What drives elevational pattern of phytolith diversity in Thysanolaena maxima (Roxb.) O. Ktze? A study from the Darjeeling Himalayas

Flora 211 (2015) 51–61

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What drives elevational pattern of phytolith diversity in Thysanolaena maxima (Roxb.) O. Ktze? A study from the Darjeeling Himalayas Subha Brata Dey a , Ruby Ghosh b , Mayank Shekhar b , Biswajit Mukherjee a , Subir Bera a,∗ a Centre of Advanced Study, Palaeobotany–Palynology Laboratory, Department of Botany, University of Calcutta, 35, Ballygunge Circular Road, Kolkata 700019, India b Birbal Sahni Institute of Palaeobotany, 53, University Road, Lucknow 226007, India

a r t i c l e

i n f o

Article history: Received 20 August 2014 Received in revised form 3 January 2015 Accepted 23 January 2015 Edited by Hermann Heilmeier. Available online 24 January 2015 Keywords: Morphometric sensitivity Elevation gradient Climate Moisture index Soil pH Opal A

a b s t r a c t Phytolith formation in plants is commonly thought to be influenced by variations in temperature, precipitation and other environmental factors. The objective of this study is to test how variations in different climatic and edaphic factors i.e., temperature, rainfall, actual evapotranspiration (AET), potential evapotranspiration (PET), moisture index (MI) and soil pH along a tropical–temperate elevation gradient (150–2456 m a.s.l.) in the Darjeeling Himalayas would influence the variability and plasticity of formation and frequencies of phytoliths in Thysanolaena maxima. Among the 16 phytolith types, frequency, diversity and morphometric sensitivity of some consistently occurring morphs viz., stomate, cuneiform bulliform, three-lobates and bilobates have been studied in detail to trace their sensitivity to changing environmental factors along the studied elevation gradient in Darjeeling. We used regression analysis to establish elevational pattern of phytolith diversity and sensitivity of T. maxima and to relate environmental factors with morphometric attributes. Frequencies of bilobate, three-lobates and stomate types increase with increasing elevation and MI, but decrease with MAT (mean annual temperature), AET, PET and soil pH. However, a reverse trend is noticed in frequency of bulliform types. Morphometric measurements show a positive correlation between dimensions of stomate, three-lobates and bilobates with MAT, AET, PET and soil pH and a negative correlation with MI. Length of bilobate shanks and dimension of bulliform cells are found to be negatively correlated with MAT, AET, PET and soil pH and positively correlated with MI; however, an opposite trend is noticed for shank width. No significant relationship is observed with rainfall, but MI, which is a measure of the water balance between rainfall and PET of an area shows significant correlation with morphometric traits. The study thus demonstrates that the diversity and morphometric attributes of T. maxima phytoliths are plastic in their response to different environmental factors. © 2015 Elsevier GmbH. All rights reserved.

Introduction Phytoliths are the opal A particles formed as a result of biological and physical interaction between plants and their environment by which certain plants deposit solid silica in intracellular or extracellular locations after absorbing them in a soluble state (monosilicic acid) from the soil solution (Piperno, 2006). These silica bodies thus formed retain shapes of the cells of their origin (Pearsall, 2000). For normal growth and development of plants, silica has been proved to be an important factor (Agarie et al., 1996). Due to their siliceous nature these tiny particles show resistance to biological and mechanical degradation. Variations in phytolith morphologies

∗ Corresponding author. Tel.: +91 33 2461 4959; fax: +91 33 2461 4849. E-mail address: [email protected] (S. Bera). http://dx.doi.org/10.1016/j.flora.2015.01.004 0367-2530/© 2015 Elsevier GmbH. All rights reserved.

at different scales are indicative of their plant taxonomic groups as well as photosynthetic pathways (especially in grasses) and are thus helpful in palaeoecological studies (Pearsall, 2000; Madella et al., 2012). Variability in the presence, abundance and frequency of certain morphotypes as well as variability in phytolith size may be useful in ecological interpretations (Pearsall, 2000; Das et al., 2014). Although the genetic control of plant silicification is known (Hodson et al., 2005; Mitani and Ma, 2005; Piperno, 2006), environmental influences also cannot be ruled out (Epstein, 1999; Bauer et al., 2011; Das et al., 2013, 2014). Li et al. (2014) observed that under elevated CO2 concentration the size of the phytolith morphotypes and the ratio of different types change significantly in Phragmites communis. Frequency and dimension of some phytolith types also show significant oscillations with changing soil pH, as was inferred from a study on Leymus chinensis collected from habi-

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tats differing in pH value from Songnen Plain in China (Jie et al., 2010), indicating that phytolith formations are at least partially controlled by soil pH. Humidity was also found to be influencing phytolith formation in Phragmites communis from three different climatic zones of northeast China (Liu et al., 2013). Irrigation was found to have positive influence on phytolith formation when studied in some annual Poaceae species from Middle-Eastern desert fringes (Rosen and Weiner, 1994; Mithen et al., 2008; Madella et al., 2009; Jenkins et al., 2011) and also in Triticum aestivum and Agropyron desertorum from North America (Mayland et al., 1991, 1993). Positive effects on phytolith formation were also noticed for both higher rainfall and irrigation in T. durum growing in Jordan, however, effect of irrigation was observed to be stronger than rainfall (Mithen et al., 2008; Jenkins et al., 2011). Brachiaria decumbens, B. brizantha (Melo et al., 2003) and Sorghum bicolor (Hattori et al., 2005) have also shown similar trends under laboratory conditions. However, influence of water availability on phytolith formation is not always straightforward. Earlier studies indicated that for phytolith production, rainfall may not always be the limiting factor. Climatic variables like solar irradiance and evapotranspiration may also influence plant’s phytolith production depending on the ecological conditions (Rosen and Weiner, 1994). Webb and Longstaffe (2002) found that in Calamovilfa longifolia growing in the North American prairies, average silica content of the transpiring tissues is higher in sites that are more arid than with a higher humidity. Usually, rain water stays in longer contact with soil than water from irrigation, and thus may absorb more available silica. Hence, a greater effect of rainfall compared to irrigation could be expected during phytolith formation. However, sometimes a deviation from the trend may be due to the fact that more arid conditions may not always mean less water, but also higher evapotranspiration in drier areas. Hence, aridity may have both potential negative (due to lower water availability) and positive (due to higher evapotranspiration) effects on phytolith formation. Studies on the relationship of plant silica content and water availability in some Poaceae (e.g., Festuca scabrella, Stipa comata and Bouteloua gracilis) and Cyperaceae (Carex filifolia) species from Canada (Johnston et al., 1967) and some Poaceae species (Pennisetum purpureum and Panicum maximum) grown in Benin (Kindomihou et al., 2010) showed no significant correlation between the two parameters. Katz et al. (2013) observed that in humid habitats, the non-spiny Asteraceae members showed a more or less similar pattern of change in silicification according to water availability, and showed higher phytolith concentrations in the most arid site, whereas the spiny species showed inconsistent patterns of silicification which could not be related to water and soil silica availabilities. A positive correlation between grass phytolith concentration with water availability and a minor effect of soil silica availability was also observed. Studies of Ghosh et al. (2011) and Das et al. (2013, 2014) from the world’s largest delta Sunderbans demonstrated how the frequency variations of grass and non-grass phytolith types and changes in the dimension of some blocky types can be related to change in salinity. However, to elaborate the complex influence of various environmental factors on the pattern of phytolith formation, studying naturally growing plants along an elevation gradient may be helpful, as, with rising elevation different environmental factors like temperature, rainfall, evapotranspiration, soil pH etc. change significantly and thus their effect on phytolith formation of selected plant species may help in resolving the issue. Moreover, the result may aid in future studies related to phytolith variability in plants from similar elevation gradients of mountainous region. Eastern Himalayan mountain ranges which represent one of the global hotspots of biodiversity (Mittermeier et al., 2005) due to their location at the juncture of many biogeographical realms are one of the potential places for studying the effect of climate drivers on plant systems. Wide altitudinal variations and corresponding

