Response of grassland ecosystem to monsoonal precipitation variability during the Mid-Late Holocene: Inferences based on molecular isotopic records from Banni grassland, western India.

Banni, located in the arid western India, is one of the largest tropical grasslands of the Asian continent. The net primary production in this grassland ecosystem is currently mediated by precipitation during the Indian summer monsoon (ISM). However, timing of the grassland expansion and its link to the intensity of monsoonal precipitation remains enigmatic due to the paucity of datasets. The major objective of this study is to understand the changes in monsoonal precipitation and vegetation for the last 4600 cal yr BP using hydrogen and carbon isotopic composition of n-alkanes (δDn-alkane and δ13Cn-alkane) measured from two core sediments (Chachi and Luna) in Banni region. The δ13CC29 and δ13CC31 values for Chachi core sediments vary from -30.9 ‰ to -27.2 ‰ and -34.4 ‰ to -25 ‰ respectively. The δ13Cn-alkane values from the core sediments are converted into %C4 plants based on a binary mixing model using the end-member δ13Cn-alkane values derived from the dominant modern vegetation in the Banni region. The prominent feature of the paleovegetation curve is the marked increase in the δ13Cn-alkane values after 2500 cal yr BP, which suggests proliferation of C4 grasses in this region. Similar changes after 2500 cal yr BP have also been observed in the δDn-alkane values. The δDC29 values are used to calculate δD value of paleoprecipitation that varied from 10 ‰ to -60.2 ‰. A significant increase in the δD values of paleoprecipitation (ca. 25 ‰) indicates a weakened ISM precipitation after ca. 2500 cal yr BP. The regional aridification and frequent fire events may have helped the expansion of C4 plant dominated grassland ecosystem in Banni region. Correlation between paleoclimatic records suggests that the southward migration of intertropical convergence zone and more frequent warm phases of El-Nino Southern Oscillation have triggered the weakening of monsoonal precipitation in the tropical region.

Introduction

Grasslands are globally important as they comprise nearly one-fifth of the world’s land surface and 80% of the total agriculturally productive land [1-2]. Geographical distribution, community composition and net primary production of grassland ecosystems not only play crucial roles in global carbon budget [3] but also influence regional climate by modulating the evapotranspiration flux [4]. The vegetation composition of grassland ecosystems reflects the competing influence of precipitation, fire and atmospheric CO2 concentration. For instance, changes in CO2 level and fire events in the grassland of West Africa during the Late Holocene shifted the vegetation composition towards woody plant cover, despite the increased regional aridity [5]. It is important to note that the interaction between above-mentioned forcing factors varies across the globe and thus produces a site-specific structure of the grassland ecosystem. Furthermore, the magnitude of the climatic factors and their effects on vegetation composition are also globally variable, depending upon duration of the rainy season, precipitation amount and number of precipitation events [6]. Prolonged and recurrent droughts predicted in a warming environment, combined with human-induced changes in the natural hydrological regime, will likely have an adverse effect on biodiversity of the grasslands [7, 8]. The understanding of past climate-ecosystem coupling is thus vital to predict future ecosystem response to changing natural variables.

The available Mid-Late Holocene paleoclimate records from Indian summer monsoon (ISM) domain suggest significant modifications in seasonal distribution of monsoonal precipitation in response to shifts of the Intertropical Convergence zone (ITCZ) [9-11]. Changes in hydrological regime will likely to have a major impact on the biodiversity of the Banni grasslands in western India—one of the largest tropical grassland of Asian continent. The modern day precipitation of Banni grassland is characterized by extremely sparse rain events (n = 10–12), short length of monsoon season and very high temperature difference (ca. 40 °C) between summer and winter months. This extreme climate setting makes the Banni grassland an ideal site to investigate the past climate-grassland interactions.

A previous study from the Banni grassland used the carbon isotopic composition of bulk organic matter (δ13Corg) and oxygen isotopic composition of gastropod shells (δ18Oshell) to decipher paleoenvironmental changes [12]. The δ13Corg values from the Banni grassland suggested Late Holocene expansion of a C4 grass dominated ecosystem in response to changes in moisture availability and local fire-events [12, 13]. However, δ13Corg value based estimates of %C4 plants have uncertainties due to thermal degradation of selective organic compounds [14], which can alter the pristine isotopic character [15]. Furthermore, δ13Corg values integrate organic matter from numerous sources (each with distinct δ13C value) that can often led to bias in paleovegetation reconstruction [16]. Likewise, uncertainty has also been associated with δ18Oshell based climate interpretation due to interplay of factors such as local precipitation variability, changes in the moisture pathway, lake water evaporation, residence time, temperature as well as biological effects controlling the δ18O values [17]. Therefore, more robust hydrological and vegetational proxies are required for an improved assessment of the paleoenvironmental changes from this region.

To achieve a better understanding of the past vegetation composition and its linkage with climatic factors, the present study measured carbon and hydrogen isotopic composition of n-alkanes (δD and δ13C) extracted from two seasonal wetland sediment cores (Chachi and Luna) (Fig 1). The relative phasing of vegetation and precipitation are assessed from the isotopic composition of same compound (i.e., n-alkane). Comparison of records from multiple seasonal wetlands provides a synoptic view of past environmental changes in this region. The objectives of the present study include (i) understanding the potential sources of organic matter (OM) in the Banni region; (ii) reconstruction of the climatic conditions and local vegetation history; (iii) recognizing the spatial variability of ISM; (iv) interpreting the role of teleconnections on the intensity of ISM precipitation and; (v) understanding the feedback mechanism loop between monsoonal precipitation, fire and CO2. To our best of knowledge, this is the first continuous biomarker-based record of climate change from western India during the Mid-Late Holocene.

Fig 1

Study area, sampling and modern climate in Banni grassland.

(A) Schematic representation of plant functional types around the seasonal wetlands [13, 18]. Images showing core sampling in (B) Chachi and (C) Luna wetlands. (D) Monthly precipitation in the Banni region for past 5 years. (E) Back-trajectory analysis of the air-parcel demonstrates that Arabian Sea is the dominant moisture contributor to the precipitation in Banni region.

