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208179 Abstract 5
UNIVERSITY OF PORTSMOUTH SCHOOL OF BIOLOGICAL SCIENCES
BSc (Hons) Environmental Biology
The use of ferns as biological indicators to assess local rainforest habitat in Southeast Sulawesi, and evaluation of the ecological factors that influence species diversity,
abundance and distribution
By
Rebecca S Wheeler
2004 / 2005 academic year
Supervisor: Dr Andrew Powling
208179 Abstract 6
Declaration.
I, Rebecca S Wheeler, declare that I am the sole author of this work and that all work
is my own except where references have been cited. All diagrams and photographic
images are my own unless otherwise cited. Any received guidance has been detailed
in the acknowledgements.
Signed .
Word content (excluding titles, table contents, appendix and references) 8370
208179 Abstract 7
1. Abstract.
Fern communities and their required microhabitats have not been previously
studied in the Lambusanga reserve on Buton Island, Southeast Sulawesi. The reserve
area is greatly affected by people from local communities. They rely on the forest for
subsistence agriculture (Byrne 1997), building materials and especially rattan
harvesting from which profits support the local economy. Many different forest
ecosystems from undisturbed primary forest to cleared forest areas exist within the
reserve forming a fragmented (Pimm 1998) array of these forest types.
This allowed the comparison of fern and fern ally species across these
different habitats to be completed. Identification of the ferns was done after
specimens were collected during fieldwork. Various transects and forest surveys were
completed in three different forest areas, varying in altitude, disturbance and
topography. Transects were also completed along the rivers of those areas. This
enabled the effect of environmental variables such as light intensity, canopy height
and soil properties on specific fern species to be established across different forest
ecosystems. Data from this permits some of the ferns to be used as biological
indicators of forest disturbance.
Ground ferns, epiphytes, climbing ferns, river ferns and tree ferns were all
found throughout the measured areas of the reserve. The results show that these
groups of ferns are affected by different environmental variables and individual
habitat preferences have been established for some of these species.
208179 Abstract 8
Contents.
Chapter Page
1 ABSTRACT 4
2 INTRODUCTION 5
2.1 Fern life history 5-6
2.2 Ferns as study subjects 7
2.3 Ferns and rainforest structure 7-8
2.4 Habitat requirements 8-9
2.5 Forest disturbance 10
2.6 The area of study 10-12
2.7 Geological history 12-13
2.8 Deforestation and fragmentation 13-14
2.9 Aims of project 14
3 MATERIALS AND METHODS 15
3.1 Materials 15
3.2 Methods 15
3.2.1 Vegetation sampling and
environmental measurements
15-20
3.2.2 Statistical methods 20-21
4 RESULTS 22
4.1 Biodiversity inventory from transect
data
22-23
4.2 Transect data 23-44
4.3 Problems encountered 44-45
208179 Abstract 9
4.4 Binary logistic regression for transect
data
46-53
4.5 Binary logistic regression survey in
Kakenauwe grid
53-57
4.6 River surveys and biodiversity
inventory
58-60
4.7 Specimen images 61-65
5 DISCUSSION 66-76
6 CONCLUSION 77-78
7 ACKNOWLEDGEMENTS 79
8 REFERENCES 80-85
9 APPENDIX 86
9.1 Light conversions to units of Lux 86-87
10 DECLARATION 88
208179 Abstract 10
2. Introduction. Ferns belong to the Pteridophyta division of the plant kingdom (Raven et al, 1999).
Pteridophyta (formerly Filicophyta) are defined as a major group of spore-bearing
vascular plants (Lawrence, 2000), also including the club mosses (Selaginellaceae),
horsetails (Equisetum) and the tropical Psilophyta. Ferns occur in abundance across
tropical and temperate climates.
2.1 Fern Life History
The life history of a fern consists of a cycle between a diploid sporophyte
generation and a gametophyte generation. During the sporophyte stage, spore-forming
organs known as sori, form on the fronds. Sori can sometimes be covered and
protected by an extension of the lower epidermis known as an indusium. The
organisation, size, shape and colour of the sori are unique to every different species.