climatic changes (tropical hot to extreme cold) may also help in exploring the influence of different climatic factors on plant phytolith production patterns. In Darjeeling, these changes are noticed within a very small geographical extent making the region ideal for elevational pattern studies. The members of Poaceae are one of the dominant and diverse phytolith producers among the phytolith producing angiosperms. In Darjeeling, the C3 grasses dominate the grass assemblages, and thus we have selected Thysanolaena maxima (tiger grass) for the present study as this is a vigorous perennial centothecoid C3 grass growing under a wide range of habitat conditions (like in temperate and sub-tropical regions) with variable soil pH (5.3–9.3), moisture (11.6–37.6%) and soil types varying from sandy loam to clayey loam. We collected naturally growing T. maxima grass along a large elevation gradient (150–2456 m a.s.l.) in the Darjeeling Himalayas with the aims to (1) study and compare the effects of different environmental factors like temperature, precipitation, actual evapotranspiration (AET), potential evapotranspiration (PET), moisture index (MI) and soil pH on phytolith production pattern of T. maxima and (2) find out which of these factors are most important in case of the observed variability in phytolith production along the elevation gradient.

Materials and methods Study area This study was undertaken during 2012–2013 in Darjeeling (26◦ 31 –27◦ 13 N and 87◦ 59 –88◦ 53 E), a hilly district of West Bengal, India (Fig. 1). Being a part of the inner mountain ranges of the eastern Himalaya, the district represents a hilly and rugged terrain. The district falls naturally into two distinct tracts, (1) the Tarai – immediately beneath the hill, one of the low-lying belts of the country, traversed by a number of rivers and streams flowing down from the hills and the ridges and (2) deep valleys of the lower Himalayas. The flora of the district shows distinct variation with the changing elevation. The unique topographical features of the hills as well as the impact of strong moisture laden winds from the south greatly influence the character of the vegetation from place to place. Due to significant variations in geographical location, relief and altitude, the climate of the district varies from hot tropical in the foothills to Arctic cold at the summits. In the plains, the mean annual temperature is around ∼24 ◦ C and drops below 12 ◦ C on the ridges. The summer (April–mid June) is very pleasant in the district when temperature varies between 16 ◦ C and 17 ◦ C. However, winter (December–March) is extremely cold and temperature drops to 5–6 ◦ C. Precipitation is heavy throughout the monsoon (June–September) in all parts of the district though average rainfall varies considerably from place to place, being dependent on a number of local conditions such as the configuration and height of local mountains. Orography is one of the most important factors except seasonality causing the vertical zonation of temperature and precipitation. This significant variation in climatic profiles across the district is brought about by the obstruction in movement of monsoon winds by high mountains (Mani, 1974) and as a result the mountain front is exposed to heavy rainfall, especially the middle parts of the southern hills. No distinct relationship between total rainfall and altitude exists. Windward sides (the southern slopes of the ridges) receive much higher (4000–5000 mm) rainfall than the leeward sides (2000–2500 mm). We collected Thysanolaena maxima plants from 8 different habitats of the Darjeeling Himalayas viz., Mahananda Wildlife Sanctuary, Bamonpokhri, Peshok, Saurene, Manebhanjyang, Sixth mile, Darjeeling and Chitrey (Table 1; Fig. 1) along an elevation gradient of 150–2456 m covering tropical to temperate vegetation belts

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Fig. 1. Location of Darjeeling in northern part of West Bengal, India and sampling location distributed over different elevation in Darjeeling along with the nearest CRU TS 3.21 grid climate data points.

in the Darjeeling Himalayas. The grasses were collected at the end of their annual growing cycles (during the dry season) to ensure better phytolith build up in their tissues. Climate data IMD (Indian Meteorological Department) climatic dataset for the eight sampling sites along the entire elevation gradient is not available. To have a thorough knowledge of the modern climate and its variation along the studied elevation range of the Darjeeling Himalayas, we have selected CRU TS 3.21 (0.5◦ × 0.5◦ gridded) climate data (Harris et al., 2014) which extends from the year 1901 to 2012. Three grid points from the CRU TS 3.21 data set nearest to the study sites are selected for estimation of regional temperature and precipitation (Fig. 1). Climate parameters derived from this dataset are adjusted for the exact location and elevation of the modern sampling sites. In the Eastern Himalayas, a monotonic

decline in temperature with increasing elevation was observed by Acharya et al. (2011) and the rate of this decline along the elevation gradient was estimated to be −0.62 ◦ C for every 100 m rise in elevation. Temperature adjustments are made to the exact sampling locations following Acharya et al. (2011). We also estimated MI (moisture index) which is a measure of the water balance of an area in terms of gain from precipitation and loss from potential evapotranspiration (PET) using the formula MI = PET/mean annual rainfall (MAP), with PET (mm) [mean annual bio-temperature (i.e., temperature >0 ◦ C) × 58.93] following Holdridge et al. (1971), Bhattarai and Vetaas (2003) and Bhattarai et al. (2004). Moisture index was inverted for simplicity, where values above 1 indicate a positive water balance and those below 1 indicate a negative water balance (Vetaas, 2002; Bhattarai and Vetaas, 2003). AET (actual evapotranspiration) which is considered as the surrogate of productivity has been calculated using the equation AET = P/[0.9 + (P/L) 2 ]1/2 with L = 300 + 25 T + 0.05T3 , where P (mm) = mean annual precipitation