Study area

Modern climate

The Banni terrain (total area: 3,847 km2, 23°19' to 23°52' N and 68°56' to 70°32' E) is a flat transitional land which lies between the mainland Kachchh and the Great Rann, one of the world’s largest hypersaline marshy area. This region accounts for 8.4% of the total area of Kachchh Desert, western India [19]. The Banni region receives the dominant fraction (80–90%) of annual precipitation (avg. 310 mm) during ISM (Fig 1D). The average seasonal temperature in this region varies from 50 °C in summer (May-July) to 10 °C in winter (December-February) [18].

Geomorphology

Banni, a tectonically raised mudflat, is located on the southern edge of the Great Rann of Kachchh [20]. This unique geomorphic terrain is slightly elevated (ca. 4 to 20 m) from the Great Rann with sediments comprising of fluvial origin [20]. The sediments in the Banni region are derived from the Mesozoic sedimentary rocks of northern Kachchh Mainland brought down by the north flowing drainage system comprising of Khari, Chhari and Kaila rivers [21-22]. The fine textured Banni sediments are composed of stratified silt and clay with thickness more than 30 m [23]. Soil salinity is highly variable, and pH ranges between 6.5 and 8.5 [23].

Modern vegetation

The modern-day vegetation in Banni region is characterized by the mixed C3-C4 plants [13] (Fig 1A). Vegetation of Banni comprises of herbs (89 species, accounts for ca. 46% of all the plant species), grasses (37 species, ca. 19% of all the plant species,) shrubs (31 species, ca. 16% of all the plant species), trees (17 species, ca. 9% of all the plant species), and sedges and climbers (ca. 10% of plant species) [24]. The Chachi site comprises of Suaeda scrub and grassy vegetation with sparse cover of Prosopis juliflora and other herbaceous vegetation, whereas the catchment of Luna is composed of grassland with moderate density of Prosopis juliflora [13]. The region is covered with coarse and low perennial grasses like the Desmostachya bipinnata, Sporobolus marginatus, Dichanthium annulatum, Cenchrus ciliaris, Sporobolus fertilis and Chloris barbata [25]. Prominent tree species of this region are Acacia nilotica subsp. indica, Prosopis juliflora, Prosopis cineraria, Salvadora oleoides and Salvadora persica [25]. The region is currently degraded mostly due to frequent droughts events coupled with intense grazing and invasion of Prosopis juliflora [19].

Methods

Field sampling

Core sediments were collected from two closed shallow seasonal wetlands (Chachi and Luna) during a field expedition in summer 2012 when the region was completely dried up (Fig 1B and 1C). The cores (ca. 138 cm and 85 cm depth for Chachi and Luna, respectively) were retrieved using 6 cm diameter, long PVC pipes. No specific permissions were required for the sample collections from these locations. All the samples were stored at 4 °C before the analysis. The sediment cores collected from Chachi and Luna were clayey with occasional silt laminations [12]. The Chachi core was sub-sampled at 5–8 cm intervals until 70 cm depth followed by 4 cm intervals and Luna core was sub-sampled at 6 cm intervals. In addition to core sediment samples, leaf samples of dominant vegetation (grasses, herbs and trees) were also collected. The surface of the leaf samples were cleaned with 0.5 N HCl and distilled water in an ultrasonic bath to remove adhered contaminants. Leaf samples were dried at 40 °C for 48 hours, and core sediment samples were dried at 50 °C for 8 hours. Leaves and sediment samples were subsequently powdered in an agate mortar for further analysis. All the biomarker-based analyses were conducted at the stable isotope laboratory of Indian Institute of Science Education and Research Kolkata.

Extraction and analysis of n-alkane

Total lipid extract (TLE) is obtained via accelerated solvent extractor (Dionex, ASE350) using dichloromethane/methanol (93:7) mixture at 100 °C and 1600 psi pressure for 15 min (2 cycles). For this purpose, 0.3–0.5 g leaf and 7–8 g sediment samples were used. Sample preparation and lipid extraction methods have been described in detail elsewhere [12, 26, 27]. TLE fraction was concentrated in a Rotavapour (R-210, Buchi) by evaporating the dichloromethane and methanol mixture. The non-polar hydrocarbon fraction (n-alkane) was separated from the TLE using short-column silica gel column chromatography, using activated silica gel (100–200 mesh) in Pasteur pipette plugged with glass wool and hexane as eluent. Then n-alkane fraction has been concentrated up to 0.5 ml under a stream of dry N2.

The concentration of individual n-alkane was measured in a gas chromatograph (Agilent 7890A, GC system) equipped with a split/splitless injector, non-polar capillary column (HP5-MS, 30 m × 250 μm × 0.25 μm) and flame ionized detector (FID). Samples were injected in 1:1 split mode with an initial inlet temperature of 320 °C. The GC oven temperature was started at 60 °C (held 2 min) and then increased to 320 °C at 8 °C/min (held 12 min). The characteristic retention time obtained from the external n-alkane standard mixture (SUPELCO C8–C40 alkanes) was used for the identification and quantification of individual n-alkanes.

The δD and δ13C analyses of n-alkane

For the Chachi core sediments, a total of 19 and 16 samples were measured for δ13C and δD values respectively. For Luna core sediments, δ13C analysis has been carried out on 14 samples. All samples were not analyzed for δD values compared to δ13C due to limited sample material.

The δ13C values were measured in Thermo Trace GC Ultra, connected via combustion interface (GC-Isolink) and Conflo IV interface to a MAT 253 isotope ratio mass spectrometer (GC-IRMS). The GC oven temperature was raised from 40 °C (held 1 min) to 320 °C (held 12 min) at 10 °C/min. 1 μl samples were injected in splitless mode with an inlet temperature at 280 °C and He as carrier gas at 1 ml/min. Individual compounds were combusted over Nickel and Copper wire in the presence of O2 with He (1% v/v) at 950 °C to produce CO2. Calibration of δ13C value was performed by injecting several pulses of CO2 at the beginning and the end of each GC run. Isotopic ratios are expressed in ‰ relative to the Vienna Pee Dee Belemnite (VPDB) standard. A standard mixture of n-alkanes (C16-C30) procured from Prof. Arndt Schimmelmann of Indiana University, with a known isotopic composition was used at different dilution (0.5, 1, 1.5, 7.5 and 15 ng/μl) to monitor the accuracy of the measurement.

