IAWA Journal, Vol. 31 (2), 2010: 121–138

PCA OF CITES LISTED SANTALINUS (LEGUMINOSAE)

Ian R. MacLachlan1,2 and Peter Gasson2*

SUMMARY

Pterocarpus santalinus L. f. is endemic to south eastern where it is known as ‘red sandal wood’ or ‘red sanders wood’. A highly valued timber for its structural and medicinal properties P. santalinus is listed under CITES Appendix II, reflecting the possibility of extinction should unregulated trade continue. Currently timber identification uses compara- tive wood anatomy. Some P. santalinus specimens are very distinct from closely related species, but this is not always the case. PCA was applied to data on 17 wood anatomical characters and one physical character of several Pterocarpus species including P. santalinus. A comparative description of P. santalinus is presented using the same data as PCA. The primary quantitative outcome was discrete clustering of P. santalinus in PCA axes scores plots, distinguishing it from the other included species. PCA eigenvector data indicated which characters were responsible for the greatest amount of variance in the data set. With simple modifica- tions PCA has considerable potential in quantitative wood anatomy as a complementary technique to comparative wood anatomy for the identi- fication of cryptic wood specimens. Key words: Pterocarpus santalinus, Dalbergioideae, Leguminosae, red sandal wood, CITES, quantitative wood anatomy, principal components analysis, morphometrics.

INTRODUCTION

Pterocarpus Jacq. is a long-recognised, well defined pantropical genus of forest , containing 35–40 species (Klitgaard & Lavin 2005), but estimates vary from 20 (Rojo 1972) to 60 species (Chauhan & Vijendra Rao 2003). Five species, exclusively with winged , including P. santalinus L.f., are restricted to south and southeast Asia. Rojo (1972) provides the most recent taxonomic revision of Pterocarpus, recognis- ing 20 species, although he is regarded to have reduced a number of “good” species to synonyms (B.B. Klitgaard & G.P. Lewis, personal communication). Rojo (1972) suggests inclusion of all Asian Pterocarpus taxa under two forms of P. indicus Willd.; forma indicus and forma echinatus, with binomials for the remaining Asian Pterocarpus

1) Royal Botanic Gardens Edinburgh, 20 Inverleith Row, EH3 5LR, Edinburgh, U.K. 2) Jodrell Laboratory, Royal Botanic Gardens Kew, Kew Road, TW9 3DS, Richmond, Surrey, U.K. *) Corresponding author [E-mail: [email protected]].

Downloaded from Brill.com09/28/2021 08:56:13AM via free access 122 IAWA Journal, Vol. 31 (2), 2010 taxa placed in synonymy under each. This conflicts with the widely accepted of Prain (1900) who recognised five Asian Pterocarpus species. Pterocarpus santalinus is endemic to a 0.2 million hectare area (Lohi Das & Daya- nand 1984; Raju & Nagaraju 1999) in the south East Indian state of (Gamble 1902; Troup 1921; Lohi Das & Dayanand 1984; Raju & Nagaraju 1999). This species grows exclusively on quartzite (80%) and shale (20%) substrates of the hills (Raju & Nagaraju 1999) in deciduous tropical savannah woodland, forming a small to medium-sized with a dense, rounded crown attaining a height of 10–15 m and girth of 90–160 cm. The bark is dark brown, divided into rectangular plates and deeply fissured (Gamble 1902; Jain 1996), are pinnately compound with 3–5 oval-orbicular leaflets and the yellow are of typical papilionoid form. Slow growth results in very heavy, dense heartwood, with air-dry specific gravity ranges of 0.87–1.2 (Gupta & Uniyal 2003) and 1.04–1.15 (Chauhan & Vijendra Rao 2003). Pterocarpus santalinus heartwood is also extremely decay and insect resist- ant, making it a highly prized commodity. Two classes of P. santalinus wood exist: [high] ‘quality’ and ‘non-quality’ (Troup 1921; Lohi Das & Dayanand 1984). ‘Quality’ timber has wavy grain, traditionally demanded for small carvings and furniture where a figured appearance is desirable, attracting a considerable financial premium over straight grained ‘non-quality’ timber. Historically, P. santalinus has been used to produce textile dyes, owing to the presence of red ‘santalin’ heartwood pigments (Mulliken & Crofton 2008), but this was largely supplanted in the late 19th Century by synthetic dyes (Gamble 1902; Pearson & Brown 1932; Green 1995). Preparations and extracts of P. santalinus heartwood have widely documented uses in traditional Indian ayurvedic medicine for treatment of skin diseases, ulcers, swellings, bilious infections, fever, diarrhoea, vomiting, leprosy, diabetes, mental problems and even poisoning (Perry 1980; Raju & Nagaraju 1999; Gupta & Uniyal 2003; Jadhav 2008). The treatment of diabetes is particularly important because P. santalinus contains very high concentra- tions of pterostilbene, a notable insecticidal and anti-diabetic polyphenolic compound (Seshadri 1972). Despite protection as a reserved species some years prior to Troup (1921), P. santali- nus was overexploited during the 1950’s and 1960’s in search of quality timber for ex- port (Green 1995). Further protection was assigned under Andhra Pradesh preservation of private forest rules (1978) and a felling ban in state forests since 1982 (Mulliken & Crofton 2008). Pterocarpus santalinus is classified as ‘endangered’ by the 1997 International Union for the Conservation of Nature Red List of Threatened Species (Walter & Gillet 1998), and was first listed in legislation of the Convention on Interna- tional Trade in Endangered Species (CITES) on 16th February 1995 under Appendix II to regulate trade in logs, wood chips and unprocessed broken material. On 13th September 2007 this was amended to include powder and extracts of P. santalinus (www.unep-wcmc.org). According to Mulliken and Crofton (2008) international trade of P. santalinus is now dominated by wood chips, extracts, carvings and appar- ently illegal timber. Currently CITES has limited resources to guide the identification of Pterocarpus wood. The ‘CITES Identification Guide – Tropical ’ (CITES 2002) dedicated

