a morphometric and visual method of fossil phytolith
TRANSCRIPT
1
A morphometric and visual method of fossil phytolith identification using regionally
specific digital herbaria databases
PARR, J.F
Centre for Geoarchaeology and Palaeoenvironmental Research
School of Environmental Science and Management
Dr Jeff Parr
Centre for Geoarchaeology and Palaeoenvironmental Research
School of Environmental Science and Management
Southern Cross University
PO Box 157 Lismore, NSW 2470 Australia.
OFFICE 66203789
LAB 66203357
Email [mailto:[email protected]]
2
Abstract
This paper describes a method of fossil phytolith identification using regionally specific
herbaria databases. The method presented in this paper is described in detail with step by
step instructions in order that it may be applied with ease to other data sets. It is based on a
relatively simple visual and morphometric procedure for phytolith identification that has
been used successfully to identify spatial and temporal changes in vegetation for the
palaeolandscapes of West New Britain, PNG. With minor modifications this method can
also be applied to other geographical data. Once a regional database is set up in the manner
described in this paper the method provides a reasonably rapid and usefull method of fossil
phytolith identification.
Keywords: phytoliths, palaeo-landscapes, likelihood analysis, phytolith identification,
phytolith counting
3
Introduction
There are numerous ways by which fossil phytolith identification can be achieved,
however, all methods have one thing in common in that they rely on morphological and/or
metric assessment by the practitioner. The preferred method will ultimately depend on the
practitioner’s preference, the research question and the degree of precision required. In this
paper I describe a relatively simple morphometrical method of phytolith identification
using a regionally specific digital phytolith database which was applied successfully to
identify spatial and temporal vegetation differences in the palaeolandscapes of West New
Britain, PNG (Parr 2003).
When it comes to critical forms of phytolith identification as is required in the analysis of
plant domesticates, [Rovner, 1983 #88] p250 has reasonably emphasised the importance of
morphometrical comparisons between a wide range of species with possible overlapping
morphotypes to achieve a level of confidence in such an identification. Visual methods of
phytolith identification have included comparing images of phytolith morphology from a
practitioner’s personal database or from the databases and/or published images of others.
Databases of phytolith images have been produced by scanning electron micrographs
(SEM) (Ball et al. 1993; Bowdery 1989; Mbida et al. 2000)[Runge, 1999 #580] or,
composite drawings, often but not always made by using a drawing tube attachment on a
microscope [Krishnan, 2000 #309](Lentfer et al. 1997; Lentfer et al. 2001; Piperno 1988;
Wallis 2003) and film or digital photography (Kealhofer and Piperno 1998; Parr and Carter
2003; Parr et al. 2001a; Runge 1996). Metrical analysis of phytoliths has been employed by
a number of practitioners particularly for plant family or species differentiation cf. [Mbida,
4
2000 #284][Krishnan, 2000 #309; Zhao, 1998 #214; Pearsall, 1995 #70][Pearsall, 1982
#581]. The use of a combination of both visual and metrical analysis is probably the most
commonly used process and is obviously the most accurate method of fossil phytolith
identification.
A large number of plant families are now generally accepted by established practitioners to
have diagnostic phytoliths for the particular geographical locations from which they have
been recorded (Bowdery 1996; Horrocks et al. 2000; Kealhofer and Piperno 1998; Runge
1996; Welle 1976)[Rovner, 1983 #88][Runge, 1999 #580]. For example, in the study
conducted by (Kealhofer and Piperno 1998), of the initial 377 species analysed from 17
monocotyledon and 59 dicotyledon families, 9 monocotyledon and 26 dicotyledon families
contained diagnostic phytoliths. Of these families 154 species were found to contain
diagnostic phytoliths (Kealhofer and Piperno 1998). In summary of their analysis, they
point out that "plant age, soil chemistry, and other environmental factors may influence
phytolith abundance and the diversity of forms present in a given plant" (Kealhofer and
Piperno 1998) p22. [Wallis, 2003 #579] has recently pointed out the contrasting results of
her study on phytolith deposition in some plant families to that reported by [Bowdery, 1996
#191; Piperno, 1988 #66]. This is now a well-known phenomenon. Thus, one of the most
important aspects of microfossil analysis is the establishment of an appropriate reference
database. [Piperno, 1988 #66; Sangster, 2002 #459] and [Bowdery, 1996 #191] point out
the importance of producing specific phytolith databases with specimens collected from the
intended study area due to both regional and edaphic variability. [Bowdery, 1996 #191]
emphasises the importance of this point by comparing the results of an attempted phytolith
extraction from the species Salsola kali by [Mulholland, 1987 #458], which failed to
5
produce any diagnostic types, whereas diagnostic types were recovered from the same
species collected at the Puritjarra site in Australia. Thus, the absence of phytoliths in a
particular species in one geographical area does not guarantee the same result in another
(Sangster and Hodson 2002).
In this paper I describe a method of fossil phytolith identification that uses a regionally
specific digital phytolith image database. The method was used to identify spatial and
temporal vegetation differences in the palaeolandscapes of West New Britain, PNG in a
previous study (Parr 2003). Examples are drawn from this previous study to demonstrate
how this identification method can be employed to the spatial and temporal analysis of
phytolith assemblages.
Methodology
The process of sediment sampling
Any established method of soil sampling and processing for microfossil analysis can be
used to produce microscope slides for the identification method described below (cf.