This means that identification to genus and species level heavily relies upon sori
analysis and is often the only differing characteristic between species of the same
genus. Ferns can only be confidently identified if they are sporulating, causing a
major limiting factor during sample collection.
Each sorus contains sporangia, inside which individual spore mother cells
produce four haploid spores via meiosis (Kimball, 2004). The spores remain in place
until conditions become favourable and the humidity drops. The annulus (a row of
specialised cells) straightens due to separation of the sporangia lip cell membranes.
Once a critical tension point is reached, the annulus snaps back and the spores are
released. A spore germinates and grows into a young plant known as a prothallus.
This produces the archegonia and antheridia, which produce a single egg and
swimming sperm by mitosis respectively. The prothallus is able to support gamete
synthesis by the presence of rhizoids (root-like stems), which absorb vital nutrients
208179 Abstract 11
and sufficient water from the surrounding environment. Ferns in general prefer to
inhabit moist substrate conditions, which allow the mobile biflagellate zoospores to
swim to the archegonia (female) on a different prothallus. As the male and female sex
organs have differing maturation times, the sperm seeks a separate individual on
which the archegonia are already established so fertilisation can occur. This restores
the haploid gametophyte into a diploid sporophyte, producing a new independent
generation (Kimball, 2004). The fern reproductive cycle shown below illustrates the
above processes. Vegetative growth allows the plant to reach maturity. The process of
leaf unrolling known as circinate vernation produces new fronds, eventually
constructing a well-established adult.
Figure 1. Fern Life Cycle
208179 Abstract 12
2.2 Ferns As Study Subjects
The abundance and diversity of fern habitats make them ideal as subjects for
studying forest ecosystems. Fern ‘allies’ such as Selaginella and Lycopodium species
will also be included in this study. The ferns discovered in Southeast Sulawesi can be
compared and identified in accordance with Series II of Holttum’s Flora Malesiana
literature.
2.3 Ferns and Rainforest Structure
Rainforest biomes are found at low latitudes either side of the equator. They
experience more than 254cm of rainfall per year (Lawrence, 2000). Tropical
rainforest structure is assembled in a layer formation, comprising of ground level
vegetation, under-storey vegetation (shrubs, herbaceous plants and small trees) and
finally the upper canopy layer. Not all plants are restricted to inhabit a single layer
niche for example; Lianas (vines) and Rattan are both well-established climbing
species that exist from the ground up to the canopy.
Together, the many different types of ferns inhabit niches across the entire
forest structure, making them useful species to study. Ground ferns range from small,
ubiquitous individuals such as Lygodium circinnatum to large and entrenched
individuals like the Elephant fern (Angiopteris evecta). Climbing ferns such as
Teratophyllum aculeatum and Drynaria sparsisora inhabit areas from ground level up
to mid-canopy levels. Birds nest ferns (Asplenium nidus) and other epiphytic ferns
such as Pyrrosia piloselloides are found growing up at the canopy level, using other
plants and trees for substrate. Some ferns are only found in river environments and in
alluvial deposits such as Tectaria aurita and Tectaria rheophytica.
208179 Abstract 13
Tree ferns (family Cyatheaceae) are mainly arboreal and palm-like. They can
reach heights of 50ft or more, with long and complex tripinnate fronds. Tree ferns are
naturally tropical, inhabiting a wide range of topographical ecosystems.
2.4 Fern Habitat Requirements
Due to the huge diversity of the Pteridophyta, habitat requirements of different
fern species vary enormously. Many different environmental factors potentially
influence the abundance and diversity of ferns in a particular area. Pinpointing the
main factor for making an area ‘habitable’ would inevitably be impossible. This is
made harder if interactions between the ferns and environmental factors are also taken
into account. Particular habitat requirements for individual fern species can only be
suggested if several examples of the species were found within the specified habitat.