Downloaded from Brill.com09/28/2021 08:56:13AM via free access MacLachlan & Gasson — PCA of Pterocarpus 123 to identification of tropical timbers (including P. santalinus) uses low magnification photographs and keys for operatives e.g. customs officers, to assess the requirement for further clarification of specimen identity. However, this publication assumes greater expertise than can be expected of its users and contains insufficient detail for systematic wood anatomy. Additionally the CITESwoodID database (Richter et al. 2007; Koch et al. 2008) does not include P. santalinus, and P. macrocarpus Kurz (a non-CITES listed species) is the only representative of the genus. Molecular techniques have also been recognised as a promising tool for CITES wood identification (Koopman & Diemont 2004), but until the recent work of Ogden et al. (2008) a tentatively suitable DNA extraction, amplification and analysis protocol to identify wood samples for law enforcement purposes has been lacking. Several previous comparative wood anatomy studies including Gamble (1902), Pearson & Brown (1932), Ramesh Rao & Purkayastha (1972) and Chauhan & Vijendra Rao (2003) treat the genus Pterocarpus in Asia from forestry, taxonomic and medici- nal perspectives, but their descriptions of P. santalinus are unsatisfactory for CITES application. Pearson and Brown (1932) provide an extremely detailed description of P. santalinus wood anatomy including large numbers of measured characters, but this description is derived from only one specimen. By comparison, Gamble (1902) and Ramesh Rao and Purkayastha (1972) provide descriptive accounts of P. santalinus using recognised comparative wood anatomy characters. Chauhan and Vijendra Rao (2003) include measurements of specific gravity, tangential vessel diameter, vessel element length, number of crystal locules per chain, fibre length, fibre diameter, ray width and height (the latter two being expressed as number of cells and in µm). Conventional approaches to quantitative wood anatomy are usually applied to ecological rather than taxonomic questions, where parametric statistics are used to elu- cidate relationships between variables and environmental gradients, for example De Micco et al. (2007), De Micco (2008) and Lens et al. (2003). Although applications of multivariate numerical techniques have been effectively applied to taxonomic problems for several decades (Dunn & Everitt 1982), they have been limited in quan- titative wood anatomy. Two examples include Coulaud (1989) who compared the wood anatomy of genera in the Loganiaceae using fourteen quantitative variables, and Terrazas et al. (2008) who applied multivariate analyses to Buddleja L. (Buddlejaceae) wood anatomy in a combined ecological and taxonomic study. Both these studies showed clustering patterns for taxa that were obvious but not discrete in discriminant axes plots. The aim of our study was to test whether P. santalinus wood can be reliably delimited from other related timbers using multivariate morphometric techniques. The objec- tives were three-fold: 1) To expand on current applications and develop a repeatable approach for the use of multivariate techniques in quantitative wood anatomy; 2) To establish whether multivariate wood anatomy is suitable for delimiting P. santalinus from closely related species; 3) To provide a comparative description of P. santalinus, based on several specimens incorporating quantitative data and other appropriate ob- servations for CITES identification purposes.