Table 1 Sampling sites with their coordinates, altitude and corresponding dominant vegetation. Sites

Coordinates

Mahananda Wildlife sanctuary (MWS)

26◦ 42 43.78 N;88◦ 18 26.78 E ◦





Elevation (m)

◦





150

Bamonpokhri Forest (BPF)

26 49 0.50 N; 88 17 1.19 E

Peshok (PSK)

27◦ 03 56.49 N;88◦ 23 13.13 E

1300

Saurene (SAU)

26◦ 58 49.46 N;88◦ 11 20.32 E

1458

Mane bhanjyang (MAN)

26◦ 59 17.22 N; 88◦ 7 44.28 E

1900

Sixth mile (SM)

27◦ 01 48.17 N;88◦ 19 27.45 E

1961

◦





◦





408

Darjeeling (DAR)

27 0 27.38 N; 88 15 10.02 E

2105

Chitrey (CHIT)

26◦ 59 57.75 N; 88◦ 6 22.89 E

2456

Dominant vegetation Terminalia sp., Lagerstroemia speciosa, Alstonia sp., Shorea robusta, Tectona grandis, Floscopa scandens, Dillenia pentagyna, Lygodium flexuosum Diplazium esculentum, Justicia betonica var. villosa, Pogostemon parviflorus, Ruellia sp., Sida sp., Dendrocalamus sp., Arundinaria sp., Craniotome sp., Bauhinia sp., Rubus ellipticus Panicum repens, Cymbopogon nardus, Oplismenus burmannii, Arundo donax, Centotheca lappacea, Pronephrium sp., Leguminosae, Cedrela toona, Mikania scandens, Cyrtococcum patens Rubus lineatus, Equisetum arvense, Lepidagathis incurva, Palmae, Pteris biaurita Phragmites karka, Thysanolaena maxima, Phlogacanthus thyrsiflorus Carex filicina, Craniotome versicolor, Cryptomeria japonica, Primula sp., Boehemeria sp., Anaphalis subumbellata, Cheilanthes albomarginata, Dicranopteris sp., Polystichum lentum, Polystichum aculeatum Selaginella monspora, Lycopodium clavatum, Betula sp., Senecio scandens, Gaultheria sp., Maesa chisia, Alnus nepalensis Boehmeria sp., Edgaria darjeelingensis, Carex baccans, Sechium edule, Dahlia imperialis, Isachne albens, Muhlenbergia sp., Saccharum sp., Senecio cappa, Anaphalis contorta, Acer sikkimensis, Gynura sp., Solanum jasminoides Selaginella bisulcata, Dicranopteris sp., Gaultheria nummularioides, Elsholtzia strobilifera, Primula sp., Stachyurus himalaicus, Rhododendron grande, Rhododendron arboreum

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Fig. 2. Variations of (a) mean annual precipitation (MAP) and mean annual temperature (MAT) (b) actual evapotranspiration [AET], potential evapotranspiration [PET] and moisture index (MI) (c) soil pH along the studied elevation gradient in Darjeeling.

and T (◦ C) = mean annual temperature (Turc, 1954; Kluge et al., 2006). A monotonic temperature decline with increase in elevation in Darjeeling has been noticed (Fig. 2). However, precipitation does not show a clear trend with respect to elevation variations. The windward sites i.e., Mahananda Wildlife Sanctuary, Bamonpokhri, Peshok, Saurene, Sixth mile and Darjeeling receive more rainfall than leeward sites like Manebhanjyang, and Chitrey (Fig. 2). AET and PET show declining trends along the elevation gradient, whereas the moisture index showed a significant increase up to an elevation of 2456 m (Fig. 2). A general trend of decline with increasing elevation is noticed in soil pH data for all the sampling sites with minor fluctuations (Fig. 2). Phytolith extraction and quantification Following dry ash methodology outlined by Albert and Weiner (2001); cf. Parr et al., 2001), phytoliths were extracted from plant samples with minor modification. Different parts of the specimen (leaf and stem) were cut into small pieces and washed with distilled water in an ultrasonic water bath to remove the debris attached to the surface of the tissues. Specimens were then dried at >100 ◦ C in hot air oven for overnight. After cooling, the dry mass of each specimen was measured using a high precision balance. The dried samples were then chopped and placed on porcelain crucibles and were transferred to a digital muffle furnace for combustion, and maintained at a temperature of about 750–800 ◦ C for 2 h to get the resulting mass of white ash. The ash samples were subsequently subjected to oxidation by boiling with HCl: HNO3 [3(N) in 1:1 ratio]. For acid elimination from the oxidized samples, successive washing cycles were carried out by centrifugation at 3000–5000 rpm for 5 min. After acid elimination, the post acid residues were dried and weighed. The post acid residues were again subjected to oxidation by boiling them with 10 ml of 30% H2 O2 (boiling is continued till the H2 O2 frothing stops) to remove the organic content of the plant tissue. After H2 O2 oxidation, samples were subjected to successive washing cycles to remove the remaining H2 O2 . The resulting masses i.e., post H2 O2 residues were dried at >100 ◦ C overnight and subsequently weighed on a high precision balance. The resulting biominerals formed the AIF (acid insoluble fraction) within which phytoliths were found. Using the AIF, slides were prepared with PVA (Polyvinyl alcohol) as fixative and DPX (Di-n-butyl phthalate) as mounting agent for photomicrography using ‘ZEISS AXIOSKOP 2’ to record significant types of phytoliths found in T. maxima. As combustion of plant parts in such high temperatures (750–800 ◦ C) may alter the physico-chemical properties of phytolith morphs and fuse them (Elbaum et al., 2003; Jenkins, 2009), sometimes deformed phytolith morphs where encountered in large amounts in slides; hence, wet oxidation method was employed (oxidation by boiling with HCl + HNO3 , followed by H2 O2 treatment) to get the original fireunaltered assemblage. More than 300 phytoliths were counted for

each sample and for morphometric analysis at least 50 observations have been taken into consideration in order to calculate the mean values. The morphotypes were named and described according to International Code for Phytolith Nomenclature (Madella et al., 2005). Silica content was calculated as the percentage of dry mass, following the equation:

Silica content(%) = (

ash mass ) × 100 dry matter mass

(Pepi et al., 2012). The measurements for the analyses of variation in size along the climatic and elevational gradients include the length (L) and width (W) of stomate and three-lobates; vertical length of bilobates, lobe width, shank length, shank width; vertical, horizontal and lateral lengths of bulliform types. These measurements were also used to calculate the dimensions of these morphs. A total of ca. 1600 opaline silica particles in T. maxima have been measured.