The δD values were measured using a Thermo Trace GC Ultra coupled via a GC Isolink pyrolysis interface operated at 1420 °C to a Thermo MAT 253 GC-IRMS. The δD values were measured against calibrated H2 reference gas. The δD values are reported in ‰ notation against the Vienna Standard Mean Ocean Water (VSMOW). The Schimmelmann standard was used at different dilution (35, 50, 75 and 105 ng/μl) to assess the accuracy of the analysis. Long-term repeated analysis of the external standard mixture rendered a precision of ±3 ‰ (1σ) and an average accuracy of 1 ‰. The H3+ factor varied around 11.1 ± 0.02 ppm nA-1 throughout the measurement period.

Chronology

The published 14C activity measurements made on bulk organic matter are used to constrain an age model for Chachi and Luna core sediments [12]. However, using the radiocarbon activity of bulk organic carbon to date the deposition of lake sediments is often hampered by i) the “reservoir effect”, carbon in the organic fraction derived from old groundwater or dissolution of limestone [28, 29, 30]; (ii) contamination by reworked organic materials from weathering and erosion in the upstream catchment can dilute the 14C content of organic materials with respect to the atmosphere at the time of deposition [31, 32]. The chronology based on the AMS dating of bulk organic sediments in Chachi and Luna core sediments are stratigraphically consistent [12]. The bulk organic matter in the Banni core sediments has dominant source derived from the terrestrial vascular plants [12] that incorporate 14C in cellulose sourced from 14CO2 in the atmosphere [33]. Therefore, the bulk organic matter in the Banni core sediments is relatively free of any “reservoir effect” making it an ideal material for applying 14C to date the sediments. The 14C dates were calibrated using OxCal 4.1 software [34] with the IntCal 09 calibration curve [35]. The average sedimentation rates of the Chachi and Luna sediments are 0.03 and 0.06 cm yr-1 respectively. The age-depth relationship was modeled using linear interpolation between adjacent radiocarbon measurements.

Quantification of organic source using n-alkane indices

The n-alkane indices have been used to obtain information on OM sources using the source-specific carbon chain length [36, 37]. The long-chain n-alkanes (C27–C33) tend to dominate terrestrial leaf waxes, short-chain n-alkanes less than C21 are more characteristic of algae, and mid-chain n-alkanes (C21, C23, and C25) are mainly produced by aquatic macrophytes [38, 39].

In the present study, Paq, Terrigenous versus aquatic ratio (TAR) and carbon preference index (CPI) have been calculated. The Paq and TAR indices have widely been used to estimate the relative proportion of terrestrial flux in the sediments [39, 40].

Following equations have been used in this study:

[41].

Back-trajectory computation

The probable moisture source of the air masses has been deduced based on the backward trajectories computed from the database of the National Oceanic and Atmospheric Administration (NOAA, http://www.arl.noaa.gov/ready/hysplit4.html) using Hybrid Single-Particle Lagrangian Trajectories (HYSPLIT-4). Trajectories were computed for 48 hours at 500, 1500 and 2500 m.a.s.l. as precipitation in India is expected to originate from these altitudes [42]. In daily time-scale, trajectories did not show much variation with altitude, and hence 500 m.a.s.l. was considered to be cloud base height for precipitation over the Banni region.

The δD value of precipitation in Banni grassland

In the absence of meteorological station in Banni grassland, the Isotope Global Spectral Model version 2 (IsoGSM2) prediction of the stable isotopic composition of regional precipitation [43] has been used in the present study. This model provides monthly averaged δD and δ18O values of precipitation for the years 2011 to 2015. The spatial resolution of the model is 1.9° latitude 1.8° longitude. In IsoGSM2, atmospheric processes and temperature fields are constrained by spectral nudging. The detailed description of the model has been provided elsewhere [43]. In this study, 5 years weighted averaged δD and δ18O values of precipitation have been used as a reference dataset to interpret paleoclimatic signals.

Statistical analyses

The Mann-Whitney U test has been used to assess statistically significant differences between δD and δ13C values of the Chachi core sediments before and after 2500 cal yr BP. This test is commonly used to identify the significantly differing groups, which are not normally distributed [44]. The null hypothesis of this non-parametric test is that the difference of location between the samples is equal to zero, while the alternative hypothesis is that the difference of location between the samples is different to zero. The analysis has been conducted at a significance level of 95%. In this study, this analysis has been conducted on both δDC29 and δDC31 values for two time-slices; 4600 to 2500 cal yr BP and 2500 cal yr BP to present. Mann-Kendall trend analysis, a nonparametric test, has been employed to identify the trends in time-series of δD and δ13C values of the Chachi core sediments. The null hypothesis of this non-parametric test is that there is no trend in the time-series. The alternative hypothesis is that there is a trend in the time-series. The analysis has been conducted at a significance level of 95%. The correlation between dependent and independent variables has been calculated using linear regression analysis. All the statistical analyses have been conducted using PAST (Paleontological Statistics) and Sigma Plot software (version 12.5).

Results

Molecular level characterization of the OM

The n-alkane distributions in leaves of modern plants showed the dominance (ca. 80% of total concentration) of C29 and C31 homologues (S1 Fig). On the contrary, the n-alkanes distribution in the core sediments displayed a distinct bimodal distribution with carbon numbers ranging from C16–C35 (S2 Fig). In the Chachi core sediments, total n-alkane concentration ranged from 120 μg g-1 to 700 μg g-1 and reached the maximal value at the topmost section of the core. The short-chain and long-chain n-alkanes accounted for ~ 40% and ~25% of the total n-alkane concentration respectively. The CPI values for the Chachi and Luna core sediments varied from 2.9 to 15.7 (Fig 2A) and 3.2 to 4.2 respectively. The Paq values for the Chachi and Luna core sediments varied from 0.07 to 0.33 (Fig 2B) and 0.10 to 0.25, respectively. The TAR values of the Chachi sediments ranged from 1.1 to 6.5 (Fig 2C), whereas the Luna core sediments varied from 4.1 to 5.1.

Fig 2

Down core profile of n-alkane indices in Chachi core sediments.

(A) CPI, (B) Paq, and (C) TAR values for the Chachi core sediments.

