though given the shorter time period the probable changes in fire occurrence are lower than between other time intervals. Unsurprisingly future increases in fire risk are predicted for most areas across Yunnan, though deep valleys in Northern Yunnan show variable patterns across space and time (Fig. 5d). 4. Discussion

Small charcoals Particles

Large charcoals Particles

Total charcoal Particles

Factors

R

P

R

P

R

P

MAT (°C) MTWM (°C) MTCM (°C) MTWETQ (°C) MTDRYQ (°C) MTWQ (°C) MTCQ (°C) MAP (mm) PWETM (mm) PDRYM (mm) PWETQ (mm) PDRYQ (mm) PWQ (mm) PCQ (mm) PCA1 PCA2 PCA3 Mega-mesothermal elements Mesothermal elements Meso-microthermal elements Microthermal elements

.379** .097 .351** .309** .380** .242* .420** .191 .302* .042 .266* .042 .295* .305** .252* .385** .026 .284*

.001 .415 .002 .008 .001 .040 b.001 .108 .010 .728 .024 .728 .012 .009 .033 .001 .829 .016

.612** .161 .415** .306** .488** .220 .460** .153 .138 .071 .110 .071 .273* .268* .149 .530** .105 .389**

b.001 .178 b.000 .009 b.001 .063 b.001 .200 .247 .554 .357 .554 .020 .023 .211 b.001 .381 .001

.453** .117 .385** .326** .426** .251* .453** .193 .281* .051 .246* .051 .307** .314** .243* .439** .045 .323**

b.001 .327 .001 .005 b.001 .034 b.001 .104 .017 .673 .038 .673 .009 .007 .040 b.001 .705 .006

.022 −.140

.852 .240

.075 −.182

.533 .126

.035 −.158

.767 .186

−.280* .017

173

−.424** b.001 −.328** .005

correlated with small and total charcoal particles (Table 1), the R-values are obviously lower than that of the cold/dry season temperature variables (e.g. MTCM, MTDRYQ and MTCQ). These results imply that the precipitation related factors may have less important effects on wildfire as the cold/dry season temperature variables during the Miocene in Wenshan. For the pollen taxa, the results indicate that large charcoal particles and total charcoal particles were highly positively related (P b 0.01) to mega-mesothermal elements, but negatively related (P b 0.01) to microthermal elements (Table 1). Since mega-mesothermal elements represent species living in areas with a subtropical climate, and microthermal elements were species living in areas with a cold climate (Li et al., 2015), the results further prove that the charcoals ratios related to temperature. 3.4. Model performance and influencing factors Both of the percentage contribution and permutation importance show that temperature in the dry season (e.g. MTCM, MTCQ, MTDRYQ) are the most important parameters although importance varies between variables (Table S3). The results of the Maxent tests (Figs. S3– S5) also show that dry season temperature related parameters are the most important factors influencing fire occurrence. 3.5. Predicted fire prevalence of different time-phases Maxent predicted the potential distribution of active fires in Yunnan Province. The resulting map shows that the high probability of active fire mainly located in southern Yunnan (Fig. 5a), though bands of high fire prevalence occurred in some areas to the north of the province associated with the effect of local topology on climate. We explored probable changes in fire prevalence across Yunnan during different time-periods. During the LGM most areas are predicted to have lower fire prevalence (Fig. 5b). The mid-Holocene conversely is