(Piperno 1988) 110-116). The example provided here is from seven hill, hill-slope and
coastal plain sites at Numundo (Parr 2003). These sites overlook or sit on the coastal plain,
and contain stratified sediments from which samples were collected dating from the period
of the Witori Kimbe one (W-K1) eruption (c. 5,900 BP) to the present (Torrence 2002).
The sediments comprise air-fall tephra and associated soil development. At all seven sites,
sediment samples were collected in 5cm increments during the 1998, 1999 and 2000 field
seasons (Torrence 2000; Torrence et al. 1999). Samples were selected with the aim of
6
differentiating between vegetation disturbances of an anthropogenic nature and those
caused by natural disturbance from volcanic events. Two to three samples (depending on
the extent of soil development) were taken from within each soil horizon. Samples from
each site were analysed for phytolith types, quantities of charcoal particles and starch
grains. In addition, topsoil samples were taken from the nearby Kulu Dagi rainforest, which
were to be used as representative forest reference material during phytolith analysis. Also
an earlier study by (Boyd et al. 1998b) examined samples from a range of sites in West
New Britain associated with various degrees of anthropogenic disturbance, and their results
were also considered during phytolith analysis.
Plant sampling and compiling comparative phytolith reference database
Plant succession in the volcanic environments of the Southwest Pacific has been well
documented by (Cronin and Neall 2000; Lees and Neall 1993; Lentfer and Boyd 2001;
Paijmans 1973; Thornton 1996; Turner and Hurst 2001) and (Whittaker et al. 1989).
During phytolith analysis these studies provide vital information for identifying vegetation
changes resulting from volcanic disturbance. In addition, studies by (Duar 1999; Peekel
1984) and (Floyd 1954) provided valuable information on the plant communities, in the
study area and the habitats they represent. To take advantage of such studies a plant
reference collection was established for the study area.
Plants samples were collected in the study area by Carol Lentfer, Robin Torrence, Bill
Boyd, Lisa Kealhofer and Michael Therin and others during fieldwork and/or supplied by
the Lae Herbarium Papua New Guinea, Southern Cross University Herbarium and the
7
Royal Botanical Gardens Sydney. From this plant collection, 81 families, comprising 213
species, were selected on the basis of their particular relationship to the study area, plant
habitat and the plant succession studies based on local volcanic sediments described above.
For the purpose of this study recently introduced plants were excluded, with the exception
of a few economically important varieties in terms of subsistence, such as Ipomea batatas.
Phytoliths were extracted from the above herbarium specimens using the microwave
digestion method described in (Parr et al. 2001). However, the phytolith identification
protocol described in this paper is not dependent on any particular extraction method. Thus
any the established methods of phytolith extraction from herbarium material can be used
(Jones and Beavers 1963; Lanning et al. 1958; Parry and Smithson 1957; Pearsall 1979;
Piperno 1988; Raeside 1970; Rovner 1972; Zimmerman 1901).
The process of building a text database
A text database was produced in Microsoft Excel for the herbarium collection. The
database contained the plant family, species name, the herbarium reference and/or
acquisition number provided by the collector and, a description of the habitats in which
each of the species occurs, based on the vegetation reports of (Duar 1999; Floyd 1954;
Peekel 1984) for West New Britain. In addition, information of plant succession by (Boyd
et al. 1998b) on sites in West New Britain associated with anthropogenic disturbance and,
of volcanic disturbance documented by (Cronin and Neall 2000; Lees and Neall 1993;
Lentfer and Boyd 2001; Paijmans 1973; Thornton 1996; Turner and Hurst 2001) and
(Whittaker et al. 1989) were also used as a reference to the various modes of disturbance.
Habitats or vegetation assemblages described by (Duar 1999; Floyd 1954; Peekel 1984) for
8
West New Britain were placed within groups in the database. Each group was provided
with an alphabetical reference e.g. “Group A” or “Group B” etc. Eight main categories
were used to broadly describe these vegetation assemblages. Group A, representing coastal
habitat, Group B closed forest, Group C anthropogenically disturbed areas, Group D natural
disturbed areas, Group E wetlands, Group F open grassland, Group G herbaceous
woodland, and Group H. non-specific or redundant types that can be identified but are non-
habitat specific or those not identified. A link was then established between each Group and
associated plant species represented by the images in the digital phytolith database
described below.
Creating a digital phytolith image database from herbarium material
A digital image database was produced with an Olympus CX40 trinocular microscope
fitted with an Olympus DP10 digital camera. Images were taken of phytoliths in various
orientations at 400x magnification and a scale bar was placed in the bottom right hand
corner of each image. The scale bar was produced in Adobe PhotoShop. This was achieved
by first measuring a single phytolith under the microscope using the optical micrometer and
then measuring the distance across the same access of the phytolith in pixel’s on the digital
image in Adobe PhotoShop. A digital image database and counter was combined with the
text database described above. The database was produced in Microsoft Excel and saved as
a template. Images were placed into the database under their morphological typology. For
example, phytolith types from the family Poaceae were placed under the headings bilobate,
trilobate and bulliform etc (cf. Plate 1). In addition, each entry can be measured in microns
9
with a moveable scale bar provided on each page and is given a group category (e.g. Group
A, B or C etc.) as described above.