The δD and δ13C values of long-chain n-alkane in modern plants

The δD and δ13C values of the modern vegetation from Banni region are listed in Table 1. The modern C3 vegetation in Banni region showed average δ13CC29 value of −33.6 ± 1.8 ‰ and δ13CC31 values of −35.5 ± 2.4 ‰ (n = 8, 1σ). The average δ13CC29 and δ13CC31 values of modern C4 plants are −22.4 ± 1.1 ‰ and −23.6 ± 1.6 ‰ (n = 5, 1σ) respectively. In C3 plants, δDC29 and δDC31 values varied from −173 ‰ to −98 ‰ (avg = −133 ± 25.1 ‰) and −198 ‰ to −132 ‰ (avg = −162 ± 24.7 ‰). The δDC29 and δDC31 values of C4 plants ranged between −218 ‰ to −144 ‰ (avg = −176 ± 28.9 ‰) and −219 ‰ to −150 ‰ (avg = −185 ± 28.6 ‰), respectively.

Table 1

Compound specific δD and δ13C values for the long chain n-alkanes of modern vegetation from Banni region.

SD = standard deviation (1σ).

	Sample	δ13CC29 (‰, VPDB)	S.D.	δDC29 (‰, VSMOW)	S.D.	δ13CC31 (‰, VPDB)	S.D.	δDC31 (‰, VSMOW)	S.D.
C3 plants	Solanum viarum	-34.9	0.4	-122	4	-37.4	0.3	-198	2
	Mangifera indica	-34.9	0.4	-173	2	-37.2	0.3	-184	3
	Eupatorium odoratum	-32.7	0.2	-133	3	-37.6	0.3	-141	2
	Salvadora persica	-30.1	0.8	-116	4	-30.7	0.5	-132	1
	Acacia nilotica	-36.1	0.3	-165	5	-36.1	0.4	-189	2
	Azadirachta indica	-33.4	0.1	-141	4	-33.6	0.2	-154	3
	Physalis minima	-34.0	0.3	-123	3	-36.9	0.4	-143	1
	Citrus lemon	-32.6	0.4	-98	4	-35.0	0.3	-160	1
	Average	-33.6 ± 1.8		-133 ± 25.1		-35.5 ± 2.4		-162 ± 24.7
C4 plants	Cenchrus ciliaris	-21.0	0.5	-218	4	-22.2	0.3	-150	2
	Chloris barbata	-22.8	0.3	-144	3	-23.7	0.4	-177	1
	Panicum miliaceum	-22.1	0.3	-177	1	-24.4	0.4	-209	4
	Suaeda fruticosa	-21.9	0.4	-187	4	-21.9	0.5	-219	4
	Desmostachya bipinnata	-23.9	0.3	-155	2	-25.8	0.2	-169	2
	Average	-22.4 ± 1.1		-176 ± 28.9		-23.6 ± 1.6		-185 ± 28.6

The δ13C value of long-chain n-alkanes in core sediments

The δ13CC29 and δ13CC31 values of the Chachi core sediments varied from −30.9 ‰ to −27.2 ‰ and −34.4 ‰ to − 25.0 ‰ respectively (Table 2). The δ13C values of both the homologues showed more negative values during 4600 to 2500 cal yr BP (Fig 3A). Afterwards, δ13CC31 values displayed a progressively increasing trend with maximum values at 1500 cal yr BP. Similar trends are also observed for the δ13CC29 values. The δ13CC29 and δ13CC31 values of the Chachi core sediments exhibit strong and significant positive correlation (R2 = 0.66, p < 0.05). The p-values of Mann-Kendall analyses for the both homologues are less than 0.05 and Kendall’s tau value for δ13CC29 and δ13CC31 values are 0.45 and 0.61. The δ13C values of Luna core sediments for the last ca. 1000 parallels the Chachi record (Fig 3). The δ13CC29 and δ13CC31 values of the Luna core sediments ranged from –25.3 to –27.7 ‰ and –23.9 to –28.0 ‰ respectively (Table 3).

Table 2

δD and δ13C values for the long chain n-alkanes of Chachi core sediments N.A. = not available/not determined.

SD = standard deviation (1σ).

Sample	Depth	Age cal yr BP	δ13CC29 (‰, VPDB)	S.D.	δ13CC31 (‰, VPDB)	S.D.	δDC29 (‰, VSMOW)	S.D.	δDC31 (‰, VSMOW)	S.D.
Chachi sediments
Ca-1	0	0	-28.3	0.4	-28.7	0.1	-77	2	-93	4
Ca-2	8	471	-27.9	0.2	-26.2	0.0	N.D.		N.D.
Ca-4	20	1177	-27.5	0.4	-27.2	0.3	-77	3	-72	0
Ca-8	40	1631	-27.6	0.1	-25.0	0.2	-71	2	-83	2
Ca-12	60	1844	-27.2	0.4	-26.8	0.2	-97	0	-108	0
Ca-15	74	2140	-28.7	0.3	-27.9	0.7	-106	4	-98	2
Ca-17	82	2310	-29.2	0.2	-28.0	0.2	-88	0	-90	3
Ca-19	90	2480	-29.3	0.2	-29.4	0.8	N.D.		N.D.
Ca-20	95	2586	-29.7	0.5	-31.7	0.4	-104	4	-116	2
Ca-21	100	2692	-28.1	0.0	-28.9	0.1	-96	1	-116	2
Ca-22	104	2777	-27.2	0.2	-27.3	0.4	-121	2	-107	3
Ca-23	108	2862	-28.4	0.2	-28.8	0.9	N.D.		N.D.
Ca-24	112	3090	-29.9	0.1	-30.0	0.4	-99	2	-113	1
Ca-25	116	3319	-28.5	0.2	-29.9	1.0	-129	1	-126	4
Ca-26	120	3548	-30.9	0.2	-34.4	0.5	-113	1	-124	1
Ca-27	126	3892	-29.0	0.2	-31.3	0.2	-121	7	-121	2
Ca-28	130	4121	-29.5	0.1	-30.2	0.6	-115	5	-129	2
Ca-29	134	4350	-30.3	0.6	-29.8	0.1	-122	4	-134	1
Ca-30	138	4578	-29.1	0.1	-31.3	0.3	-110	2	-132	3

Fig 3

The isotopic profile and related calculations from the core sediments.

[A] δ13C and [B] δD values for both n-C29 and n-C31 homologues. [C] Down-core calculated fractional C4 contributions from both n-C29 and n-C31 homologues. [D] Down-core apparent hydrogen isotopic fractionation (εapp) for each homologue calculated using the measured end-member compositions and calculated fractional C4 contributions. [E] Down-core reconstructed δDrainwater values using the measured δD and calculated εapp values for each homologue. The horizontal lines in the figure represent the error associated with the calculations.