4.1. The climate factors on wildfire activity Some contention exists regarding the main factors which influence wildfire activity. Fire regime is known to be affected by temperature, wind speed, humidity, and precipitation (Flannigan et al., 2000), and fire frequency may decrease with increasing precipitation despite warm temperatures (Bergeron and Archambault, 1993). Recent studies tracking wildfire trends over the past century show that both fire frequency (Brown and Swetnam, 1994; Clark, 1990), and area burned (Flannigan and Wagner, 1991) are tightly correlated with air temperature, especially in the dry season. Our results are consistent with the research on fire activities in western United States, where higher frequency of forest wildfire was due to warming and earlier spring (Westerling et al., 2006). Former studies in Yunnan also show that temperature, especially in the dry season is the main factor influencing wildfire activity over the last decade (Chen et al., 2012; Zhao et al., 2009; Zhou et al., 2012). Based on daily meteorological data and fire data over the past 50 years, Zhao et al. (2009) suggested that in Yunnan, the temperatures during the cold period each year determines the forest fire season. Large daily temperature ranges can cause increases in fire frequency, and the annual mean daily temperature range is more important than annual mean precipitation on the activity of wildfires based on the forest fires during 1982–2008 in Yunnan Province and meteorological factors (Chen et al., 2012). Previously reconstructed palaeoclimates indicate significant precipitation seasonality and typical monsoonal climate in Wenshan during the Miocene (Li et al., 2015). This evidence for a monsoonal climate in Miocene further supports our results. The precipitation in the dry season during the Miocene was higher than that of today (Li et al., 2015), whereas, the temperatures in the dry season during the Miocene are close to that of the present (Li et al., 2015). No significant relationships were found between precipitation parameters and large charcoal particles in our results (Table 1), possibly due to the relatively small change in reconstructed precipitation parameters (Li et al., 2015, Table S1). This lack of precipitation variability may reflect the stability of rainfall patterns during the Miocene. The similarity of the Miocene palynoflora in Wenshan Basin to modern evergreen broadleaf vegetation in subtropical East Asia (Li et al., 2015) also supports this assumption. Since the distribution of the evergreen broadleaf forests in China is mainly limited by temperatures rather than precipitation (Fang and Yoda, 1991), thus the precipitation variability cannot be detected by bioclimatic analysis in this study. All significant correlations between precipitation and charcoal ratios were positive. This implies that high precipitation in the dry season may increase the number of charcoal particles. This may result from two different mechanisms, either higher precipitation increases vegetation growth rates, providing more fuel to burn, or the relationship between precipitation and charcoal particle abundance is not a causal relationship, but merely a reflection of the positive correlation between temperature and precipitation. 4.2. Other factors The fire-adapted vegetation is an important factor for the wildfire activity. The wood characters of the charcoals indicate that most of these charcoals are conifers. The dominant pollen taxa in Wenshan Basin includes Pinus, Tsuga, Abies, and Taxodiaceae (Li et al., 2015),

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Fig. 5. The predicted fire risk and changes in Yunnan Province. (a) The predicted potential suitable habitat for active fires. The different colors of (a) indicate the probability of active fire. (b) The predicted fire risk changes from LGM to present. (c) The predicted fire risk changes from the Mid-Holocene to present. (d) The predicted fire risk changes from present to 2070. The different colors of (b–d) indicate the probability of active fire changes. The orange and red colors represent an increase in fire probability from the LGM/Mid-Holocene to the present (b/c) or from present to 2070 (d), whereas the blue colors indicate a decrease in fire probability (green indicates no change).

further support the identification of the conifer charcoals. The wildfires in Yunnan Province were found mainly occurred at the conifer dominant forest in present, because plenty of fuels produced by conifer needles, this may increase probability of forest fire when temperature in the dry season reaches high levels (Chen et al., 2012). Therefore, we can infer that the similarity of the conifer dominant forest during Miocene in Wenshan Basin may lead to high frequencies of wildfire in the dry season under the monsoonal climate region. These results confirm that the comparison of fire regime between the Miocene and modern are reasonable. Human development and activity can affect fire regimes through direct (e.g. fire ignition and suppression) or indirect mechanisms (e.g. landscape-level alteration; Hawbaker et al., 2013). Some studies show that fire occurrence is predominantly influenced by human factors in some parts of Yunnan (Chen et al., 2012, 2014). The Maxent model

results show that human factors were less important than climate parameters by including population grid data into the model, although land cover modification or disturbance is likely to heavily influence fire activity. 4.3. The inconsistency and uncertainty The results show some inconsistencies between small charcoal particle ratios and large charcoal particle ratios relating to climatic parameters (e.g. PCQ, Table 1). Inconsistencies may be because large charcoal particles are heavier than small ones, so wind or rivers may disperse smaller fragments over long distances (Patterson et al., 1987). Generally, smaller charcoal particles are deposited further from the fire as they can be carried further in air and smoke particles, whereas larger particles are deposited closer to the source (Whitlock and Larsen, 2001),