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MATERIALS AND METHODS

Twenty-one Pterocarpus specimens from the Royal Botanic Gardens Kew (RBGK) Economic Botany Collection were processed for data acquisition, including all five Asian Pterocarpus species accepted by Prain (1900) and Rojo (1972), and two African Pterocarpus species (see Table 1 for specimen details). Sectioning blocks, approximately 1 cm3 were boiled in water to soften. After three weeks of daily boiling, P. santalinus remained too hard to section, so we used the wood softening technique of Kukachka (1977) employing ethylenediamine solution, modified to a 5% solution of ethylene- diamine diluted in distilled water and under a 1000 mb vacuum for 20–40 hours.

Table 1. Specimens of Pterocarpus from which data were collected for PCA.

Species RBG Kew economic Origin botany collection specimen number P. dalbergioides 6655 Andaman Islands, India P. dalbergioides 6657 Andaman Islands, India P. dalbergioides 6671 India P. erinaceus 6660 Shupanga, East Africa P. erinaceus 6662 Gambia P. indicus 6673 Thailand P. indicus 6674 North Borneo P. indicus 8262 Singapore P. macrocarpus 6666 Burma P. macrocarpus 6684 Burma P. macrocarpus 70552 Upper Burma P. marsupium 6689 India P. marsupium 6693 Sri Lanka P. osun 6702 Nigeria P. osun 38448 Nigeria P. santalinus 6712 Ceylon & Coromandel, India P. santalinus 6713 Bengal, India P. santalinus 6716 North Arcot, India P. santalinus 6717 Cuddapah, India P. santalinus 21363 Madras, India P. santalinus 21364 Unknown

Microscopy and Measurements We identified 17 wood anatomical characters (variables) and one physical char- acter (specific gravity) for analysis, based on defined characters from the IAWA list of microscopic features for hardwood identification (IAWA Committee 1989) (see Table 2). For several characters (including vessel frequency, tangential vessel diameter, rays mm-1 and ray height) discrete IAWA list characters were measured and analysed as actual values, because arbitrary size classes are potentially insensitive to inter and intraspecific variation. Characters not covered in the IAWA list were selected to be rigorous and easily repeatable.

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Camera lucida drawings were used to make transverse section (TS) measurements superimposed on a 50 × 500 mm grid corresponding to a 1 × 10 mm transect on the slide. Cell and crystal counts made in tangential longitudinal section (TLS) used a 1 mm line transect. TS observations, counts and measurements were made at 40× mag- nification, whereas 100× magnification was used for TLS counts. Two criteria were applied to influence transect position: firstly to ensure the longest TS transect possible (maximum 10 mm) and secondly to avoid damage or decay, other- wise transect position in TS or TLS was determined randomly. Data were collected from five replicates (slides) of each specimen, to gain a greater sample size per specimen, whilst reducing the impact of factors including differences in transect situation and vessel wall damage. Specific gravity was calculated using mass (g)/volume cm3 of sectioning blocks at 11% moisture content. Block volume was measured using the water displacement method (Wiemann & Williamson 1989; Chave 2005).

Manipulation of raw data Counts and measurements for each continuous variable from the five replicates of each specimen were summed, generating 25 values per specimen for variables requir- ing calculation of mean values, but for which only five values were recorded per slide. Values for some continuous variables were calculated from basic counts and measure- ments (see Table 2 for calculations). To determine equality of data distribution between variables the Anderson-Darling normality test was performed on all continuous data in the statistical package Minitab (version 15, Minitab Inc. USA). Continuous variable data that were likely to be not normally distributed (Anderson-Darling p < 0.05) were Log10 transformed in Microsoft Office Excel and re-tested for normality. Subsequently all continuous variable [either raw or Log10 transformed] data were likely to be normally distributed (Anderson- Darling p > 0.05).

Principal Components Analysis (PCA) PCA was based on the methodology of Möller et al. (2007) and carried out in the statistical package R-pack Le Progiciel R.4.0d10 (http://www.bio.umontreal.ca/ Casgrain /en/labo/R/v4/progress.html). PCA was implemented through a correlation matrix using 16 variables (axial parenchyma storeying and ray storeying were excluded) and 21 Pterocarpus specimens to account for the inclusion of variables expressed on different scales and produce variables without physical dimensions (Everitt & Dunn 2001). Eigenvalues, eigenvectors and principal component (PC) axes scores were produced for the data set. Scree testing of the PCA eigenvalues (Cattell 1966; Nelson 2005) indicated that PCs 1–3 would be suitable for subsequent data analysis. Eigenvectors indicate the proportion of variance attributable to each variable in a data set. PC 1–3 eigenvectors were exported to Excel and negative eigenvector values were converted to equivalent positive values (negative values hold equal variation to corresponding positive values) to prevent misinterpretation of informative informa- tion through subsequent ranking. Eigenvectors for the first three PC axes were then

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Table 2. Variables used in PCA analysis; their types, states, coding or units of measurement and calculations used where applicable.