Statistical analyses Correlations between dimension of some significant phytolith morphotypes produced by T. maxima and environmental variables like MAT (mean annual temperature), MAP (mean annual precipitation), actual evapotranspiration (AET), potential evapotranspiration (PET), moisture index (MI) and soil pH have been tested. The relationships between changing dimensions of these morphotypes and the above-mentioned environmental factors along the studied elevation gradient were assessed using regression analysis to elaborate the effect of these environmental variables on changes in dimension of these phytolith types. In some cases compared to linear models, quadratic regression models were found to be better fits. Quadratic models were selected based on r2 and p values and for the meaningful comparison of the two regression models F-test was performed on the residual sum of squares (Zar, 1999). In every case the outcome of the F-test was considered for selection of the regression model. To further test whether these environmental factors influence phytolith diversity in T. maxima we have performed canonical correspondence analysis (CCA) using CANOCO 4.5 for Windows in the complete phytolith data set from stem and leaves of eight different sampling sites along the elevation gradient. CCA is a multivariate analysis that extracts the relationships between biological assemblages of species and their environment (ter Braak, 1986, 1987; ter Braak and Verdonschot, 1995). A constrained ordination of species data in response to climate variables has been performed by this method. CCA has been used here to reveal the climatic parameters (MAT, MAP, AET, PET, MI and soil pH) that best reflect the principal patterns of variation in the phytolith data.

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Table 2 Comparative account of phytolith production in leaves and stem of Thysanolaena maxima along elevation gradient of the Darjeeling Himalayas (in every case 5 g of the dry weight was taken). Sites

Elevation (m)

Part

Ash (gm)

Ash (%)

Post-acid residue (g)

AIF (g)

Ash-AIF (%)

Dry wt.-AIF (%)

MWS

150

BPF

408

PSK

1300

SAU

1458

SM

1900

MAN

1961

DAR

2105

CHIT

2456

Leaf Stem Leaf Stem Leaf Stem Leaf Stem Leaf Stem Leaf Stem Leaf Stem Leaf Stem

0.346 0.205 0.252 0.116 0.243 0.221 0.266 0.115 0.238 0.052 0.26 0.06 0.092 0.028 0.048 0.06

6.92 4.1 5.04 2.32 4.86 4.42 5.32 2.3 4.76 1.04 5.2 1.2 1.84 0.56 0.96 1.2

0.328 0.096 0.248 0.112 0.204 0.118 0.232 0.108 0.224 0.048 0.23 0.058 0.085 0.024 0.044 0.02

0.2225 0.044 0.2005 0.0545 0.191 0.046 0.143 0.04 0.168 0.033 0.099 0.027 0.0475 0.019 0.037 0.0175

64.30 21.46 79.56 46.98 78.60 20.81 53.57 34.78 70.58 63.46 38.07 45.00 51.63 67.85 77.08 29.16

4.45 0.88 4.01 1.09 3.82 0.92 2.85 0.8 3.36 0.66 1.98 0.54 0.95 0.38 0.74 0.35

Results Variations in phytolith diversity in T. maxima along elevation gradient In Table 2, AIF (acid insoluble fraction) percentages of leaf and stem are expressed in two separate columns as a function of both the plant’s dry weight and ash. Quantitative estimation of biogenic silica of T. maxima along the elevation gradient of the Darjeeling Himalayas revealed that silica content was higher in the leaves than stem (Fig. 3; Table 2). Highest silica content was noticed in leaves (4.45%; dry weight AIF%) and stems (0.88%; dry weight AIF%) of plants growing in lower elevation sites like Mahananda Wildlife Sanctuary (150 m) and Bamonpokhri (408 m) but gradually decreased with increasing elevation (Fig. 3; Table 2). Phytoliths from leaves and stems of T. maxima were categorized into 16 different types (Fig. 4; Table 3). Among the 16 phytolith types, 14 types were observed in leaves and only 5 types were recovered from stems (Table 3). Diversity, frequency (Fig. 5) and size of some leaf and stem phytolith types were found to change significantly along the elevation and climatic gradients in the Darjeeling Himalayas. Only four characteristic morphs such as, bilobate, stomate, cuneiform bulliform and three-lobates which were consistently recovered from T. maxima plants up to an elevation of 1961 m together accounted for 92.1% of all the forms. Rest of the morphotypes from stem and leaf showed inconsistency along the elevation

gradient and hence were not considered for further statistical analyses. Frequencies of different variants of bilobate (both in stem and leaf) were higher than for other phytolith types. Percentages of bilobate, three-lobates and stomate types were found to increase with rising elevation and MI and decreasing soil pH, AET and PET. However, the percentages of cuneiform bulliforms showed a reverse trend. Absence of cuneiform bulliform types above 2100 m elevation in Chitrey (2456 m) was remarkable. Variations of size in phytoliths of T. maxima along elevation gradient Length and width of the stomate types ranged between 4.0–34.2 ␮m and 2.2–15.95 ␮m, respectively, showing a gradual decreasing trend with increasing elevation (Table 4). Threelobate types have also demonstrated a similar trend, where length (11.85–26.85 ␮m) and width (15.80–31.35 ␮m) decreased along the increasing gradient. In case of bilobates, length (12.05–41.95 ␮m) and width (5.20–25.05 ␮m) i.e., the overall dimensions showed a declining trend with increasing elevation, but the shanks of the morphs showed a somewhat different tendency (Table 4). Length (6.25–18.95 ␮m) and width (1.75–12.65 ␮m) of bilobate shanks revealed a contrasting trend, showing increasing and decreasing tendencies, respectively, with elevation (Table 4). Result of correlation analysis between dimensions of stomate, three-lobates, cuneiform bulliform and bilobates along with length and width of bilobate shank and environmental variables showed Table 3 Phytolith morphotypes recovered from Thysanolaena maxima and their acronyms used in this study.

Fig. 3. Dry weight-AIF percentage of leaf and stem parts of Thysanolaena maxima along an elevation gradient of Darjeeling.