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with a progressive decrease in average particle size with increasing distance. Thus, the large charcoal particles in this study are considered to represent local fires while small charcoal particles are assumed to represent fires from across a broader region. Therefore the interpretation of fossil charcoal records must to be treated with caution, especially for the small charcoal particles. Our results shows MTWETQ, PWETM, and PWETM are positively correlated with charcoal ratios. These correlations may result from errors, e.g., some pollen misidentification or counting, the misinterpretation of the anemophilous taxa (which tend to be widely dispersed; Palazzesi et al., 2014), and the errors of palaeoclimate reconstruction using bioclimatic analysis. It is impossible to avoid these errors completely; despite improvements in pollen identification, counting, and the removal of some anemophilous taxa with lower percentages (Li et al., 2015) to reduce errors. The dry season temperature showed a stronger relationship with fire frequency than other palaeoclimate parameters, which provide reasonable explanations that the dry/cold season temperature was the main factor in controlling fire frequency during the Miocene. Anemophilous pollen normally represents regional vegetation and thus the reconstructed palaeoclimates may represent a regional climate; however, we have used bioclimatic analysis to minimise any regional climate signal by excluding low abundances of anemophilous taxa which may have been transported long distances (Li et al., 2015), thus the reconstructed palaeoclimate in Wenshan Basin could represent local climate signals rather than larger regional climate. 4.4. The modeling results Additionally using models, with calibration from our charcoal analysis provides us the ability to interpolate between the present day and the Miocene. Rather than solely examining the two time points for which both empirical fire record data exists, we can use these data to verify the outcomes of the model, and therefore further explore the changing environment and fire activity at various interim points. Here we also explore the changing distribution of fire activity at extreme points between the Miocene and present, as such analysis has implications for understanding the biogeography and landscape structure both during those periods, and the modern day legacies of such patterns. Fire is a particularly important factor in savannah ecosystems, and there is considerable evidence for the aridification of large parts of the Southeast Asia during the LGM and the mid-Holocene (An, 2000; An et al., 2000; Cosford et al., 2008; He et al., 2004), thus analyzing these changing patterns is crucial to understanding modern ecological patterns across the region. The results of modeling fit well with that of the palynological and charcoal based analyses. Fires across Yunnan Province have changed in activity level considerably over time. The distribution of fires during former time periods, such as the LGM has implications for the present distributions of various ecosystems and thus exploring these patterns has implications for the understanding of present patterns of diversity and biogeography. Models also predict increases in fire activity for much of Yunnan into the future, with only some valleys in northern Yunnan showing predicted reductions in fire activity. As a result, stricter management controls will be required to prevent a marked increase in wildfires, especially in an increasingly agriculturalised landscape. 5. Conclusion The combination of Miocene charcoal data and modern active fire data provides a detailed insight into the climate and wildfire dynamics from Miocene to the present day. This study shows the future potential of charcoal analysis represents a novel method of investigation and demonstrates the value of models in understanding environmental processes and how these changes over time. Based on the relationships between the charcoals and climatic parameters, we show that the