Variable Variable Variable states Coding/ Calculation used type Units

Wood porosity Discrete Ring porous 0 – Semi-ring porous 1 – Diffuse porous 2 – Diffuse apotracheal axial Discrete No 0 – parenchyma Yes 1 – Paratracheal axial Discrete Winged-aliform 0 – parenchyma mostly Confluent 1 – winged-aliform or confluent Evidence of uniform Discrete No 0 – apotracheal axial Yes 1 – parenchyma radial flattening Axial parenchyma Discrete No 0 – storeying Yea 1 – Ray storeying Discrete Regularly storied 0 – Irregularly storied 1 – Uniform fibre wall Discrete Not uniform 0 – thickness Uniform 1 – Fibre wall thickness Discrete Very thin-walled 0 – Thin- to thick-walled 1 – Very thick-walled 2 – Percentage of vessels Continuous Actual value % (No. of solitary vessels/ solitary Total no. of vessels) x100 Tangential vessel Continuous Mean µm Summed vessel diameter/ diameter No. of vessels Vessel frequency Continuous Actual value mm-2 Total no. of vessels/TS transect area Vessel frequency/ Continuous Actual value N/A Vessel frequency/Mean Mean tangential tangential vessel diameter vessel diameter Parenchyma band width Continuous Mean No. of Summed parenchyma band cells width/No. of parenchyma bands Rays mm-1 Continuous Mean No. of Total no. of rays/25 rays mm-1 (transect length mm) Percentage of biseriate Continuous Actual value % (No. of biseriate rays/ rays Total no. of rays) x 100 Ray height Continuous Mean No. of Summed ray height/Total cells no. of rays No. of crystals in axial Continuous Mean No. of Summed no. of crystals/25 parenchyma crystals (transect length mm) mm-1 Specific gravity Continuous Mean N/A Summed SG/No. of sec- tioning blocks for which it was measured

Downloaded from Brill.com09/28/2021 08:56:13AM via free access MacLachlan & Gasson — PCA of Pterocarpus 127 multiplied by the proportion of variation for which their respective PC was responsible. This generated new eigenvector values for each variable that were proportional to the variance each respective PC contributes. Proportional eigenvector values were summed across PCs 1–3 (penultimate column, Table 4) and used to rank the whole PC 1–3 data set, as a simple indication of which variables were responsible for the greatest overall proportion of variance. Principal component axes scores were plotted as scatter plots of PCs 1 vs 2 and 1 vs 3 to identify clustering patterns.

RESULTS Preliminary observations Macroscopic characters were relatively consistent within species. Heartwood varies from a dark red/purple colour in P. santalinus to light brown in P. marsupium Roxb.; remaining species were intermediate. Where present, sapwood was a pale brown to cream colour in all species. Indistinct growth rings were present in some specimens, requiring magnification for confirmation. Wide parenchyma bands, paler than surrounding fibre tissue were macroscopically visible in P. erinaceus Lam. (Kw 6660), P. marsupium (Kw 6689) and P. osun Craib (Kw 6702). Diffuse apotracheal axial parenchyma was absent, except in three specimens; P. erinaceus (Kw 6660), P. indicus (Kw 8262) and P. osun (Kw 6702). Both uniform fibre wall thickness and actual fibre wall thickness were most consistent within species. Wood porosity was reasonably consistent for a given species. Pterocarpus santalinus was semi-ring- or diffuse porous, but P. dalbergioides Roxb. was ring porous and the only species with uniform wood porosity for all specimens. Most specimens have straight to mildly sinuous grain, although 4/5 specimens of P. santalinus (Kw 6712, Kw 6713, Kw 6717 & Kw 21364) have tightly interlocked wavy grain, characteristic of “quality” timber. Trends were present in continuous character raw data (see Table 3). Two variables, axial parenchyma storeying and ray storeying were ubiquitous in Pterocarpus, as they are in many other taxa of the Papilionoideae (Gasson 1994; 1999; Gasson et al. 2004); and were therefore excluded from PCA. Pterocarpus specimens generally had uniseriate rays (e.g. Fig. 6), although the highest value for percentage of biseriate rays was 57% (P. erinaceus Kw 6660) with four other Pterocarpus specimen values >20% (P. indicus Kw 6673: 41%; P. macrocarpus Kw 70552: 34%; P. santalinus Kw 6712 & 6716: 25 & 49% respectively). The overall range of mean tangential vessel diameter values was 77–202 μm. Ptero- carpus santalinus dominated the lower range with values of 77–123 μm, but individual specimens of P. dalbergioides (Kw 6671), P. indicus (Kw 8262), P. marsupium (Kw 6693) and P. osun (Kw 6702) had values within the range of P. santalinus. Mean tan- gential vessel diameters for all other specimens were greater than any P. santalinus value. Specific gravity (range for all species: 0.56–1.25) showed the most obvious bias of any variable towards P. santalinus, and the lower limit of its range (0.96–1.25) exceeded the upper range limit of any other specimen.