Table 3

δ13C values for the long chain n-alkanes of Luna core sediments.

SD = standard deviation (1σ).

Lune sediments
Sample	Depth	Age cal yr BP	δ13CC29 (‰, VPDB)	S.D.	δ13CC31 (‰, VPDB)	S.D.
Lb-1	3	10	-27.2	0.1	-27.5	0.1
Lb-2	9.5	109	-26.6	0.4	-27.3	0.5
Lb-3	15.5	274	-26.3	0.6	-25.8	0.8
Lb-4	21.5	438	-27.6	0.2	-28	0.4
Lb-5	27.5	603	N.D			N.D
Lb-6	33.5	670	-25.7	0.3	-25.9	0.1
Lb-7	39.5	737	-27.4	1.1	-27.7	0.3
Lb-8	45.5	804	-26.6	0.1	-26.2	0.7
Lb-9	51.5	871	-26.3	0.1	-25.9	0.3
Lb-10	57.5	937	-26.2	0.7	-26.6	0.6
Lb-11	62.5	951	-27.7	0.3	-27.5	0.1
Lb-12	67.5	962	-25.3	0.0	-23.9	0.5
Lb-13	72.5	973	N.D		N.D
Lb-14	77.5	984	-25.8	0.6	-25.3	0.4
Lb-15	82.5	995	-27.3	0.2	-27.9	0.6

The δD values of long-chain n- alkanes in core sediments

The p-values of Mann-Kendall analyses for the both homologues are less than 0.05 and Kendall’s tau value for δDC29 and δDC31 values are 0.72 and 0.76, respectively. The magnitudes of fluctuation in δD values were similar for both C29 and C31 homologues (Table 2). In most of the samples, the δDC29 values were more positive compared to δDC31 value. The most prominent feature of the curve is the shift towards positive values after 2500 cal yr BP (Fig 3B). Accordingly, we have compared the values for two time-slices; 4600–2500 cal yr BP (phase-1) and 2500 cal yr BP to present (phase-2). The average δDC29 and δDC31 values during the phase-1 were −113.0 ± 10.7 ‰ and −121.8 ± 8.8 ‰ (n = 10, 1σ) respectively. The average δDC29 and δDC31 values during phase-2 were −86.0 ± 13.5 ‰ and −90.7 ± 12.3 ‰ (n = 6, 1σ). The difference in the δDC29 (and δDC31) values for phase-1 and phase-2 has also been exhibited by Mann-Whitney test statistics. For both the homologues, we have found the p-values are less than significance level of 0.05 and thus reject the null hypothesis.

The stable isotopic composition of modern precipitation in Banni region

Monthly-weighted averaged δD values are positively correlated (R2 = 0.9, p < 0.05) with the δ18O values of precipitation. The Local Meteoric Water Line for the Banni region is:

(Fig 4A)

Fig 4

Isotopic variation of modern regional precipitation (IsoGSM2).

(A) Local meteoric water line in the Banni region has been obtained using cross-plot between δD and δ18O values. (B) The monthly averaged δD values of precipitation become progressively depleted in heavier isotope with the establishment of the ISM.

Due to the significant correlation (R2 = 0.9), only the results of δD values of precipitation are further discussed. During 2011 to 2015, the δD values of precipitation varied from −4 ‰ to −77 ‰, and lower δD values coincide with the peak ISM precipitation (Fig 4B).

Discussion

Sources of organic matter

The n-alkane assemblage in the sediments is an effective biomarker tool for evaluating sources of organic matter due to the distinct carbon chain-lengths of terrestrial higher plants and microorganisms [26, 45]. In general, cuticular waxes of higher plants have a strong odd over even predominance in the long-chain n-alkanes and show CPI values >1, whereas the bacteria and algae are characterized by weak odd over even predominance and produce low CPI values (~1) [38]. Terrestrial higher plants are characterized by relatively higher abundances of odd-numbered long-chain n-alkanes, whereas microorganisms contribute short-chain n-alkanes [16, 36]. In Banni core sediments, the higher abundance of the short-chain n-alkanes (S2 Fig) indicates a significant contribution from microorganisms to the n-alkane pool in the sediments. On the other hand, peaks observed in the long-chain n-alkanes maximise at C29 and C31. The CPI values of both the sediment cores are consistent with the modern higher plants growing in the Indian subcontinent [27]. High CPI values (Fig 2A) suggest that long-chain n-alkanes in the sediments were derived from terrestrial higher plants. Further, the low Paq values of Chachi core sediment indicate higher contribution of terrestrial biomass to the organic matter compared to aquatic productivity, and this observation is concordant with the TAR values (Fig 2B and 2C). Therefore, the long-chain δ13C values would be suitable for the paleovegetational reconstruction.

Estimates of shifts from C3 to C4 plant type

A previous study from Banni grassland using bulk δ13Corg values showed that %C4 plant varied from 16% to 43% [12] (S1 Table). However, bulk δ13Corg values reflect contribution from multiple organic matter sources [16, 27], which are characterized by distinct isotopic composition. Furthermore, C4 plant derived components degrades faster compared to its C3 counterpart [46]. Therefore, paleovegetation reconstruction using the δ13Corg values may provide an erroneous estimate of %C3-C4 plants. The δ13C values can refine the interpretation of bulk δ13Corg data due to its resistance to biodegradation. Result of linear regression analysis shows that the δ13CC29 and δ13CC31 values of the Chachi core sediments exhibit a strong and significant positive correlation (R2 = 0.66, p < 0.05). The large spread in the δ13CC29 and δ13CC31 values (Fig 3A) shows substantial variations in the vegetation composition. The δ13C values are not only governed by the vegetation composition but also by physiological changes against moisture stress and δ13C value of atmospheric CO2 (δ13Catmospheric CO2) [42, 47, 48]. The results from the Indian subcontinent show that δ13C values of C3 plants change only −0.4 ‰ for every 100 mm increase in the precipitation amount [49]. Therefore, ca. 2350 mm change in the annual precipitation would be required to explain the observed spread in the δ13CC31 values (Table 2). Thus, we exclude moisture stress on the δ13C values as the estimated precipitation change in the region seems unrealistic. The δ13Catmospheric CO2 value has been declining rapidly in the recent decades as a consequence of the Suess effect (http://cdiac.ornl.gov/trends/co2/iso-sio/iso-sio.html). The modern δ13Catmospheric CO2 value (−8.2 ‰) is 1.7 ‰ lower compared to that in the Mid-Late Holocene period [47], and accordingly the end-member δ13C values of modern plants are corrected. The δ13C values are converted into %C4 plants based on mixing model, where end-member values are the average isotopic composition of the dominant C3-C4 plants in Banni region (Table 1).