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temperature of the dry/cold period in a year was the main factor in controlling fire frequency during the Miocene. The Maxent model of the relationship between current climate and fire activity is consistent with the conclusion from fossil sediments that the main factors which have effects on fire activity is dry/cold season temperature. Our results contribute to new insights into the relationship between forest wildfire activity and seasonal climate through the Cenozoic of Asia, and show that temperatures are one of the most significant determinants of fire activity and provide new insights into past and future fire activity. Supplementary data to this article can be found online at doi:10. 1016/j.palaeo.2016.10.028. Acknowledgements The authors thank Prof. Shi-Tao Zhang and Dr. Hong-Jie Ma from Kunming University of Science and Technology for a preliminary investigation of the geology of this study site; the fellow researchers of the Paleoecology group in Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences for sample collection, and Dr. Li Wang for his guidance during SEM observation. The SEM photos were taken in the Central Laboratory of the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. We thank the editor Prof. Thierry Corrège for his helpful suggestions. We also gratefully acknowledge the comments and many constructive suggestions from Dr. Torsten Utescher and another anonymous reviewer. This study was supported by, the National Natural Science Foundation of China (No. 41372035 and No. U1502231), the Foundation of the State Key Laboratory of Paleobiology and Stratigraphy (Nanjing Institute of Geology and Paleontology, Chinese Academy of Sciences) (No. 153107). References Aldersley, A., Murray, S.J., Cornell, S.E., 2011. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 409, 3472–3481. An, Z.S., 2000. The history and variability of the East Asian paleomonsoon climate. Quat. Sci. Rev. 19, 171–187. An, Z.S., Porter, S.C., Kutzbach, J.E., Wu, X.H., Wang, S.M., Liu, X.D., Li, X.Q., Zhou, W.J., 2000. Asynchronous Holocene optimum of the East Asian monsoon. Quat. Sci. Rev. 19, 743–762. Behling, H., Pillar, V.D., Orlóci, L., Bauermann, S.G., 2004. Late Quaternary Araucaria forest, grassland (Campos), fire and climate dynamics, studied by high-resolution pollen, charcoal and multivariate analysis of the Cambará do Sul core in southern Brazil. Palaeogeogr. Palaeoclimatol. Palaeoecol. 203, 277–297. Bergeron, Y., Archambault, S., 1993. Decreasing frequency of forest fires in the southern boreal zone of Quebec and its relation to global warming since the end of the'Little Ice Age. The Holocene 3, 255–259. Blokhina, N.I., Afonin, M.A., 2009. New species of Piceoxylon Gothan (Pinaceae) from the Cretaceous and Paleogene of the northwestern Kamchatka Peninsula. Paleontol. J. 43, 1190–1201. Bond, W.J., Woodward, F.I., Midgley, G.F., 2005. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538. Bowman, D.M., Balch, J.K., Artaxo, P., Bond, W.J., Carlson, J.M., Cochrane, M.A., D'Antonio, C.M., DeFries, R.S., Doyle, J.C., Harrison, S.P., 2009. Fire in the earth system. Science 324, 481–484. Braconnot, P., Joussaume, S., de Noblet, N., Ramstein, G., 2000. Mid-Holocene and last glacial maximum African monsoon changes as simulated within the paleoclimate modelling Intercomparison project. Glob. Planet. Chang. 26, 51–66. Brown, A., 2012. Palaeoclimate: a climate for fire. Nat. Clim. Chang. 2, 769. Brown, P.M., Swetnam, T.W., 1994. A cross-dated fire history from coast redwood near Redwood National Park, California. Can. J. For. Res. 24, 21–31. Bruch, A.A., Utescher, T., Mosbrugger, V., Gabrielyan, I., Ivanov, D., 2006. Late Miocene climate in the circum-alpine realm—a quantitative analysis of terrestrial palaeofloras. Palaeogeogr. Palaeoclimatol. Palaeoecol. 238, 270–280. Chen, F., Lin, X.D., Niu, S.K., Wang, S., Li, D., 2012. Influence of climate change on forest fire in Yunnan Province, southwestern China. J. Beijing For. Univ. 34, 7–15 (in Chinese with English abstract). Chen, F., Fan, Z.F., Niu, S.K., Zheng, J.M., 2014. The influence of precipitation and consecutive dry days on burned areas in Yunnan Province, Southwestern China. Adv. Meteorol. 2014, 1–11. Clark, J.S., 1990. Fire and climate change during the last 750 yr in northwestern Minnesota. Ecol. Monogr. 60, 135–159. Clark, J.S., Patterson III, W.A., 1997. Background and local charcoal in sediments: scales of fire evidence in the paleorecord. Sediment Records of Biomass Burning and Global Change. Springer, pp. 23–48. Cosford, J., Qing, H., Eglington, B., Mattey, D., Yuan, D.X., Zhang, M.L., Cheng, H., 2008. East Asian monsoon variability since the mid-Holocene recorded in a high-resolution,