Morphotype

Acronym

Plant part

Bilobate 3-Lobate Cross Rondel Saddle Bi-horned tower Multiple toothed Cylindrical sulcate tracheid Elongate spiny Multicellular articulated epidermal cell Point shaped Cuneiform bulliform Parallelepipedal bulliform Stomatal complex Vessel element Flat tower

Bl 3L 4L Rd Sd Bt Mt Cst Esp Mac Ps Cb Pb Sc Ve Ft

Leaf, stem Leaf Leaf Leaf Leaf Leaf Leaf Leaf Leaf, Stem Leaf Leaf, Stem Leaf Leaf Leaf Stem Stem

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Fig. 4. SEM (a–d, f, h, m, n, q–t and v) and light microscopic images (e, g, i, j–l, o, p, u, w–aa) of phytolith morphotypes recovered from leaves and stems of Thysanolaena maxima. Bilobate (a–e, h, I, v); three-lobate (f, g); cross (j, k); multiple toothed (l); bi-horned tower (m, n); point-shaped (o); rondel (p); flat-tower (q); saddle (r); elongate spiny (s, u); cuneiform bulliform (t, w); parallelepipedal bulliform (x); vessel element (y); stomata (z) and cylindrical sulcate tracheid (aa).

no significant association with MAP. Dimensions of stomate, threelobate and bilobate types correlated positively with MAT, AET, PET and soil pH, and negatively with MI. However, the dimensions of bulliform morphotypes showed a reverse trend (Table 5). In case of length and width measurements of bilobate shanks, an opposite trend was noticed, where shank length correlated positively with MI, but negatively with the rest of the environmental parameters. Width of shanks was found to be negatively correlated with MI, and positively correlated with the rest of the environmental variables (Table 5). The relationships between changing environmental factors along the elevation gradient with size variability of each phytolith morphotype are shown in Table 6 and Fig. 6. Stomate dimension showed a positive linear relationship with MAT and soil pH and a negative linear relationship with MI (Fig. 6). Positive log-linear relationships were found to exist with AET and PET. Bulliform dimension illustrated a significant negative linear relationship with MAT and negative log-linear relationship with AET, PET and soil pH. With moisture index, it showed a positive log-linear relationship (Fig. 6). Dimension of three-lobate types also demonstrated a

positive linear relationship with MAT, AET, PET and soil pH and a negative log-linear relationship with MI (Fig. 6). Bilobate dimension showed significant positive linear relationships with MAT and soil pH. With AET and PET, bilobate dimensions exhibited positive log-linear relationships; however, with MI it showed a negative loglinear relationship. Interestingly bilobate shank lengths showed a negative log-linear relationship with AET and PET. With MAT and soil pH it displayed a negative linear trend and with MI a positive linear trend. In contrast, width of bilobate shank represented a positive log-linear relationship with AET and PET, and a positive linear relationship with MAT and soil pH. With MI, it showed a negative linear relationship (Fig. 6). Phytolith assemblages and environmental variables along the elevation gradient Result of canonical correspondence analysis performed on leaf phytolith frequency dataset with the environmental variables showed that the first two axes explained 44.1% and 19% of the total variation in the phytolith dataset (Fig. 7a). The amount of

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Fig. 5. Diversity and frequency distribution (%) of phytolith morphotypes in (a) leaf and (b) stem of Thysanolaena maxima along an elevation gradient of Darjeeling and their relation to environmental parameters. Acronyms used in the figures are: [bilobate = Bl; 3-lobate = 3L; cross = 4L; rondel = Rd; saddle = Sd; bi-horned tower = Bt; multiple toothed = Mt; cylindrical sulcate tracheid = Cst; elongate spiny = Esp; multicellular articulated epidermal cell = Mac; point shaped = Ps; cuneiform bulliform = Cb; parallelepipedal bulliform = Pb; stomatal complex = Sc; vessel element = Ve; flat tower = Ft].

variations explained by the axes as a fraction of total explainable variation is represented by the cumulative percentage of variance of species–environment relation. The two axes when taken together displayed 73.9% of variations that can be explained by the variables. CCA axis 1 accounted for 51.6% of the species–environment relation. The eigenvalues of axis 1 and axis 2 were 0.34 and 0.15, respectively. A fairly high first eigenvalue implies that the first axis represented a fairly strong gradient. The species–environment correlations indicated how much of the variation in the phytolith data on one CCA axis has been explained by the environmental variables. The value 0.98 suggests that the environmental variables accounted for most of the variations in the phytolith data on CCA axis 1. The length and position of the arrows depend on

the eigenvalues and provide information about the relationship between the climatic variables and the derived axes (Jongman et al., 1987). Arrows parallel to an axis indicate a correlation. The length of the arrow reflects the strength of that correlation. Environmental variables with long arrows are more strongly correlated with the ordination axes than those with short arrows. Thus, MAT, AET, PET and MI were strongly related to axis 1 and least to axis 2, with MI being inversely related to MAT, AET and PET. MAP and pH were not highly related to either axis 1 or axis 2. Bilobate, three-lobate and stomate types showed strong relation with moisture index, whereas cuneiform bulliform types demonstrated close association with MAT, AET, PET and pH parameters.

± ± ± ± ± ± ± 9.48 11.91 12.78 14.95 17.69 18.51 19.65 – 2.4 1.7 1.2 2.1 0.6 0.5 0.8 ± ± ± ± ± ± ± 35.77 55.47 63.22 67.52 73.2 4 76.45 77.85 – 2.0 1.3 1.8 0.6 0.9 0.3 0.3 ± ± ± ± ± ± ± 68.49 77.58 85.86 90.71 92.28 95.56 97.50 – 11.4 ± 0.7 8.36 ± 0.3 6.57 ± 0.4 5.53 ± 0.6 3.81 ± 0.4 2.93 ± 0.2 2.42 ± 0.3 2.01 ± 0.1 6.87 ± 0.5 10.75 ± 0.6 12.36 ± 0.3 13.29 ± 0.4 14.51 ± 0.3 15.47 ± 0.3 16.74 ± 0.3 18.48 ± 0.3 23.25 ± 0.9 19.49 ± 1.2 17.58 ± 0.4 13.95 ± 0.5 9.17 ± 0.5 7.39 ± 0.3 6.79 ± 0.5 5.56 ± 0.7 40.14 ± 0.8 37.92 ± 0.8 28.82 ± 0.6 27.29 ± 1.0 21.52 ± 1.1 17.99 ± 0.7 15.14 ± 0.7 12.70 ± 0.3 29.89 ± 0.7 26.12 ± 0.4 22.27 ± 0.9 19.34 ± 0.3 18.37 ± 0.3 16.35 ± 0.3 – – 25.72 ± 0.6 22.14 ± 0.7 18.47 ± 0.3 16.48 ± 0.3 14.98 ± 0.4 12.35 ± 0.3 – – 15.11 ± 0.42 12.89 ± 0.9 10.38 ± 0.29 8.99 ± 0.35 6.98 ± 0.40 4.87 ± 0.5 3.62 ± 0.3 2.71 ± 0.5 150 408 1300 1458 1900 1961 2105 2456 MWS BPF PSK SAU MAN SM DAR CHIT

27.09 ± 4.1 16.84 ± 1.7 11.42 ± 1.1 8.76 ± 0.4 8.01 ± 0.4 6.91 ± 0.5 5.80 ± 0.4 4.77 ± 0.4

Horizontal length (␮m) Vertical length (␮m) Shank width (␮m)

Bulliform

Shank length (␮m) Lobe width (␮m) Vertical length (␮m) Width (␮m)

Bilobate

Length (␮m) Width (␮m)

Three-lobate

Length (␮m) Altitude (m a.s.l.)