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Figures 1–4. (TS) The non-Pterocarpus santalinus species. – 1: P. dalbergioides (Kw 6671). – 2: P. indicus (Kw 6674). – 3: P. macrocarpus (Kw 6666). – 4: P. marsupium (Kw 6693) (all scale bars = 500 µm). — Figures 5 & 6, P. santalinus (Kw 21364) showing gum & resin deposits in vessel lumina. – 5: (TS) Indistinct growth rings with fibres very thick-walled and uniform fibre wall thickness throughout growth ring (scale bar = 500 µm). – 6: (TLS) Storied uniseriate rays (scale bar = 200 µm).

Downloaded from Brill.com09/28/2021 08:56:13AM via free access MacLachlan & Gasson — PCA of Pterocarpus 129 0 1 0 1 2 1–3 5–9 4–7 7–12 3–49 1 & 2 0 & 1 0 & 1 23–47 80–120 0.3–2.6 santalinus 0.96–1.25 44.4–102.7 2 1 0 1 0 1 1 3 1–2 6–7 osun 2–21 8–11 0 & 1 34–49 0.1–0.3 90–200 9.0–16.6 0.68–0.84 . Pterocarpus Explanations of 1 0 7 0 1 0 0 1 7–8 4–7 2–4 3–11 0 & 1 36–38 0.6–0.0 120–130 34.2–54.5 0.70–0.75 marsupium 0 1 1 0 2 2 4–7 6–8 8–11 5–34 0 & 1 0 & 1 0 & 1 30–52 0.9–2.3 130–140 0.82–0.88 31.0–53.0 macrocarpus 1 0 1 0 1 2–3 5–7 3–7 0 &1 8–10 4–41 0 & 1 0 & 1 29–66 indicus 0.4–1.1 120–170 0.57–0.82 19.3–45.7 1 0 1 0 1 8–9 5–7 2–6 2–4 1 & 2 0 & 1 0 & 1 31–49 10–57 0.2–0.8 140–160 0.56–0.9 erinaceus 17.5–51.2 0 0 1 1 0 0 1 2 6–7 3–4 8–10 2–17 6–40 0 & 1 0.5–2.3 120–150 21.7–26.9 0.71–0.93 dalbergioides

-1 variable coding and mean value calculations (including units of measurement) are in Table 2. Table variable coding and mean value calculations (including units of measurement) are in porosity Wood Table 3. Table Raw data showing all variable codes or mean value ranges recorded for each variable and species of Characters Diffuse apotracheal axial parenchyma Diffuse Percentage of biseriate rays Paratracheal axial parenchyma mostly winged-aliform or confluent Ray height Evidence of uniform apotracheal axial parenchyma radial flattening No. of crystals in axial parenchyma Specific gravity Axial parenchyma storeying Ray storeying Uniform fibre wall thickness Fibre wall thickness Percentage of vessels solitary Tangential vessel diameter Tangential Vessel frequency Vessel Vessel frequencyVessel / Mean tangential vessel diameter Parenchyma band width Rays mm

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Important characters not included in the data analysis were also noted. Combined with densely packed, thick-walled fibres, P. santalinus had abundant gum and resin deposits in its cell lumina. Other specimens also exhibited gum or resin deposits, but these were largely confined to vessels,e.g. in P. dalbergioides (Kw 6657 & Kw 6655) and P. macrocarpus (Kw 6666, Kw 6684 & Kw 70552). Pterocarpus santalinus dif- fered by having gum and resin deposits in all cell types, even fibres (Fig. 5 & 6). This was particularly apparent in P. santalinus specimens Kw 6712, Kw 6713 & Kw 21363, that were the most challenging to section.