176

S. Li et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 465 (2017) 168–176

absolute-dated aragonite speleothem from eastern China. Earth Planet. Sci. Lett. 275, 296–307. Daniau, A.L., Bartlein, P.J., Harrison, S.P., Prentice, I.C., Brewer, S., Friedlingstein, P., Harrison-Prentice, T.I., Inoue, J., Izumi, K., Marlon, J.R., Mooney, S., Power, M.J., Stevenson, J., Tinner, W., Andrič, M., Atanassova, J., Behling, H., Black, M., Blarquez, O., Brown, K.J., Carcaillet, C., Colhoun, E.A., Colombaroli, D., Davis, B.A.S., D'Costa, D., Dodson, J., Dupont, L., Eshetu, Z., Gavin, D.G., Genries, A., Haberle, S., Hallett, D.J., Hope, G., Horn, S.P., Kassa, T.G., Katamura, F., Kennedy, L.M., Kershaw, P., Krivonogov, S., Long, C., Magri, D., Marinova, E., McKenzie, G.M., Moreno, P.I., Moss, P., Neumann, F.H., Norström, E., Paitre, C., Rius, D., Roberts, N., Robinson, G.S., Sasaki, N., Scott, L., Takahara, H., Terwilliger, V., Thevenon, F., Turner, R., Valsecchi, V.G., Vannière, B., Walsh, M., Williams, N., Zhang, Y., 2012. Predictability of biomass burning in response to climate changes. Glob. Biogeochem. Cycles 26, GB4007. Ding, Y.H., Ren, G.Y., Zhao, Z.C., Xu, Y., Luo, Y., Li, Q.P., Zhang, J., 2007. Detection, causes and projection of climate change over China: an overview of recent progress. Adv. Atmos. Sci. 24, 954–971. Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57. Fan, Z.X., Thomas, A., 2013. Spatiotemporal variability of reference evapotranspiration and its contributing climatic factors in Yunnan Province, SW China, 1961–2004. Clim. Chang. 116, 309–325. Fang, J.Y., Yoda, K., 1991. Climate and vegetation in China V. Effect of climatic factors on the upper limit of distribution of evergreen broadleaf forest. Ecol. Res. 6, 113–125. Fesenmyer, K.A., Christensen Jr., N.L., 2010. Reconstructing Holocene fire history in a southern Appalachian forest using soil charcoal. Ecology 91, 662–670. Field, C.B., Barros, V.R., Mach, K., Mastrandrea, M., 2014. Climate change 2014: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Flannigan, M., Wagner, C.V., 1991. Climate change and wildfire in Canada. Can. J. For. Res. 21, 66–72. Flannigan, M., Stocks, B.J., Wotton, B., 2000. Climate change and forest fires. Sci. Total Environ. 262, 221–229. Fu, J.Y., Jiang, D., Huang, Y.H., 2014. 1 km grid population dataset of China. Global Change Research Data Publishing and Repository 69, pp. 41–44. Gavin, D.G., Brubaker, L.B., Lertzman, K.P., 2003. Holocene fire history of a coastal temperate rain forest based on soil charcoal radiocarbon dates. Ecology 84, 186–201. Giglio, L., Randerson, J.T., Werf, G.R., 2013. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosci. 118, 317–328. Gill, A.M., Catling, P.C., 2002. Fire Regimes and Biodiversity of Forested Landscapes of Southern Australia, Flammable Australia: The Fire Regimes and Biodiversity of a Continent. Cambridge University Press, Cambridge, UK, pp. 429–446. Gillett, N., Weaver, A., Zwiers, F., Flannigan, M., 2004. Detecting the effect of climate change on Canadian forest fires. Geophys. Res. Lett. 31, 1–4. Hawbaker, T.J., Radeloff, V.C., Stewart, S.I., Hammer, R.B., Keuler, N.S., Clayton, M.K., 2013. Human and biophysical influences on fire occurrence in the United States. Ecol. Appl. 23, 565–582. He, Y., Theakstone, W.H., Zhang, Z.L., Zhang, D., Yao, T.D., Chen, T., Shen, Y.P., Pang, H.X., 2004. Asynchronous Holocene climatic change across China. Quat. Res. 61, 52–63. Hijmans, R.J., Graham, C.H., 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Glob. Chang. Biol. 12, 2272–2281. Hoetzel, S., Dupont, L., Schefuß, E., Rommerskirchen, F., Wefer, G., 2013. The role of fire in Miocene to Pliocene C4 grassland and ecosystem evolution. Nat. Geosci. 6, 1027–1030. Jacques, F.M.B., Guo, S.X., Su, T., Xing, Y.W., Huang, Y.J., Liu, Y.S.C., Ferguson, D.K., Zhou, Z.K., 2011. Quantitative reconstruction of the Late Miocene monsoon climates of Southwest China: a case study of the Lincang flora from Yunnan Province. Palaeogeogr. Palaeoclimatol. Palaeoecol. 304, 318–327. Kasischke, E.S., Turetsky, M.R., 2006. Recent changes in the fire regime across the north American boreal region—spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, 1–5. Keeley, J.E., Rundel, P.W., 2005. Fire and the Miocene expansion of C4 grasslands. Ecol. Lett. 8, 683–690. Lebreton-Anberrée, J., Li, S., Li, S.-F., Spicer, R.A., Zhang, S.T., Su, T., Deng, C., Zhou, Z.K., 2016. Lake geochemistry reveals marked environmental change in Southwest China during the mid Miocene climatic optimum. Sci. Bull. 61, 897–910. Li, S.Y., Wang, Q.H., Li, B.F., Gao, F., 2006. Comparative study on flammability of fresh leaves and living branches of 10 tree species in Central Yunnan Province. J. Southwest For. Coll. 26, 56–58 (in Chinese with English abstract). Li, S.Y., Ma, C.L., Luo, W.B., Li, S.H., Wang, X.F., Yuan, J.J., Yang, L., 2008. Combustibility ordering and sorting of 35 woody plant species in Kunming area. Chin. J. Ecol. 27, 867–873 (in Chinese with English abstract). Li, S.F., Mao, L.M., Spicer, R.A., Lebreton-Anberrée, J., Su, T., Sun, M., Zhou, Z.K., 2015. Late Miocene vegetation dynamics under monsoonal climate in southwestern China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 425, 14–40. Littell, J.S., McKenzie, D., Peterson, D.L., Westerling, A.L., 2009. Climate and wildfire area burned in western US ecoprovinces, 1916-2003. Ecol. Appl. 19, 1003–1021. Long, C.J., Whitlock, C., Bartlein, P.J., Millspaugh, S.H., 1998. A 9000-year fire history from the Oregon Coast Range, based on a high-resolution charcoal study. Can. J. For. Res. 28, 774–787.