Stomate

In the stem phytolith dataset the first two axes explained 82.8% and 4.6% of the total variation (Fig. 7b). The eigenvalues of axis 1 and axis 2 were 0.11 and 0.01, respectively. CCA axis 1 accounted for 93.2% of the species-environment relation. In Fig. 7b, the climatic variables MAT, AET and PET showed strong relations with axis 1 and least related to axis 2, while MI was found to be inversely related to these variables and was related to axis 1, whereas MAP did not show any relation to axes 1 and 2. Soil pH demonstrated a comparatively stronger relationship with axis 1 than MAP. Bilobate types were found to be closely associated with MI as well as with axis 1.

Discussion

Sites

Table 4 Measurement data of significant phytolith morphotypes of Thysanolaena maxima (mean ± standard deviation; n = 50).

0.8 0.6 0.2 0.5 0.4 0.3 0.4

S.B. Dey et al. / Flora 211 (2015) 51–61 Lateral length (␮m)

58

Our results on the phytolith diversity and size variability in T. maxima demonstrate a complex influence of different environmental factors on the phytolith formation of this C3 grass. Before discussing on the effects of different environmental factors on the observed phytolith diversity and variability in this C3 grass, a brief knowledge of the relationship of elevation and different environmental variables is essential. A strong negative correlation between MAT, AET and PET with elevation (r = −0.99) reveals the elevation gradient to be a climate-energy gradient, where PET and AET show a positive linear relation to MAT (Lambert and Chitrakar, 1989). Generally, rainfall is commonly found to have a relatively complex pattern (Lomolino, 2001) and it appears to be statistically independent of the linear change of energy along the studied gradient in the Darjeeling Himalayas, where orographic factors influence rainfall. Dry acid insoluble fractions of silica retrieved from leaves and stems of T. maxima from the eight sites along an elevation gradient show a decrease with rising elevation. Leaves are found to deposit more silica than stems in T. maxima. As silica formation in grasses is related to abrasiveness (Massey et al., 2006), hence a diminishing trend of abrasiveness with increasing elevation may be inferred. However, to answer the question as to what drives this decrease in abrasiveness with rising elevation, we have to understand the influence of different environmental factors on silica formation in T. maxima in Darjeeling. A strong influence of climatic and energy related factors (temperature, AET, PET and MI) on the elevational pattern of phytolith size variability in Darjeeling has also been observed. Prevalence of comparatively larger phytoliths at low elevation sites may be correlated to the larger available intercellular space for silica precipitation. These larger available intercellular spaces may be attributed to higher photosynthetic productivity. Temperature is one of the principle controlling factors of plant productivity, with large effects on physiological activity. However, temperature ranging between 0 and 30 ◦ C do not have any stressful effect on photosynthetic rates in C3 grasses, which actively grow in the winter and early spring seasons at high altitudes and latitudes (Regehr and Bazzaz, 1976; Mawson et al., 1986; Larcher 2003). As the sites along the studied elevation gradient show a MAT ranging between 10 and 25 ◦ C, hence, the decrease in size and frequency of phytolith morphs at higher elevation sites is probably not due to changes only in temperature regime vis-à-vis net photosynthetic productivity. Climatic variables related to energy, such as temperature and PET (typically used as measures of energy) and AET (surrogate of primary productivity) in combination with water availability are most commonly related to photosynthesis as well as productivity of plants. When water is adequate, photosynthesis will not be restricted by low stomatal conductance, while under limited water supply stomatal closure slows down photosynthesis. Higher temperature, low moisture content in the air and high solar irradiance may result in a higher rate of evapotranspiration. Larger sized and higher frequencies of phytolith types of T. maxima in the sites from comparatively lower elevation zones may be due to higher photo-

S.B. Dey et al. / Flora 211 (2015) 51–61

59

Table 5 Pearson’s correlation coefficients between environmental drivers and phytolith morphotype measurements in Thysanolaena maxima.

Dimension stomate Dimension 3-lobate Dimension bilobate Bilobate shank L Bilobate shank W Dimension bulliform **

MAT (◦ C)

MAP (mm)

AET (mm yr−1 )

PET (mm yr−1 )

MI

pH

0.932** 0.982** 0.988** −0.973** 0.981** −0.964**

0.170 −0.005 0.224 −0.265 0.216 −0.093

0.948** 0.984** 0.991** −0.966** 0.982** −0.957**

0.932** 0.982** 0.988** −0.973** 0.981** −0.964**

−0.859** -0.957** −0.955** 0.963** −0.954** 0.925**

0.936** 0.880** 0.891** −0.925** 0.927** −0.845**

p < 0.01.

Fig. 6. The relationships between dimension of stomates, bulliform cells, three-lobates and bilobates, shank length and shank width of bilobates, in leaves of Thysanolaena maxima with mean annual temperature (MAT), actual evapotranspiration (AET), potential evapotranspiration (PET), moisture index (MI) and soil pH. Only relationships with p < 0.05 and p < 0.01 are represented. The fitted lines are based on Generalized Linear Models with a significant F-test.

synthetic productivity by the plants owing to adequate rainfall, low atmospheric humidity and higher temperature and to higher rate of transpiration, while, in the studied elevational gradient of Darjeeling, declining trends in temperature along the gradient facilitates low evapotranspiration and high MI. In Darjeeling, where rainfall is adequate throughout the district except for a variation due to orographic factors, stronger

influence of climatic and energy related factors (temperature, evapotranspiration and MI) on the elevational pattern of phytolith production of T. maxima is noticed over any single factor like rainfall. Possibly lower rate of evapotranspiration at higher elevation sites causes low silica uptake by plants, which perhaps indirectly influences phytolith frequency and size variability in T. maxima. Similarly, higher rate of evapotranspiration in the low elevation

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S.B. Dey et al. / Flora 211 (2015) 51–61 Table 6 Summary of simple linear regression when related to environmental factors like MAT (mean annual temperature), actual evapotranspiration (AET), potential evapotranspiration (PET) and moisture index (MI) and soil pH. MAT (◦ C) Dimension: stomate r2 p

AET (mm yr−1 )

PET (mm yr−1 )