In the core sediments, the δ13CC31 values correspond to 13% to 70% abundance of C4 plants, while δ13CC29 values indicate 5% to 38% abundance of C4 plants (Fig 3C). Chemotaxonomic investigations showed that C4 grasses in the savannah region synthesize more C31, whereas C29 is the dominant homologue in C3 dicots (S1 Fig) [50, 51]. The δ13CC29 and δ13CC31 values in the core sediments are believed to represent two distinct organic matter sources. Accordingly, in the present study, it has been assumed that δ13CC29 values of the sediment samples reflect the changes in the abundance of C3 trees, whereas δ13CC31 values indicate the changes in %C4 grasses. The measured δ13CC31 values provide higher estimation of C4 plants compared to both δ13Corg and δ13CC29 values (Fig 3C, S1 Table). Therefore, it can be suggested that the δ13CC31 values would be a more suitable proxy to infer vegetation change in the grassland setting.

The δ13CC31 value-based calculation indicates ca. 20% higher abundance of C4 plant during phase-2 compared to phase-1 (Table 4). Estimate of lower %C4 plant till 2500 cal yr BP is concurrent with the pollen and phytolith records which showed the dominance of woody taxa (i.e., C3 plants) during this interval [13]. The δ13C values from the Luna core sediments for the last 1000 cal yr BP also shows high %C4 plants in the Banni region. The trend in the δ13C values corresponds well with the vegetation record from central India [42].

Table 4

%C4 plants calculated using δ13CC29 and δ13CC31 values of the Chachi core sediments.

Sample Number	Age (cal yr BP)	C4% (δ13CC29)	Uncertainty	C4% (δ13CC31)	Uncertainty
Ca-1	0	27.7	4.1	38.7	8.0
Ca-2	471	31.3	4.9	59.7	8.5
Ca-4	1177	34.8	4.1	51.3	7.2
Ca-8	1631	33.9	5.3	69.7	7.6
Ca-12	1844	37.5	4.1	54.6	7.6
Ca-15	2140	24.1	4.5	45.4	5.5
Ca-17	2310	19.6	4.9	44.5	7.6
Ca-19	2480	18.8	4.9	32.8	5.1
Ca-20	2586	15.2	3.7	13.4	6.7
Ca-21	2692	29.5	5.7	37.0	8.0
Ca-22	2777	37.5	4.9	50.4	6.7
Ca-23	2862	26.8	4.9	37.8	4.7
Ca-24	3090	13.4	5.3	27.7	6.7
Ca-25	3319	25.9	4.9	28.6	4.3
Ca-26	3548	4.5	4.9	-9.2	6.3
Ca-27	3892	21.4	4.9	16.8	7.6
Ca-28	4121	17.0	5.3	26.1	5.9
Ca-29	4350	9.8	3.3	29.4	8.0
Ca-30	4578	20.5	5.3	16.8	7.2

δDC29 value as a proxy for paleoprecipitation

The δD values from plant leaf wax have been increasingly used for the reconstruction of paleohydrological conditions [52-54]. The δD variability in leaf n‐alkanes are mostly explained in terms of the changes in the δD values of regional meteoric water [54-59]. In addition, plant functional types and photosynthetic pathways can also affect the δD values [60-63]. In the Chachi core sediments, the δDC29 values differ by -14 ‰ to 22 ‰ to the δDC31 values of the same sample. Difference sources of n-alkane can be assumed to explain this offset [64] as net apparent hydrogen fractionation between source water (i.e., MAP) and lipid (εMAP-) of C3 dicots is ca. 30 ‰ more positive compared to that of C4 grasses [65].

Results from Banni grassland also show that εMAP-C29 value for C3 plant is −113±25.1 ‰ while for C4 plant the value is −156±28.9 ‰ (Table 1). Similar differences have been inferred from C31 homologue as well. The difference in water absorption systems in plant forms plays an important role that cause more negative δD values in n-alkanes from grasses than those from woody plants. The grasses (C4) in the open ecosystem utilize water from deeper water sources (due to greater rooting depth of perennial grasses) compared to trees (C3) in woodland setting as woody plants draw most of their water from soil surface through radial and shallow roots [65]. The different water-usage strategies of C3 and C4 plants may lead to a significant offset in the εMAP- (as well as δD) values and required to be addressed when the vegetation changes are large (as in the case of the present study).

In this study, the proportion of C4 plants for each time-point has been calculated from the δ13CC29 and δ13CC31 values of the sediment samples (Table 3). Using the δ13C based estimates of vegetation changes, the weighted averaged apparent fractionation factor between δD and the δDprecipitation (ε-precip) has been estimated using the following equation; Where, A is the %C3 plants. The weighted averaged ε-precip values for each time-point have been presented in Fig 3D. Before the estimation of δDprecipitation values, it is required to calculate the hydrogen isotopic fractionation during organic matter transport from leaf-wax to soil. Using the above-mentioned equation, the δD value of modern leaf would be −125 ‰. On the other hand, the δDC29 value of the core-top sediment is −78 ‰, indicating incorporation of n-alkane into soil resulted in ca. 47 ‰ increase of the δDC29 value. Accordingly, the δD values for each time-point have been corrected [66]. The δD value of regional precipitation has been calculated using the following equation [67];

The correlation analyses have been conducted between δ13C and δD values of both the homologues for further interpretation of paleohydrological conditions. Results of the linear regression analysis have shown strong and significant positive correlation (R2 = 0.58, p < 0.05, Fig 5) between δDC31 and δ13CC31 values. The vegetation change as primary driver of hydrogen isotopic variability of C31 homologue can result in negative correlation between δ13CC31 and δDC31 values. In contrast, the positive correlation indicates that the vegetation change has not influenced the hydrogen isotopic variations in C31 homologue. It has been further observed that the correlation between δD and δ13C values for C29 homologue is weaker and insignificant (R2 = 0.15, p = 0.13, Fig 5) suggesting that the δD values of this homologue are not influenced by the change from C3 plants to C4 grass [53]. Therefore, the δDn-alkane values of both the homologues can be used as a surrogate to understand the paleohydrological conditions. In this study, the further discussion is based on the δDC29 values. The δDprecipitation(C29) values found to vary between 10 ‰ to −60.2 ‰ (Fig 3C).