Identifying fossil phytoliths
Phytoliths were extracted from sediment samples using the microwave digestion method
described in (Parr 2002). Again the identification protocol is not dependent on any
particular method of phytolith extraction from sediments. Thus any established method of
phytolith extraction from sediments will suffice (Piperno 1988). The phytoliths extracted
from each sediment sample were mounted onto microscope slides and scanned at 400x
magnification on an Olympus CX40 microscope. The number of phytoliths counted to
accurately represent an assemblage will vary from site to site. For example, numbers used
in previous studies range from 100 [Grave, 1999 #209; Kealhofer, 1996 #74] to 200
[Kealhofer, 1996 #74][Pearsall, 1989 #71] and in some cases 300 [Parr, 2002 #229]
phytoliths per slide depending on the research question. Although, some authors have used
counts of up to 500 phytoliths per slide [Bowdery, 1996 #191; Boyd, 1998b #15]. A useful
method for assessing the number of phytolith counts required to adequately represent an
assemblage is to find the ‘point of plateau’. An example of this procedure is provided in
Figure 1. However, for the purpose of this study a total of 200 phytoliths were counted
from each sample with a minimum of 125 diagnostic phytolith types considered to be
10
Figure 1. Plot showing the mean point of plateau during counting of 200 diagnostic fossil phytoliths from each of the Numundo samples in the study area. The X-axis represents the number of fossil phytoliths counted and the Y-axis provides the number of phytolith types during various stages of the counting procedure. Note that the number of types to the total phytolith count begins to plateau out at around 100 phytoliths and after 125 phytolith there is no significant change. adequate for the task. Each count of a particular phytolith type is recorded under the image
of a phytolith type of which it matches on the bases of likelihood and morphometrical
characteristics described below (Plate 1). Where possible, phytoliths were described using
the International Code for Phytolith Nomenclature (Madella et al. 2003) and identified
using the phytolith database produced from the plant reference collection described above.
10
20
30
40
0 25 50 75 100 125 150 175 200 225
Number of phytoliths counted
11
Plate 1. Phytolith database and counter created in a Microsoft Excel spreadsheet. In this example the spreadsheet is open at the worksheet containing Bulliform phytoliths. There are matches made in the above example with ten bulliform types from the species Imperata cylindrica in the top row, fourteen counts for Ishaemum polystachyum bottom row left and eleven counts for Schizostachym brachycladum right. Entries have been made in the drop down menu counters for matches in shape, size and texture. These counts are automatically linked to other worksheets and enter likelihood scores (cf. Table 2) and phytolith type (cf. Table 3) summaries (the counters are reset after counts are summarised and printed).
12
Establishing the likelihood of identification
A non-statistical morphometric and visual likelihood procedure was applied to ‘reduce’ the
subjectivity and to improve the resolution of fossil phytolith identification. Although the
method is not the same as the statistical analysis used by (Horrocks and Walsh 1998;
Horrocks and Walsh (2001)) the concept was conceived from their method. The analysis
considers the certainty with which phytoliths, first, represent specific plant types, and,
secondly, reflect plants present in the study area. For the study discussed here the plant
reference material represents species primarily from the study area, thus (with the exception
of a few introduced cultivars) this variable is constant for the majority of plants. The
protocol for the identification of fossil phytoliths is summarised in Table 1.
Where the characteristics described in Table 1. are matched for both the fossil phytolith and
a phytolith from the digital phytolith image database (cf. Plate 1.), a tentative identification
can be made and a score applied for the likelihood analysis (cf. Table 2). Naturally, a
definite species identification cannot be claimed (cf. Joosten and de Klerk 2002). However,
this type of analysis assumes that the most accurate way of identifying a fossil phytolith
type and assign it to a habitat group, is where a matched is made to a morphotype found in
a phytolith database that is specific to the study area (cf. Plates 1.).
13
Table 1. Step by step protocol for a visual and morphometric method of phytolith identification that has been used by (Parr 2003) to identify spatial and temporal changes in vegetation for the palaeolandscapes of West New Britain, PNG.
Step A Procedure 1 Locate a fossil phytolith on the microscope slide e.g. a bulliform 2 Search for a similar morphological type in the digital image database
(see Plate 1). 3 If a no morphological match is found leave a 0 in the score column.
If a morphological match is made move to step 4 4 Record the match for morphological type (Plate 1), this will
automatically insert a score of 1 in the appropriate column (see Tables 2 and 3).
6 Use the microscope micrometer to measure the phytolith size and compare with the size of matching morphotype in the digital database (see Plate 1.).
7 If a no size match is found leave a 0 in the score column. If a size match is made move to step 8
8 Record match for size (Plate 1), this will automatically insert a score of 1 in the appropriate column (see Tables 2 and 3).
9 Compare the texture of the fossil phytolith with the morphotype in the digital image database (see Plate 1.).
10 If the texture is not a match place a 0 in the score column. If the texture is a match move to step 11
11 Record match for texture (Plate 1), this will automatically insert a score of 1 in the appropriate column (see Tables 2 and 3).
12 Identify the distribution of the species from which the phytolith bares a resemblance using herbarium information WNB.
13 If the distribution of the species from which the phytolith bares a resemblance does not occur naturally in the study area or is in a stratigraphic sequence thought to pre-date its introduction to the study area leave a 0 in the score column. If the distribution is a match move to step 14a
14 Record match for distribution this will automatically insert a score of 1 in the appropriate column (see Tables 2 and 3).
15 If other phytolith morphotypes from the same species occurs provide a score of 1 in the appropriate column (see Table 3).