MI

0.868** 0.899** 0.001 <0.001

0.867** 0.001

Dimension: three-lobate r2 p

0.938** 0.925** <0.001 <0.001

0.938** <0.001

0.950** 0.772* <0.001 0.004

Dimension: bilobate r2 p

0.976** 0.981** <0.001 <0.001

0.976** <0.001

0.911** 0.794* <0.001 0.003

Shank length: bilobate r2 P

0.946** 0.932** <0.001 <0.001

0.946** <0.001

0.926** 0.855** <0.001 0.001

Shank width: bilobate r2 p

0.962** 0.965** <0.001 <0.001

0.962** <0.001

0.910** 0.860** <0.001 0.001

Dimension: bulliform r2 p

0.928** <0.001

0.928* <0.001

0.855** 0.713* 0.003 0.002

* **

0.915* 0.001

0.737* 0.006

pH

0.876** 0.001

p < 0.05. p < 0.01.

soil pH value, phytolith types show a considerable enlargement in sizes. From this study we can conclude that the observed variability in morphometric features, frequency and production pattern of phytoliths in T. maxima is principally dependent on the balance in energy-water regime and to some extent on soil pH. Conclusions

Fig. 7. CCA biplot showing phytolith morphotypes recovered in (a) leaves and (b) stem of Thysanolaena maxima along an elevation gradient of Darjeeling and their relationship with climatic variables. Climatic variables are represented by arrows and phytolith types by dark dots.

sites (Bhattarai and Vetaas, 2003) facilitates comparatively high silica uptake by plants and thus the increase in frequency of bulliform cells. In this study we found that the width and overall dimension of bulliform increases with elevation which points toward low or no moisture stress. This might be the reason behind the higher recovery of stomate types in high elevation sites (Manebhanjyang, Peshok, Sixth Mile, Darjeeling and Chitrey) and a low occurrence of cuneiform bulliform types. Soil pH is another significant environmental factor influencing the phytolith formation. It has been observed that size of the phytolith morphs in Leymus chinensis increased with a rise in pH values (Jie et al., 2010). We also observed that at comparatively high

Phytolith diversity and size variability observed along the studied elevation gradient (150–2456 m) in the Darjeeling Himalayas depicted a complex influence of different environmental factors on phytolith formation of T. maxima where climate and energy related factors (MAT, AET, PET and MI) were found to influence the observed variability in phytolith assemblages along the elevation gradient and size variability of the morphs. As the region receives high precipitation throughout, rainfall is not found to be a limiting factor for phytolith formation. However, a complex interactive effect of water availability and evapotranspiration was observed to be influencing for phytolith formation the most in T. maxima. The study demonstrates that the diversity and morphometric attributes of phytoliths of T. maxima are plastic in their response to different environmental factors. For better understanding the physiological mechanisms involved in this observed variability in phytolith spectra in T. maxima, a future in house experimental study employing different environmental factors such as temperature, water availability, evaporation and transpiration rate etc. and silica availability along the elevation gradient will be helpful. Acknowledgements SB, SD and BM thankfully acknowledge financial assistance by SERB, New Delhi (Sanction no. SERB/SR/SO/PS/94/2010, dt. 28/05/2012). RG and MS thankfully acknowledge the Director, Birbal Sahni Institute of Palaeobotany for his encouragement, support and permission to publish this work (permission no. BSIP/RDCC/publication no. 38/2014-15). Ms Meghma Bera is thankfully acknowledged for her help in language editing of the manuscript.

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References Acharya, B.K., Chettri, B., Vijayan, L., 2011. Distribution pattern of trees along an elevation gradient of Eastern Himalaya, India. Acta Oecol. 37, 329–336. Agarie, S., Agata, W., Uchida, H., Kubota, F., Kaufman, P., 1996. Function of silica bodies in the epidermal system of rice (Oryza sativa L.): testing the window hypothesis. J. Exp. Bot. 47, 655–660. Albert, R.M., Weiner, S., 2001. Study of opal phytoliths in prehistoric ash layers using a quantitative approach. In: Meunier, J., Coline, F. (Eds.), Phytoliths: Applications in Earth Sciences and Human History. Lisse, The Netherlands: Balkema, pp. 251–266. Bauer, P., Elbaum, R., Weiss, I.M., 2011. Calcium and silicon mineralization in land plants: transport structure and function. Plant Sci. 180, 746–756. Bhattarai, K.R., Vetaas, O.R., 2003. Variation in plant species richness of different life forms along a subtropical elevation gradient in the Himalayas east Nepal. Glob. Ecol. Biogeogr. 12, 327–340. Bhattarai, K.R., Vetaas, O.R., Grytnes, J.A., 2004. Fern species richness along a central Himalayan elevational gradient, Nepal. J. Biogeogr. 31, 389–400. Das, S., Ghosh, R., Bera, S., 2013. Application of non-grass phytoliths in reconstructing deltaic environments: a study from the Indian Sunderbans. Palaeogeogr. Palaeoclimatol. Palaeoecol. 376, 48–65. Das, S., Ghosh, R., Paruya, D.K., Yao, Y.F., Li, C.S., Bera, S., 2014. Phytolith spectra in respiratory aerial roots of some mangrove plants of the Indian Sunderbans and its efficacy in ancient deltaic environment reconstruction. Quat. Int. 325, 179–196. Elbaum, R., Weiner, S., Albert, R.M., Elbaum, M., 2003. Detection of burning of plant materials in the refractive indices of siliceous phytoliths. J. Archaeol. Sci. 30, 217–226. Epstein, E., 1999. Silicon: its manifold roles in plants. Ann. Appl. Biol. 155, 155–160. Ghosh, R., Naskar, M., Bera, S., 2011. Phytolith assemblages of grasses from the Sunderbans: India and their implications for the reconstruction of deltaic environments. Palaeogeogr. Palaeoclimatol. Palaeoecol. 311, 93–102. Harris, I., Jones, P.D., Osborn, T.J., Lister, D.H., 2014. Updated high-resolution grids of monthly climatic observations – the CRU TS 3.10. Int. J. Climatol. 34, 623–642. Hattori, T., Inanaga, S., Araki, H., An, P., Morita, S., Luxova, M., Lux, A., 2005. Application of silicon enhances drought tolerance in Sorghum bicolor. Phys. Plant 123, 459–466. Hodson, M.J., White, P.J., Mead, A., Broadley, M.R., 2005. Phylogenetic variation in the silicon composition of plants. Ann. Bot. 96, 1027–1046. Holdridge, L.R., Grenke, W.C., Hatheway, W.H., Liang, T., Toshi Jr., J.A., 1971. Forest environment in tropical life zones—a pilot study. Pergamon Press, New York. Jenkins, E., 2009. Phytolith taphonomy: a comparison of dry ashing and acid extraction on the breakdown of conjoined phytoliths formed in Triticum durum. J. Archaeol. Sci. 36, 2402–2407. Jenkins, E., Jamjoum, K., Nuimat, S., 2011. Irrigation and phytolith formation: an experimental study. In: Mithen, S., Black, R. (Eds.), Water, Life and Civilisation: Climate, Environment and Society in the Jordan Valley. Cambridge University Press, Cambridge, UK, pp. 347–372. Jie, D.M., Liu, Z.Y., Shi, L.X., Liu, H.M., Ge, Y., 2010. Characteristics of phytoliths in Leymus chinensis from different habitats on the Songnen Plain in Northeast China and their environmental implications. Sci. Chi. (Earth Sci.) 53, 984–992. Johnston, A., Bezeau, L.M., Smoliak, S., 1967. Variation in silica content of range grasses. Can. J. Plant Sci. 47, 65–71. Jongman, R.H.G., ter Braak, C.J.F., van Tongeren, O.F.R., 1987. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge, U.K. Katz, O., Lev-Yadun, S., Bar (Kutiel), P., 2013. Plasticity and variability in the patterns of phytolith formation in Asteraceae species along a large rainfall gradient in Israel. Flora 208, 438–444. Kindomihou, V., Meerts, P., Kjelgren, R., Sinsin, B., 2010. Effects of moisture stress on leaf silicification of three tropical fodder grass species (Pennisetum purpureum, Panicum maximum Cv. C1 and P. maximum Jacq.) in Republic of Benin (West Africa). Am. Eurasian J. Agric. Environ. Sci. 8, 530–537. Kluge, J., Kessler, M., Dunn, R.R., 2006. What drives elevational patterns of diversity? A test of geometric constraints, climate and species pool effects for pteridophytes on an elevational gradient in Costa Rica. Glob. Ecol. Biogeogr. 15, 1–14.