Description of Pterocarpus santalinus Heartwood very dark purple-brown, with abrupt boundary to pale sapwood. Wood is usually diffuse porous, without growth ring boundaries obvious to the naked eye or under a light microscope, but often marked by a continuous uniseriate band of gum & resin filled axial parenchyma. Vessels solitary (mean range 23–48%), or in radial clusters; tangential vessel diameter mean range 77–120 µm (absolute range 20–300 µm); vessel frequency mean range 5–9 vessels mm-2. Fibres storied, very thick-walled with narrow lumina and uniform fibre wall thickness throughout growth ring. Axial parenchyma storied, in two-celled strands. Paratracheal axial parenchyma winged- aliform (earlywood) to confluent (latewood). Apotracheal axial parenchyma banded, diffuse parenchyma absent. Mean parenchyma band width ≤ 3 cells. Rays homocellular, storied, usually ≥ 90% uniseriate (≤ 50% in some specimens), mean range 7–12 mm-1, ray height mean range 4–6 cells. Prismatic crystals in chambered axial parenchyma cells, forming axial chains, occur with a mean frequency of 1–3 crystals per 1 mm TLS transect (absolute frequency range 0–9 crystals per mm). Gums and resins variable in occurrence in heartwood, but often profuse in all cell types. Heartwood specific gravity mean range 1.05–1.25.

Comparison of P. santalinus with the remaining Pterocarpus species studied These brief comparisons highlight the primary differences between P. santalinus and the other Pterocarpus species we studied. Pterocarpus dalbergioides (Fig. 1) has dark red-brown heartwood (specific grav- ity 0.76–0.93) and is ring porous with sparsely distributed vessels (3–4 mm-2), but unlike P. santalinus fibre wall thickness is non-uniform (thin in earlywood and thick in latewood). Similarly P. macrocarpus (Fig. 3) has moderately dark pink-brown heartwood (specific gravity 0.8–0.9) and is semi-ring porous, resembling P. santali- nus microscopically with uniformly thick-walled fibres throughout the growth ring. Pterocarpus indicus (Fig. 2) has light brown to moderately dark red-brown heartwood (specific gravity 0.6–0.8) and may appear very similar toP. dalbergioides, with which it also shares ring porosity and non-uniform fibre wall thickness differentiating it from P. santalinus. Pterocarpus marsupium (Fig. 4) has pale to moderately dark golden-brown heartwood (specific gravity 0.7–0.8). Uniquely amongst AsianPterocarpus species, a golden yellow pigment is immediately released from P. marsupium heartwood upon immersion in water. Heartwood of P. erinaceus is pale to mid-brown (specific gravity 0.56–0.9), with a greater mean tangential vessel diameter and mean parenchyma band

Downloaded from Brill.com09/28/2021 08:56:13AM via free access MacLachlan & Gasson — PCA of Pterocarpus 131 width than P. santalinus. Mean parenchyma band widths are also greater in P. osun than in P. santalinus and mean vessel frequency was substantially lower in P. osun (1–2 vessels mm-2) than in P. santalinus (5–9 vessels mm-2). Heartwood specific gravity of P. osun was 0.68–0.84 and the colour was mid-brown for P. osun Kw 6702, but red-brown and similar to P. dalbergioides in P. osun Kw 38448. In all P. erinaceus and P. osun specimens fibres were thin- to thick-walled, compared to the very thick-walled fibres in all P. santalinus specimens.

Principal Components Analysis PCs 1, 2 & 3 explained 34.7%, 14.5% & 11.7% of variance respectively. Cumu- latively PCs 1–3 explained 60.8% of variance and PCs 1–9 explained 95% of vari- ance. Ranking PC 1–3 summed proportional eigenvectors for each variable, indicated which variables were responsible for greatest and least variance (Table 4): number of crystals in axial parenchyma, diffuse apotracheal axial parenchyma and parenchyma band width were the three variables responsible for most variance in PCs 1–3. Data in Table 4 indicate that the ten highest ranked variables are individually responsible for similar proportions of variance. From PC axes score plots (Fig. 7 & 8) clustering of specimens was apparent. Ptero- carpus santalinus formed a discrete cluster (separate from all other specimens) in the negative half of the PC 1 axis (Fig. 7). Specimen Kw 6716 was the exception, isolated from the main P. santalinus cluster and all other specimens due to PC 2 variance (Fig. 7). Pterocarpus dalbergioides formed a marginal discrete cluster in the area of PC 2 posi- tive values. The three specimens of P. macrocarpus formed a discrete cluster adjacent to the P. santalinus cluster, effectively separating the latter from all other specimens. Clustering of the remaining four species was not obvious, but all their specimens oc- curred as positive values of the PC 1 axis.

DISCUSSION

Interpretation of Principal Components Analysis The cumulative 60% proportion of variation that PCs 1–3 account for (Table 3) is encouraging. This is 10% greater than was achieved for morphology of Taxus wallichiana Zucc. by Möller et al. (2007), but 16.5% lower than Christensen (2005) applied (after refinement to only the seven most informative variables) to cone scale morphology of Pinus contorta Dougl. PC 1 was dominated by continuous variables including specific gravity, parenchyma band width, vessel frequency/mean tangential vessel diameter and vessel frequency (Table 4) that are either functions of size and/or growth rate. However, number of crystals in axial parenchyma ranked 1st (highest) when variables are summed and ranked across PCs 1–3. Crystal type, crystal location and crystal presence or absence are commonly applied systematic wood anatomy characters (Metcalfe & Chalk 1950; Chattaway 1955; IAWA Committee 1989; Carlquist 2001), but crystal abundance in

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0.114 0.110 0.081 0.077 0.160 0.158 0.156 0.154 0.153 0.149 0.149 0.148 0.145 0.137 0.105 0.095 prop. var.