14
Table 2. An example of how an identification is made on the basis of matching variables; shape = 1 point, size = 1 point, texture = 1 point, more than one representative cell type from the tentatively identified plant species = 1 point and location =1 point. A likelihood score is applied on the basis of the cumulative points where: 5 = Conclusive, 4 = Strongly supports, 3 = Supports, 2 = Inconclusive, <1= Not identified and Redundant. Types with a score less than 3 are not used in the final analysis i.e. the minimal requirement to be counted in the final analysis is two of the three morphological variables and to qualify for a score under location. Other combinations of 3 point scores e.g. Schizostachym brachycladum below are not used. This particular process is automated in the author’s database from entries made in the drop down menus during identification (cf. Plate 1).
Typology Tentative Shape Size Texture More than one Location Likelihood
identification representative cell score
Bulliform Imperata cylindrica 1 1 1 1 1 5
Bulliform Imperata cylindrica 1 1 1 1 1 5
Bulliform I. polystachyum 1 0 1 1 1 4
Bulliform S. brachycladyum 1 0 0 1 1 3
Statistical analysis and plotting
Data obtained on type and habitat counts that are generated in the Excel worksheets can be
transferred directly into a statistical and/or plotting program. Principal component analysis
(PCA) can be employed as a method of ordination (cf. (Boyd et al. 1998c; Lentfer et al.
2001; Parr et al. 2001a,c) and the phytolith diagrams plotted using any standard software
packages such as SPSS, Abel or Tilia etc..
Results
From the initial reference collection of 81 plant families comprising 213 species, 59
families and 94 species were found were found to have diagnostic types that for the
15
Plate 2. Top left Heliconia phytoliths from the plant database and right Heliconia Phytoliths from the site FAAH c. 5900 BP. Centre left disc shaped phytoliths from Pandanus tectorus and right Pandanaceae type phytolith and, bottom left Euphorbiaceae haircell phytolith from the species Macaranga urophylla and right broken hair cell with similar morphological characteristics also from site FAAH c. 3500 BP.
16
Family
Species
Habitat bilo
bate
cla
ssic
bilo
bate
oth
er
bloc
k-ps
ilate
bloc
k-pa
pilla
te
bulli
form
cari
nate
chai
nlik
e m
ulti-
loba
te
cros
s ep
ider
mal
sho
rt
cells
cy
st-u
nilo
bate
cune
iform
epid
erm
al lo
ng c
ells
epid
erm
al s
hort
cel
ls
glob
ular
-sph
ere
hair
cell
hair
base
halfr
ound
hatli
ke c
onic
al-
depr
esse
d In
situ
epi
derm
al c
ells
irreg
ular
lent
icul
ar-o
vate
Mis
cella
neou
s ab
norm
al
Plat
e
poin
t
pric
kle
puzz
le-a
sym
emet
rical
smal
l/elo
ngat
e
sphe
re-o
paqu
e
sphe
re-p
sila
te
sphe
re-c
rena
te
sphe
re-m
ed-c
rena
te
sphe
re-la
rge-
nodu
lose
sp
here
-sm
all-e
chin
ate
sphe
re-m
ediu
m-
echi
nate
sp
here
-larg
e-ec
hina
te
quad
ra-lo
bate
reni
form
stom
ate
trac
hied
trilo
bate
Trot
h
Tota
l cou
nt
Piperaceae Piper betal L. G 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Piper mestorii F.M. Bail. E 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pittosporaceae Piper peekelii C. DC. F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Poaceae Pitosporum ferrugineum Ait. D 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bambusa forbesii D 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8
Coix lachryma-jobi L. E 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Imperata cylindrica P.Beauv. D 16 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33
Imperata exaltata (Roxb.) Brogn. F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ischaemum polystachyum (L.) E 7 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14
Seteria sphacelata (K.Schum.) Stapf. & C.E.Hubb F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Schizostachym brachycladum (Blanco) Mer. G 10 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20
Podocarpaceae Dacrycarpus imbricatus Bl. B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pteridophyta Bolbitis quogana (Gaud.) Ching B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Diplazium esculentum (Retz.) Sw. E 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nephrolepis hirstulata (Forst.) Presl D 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rhamnaceae Alphitonia macrocarpa Mansf. H 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alphitoria molaccana Reiss. ex Endl. H 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Num
ber
of
Phyt
olith
s in
eac
h ha
bita
t
Group
Habitat description
0 Group A. Coastal habitat 0 Group B. Closed forest
20 Group C. Anthropogenically disturbed areas 41 Group D. Naturally disturbed areas 14 Group E. Wetlands 0 Group F. Open grassland 0 Group G. Herbaceous woodland 0 Group H. Non-specific and redundant types
Table 3. An example of the Microsoft Excel type and habitat count worksheet, which is automatically, linked to the digital image database and the likelihood worksheet shown in Plate 1 and Table 2 respectively.
{
17
particular geographical area studied did not overlap morphologically with each other (Parr 2003).
Examples of phytoliths from the database and those tentatively identified are provided in plate 2.
Phytolith identification by morphological comparison between fossil cells and those in the area
specific database took from 8 to 10 hours per microscope slide (Parr 2003).
Each count made in the dropdown menu under each morphotype image (Plate 1) during the
identification process is automatically linked to the other procedures outlined in Table 1. Thus the
likelihood score for each phytolith type (Table 2) and the counts for morphotypes that occur in
each habitat group are automated (Table 3).
Discussion
A total of 94 species were found to have diagnostic phytolith types that for the particular
geographical area studied did not overlap morphologically with each other (Parr 2003). Examples
of phytoliths tentatively identified by matching types in the digital database are provided in plate
2. Phytolith identification by morphometric and visual comparison of fossil cells to those in the
area specific database took from an average of 8 hours and in some cases up to 10 hours per
microscope slide (Parr 2003). At first this process appears to be a very time consuming.