61

Lambert, L., Chitrakar, B.D., 1989. Variation in potential evaporation with elevation in Nepal. Mt. Res. Dev. 9, 145–152. Larcher, W., 2003. Physiological Plant Ecology, 4th ed. Springer, Berlin, Germany. Li, N.N., Jie, D.M., Ge, Y., Guo, J.M., Liu, H.Y., Liu, L.D., Qiao, Z.H., 2014. Response of phytoliths in Phragmites communis to elevated CO2 concentration in Songnen Grassland China. Quat. Int. 321, 97–104. Liu, L., Jie, D.M., Liu, H.Y., Li, N., Guo, J., 2013. Response of phytoliths in Phragmites communis to humidity in NE China. Quat. Int. 304, 193–199. Lomolino, M.V., 2001. Elevation gradients of species-richness, historical and prospective views. Glob. Ecol. Biogeogr. 10, 3–13. Madella, M., Alexandre, A., Ball, T., 2005. INternational code for phytolith nomenclature 1.0. Ann. Bot. 96, 253–260. Madella, M., Jones, M.K., Echlin, P., Powers-Jones, A., Moore, M., 2009. Plant water availability and analytical microscopy of phytoliths: implications for ancient irrigation in arid zones. Quat. Int. 193, 32–40. Madella, M., Lancelotti, C., Osterrieth, M. (Eds.), 2012. Quat. Int. 287, 1–2. Mani, M.S., 1974. Ecology and Biogeography in India. Dr. W Junk Publishers, The Hague. Massey, F.P., Ennos, A.R., Hartley, S.E., 2006. Silica in grasses as a defence against insect herbivores: contrasting effects on folivores and a phloem feeder. J. Anim. Ecol. 75, 595–603. Mawson, B.T., Svoboda, J., Cummins, R.W., 1986. Thermal acclimation of photosynthesis by the arctic plant Saxifraga cernua. Can. J. Bot. 64, 71–76. Mayland, H.F., Wright, J.L., Sojka, R.E., 1991. Silicon accumulation and water uptake by wheat. Plant Soil 137, 191–199. Mayland, H.F., Johnston, D.A., Asay, K.H., Read, J.J., 1993. Ash carbon isotope discrimination, and silicon as estimators of transpiration efficiency in crested wheatgrass. Austr. J. Plant Phys. 20, 361–369. Melo, S.P., Korndörfer, G.H., Korndörfer, C.M., Lana, R.M.Q., Santana, D.G., 2003. Silicon accumulation and water deficit tolerance in Brachiaria grasses. Sci. Agric. 60, 755–759. Mitani, N., Ma, J.F., 2005. Uptake system of silicon in different plant species. J. Exp. Bot. 56, 1255–1261. Mithen, S., Jenkins, E., Jamjoum, K., Nuimat, S., Nortcliff, S., Finlayson, B., 2008. Experimental crop growing in Jordan to develop methodology for the identification of ancient crop irrigation. World Archaeol. 40, 7–25. Mittermeier, R.A., Gill, P.R., Hoffman, M., Pilgrim, J., Brooks, T., Mittermeier, C.G., Lamoreux, J., da Fonseca, G.A.B., 2005. Hotspots Revisited: Earth’s Biologically Richest and Most Endangered Terrestrial Ecoregions. CEMEX, Mexico. Parr, J.F., Dolic, V., Lancaster, G., Boyd, W.E., 2001. A microwave digestion method for the extraction of phytoliths from herbarium specimens. Rev. Palaeobot. Palynol. 116, 203–212. Pearsall, D.M., 2000. Paleoethnobotany: A Handbook of Procedures, 2nd ed. San Diego, Academic Press. Pepi, M.G.F., Arriaga, M.O., Zucol, A.F., 2012. Leaf anatomy and biomineralization in Empetrum rubrum Valh ex Willd (Ericaceae). Bot. Complutensis 36, 113–121. Piperno, D.R., 2006. Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists. AltaMira Press, Cambridge, UK. Regehr, D.L., Bazzaz, F.A., 1976. Low temperature photosynthesis in successional winter annuals. Ecology 57, 1297–1303. Rosen, A.M., Weiner, S., 1994. Identifying ancient agriculture: a new method using opaline phytoliths from emmer wheat. J. Archaeol. Sci. 21, 125–132. ter Braak, C.J.F., 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179. ter Braak, C.J.F., 1987. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetation 69, 69–77. ter Braak, C.J.F., Verdonschot, P.F.M., 1995. Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat. Sci. 57, 255–289. Turc, L., 1954. Le bilan d’eau des sols: relation entre les precipitation: l’evaporation et l’ecoulement. Ann. Agron. 5, 491–596. Vetaas, O.R., 2002. Realized and potential climate niches: a comparison of four Rhododendron tree species. J. Biogeogr. 29, 545–554. Webb, E.A., Longstaffe, F.J., 2002. Climatic influences on the oxygen isotopic composition of biogenic silica in prairie grass. Geochim. Cosmochim. Acta 66, 1891–1904. Zar, J.H., 1999. Biostatistical Analysis. Prentice Hall International, USA.