× Sum PCs 1–3

4 7 3 2 9 6 8 1 5 11 12 13 16 10 15 14 rank PC 3

PC 3 0.380 0.170 0.069 0.064 0.430 0.017 0.439 0.090 0.124 0.080 0.177 0.046 0.154 0.053 0.525 0.274

6 3 7 9 2 8 4 1 5 11 13 10 12 16 15 14 rank PC 2

PC 2 0.115 0.158 0.284 0.314 0.173 0.251 0.153 0.173 0.390 0.204 0.310 0.009 0.043 0.499 0.048 0.305 6 8 3 4 5 9 2 1 7 11 16 15 10 14 13 12 rank PC 1

PC 1 0.040 0.047 0.309 0.239 0.340 0.234 0.338 0.226 0.318 0.237 0.347 0.375 0.260 0.078 0.079 0.098

-1 Pterocarpus eigenvector values (PC 1, PC 2 & PC 3) and summed proportional eigenvector values (Sum PCs 1–3 × proportion of Percentage of biseriate rays No. of crystals in axial parenchyma apotracheal axial parenchyma Diffuse Parenchyma band width vessel diameter Tangential frequency/Vessel Mean tangential vessel diameter Evidence for uniform apotracheal axial parenchyma radial flattening frequency Vessel Uniform fibre wall thickness Fibre wall thickness Specific gravity Paratracheal axial parenchyma mostly winged-aliform or confluent Table 4. Table variance) with rankings for each. Variable Wood porosity Wood Ray height Rays mm Percentage of vessels solitary

Downloaded from Brill.com09/28/2021 08:56:13AM via free access MacLachlan & Gasson — PCA of Pterocarpus 133 axial parenchyma is considered very variable at the species level and not a reliably diagnostic feature (P. Baas, personal communication). Responsible for the greatest variance in PC 1–3 eigenvector data, number of crystals in axial parenchyma supports this. By contrast rays mm-1 and ray height are established variables in systematic wood anatomy (Chattaway 1932; IAWA Committee 1989), but their low summed PC 1–3 eigenvector rank (Table 4) suggests that in this genus their diagnostic function is restricted. Variance attributable to rays mm-1 exemplifies this with little similarity of values between specimens of the same species, e.g. P. santalinus had a range of 7–12 rays mm-1 (Table 3), equivalent to the entire range for specimens of all species (also 7–12 rays mm-1). PCA is limited in the context of this study because it exposes sources of variance between individual specimens, not specific species. Inference of which variables are responsible for greatest variance between species is possible by relating the position of obvious clusters within a PC axes scores plot, to the PC axis which best defines them. The PC axes score plots (Fig. 7 & 8) were undoubtedly the most informative and con- clusive aspect of the data, with three species forming discrete clusters. Encouragingly, discrete clustering of P. santalinus is apparent (Fig. 7 & 8). Explaining this P. santalinus clustering, the four variables responsible for greatest PC 1 variance are specific gravity, fibre wall thickness, parenchyma band width and vessel density / mean tangential vessel diameter. The impact of specific gravity as a non-anatomical variable in the PC 1 eigenvector data appears to be considerable; potentially accounting for much of the 35% variance attributable to PC 1 compared with 14.5% and 11.7% for PCs 2 & 3 in which specific gravity is ranked 15th & 16th respectively (Table 4). Furthermore, from the raw data heartwood specific gravity is the only variable to com- pletely separate P. santalinus from all other specimens. Pterocarpus santalinus specimen Kw 6716 was isolated from the main P. santalinus cluster due to variation in PC 2. Compared with other P. santalinus specimens, raw data (Table 3) for Kw 6716 showed a particularly low value for number of crystals in axial parenchyma and atypically high values for percentage of biseriate rays, parenchyma band width and rays per mm. This evidence, combined with macroscopic and specific gravity similarities indicates that specimen Kw 6716 represents an extreme of variation for this species, rather than a wrongly-determined specimen. Discrete clustering was also observed in P. dalbergioides and P. macrocarpus. Pterocarpus dalbergioides clustering is evident in Figure 7, while P. macrocarpus clusters in a central position for Figures 7 & 8. Therefore the variables used in this study appear suitable for effective PCA of Pterocarpus specimens and give us a basis to refute the suggestion of Rojo (1972) that all Asian Pterocarpus species should be included under two forms of P. indicus. The two remaining Asian Pterocarpus species, P. indicus and P. marsupium, showed considerable overlap in their tentative clusters that reflected their macroscopic similarity. AfricanPterocarpus specimens showed a scat- tered PC coordinate distribution, providing no useful comparison to each other or the Asian species except P. santalinus.