However, when the various procedures in the alternative approaches are considered this
morphometrical and visual method of fossil phytolith identification becomes comparable.
18
One approach to phytolith analysis is to look for specific morphological types associated with
plant domestication. Many of the phytoliths found in domesticated plants have now been describe
in detail. For example, phytolith types found in Cucurbitaceae have been studied by (Bozarth
1987; Piperno et al. 2000), Musaceae sp. (Mbida et al. 2000; Piperno 1988; Wilson 1985), and
domesticated Gramineae sp. such as the Titicum sp. (wheat) by (Baker 1961; Ball et al. 1993;
Ball et al. 1996; Gebbing et al. 1998; Hodson and Sangster 1989; Larney et al. 1998; Lentfer et
al. 1997; Rosen 1994), Oryza sp. (rice) by (Jones 1993; Pearsall et al. 1995; Yoshida et al. 1962;
Zhao et al. 1998; Zhao and Piperno 2000) and Zea maize (corn) (Lanning et al. 1980; Pearsall
1999; Piperno and Pearsall 1993). For the experienced practitioner familiar with the particular
domesticated species they are looking for, scanning microscope slides for these specific
morphotypes is possibly second nature.
Another common approach is to establish evidence for climate change or land clearing which
might be inferred by a transition from forest phytolith types to those associated with grassland.
Importantly, the morphological characteristics of grasses have been well documented in
numerous studies (Bozarth 1992; Brown 1984a, 1984b; Clifford and Watson 1977; Fredlund and
Tieszen 1997; Krishnan et al. 2000; Lentfer et al. 1997; Milby 1971; Mulholland 1989;
Mulholland and Rapp 1992b; Ollendorf et al. 1988; Parry and Smithson 1957; Piperno and
Pearsall 1998; Twiss 1992; Twiss et al. 1969) as have a range of arboreal phytolith types (Albert
and Weiner 1997; Bowdery 1984; Bowdery 1996; Bozarth 1992; Hodson and Sangster 1998;
Hodson et al. 1997; Kealhofer and Piperno 1998; Piperno 1994; Piperno 1988; Runge 1996;
Sangster and Hodson 2002; Sangster et al. 1997; Sharma and Rao 1970; Wallis 2003; Welle
1976). This type of analysis works particularly well in areas where the vegetation of a landscape
19
is likely to be relatively uniform over large areas and the study is based on temporal rather than
spatial changes. Thus, a distinction between grassland and forests environments will not be as
complex in these environments by comparison to, for example, trying to differentiate between the
plant communities found in individual sites across a landscape. In the tropics, pacific islands and
coastal regions the plant communities can vary considerably over a small geographical area. In
some cases there is no clear cut-off point between one habitat and another but rather a gradual
transition from one plant community to another. Thus if the research question involves
deciphering the differences between natural landscapes and associated disturbances and human
land-use and indicative patterns of anthropogenic disturbance another approach may be required
(Parr et al. 2001a; Torrence and Boyd 1997).
20
References Cited:
Albert, R. M. and Weiner, S. (1997). “Phytoliths in wood ash.” The Phytolitharien 10(2-3): 5 - 6.
Baker, G. (1961). Opal phytoliths and adventitious mineral particles in Wheat dust.
Mineragraphic Investigations Technical Paper. Melbourne, Commonwealth Scientific and
Industrial Research Organization, Australia. No. 4.
Ball, T. B., Brotherson, J. D. and Gardner, J. S. (1993). “A typologic and morphometric study of
variation in phytoliths from einkorn wheat (Triticum monococcum).” Canadian Journal of
Botany 71: 1182 - 1192.
Ball, T. B., Gardner, J. S. and Brotherson, J. D. (1996). “Identifying phytoliths produced by the
inflorescence bracts of three species of Wheat (Triticum monococcum L., T. dococcon
Schrank., and T. aestivum L.) using computer-assisted image and statistical analyses.”
Journal of Archaeological Science 23: 619 - 632.
Bowdery, D. (1984). Phytoliths: a multitude of shapes. Canberra, Australian National University,
Unpublished BA (hons) Thesis.
Bowdery, D. (1989,). Phytolith analysis: introduction and applications,. In. Beck, W., Clarke, A.
& Head, L. (eds.). Plants in Australian Archaeology, Archaeology and Material Culture
Studies in Anthropology. Watson Ferguson & Company,Brisbane.Tempus Vol. (1):161 -
86.
Bowdery, D. E. (1996). Phytolith analysis applied to Archaeological Sites in the Australian arid
zone. Canberra, Australian National University, Unpublished PhD Thesis.
Boyd, W. E., Lentfer, C. J. and Torrence, R. (1998b). “Phytolith analysis for a wet tropics
environment: methodological issues and implications for the archaeology of Garua Island,
West New Britain, Papua New Guinea.” Palynology 22: 213 - 228.
21
Bozarth, S. R. (1992). Classification of opal phytoliths formed in selected dicotyledons native to
the Great Plains. George Rapp, J. and Mulholland, S. C. (eds.). Phytolith Systematics
Emerging Issues. Society for Archaeological SciencesNew York.
Brown, D. A. (1984a). “Prospects and limits of a phytolith key for grasses in the central United
States.” Journal of Archaeological Science 11: 345 - 68.
Brown, D. A. (1984b). “Prospects and limits of phytolith key for grasses in the Central United
States.” Journal of Archaeological Science 11: 345-68.