MacDonald, G.M., Larsen, C.P.S., Szeicz, J.M., Moser, K.A., 1991. The reconstruction of boreal forest fire history from lake sediments: a comparison of charcoal, pollen, sedimentological, and geochemical indices. Quat. Sci. Rev. 10, 53–71. Mai, H.D., 1995. Tertiäre Vegetationsgeschichte Europas. Gustav Fischer Verlag, JenaSluttgart-New York. Marlon, J.R., Bartlein, P., Carcaillet, C., Gavin, D., Harrison, S., Higuera, P., Joos, F., Power, M., Prentice, I., 2008. Climate and human influences on global biomass burning over the past two millennia. Nat. Geosci. 1, 697–702. Micheels, A., Bruch, A.A., Uhl, D., Utescher, T., Mosbrugger, V., 2007. A Late Miocene climate model simulation with ECHAM4/ML and its quantitative validation with terrestrial proxy data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 253, 251–270. Millspaugh, S.H., Whitlock, C., 1995. A 750-year fire history based on lake sediment records in central Yellowstone National Park, USA. The Holocene 5, 283–292. Moritz, M.A., Parisien, M.-A., Batllori, E., Krawchuk, M.A., Van Dorn, J., Ganz, D.J., Hayhoe, K., 2012. Climate change and disruptions to global fire activity. Ecosphere 3, 1–22. New, M., Lister, D., Hulme, M., Makin, I., 2002. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25. Page, S.E., Siegert, F., Rieley, J.O., Boehm, H.-D.V., Jaya, A., Limin, S., 2002. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65. Palazzesi, L., Barreda, V.D., Cuitiño, J.I., Guler, M.V., Tellería, M.C., Santos, R.V., 2014. Fossil pollen records indicate that Patagonian desertification was not solely a consequence of Andean uplift. Nat. Commun. 5, 35–58. Parisien, M.A., Moritz, M.A., 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecol. Monogr. 79, 127–154. Patra, P.K., Ishizawa, M., Maksyutov, S., Nakazawa, T., Inoue, G., 2005. Role of biomass burning and climate anomalies for land-atmosphere carbon fluxes based on inverse modeling of atmospheric CO2. Glob. Biogeochem. Cycles 19, 1–10. Patterson, W.A., Edwards, K.J., Maguire, D.J., 1987. Microscopic charcoal as a fossil indicator of fire. Quat. Sci. Rev. 6, 3–23. Phillips, S.J., Dudík, M., 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175. Phillips, S.J., Dudík, M., Schapire, R.E., 2004. A Maximum Entropy Approach to Species Distribution Modeling, Proceedings of the twenty-First International Conference on Machine Learning. ACM, p. 83. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. Power, M.J., Marlon, J., Ortiz, N., Bartlein, P., Harrison, S., Mayle, F., Ballouche, A., Bradshaw, R., Carcaillet, C., Cordova, C., 2008. Changes in fire regimes since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data. Clim. Dyn. 30, 887–907. Renard, Q., Pélissier, R., Ramesh, B., Kodandapani, N., 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int. J. Wildland Fire 21, 368–379. Scheiter, S., Higgins, S.I., Osborne, C.P., Bradshaw, C., Lunt, D., Ripley, B.S., Taylor, L.L., Beerling, D.J., 2012. Fire and fire-adapted vegetation promoted C4 expansion in the late Miocene. New Phytol. 195, 653–666. Scott, A.C., 2000. The pre-Quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 281–329. Shearer, J.C., Moore, T.A., Demchuk, T.D., 1995. Delineation of the distinctive nature of tertiary coal beds. Int. J. Coal Geol. 28, 71–98. Smol, J.P., Last, W.M., Birks, H.J.B., 2002. Tracking environmental change using lake sediments. Terrestial, Algal, and Siliceous Indicators vol. 3. Springer. Stockman, A.K., Beamer, D.A., Bond, J.E., 2006. An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Divers. Distrib. 12, 81–89. Swetnam, T.W., Betancourt, J.L., 1990. Fire-southern oscillation relations in the southwestern United States. Science (Washington) 249, 1017–1020. Westerling, A.L., Hidalgo, H.G., Cayan, D.R., Swetnam, T.W., 2006. Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943. Whitlock, C., Larsen, C., 2001. Charcoal as a Fire Proxy, Tracking Environmental Change Using Lake Sediments. Springer, pp. 75–97. Wolfe, J.A., 1994. Tertiary climatic changes at middle latitudes of western North America. Palaeogeogr. Palaeoclimatol. Palaeoecol. 108, 195–205. Xia, K., Su, T., Liu, Y.S., Xing, Y.W., Jacques, F.M.B., Zhou, Z.K., 2009. Quantitative climate reconstructions of the late Miocene Xiaolongtan megaflora from Yunnan, Southwest China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 276, 80–86. Xing, Y.W., Utescher, T., Jacques, F.M.B., Su, T., Liu, Y.S., Huang, Y.J., Zhou, Z.K., 2012. Paleoclimatic estimation reveals a weak winter monsoon in southwestern China during the late Miocene: evidence from plant macrofossils. Palaeogeogr. Palaeoclimatol. Palaeoecol. 358–360, 19–26. Zhao, F., Shu, L., Tiao, X., Wang, M., 2009. Change trends of forest fire danger in Yunnan Province in 1957–2007. Chin. J. Ecol. 28, 2333–2338 (in Chinese with English abstract). Zhou, M.K., Wang, Y.P., Gao, Y.Z., 2012. Effect of climatic factors on forest fires in Dali. J. Sichuan For. Sci. Technol. 6, 022 (in Chinese with English abstract).

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