Fern populations are likely to be affected by one or a combination of the
environmental factors below:
• Canopy cover
• Canopy height (epiphytes and climbing ferns)
• Forest maturity and status (primary/secondary/regenerating)
• Substrate/geology (ground ferns)
• Light intensity
• Soil richness/pH/temperature/moisture (ground ferns)
• Slope gradient and topography
• Annual rainfall/climate
• Disturbance (such as paths cut/tree fall/logging)
Some Examples
Pteridium aquilinum (Bracken fern) is found to grow on a wide variety of
soils, but is most successful when water and oxygen are in adequate supply (Fletcher
208179 Abstract 14
and Kirkwood 1979). Pteridium aquilinum also thrives is dry soils although fronds are
sparse and short (Watt 1964) so photosynthesis efficiency is somewhat reduced by
stomatal closure in response to high evaporation conditions (Tinklin and Bowling
1969). Evidence shows that bracken is inhibited by waterlogged soils, due to the lack
of oxygen (Poel, 1951, 1961).
This fern is characteristic of acid, nutrient-deficient soil (Watt 1976) and tends
to dominate such areas, probably due to lack of competition. Pteridium aquilinum
alongside Dicranopteris linearis are described as pioneer organisms of lowland
rainforests (Whitten et al, 2002). They invade grassland areas that suffer infrequently
from fires, therefore preferring to be in open areas of little shade and high light
intensities (Whitten et al, 2002).
Angiopteris evecta (Elephant fern) inhabits areas of high moisture levels such
as stream banks (Page 1979). This fern is found in well-established, relatively
undisturbed forest areas in nutrient-rich soil such as alluvial deposits. Angiopteris
evecta occurs on limestone substrate. This species is rarely dominant and exists as
large solitary individuals.
Asplenium nidus (Birds nest fern) alongside other epiphytes uses trees as
substrate. As these ferns are not directly dependant on soil properties they are able to
occur within a broader habitat range, with precipitation and light intensity being key
limiting factors for distribution.
The ferns mentioned above are habitat specialists. Other species are more
generalist whose habitat ranges are more diverse. Specialist ferns are well
documented, as limiting factors on their required habitat are better understood.
208179 Abstract 15
2.5 Forest Disturbance
Forest disturbance affects many fern species. Disturbance can be caused by a
huge number of natural and anthropogenic sources. Effects on fern populations are
not always detrimental. Some species are even considered to prefer habitats with
higher disturbance levels. Natural disturbances include tree fall, landslides and
extreme weather. Anthropogenic disturbances tend to have more dramatic effects on
the vegetation, as species are not adapted to them. These include farming, cutting
access paths, hunting and resource collection.
Disturbances like the ones mentioned above cause massive disruption to the
layered structure of tropical rainforests. Disturbance causes huge alterations and
variation in the vegetation composition of the area affected, causing knock-on effects
throughout the ecosystem.
Areas unaffected by human interference are known as primary forests. There
are very few areas left that are true primary forests and these have only remained
untouched due to access problems or legislation. Secondary forests are areas that have
regenerated after destruction and where exploitation is monitored and controlled.
Regenerating forests have been exploited to a point where there is nothing left and
natural succession is allowed to take over in hope that the forest will regenerate itself
over time.
2.6 The Area of Study
The study will concentrate on the Lambusanga Reserve, Buton Island, in
Southeast Sulawesi, Indonesia. The reserve is composed of primary, secondary and
regenerating forest areas, in which comparative fieldwork will take place.
208179 Abstract 16
Location of Study.
Figure 2. Indonesia. Study area is Southeast Sulawesi. (Shown in the red circle.)
Figure 3. Shows location of Buton Island (red box) in Southeast Sulawesi.
Figure 4. Fieldwork was carried out in Lambusanga Reserve on Buton Island. (Shaded
yellow).
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Figure 5. Satellite Photograph. Shows the reserve boundaries and topographical layout
of the area. The three yellow boxes indicate the three study areas within the reserve.
(Carlisle, B., 2002, ButonGIS: GIS data sets of Buton Island, Sulawesi, Indonesia.
University of Northumbria.)
2.7 Geological History
East and West Sulawesi are separated by the Palu-Koro fault. Some 13-19
million years ago, a collision caused the East to override the West, exposing
ultrabasic rocks. This force still acts on Sulawesi, causing the surface geology to be a
mixture of ophiolites, Mesozoic sedimentary rocks, tertiary sedimentary and igneous
rocks and quaternary volcanic sediments (Whitten et al 1987).