Fig 5

Regression analyses between δDn-alkane and δ13Cn-alkane values for C29 and C31 homologues.

Poor correlation (blue line) between the δD and δ13C values for C29 homologue indicates limited control of vegetation composition on the δD values. Strong and significant positive correlation (red line) between the δD and δ13C values for C31 homologue also suggests that change in vegetation type does not have influence on the δD values in collected samples.

In the low latitude tropical region, δDprecipitation values are negatively related to the precipitation amount (i.e., amount-effect) and this inverse correlation is often used to explain the variations in the δD values [50, 51, 68, 69]. Instrumental record and back-trajectory computation of air-parcel show that precipitation in the Banni grassland is strongly seasonal (Fig 1D and 1E) with the dominant moisture contribution received from Arabian Sea. The seasonal difference in the net advection of moisture is reflected in the δDprecipitation values (Fig 4B). Seasonal precipitation amount peaks during ISM months and coincides with low δDprecipitation values, which indicates more negative δDC29 value of the sediment samples are representatives of high monsoonal precipitation events and vice-versa.

Paleohydrological changes in Banni grassland

More negative δDC29 values till 2500 cal yr BP suggest an enhanced monsoonal precipitation in Banni. Little fluctuations (max. 10 ‰) show that the region had experienced minor changes in the precipitation amount during phase-1. Relatively stable and high precipitation during this phase accompanied by higher %C3 plants (Fig 3C). After 2500 cal yr BP, the δDC29 values gradually shift towards more positive values, and the aridification peaked around 1500 cal year BP. Regional aridity is also marked with the higher abundance of C4 grasses during this interval (Fig 3C). A negative excursion (ca. 10 ‰) after 1000 cal yr BP shows a slight increase in precipitation amount in the Banni region towards the present. Overall, it can be suggested that the Banni region had experienced a gradual shift from more mesic to more arid condition since the last 4600 cal yr BP. The δDC29 value based record is consistent with the interpretation made from δ18Oshell values in Banni core sediments [12].

Critical role of fire, CO2 and precipitation on grassland stability

In general, low CO2 condition and frequent fire events promote higher %C4 grasses at the expense of C3 woody plant cover [70]. According to the CO2 crossover model [71], Banni with temperature > 30 °C, the threshold atmospheric CO2 level favoring C4 plants over C3 plants should be below 300 ppmv. The pCO2 reconstruction for the studied period showed that atmospheric CO2 level increased from 268 ppmv to 282 ppmv, a range favorable for the expansion of C4 plants. However, the trend of C4 plant expansion in Banni does not mimic the paleo-pCO2 curve and exhibits an anti-phase relationship in between 2500 to 1500 cal yr BP (S3 Fig). Therefore, pCO2 level alone cannot be attributed to the changing vegetation composition in Banni for the last 4600 cal yr BP.

The present study, in combination with published records, has attributed the increasing %C4 plants to the feedback mechanism between aridity, fire and vegetation composition. Increasing fire events (evidenced from Charcoal accumulation rate (CHAR)) from 2500 to 1500 cal yr BP helped to reduce C3 woody plant cover and favored the expansion of C4 grasses (S3 Fig). These fire events also promoted an arid understory microclimate and resulted in further drying in Banni. Continuous aridification helped fuel drying and led to more frequent fire events. In a nutshell, these effects have been enhanced by biogeophysical feedbacks; well-developed grasslands can favour the expansion of grasses by increasing light availability, reducing humidity and keeping high temperatures, all of which promote the growth of C4 plants and are capable of maintaining the grassland setting.

Expansion of C4 grasses can also impact the isotopic composition of the regional precipitation. Both IsoGSM2 model predictions and δDn-alkane value reveal extremely high precipitation δD values from Banni region. The present study suggests the following mechanism to explain this distinct isotopic character: the transpiration of grasses is higher compared to forest trees [72, 73]. Although transpiration is a non-fractionation process, it indeed enriches the atmospheric vapor in heavier isotopes [73] and resultant precipitation has more positive isotopic composition.

Regional comparison of climate data

The variations in precipitation in Banni grasslands for the last 4600 cal yr BP correspond well with published records from the ISM realm (Fig 6). The enhanced ISM precipitation in Banni during ca. 4600–2500 cal yr BP was synchronous with pollen and phytolith based investigations from Pariyaj lake in western India [74]. The geochemical proxies measured from the sediment core of an active mudflat of Diu Island in western India also indicate warm and humid conditions between 4105 and 2640 cal yr BP [75]. The results from Banni grassland are also corroborated by the geochemical and palynological analyses of relict mudflat from southern Saurashtra coast with wet climatic conditions and simultaneous occurrence of marginally high sea-level between 4710 and 2825 cal yr BP [76]. Likewise, the climatic reconstructions based on lake sediments from Nal Sarovar, central Gujarat [77] and Lunkaransar and Didwana Lakes of Rajasthan, western India [78, 79] observed a high lake stand during ca. 4680 and 3500 cal yr BP. The wetter conditions in Banni region during 4600–2500 cal yr BP is in good agreement with the record based on δ13C values of sedimentary leaf waxes from the core monsoon zone of India [9], and mineralogical and isotopic investigation on core sediments from Lonar lake, central India [10, 80]. This humid phase also matches with the geochemical investigations of core sediments retrieved from continental shelf in eastern India [81].

Fig 6

Compilation of the Mid-Late Holocene records from Indian monsoon realm.

(A) δDC29 record from Banni region (this study) (B) δD record from Lonar Lake, central India [42], (C) δ18Ocarbonate from Tso Moriri lake, NW Himalayas [11], and (D) δ18Ocarbonate from northern Bay of Bengal [82].