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7 Pterocarpus Axes Scores Plot: PC 1 versus PC 2

Taxon P. dalbergioides P. erinaceus P. indicus P. macrocarpus P. marsupium P. osun P. santalinus

Plot Axes

6716 x = PC 1 y = PC 2

8 Pterocarpus Axes Scores Plot: PC 1 versus PC 3

Taxon P. dalbergioides P. erinaceus P. indicus 6716 P. macrocarpus P. marsupium P. osun P. santalinus

Plot Axes x = PC 1 y = PC 2

Figures 7 & 8. Principal component axes score plots. – 7: PCs 1 & 2. – 8: PCs 1 & 3. Discrete clustering of P. santalinus specimens is prominent in Figures 7 & 8. NB. Ellipses are not sta- tistically significant.

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Implications for P. santalinus CITES identification It appears that a boundary occurs at variables ranked ≥ 11th separating ‘informative’ variables (PC 1–3 summed proportional eigenvector ranks 1–10) responsible for high- est variance values, from those explaining considerably less variation (ranks ≥11) (see Table 4). However, these informative variables ranked 1–10 have a narrow value range and no single variable was responsible for a definitive proportion of variation. Even if removal of presumed ‘uninformative’ characters is desirable to accelerate the pro- cess of wood identification using multivariate methods, this must be done with care (Blackith & Reyment 1971). There are two important implications for application of PCA when variables are removed. Firstly the effects on an individual PC may be greater than is inferred by summed proportional PC 1–n values, and secondly, removal of data reduces PC axes scores plot dimensionality and decreases the distance between clusters. It is also important to note that whilst specific gravity is a highly diagnostic character for P. santalinus, measurements of volume from which specific gravity is calculated are straightforward for blocks of wood, but for chipped and shredded wood, such meas- urements could be inaccurate given their small size and high surface area to volume ratio.

CONCLUSIONS

Our study indicates that morphometric PCA can be an effective tool in quantitative wood anatomy for identification and delimitation of Pterocarpus species, as demon- strated by discrete clustering of P. santalinus, P. dalbergioides and P. macrocarpus in PC axes scores plots. Although PCA was applied to P. santalinus as an arguably well defined species based on comparative wood anatomy, clear proof of concept was demonstrated for the numerical comparison of wood specimens in a way unavailable to purely descriptive wood identification techniques. This study forms the basis for a rigorous method in systematic wood anatomy, whereby a single wood specimen could be incorporated into a PCA data set to indicate its true identity. It is likely to be of greatest value when identifying infrageneric taxa, where traditional qualitative characters cannot definitively separate similar species. Furthermore those 10 or 11 variables identified from summed and ranked proportional PC 1–3 eigenvectors which explained the greatest amounts of variance can be regarded as informative and suitable for use in future multivariate quantitative wood anatomy studies of Pterocarpus. A comparative wood anatomy description of P. santalinus based on six specimens was produced for CITES wood identification and clear delimitation ofP. santalinus in PC axes score plots justifies a high degree of confidence in this description based on the same specimens and data. It should be recognised that PCA is not necessarily a definitive technique for wood specimen identification, but it has potential to offer considerable gains over compara- tive wood anatomy.

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ACKNOWLEDGEMENTS

This study was undertaken as the MSc thesis of I. MacLachlan supervised by Dr. P. Gasson. I. Mac- Lachlan wishes to thank the Royal Forestry Society and the Royal Botanic Garden Edinburgh for the award of extra funding in support of this study and its publication. The authors also acknowledge the contributions of Dr. Michael Möller and Prof. Richard Bateman for their advice on the statistical aspects of this study and Miss Kasia Zieminska, Mrs. Chrissie Prychid, Dr. Steven Jansen, and Prof. Claudia Leme for their technical help. Thanks also go to Noel McGough and Madeleine Groves of the RBG Kew Conventions and Polices Section, Dr. Gwilym Lewis and Dr. Bente Klitgaard of the RBG Kew Herbarium and Dr. Mark Nesbitt of the RBG Kew Economic Botany Collection for supplying the wood samples. Prof. Pieter Baas and two anonymous reviewers suggested important manuscript improvements.

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