Clifford, H. T. and Watson, L. (1977). Identifying Grasses: Data, Methods and Illustrations.
University of Queensland Press.Brisbane
Cronin, S. J. and Neall, V. E. (2000). “Impacts of volcanism on pre-European inhabitants of
Taveuni, Fiji.” Bulletin of Volcanology 62: 199 - 213.
Duar, P. P. (1999). New Britain Palm Oil Limited Environmental Plan: Kulu-Dagi and Inland
Kove Oil Palm Project. Papua New Guinea, Environmental Management & Monitoring
(PNG) Ltd: pp.1-3.
Floyd, A. (1954). Final report on ethnobotanical expedition - west Nakanai New Britain - July-
August 1954. Lae, Dept. of Forests: 1-20 Appendix 1-2.
Fredlund, G. G. and Tieszen, L. L. (1997). “Calibratin grass phytolith assemblages in climatic
terms: Application to late Pleistocene assemblages from Kansas and Nebraska.”
Palaeogeography, Palaeoclimatology, Palaeoecology 136: 199-211.
Gebbing, T., Schnyder, H. and Kühbauch, W. (1998). “Carbon mobilization in shoot parts and
roots of wheat during grain filling: assessment by 13C/12C steady-state labelling, growth
analysis and balance sheets of reserves.” Plant, Cell & Environment 21: 301 - 313.
22
Hodson, M. J. and Sangster, A. G. (1989). “Subcellurlar localization of mineral deposits in the
roots of wheat (Triticum aestivum L.).” Protoplasma 151(1): 19-32.
Hodson, M. J. and Sangster, A. G. (1998). “Mineral Deposition in the Needles of White Spruce
[Picea glauca (Moench.) Voss].” Annals of Botany 82: 375-385.
Hodson, M. J., Williams, S. E. and Sangster, A. G. (1997). Silica deposition in the needles of
gymnosperms. I. Chemical analysis and light microscopy. Machado., A. P. J. J.-T. M. J.
(eds.). The State-of-the-art of phytoliths in soils and plants.Monographia 4 del Centro de
Ciencias Medioambientals, CISC. Madrid. p123-133.
Horrocks, M., Jones, M. D., Carter, J. A. and Sutton, D. G. (2000). “Pollen and phytoliths in
stone mounds at Pouerua, Northland, New Zealand: implications for the study of
Polynesian farming.” Antiquity 74: 863 - 872.
Horrocks, M. and Walsh, A. J. (1998). “Forensic palynology: assessing the value of the
evidence.” Review of Palaeobotany & Palynology 103: 69-74.
Horrocks, M. and Walsh, K. A. J. ((2001)). Forensic Palynology: assessing the weight of the
evidence. Proceedings of the IX International Palynological Congress, American
Association of Stratigraphic Palynologists Foundation,pp. 613-615.
Jones, M. D. (1993). Analysis of pollen and phytoliths in residue from a colonial period ceramic
vessel. Pearsall, D. M. and Piperno, D. R. (eds.). MASCA Research Papers in Science and
Archaeology. University of PennsylvaniaPhiladelphia31 - 35.
Kealhofer, L. and Piperno, D. R. (1998). Opal Phytoliths in Southeast Asian Flora. Smithsonian
Contributions to Botany. Washington DC, Vol 88 pp1.39
Krishnan, S., Samson, N. P., Ravichandran, P., Narasimhan, D. and Dayanandan, P. (2000).
“Phytoliths of Indian grasses and their potential use in identification.” Botanical Journal
of the Linnean Society 132: 241 - 252.
23
Larney, F. J., Bullock, M. S., Janzen, H. H., Ellert, B. H. and Olson, E. C. S. (1998). “Wind
erosion effects on nutrient redistribution and soil productivity.” Journal of Soil and Water
Conservation 53(2): 133 - 140.
Lees, C. M. and Neall, V. E. (1993). “Vegetation response to volcanic eruptions on Egmont
Volcano, New Zealand, during the last 1500 years.” Journal of The Royal Society of New
Zealand 23(2): 91 - 127.
Lentfer, C. and Boyd, W. E. (2001). Maunten paia: Volcanoes people and environment: The 1994
Rabaul eruptions. Southern Cross University Press. Lismore. pp. 36-37
Lentfer, C. J., Boyd, W. E. and Gojak, D. (1997). “Hope Farm Windmill: phytolith analysis of
cereals in early colonial Australia.” Journal of Archaeological Science 24: 841 - 856.
Lentfer, C. J., Boyd, W. E. and Torrence, R. (2001). Phytolith reseach relating to the archaeology
of West New Britain, Papua New Guinea. Meunier, J. M. and Colin, F. (eds.). Phytoliths:
Applications in Earth Sciences and Human History. A.A. Balkema Publishers, Exton
(PA),Tokyo, pp. 213-224.
Madella, M., Alexandre, A. and Ball, T. B. (2003). “International Code for Phytolith
Nomenclature 1.0.” The Phytolitharien 15(1): 7-16.
Mbida, C. M., Van Neer, W., Doutrelepont, H. and Vrydaghs, L. (2000). “Evidence for banana
cultivation and animal husbandry during the first millennium BC in the forest of Southern
Cameroon.” Journal of Archaeological Science 27: 151 - 162.
Milby, T. H. (1971). “The leaf anatomy of buffalo grass, Buchloë dactyloides (Nutt.) Engelm.”
Botanical Gazette 132(4): 308 - 313.