The onset of aridity in the Banni region after 2500 cal yr BP was in phase with lowering of the sea-level during 2825 and 1835 cal yr BP, as observed from the Saurashtra coast in western India [76]. The results are also in line with the high resolution record from NW India: Nal Sarovar lake [77]; Pariyaj Lake [74], Lunkaransar and Didwana lakes [78, 79]. The onset of aridity after 3 ka is also inferred in a tidal terrace sequence at the mouth of the Kharod River in western India [83]. A stalagmite record from peninsular India also indicates an abrupt climate change characterized by the decline of ISM around 2800 yr BP [84]. The findings of drier conditions in Banni region during ca. 2500–1000 cal yr BP is good accordance with the pollen record from NE India [85]; sedimentary leaf waxes from core monsoon zone [9] and geochemical data from eastern India [81]. More positive δD values of the Lonar lake sediments (central India) also provide evidences of the Late Holocene arid conditions (Fig 6B). The δ18O values of the carbonates from the Tso Moriri lake, northern India also demonstrates a decline in the intensity of ISM precipitation during the Late Holocene (Fig 6C). Our record from Banni grassland is also in line with the climatic reconstruction from northern Bay of Bengal [82] that confirms a gradual decrease in the ISM intensity during the Late Holocene (Fig 6D).

The record from the Banni grasslands matches with the evidence of precipitation changes and vegetation responses in the other tropical grasslands across the globe. For instance, terrestrial ecosystems in the Sahara responded to the weakening of African monsoon during the Mid-Late Holocene by a conspicuous reduction in tropical trees from nearly 4300 cal yr BP to the establishment of current day desert ecosystem by ca. 2700 cal yr BP [86]. Studies from the Manga Grasslands in northeastern Nigeria have also shown that climate was the major determinant of vegetation change during the Mid-Late Holocene with a shift towards more arid conditions until 1000 cal yr BP followed by wetter conditions towards the present [87]. Distinct biostratigraphical and sedimentological evidences of the shift towards drier climatic conditions during the Mid-Late Holocene were reported from multiple sites across Western Africa [88].

Teleconnections governing long-term regional climate variability

As various components of the different monsoon systems are interconnected through feedback mechanisms, perturbation of any of those components can affect the global monsoonal circulation [89]. For instance, less boreal summer monsoonal precipitation is linked with the increased Australian-Indonesian summer monsoonal (AISM) precipitation [90]. Therefore, understanding of land-ocean-atmosphere interaction on a global scale requires quantitative data from different monsoon domains. Towards this, climate records from different monsoonal domains (Fig 7) have been compared in this study to understand the role of forcing factors that persist across Hemispheres. The mean δDC29 values of the Chachi core sediments are significantly different for the phase-1 and 2, and this difference is correlated with the mean position of the ITCZ which is driven by the solar insolation [91] (Fig 7D). Monsoonal precipitation in the Northern Hemisphere increases with northward migration of ITCZ, accompanying decreased precipitation in the Southern Hemisphere; and vice versa [92]. Paleoclimatic records showed that the Southern Hemisphere insolation became more seasonal compared to Northern Hemisphere in the Late Holocene. The mean position of ITCZ during the phase-2 is thus postulated as the driving mechanism for the drying trend in ISM and East Asian summer monsoon dominated region and wet condition in the Southern Hemisphere (Fig 7A and 7D).

Fig 7

Compilation of Mid-Late Holocene records of terrestrial records across different monsoonal domains and forcing factors.

(A) δDC29 record from Banni region (this study), (B) δ18Ocarbonate from Heshang cave, EASM [96], (C) δ18Ocarbonate from Liang Luar, AISM [90], (D) solar insolation curve from the northern hemisphere [91] and (E) frequency of ENSO activities [94].

Increasing ENSO activity couldn also be argued for the increase in the δDC29 values after 2500 cal yr BP. An investigation based on the modern instrumental record suggested that majority of droughts in the ISM dominated regions are linked with the warm phases of ENSO events (i.e., El-Nino) [93]. The modern ENSO regime was established 3000 to 4500 cal yr BP and El-Nino events significantly increased after 2500 cal yr BP [94] concurrent with the marked dry condition in the Banni region (Fig 7E). This pattern in the precipitation correlates well with the grain size data from EL Junco Crater Lake in the Galapagos Islands, inferred to reflect the higher frequency of El-Nino events [95]. Together position of ITCZ and ENSO activity appear to be linked to the inferred aridification in Banni region during the Late Holocene.

Conclusion

Climate-driven changes in grassland ecosystems of the Indian Summer Monsoon realm have been investigated, for the first time using the isotopic composition of biomarkers in lake sediments. After ca. 2500 cal yr BP, a gradual increase in the δ13C values indicates the dominance of C4 grasses at the expense of C3 plants. Difference between the δ13C values indicates that each n-alkane homologue had different C3-C4 proportional contributions. In the lake sediments, C29 and C31 homologues were mainly derived from C3 plants and C4 grasses respectively. The present study has used the δDC29 values as a proxy of the isotopic composition of paleoprecipitation. Meteorological and modeling observations show that δD values of modern precipitation in the Banni region are inversely related to the precipitation amount. This ‘amount effect’ based reconstruction suggests a marked decrease in the ISM precipitation after 2500 cal yr BP. The observed precipitation pattern in the climate record from Banni region corresponds well with published records. Coupled influence of southward migration of ITCZ and higher frequency of warm ENSO events led to the decrease in the ISM precipitation after 2500 cal yr BP and helped in establishing the grassland ecosystems.

Representative distribution of n-alkanes from the modern vegetation (trees and grasses) in Banni region.

Pr and Ph represent the concentration of pristane and phytane isoprenoids in the vegetation.

(JPG)

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Representative distribution of long-chain n-alkanes derived from Chachi core sediments.

Pr and Ph represent the concentration of pristane and phytane isoprenoids in the sediments.

(JPG)

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The dotted line represents the variations in the %Char in the Banni grassland for the last 4600 cal yr BP.

The values of %Char has been taken from Pillai et al., 2017. The dashed line represents the variability of pCO2 concentration. The source of pCO2 dataset is Lüthi et al., 2008.

(JPG)

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The %C4 plants for the last 4600 cal yr BP has been estimated using bulk δ13Corg, δ13CC29 and δ13CC31 values from the Chachi core sediments.

Numbers in bold represent outliers.

(DOCX)

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