Mulholland, S. C. (1989). “Phytolith shape frequencies in North Dakota-grasses: a comparison to
general patterns.” Journal of Archaeological Science 16: 489-511.
24
Mulholland, S. C. and Rapp, G. J. (1992b). A Morphological Classification of Grass Silica
Bodies. Mulholland, G. R. J. a. S. C. (eds.). Phytolith Systematics: Emerging Issues.
Plenum PressNew York65-89.
Ollendorf, A. L., Mulholland, S. C. and Rapp, J. G. (1988). “Phytolith analysis as a means of
plant identification: Arundo donax and Phragmites communis.” Annals of Botany 61: 209
- 214.
Paijmans, K. (1973). “Plant succession on Pago and Witori volcanoes, New Britain.” Pacific
Science 27(3): 260 - 268.
Parr, J. F. (2002). “A comparison of heavy liquid floatation and microwave digestion techniques
for the extraction of fossil phytoliths from sediments.” Review of Palaeobotany and
Palynology 120 (3-4): 315-336.
Parr, J. F. (2003). A study of Palaeo-Landscapes in the Numundo region of West New Britain,
Papua New Guinea, as indicated by Fossil Phytolith Analysis. Environmental Science and
Management, Southern Cross University, Unpublished PhD. Thesis.
Parr, J. F. and Carter, M. (2003). “Phytolith and starch analysis of sediment samples from two
archaeological sites on Dauar Island, Torres Strait.” Vegetation History and
Archaeobotany 12(2): 131-141.
Parr, J. F., Dolic, V., Lancaster, G. and Boyd, W. E. (2001). “A microwave digestion method for
the extraction of phytoliths from herbarium specimens.” Review of Palaeobotany and
Palynology 116: 203 - 212.
Parr, J. F., Lentfer, C. J. and Boyd, W. E. (2001a). Spatial analysis of phytolith assemblages at an
archaeological site in West New Britain, Papua New Guinea. G.R. Clark, A. J. A. a. T. V.
(eds.). 'The Archaeology of Lapita Dispersal in Oceania'. Pandanus Press, Australian
National University, Terra Australis (17) pp. 125-134.
25
Parry, D. W. and Smithson, F. (1957). “Detection of opaline silica in grass leaves,.” Nature,
179: 975-976.
Pearsall, D. M., Piperno, D. R., Dinan, E. H., Umlauf, M., Zhao, Z. and Benfer, J., R.A. (1995).
“Distinguishing rice (Oryza sativa Poaceae) from wild Oryza species through phytolith
analysis: results of preliminary research.” Economic Botany 49(2): 183-196.
Peekel, P. G. (1984). Flora of the Bismark Archipeligo for naturalists,. Trans. E.E. Henty,
Forests Division of Botany, Lae, PNG.
Piperno, D. (1994). “Phytolith and charcoal evidence for prehistoric slash-and-burn agriculture in
the Darien rainforest of Panama.” The Holocene 4(3): 321-325.
Piperno, D. R. (1988). Phytolith Analysis: An Archaeological and Geological Perspective.
Academic Press.London
Piperno, D. R. and Pearsall, D. M. (1998). The silica bodies of tropical American grasses:
morphology, taxonomy, and implications for grass systematics and fossil phytolith
identification. Smithsonian Institution Press.Washington D.C.
Rosen, A. (1994). “Identifying ancient irrigation: a new method using opaline phytoliths from
Emmer wheat.” Journal of Archaeological Science 21: 125-132.
Runge, F. (1996). Leaf phytoliths and silica skeletons from east African plants. CD-Rom
Database. Dept. of Physical Geography, University of Paderborn, Germany.
Sangster, A. G. and Hodson, M. J. (2002). Silicification of Conifers and the Environment. 4th
International Meeting on Phytolith Research, Cambridge University, UK, abstract only.
Sangster, A. G., Williams, S. E. and Hodson, M. J. (1997). Silica deposition in the needles of
gymnosperms. II. Scanning electron microscopy. Machado., A. P. J. J.-T. M. J. (eds.). The
26
State-of-the-art of phytoliths in soils and plants.Monographia 4 del Centro de Ciencias
Medioambientals, CISC. Madrid. p123-133.
Sharma, M. and Rao, K. R. (1970). “Investigations into the occurence of silica in indian timbers.”
Indian Forester 96: 740 - 754.
Thornton, I. (1996). Krakatau: The Destruction and Reassembly of an Island Ecosystem. Harvard
University Press.Cambridge, Massachusetts.
Torrence, R. (2000). Archaeological Fieldwork in West New Britain, PNG May - June 2000.
Sydney, Division of Anthropology, Australian Museum.
Torrence, R. (2002). What makes a disaster?. A long-term view of volcanic eruptions and human
responses in Papua New Guinea. Torrence, R. and Grattan, J. (eds.). Natural Disasters and
Cultural Change. Routledge, London. 292-402.
Torrence, R., Specht, J. and Boyd, W. E. (1999). Archaeological Fieldwork on Numudo and Garu
Plantations West New Britain, PNG, Division of Anthropology, Australian Museum and
Faculty of Resource Science, Southern Cross University.
Turner, R. and Hurst, T. (2001). “Factors influencing volcanic ash dispersal from the 1995 and
1996 eruptions of Mount Ruapehu, New Zealand.” Journal of Applied Meteorology 40(1):
56 - 69.
Twiss, P. C. (1992). Predicted World Distribution of C3 and C4 Grass Phytoliths,. Mulholland,
G. R. J. a. S. C. (eds.). Phytolith Systematics: Emerging Issues. Plenum PressNew
York113-128.
Twiss, P. C., Suess, E. and Smith, R. M. (1969). “Morphological classification of grass
phytoliths.” Soil Science Society of America Proceedings 33: 109 - 115.
Wallis, L. (2003). “An overview of leaf phytolith production patterns in selected northwest
Australian flora.” Review of Palaeobotany & Palynology 125: 201-248.
27
Welle, B. J. H. (1976). “Silica Grains in Woody Plants of the Neotropics, Especially Surinam.”
Leiden Botanical Series 3: 107-142.
Whittaker, R. J., Bush, M. B. and Richards, K. (1989). “Plant recolonization and vegetation
succession on the Krakatau Islands, Indonesia.” Ecological Monographs 59(2): 59 - 123.
Wilson, S. M. (1985). “Phytolith analysis at Kuk, an early agricultural site in Papua New
Guinea.” Archaeology in Oceania 20: 90 - 97.
Yoshida, S., Ohnishi, Y. and Kitagishi, K. (1962). “Histochemistry of silicon in rice plant II:
Localization of silicon within rice tissues.” Soil Science and Plant Nutrition 8(1): 36 - 41.
Zhao, Z., Pearsall, D. M., Benfer, R. A. J. and Piperno, D. R. (1998). “Distinguishing rice (Oryza
sativa Poaceae) from wild Oryza species through phytolith analysis, II: Finalized
method.” Economic Botany 52(2): 134-145.
Zhao, Z. and Piperno, D. R. (2000). “Late Pleistocene/Holocene environments in the middle
Yangtze River valley, China and rice (Oryza sativa L.) domestication: the phytolith
evidence.” Geoarchaeology: An International Journal 15(2): 203 - 222.
Parr, J. F. (2003). A study of Palaeo-Landscapes in the Numundo region of West New Britain,
Papua New Guinea, as indicated by Fossil Phytolith Analysis. Environmental Science and
Management, Southern Cross University, Unpublished PhD. Thesis.
Bozarth, S. R. (1987). “Diagnostic opal phytoliths from rinds of selected Cucurbita species.”
American Antiquity 52(3): 607 - 15.
Lanning, F. C., Hopkins, T. L. and Loera, J. C. (1980). “Silica and ash content and depositional
patterns in tissues of mature Zea mays L. plants.” Annals of Botany 45: 549 - 554.
Parr, J. F., Lentfer, C. J. and Boyd, W. E. (2001a). Spatial analysis of phytolith assemblages at an
archaeological site in West New Britain, Papua New Guinea. G.R. Clark, A. J. A. a. T. V.
28
(eds.). 'The Archaeology of Lapita Dispersal in Oceania'. Pandanus Press, Australian
National University, Terra Australis (17) pp. 125-134.
Pearsall, D. M. (1999). The impact of maize on subsistence systems in South Amreica: an
example from the Jama River valley, coastal Ecuador. Gosden, C. and Hather, J. (eds.).
The Prehistory of Food: Appetites for change. RoutledgeLondon, New York419 - 437.
Piperno, D. R., Andres, T. C. and Stothert, K. E. (2000). “Phytoliths in Cucurbita and other
Neotropical Cucurbitaceae and their occurrence in early archaeological sites from the
lowland American tropics.” Journal of Archaeological Science 27: 193 - 208.
Piperno, D. R. and Pearsall, D. M. (1993). “Phytoliths in the reproductive structures of Maize and
Teosinte: implications for the study of Maize evolution.” Journal of Archaeological
Science 20: 337-362.
Torrence, R. and Boyd, W. E. (1997). Distributional archaeology and buried landscapes: A new
approach to tropical subsistence and settlement studies. Society of American Archaeology
Annual Meeting, Nashville.
Piperno, D. R. (1988). Phytolith Analysis: An Archaeological and Geological Perspective.
Academic Press.London
Jones, R. L. and Beavers, A. H. (1963). “Some mineralogical and chemical properties of plant
opal.” Soil Science 96: 375-379.
Lanning, F. C., Ponnaiya, B. W. X. and Crumpton, C. F. (1958). “The chemical nature of silica in
plants.” Plant Physiology 33: 339 - 343.
Parr, J. F., Dolic, V., Lancaster, G. and Boyd, W. E. (2001a). “A microwave digestion method for
the extraction of phytoliths from herbarium specimens.” Review of Palaeobotany and
Palynology 116: 203 - 212.
29
Parr, J. F., Lentfer, C. J. and Boyd, W. E. (2001b). “A comparative analysis of wet and dry ashing
techniques for the extraction of phytoliths from plant material.” Journal of Archaeological
Science 28: 875-886.
Parry, D. W. and Smithson, F. (1957). “Detection of opaline silica in grass leaves,.” Nature,
179: 975-976.
Pearsall, D. M. (1979). The application of ethnobotanical techniques to the problem of
subsistence in the Ecuadorian Formative. Urbana, University of Illinois.
Piperno, D. R. (1988). Phytolith Analysis: An Archaeological and Geological Perspective.
Academic Press.London
Raeside, J. D. (1970). “Some New Zealand plant opals,.” New Zealand Journal of Science 13:
122 - 132.
Rovner, I. (1972). “Note on a safer procedure for opal phytolith extraction.” Quarternary
Research 2: 591.
Zimmerman, A. (1901). Botanical Microtechnique,. Henry Holt.New York
Piperno, D. R. (1988). Phytolith Analysis: An Archaeological and Geological Perspective.
Academic Press.London