The forest within the reserve is entirely lowland mainly on limestone
substrate. The lowland rainforests are home to most of the tree species in Sulawesi
and also to palms such as Licuala celebensis and multiple species of rattan. The
characteristic shallow soils and steep slopes of limestone do not provide ideal tree
habitats but give rise to calcium-tolerant plant species. The ultrabasic substrate
208179 Abstract 18
support unique forests and are responsible for high levels of plant endemism (Whitten
et al, 1987).
Sulawesi would supports a disproportionately large amount of species
diversity in a very small area. Endemism (species that are unique to a particular place)
is very high in isolated places such as Sulawesi (Whitten et al, 1987) and effort is
made to study and conserve them.
Sulawesi became isolated from Asia and Australia due to its geological
location. Wallace’s Line (a deep oceanic trench) runs between Borneo and Sulawesi
southward between Bali and Lombok, causing a relatively impassable barrier to
animals and plants. Sulawesi is isolated from the East by Lydekker’s Line, which runs
from between Timor and Australia to between Halmahera, Seram and New Guinea.
Lydekker’s Line prevented the migration of most Australasian species, and led to the
evolution of some plant, mammal, bird and herpetofauna species entirely unique to
Sulawesi (Whitten et al 1987).
2.8 Deforestation and Fragmentation
The lowland rainforests of Sulawesi and Buton Island are greatly affected by
deforestation to make room for rice paddy fields and to increase access for rattan
collection. The local villagers exploit their surrounding forest to build houses and to
establish plots used for growing and harvesting subsistence crops. It has been
calculated that over half of the original forested areas over Sulawesi have been
cleared, and the remaining forest has been fragmented with exception of few larger
areas (Whitten 2000).
Fragmentation causes dramatic biological consequences. These are extinction
of species with home ranges larger than the fragment size, delayed extinction of
species left with numbers below their minimum viable population, loss of genetic
208179 Abstract 19
diversity, inbreeding depression, increases in tolerant species, loss of ‘habitat
specialists’ and an increase in ‘edge effects’ (Pimm, 1998).
The edge surrounding a forest fragment is thought to penetrate 300m into the
fragment. It has been calculated that fragments less than 36 hectares (Ha) are
effectively all ‘edge’, but this depends on the shape of the fragment (Pimm, 1998).
‘Edge effects’ include lower humidity levels due to increased winds, causing damage
or death to plant species. There would also be higher light intensities allowing
climbing plants such as Lianas to proliferate. An estimated ten percent decrease of
plant biomass in a forest fragment occurs within a couple of years (Pimm, 1998),
causing a cascade decrease throughout the higher trophic levels dependant on the
plants for nutrition. It is therefore important to prevent further deforestation to allow
regeneration and conservation of the existing biodiversity.
2.9 Aims of Project
1) To create a species list of the ferns on Buton Island.
2) To establish effects of light intensity, soil pH, soil moisture, canopy cover,
canopy height, slope, topography, substrate type, and forest maturity and
disturbance levels on different fern species.
3) Use findings from the above to define habitat requirements for common Buton
ferns.
4) To highlight the main habitat requirements for specific species by comparing
microhabitats where it did and did not occur.
5) To use ferns as biological indicators of forest disturbance levels.
6) To assess differences of fern diversity and abundance between river, forest and
non-forest locations.
208179 Abstract 20
3. Materials and Methods.
3.1 Materials
• 100m, 50m and 5m tape measures, measures distances between sample sites.
• Polythene bags for specimen collection.
• Waterproof notebook and pencil to record data.
• Light meter.
• Soil pH meter.
• Soil moisture meter.
• Digital camera, to photograph new specimens (non-sporulating).
• Compass to measure slope.
• CD case aids visual canopy cover estimate.
• 50m ropes for river transects.
Methods
3.2.1 Vegetation sampling and environmental measurements
Transects
Transect sites were chosen to cover areas of primary (or close to), secondary
and regenerating forests at varying altitude and disturbance levels. Fieldwork was
completed at three different forest locations (Kakenauwe grid, La Pago and Anoa),
along a stretch of road and at three river locations (one in each of the forest areas).
Topography, substrate type and forest structure differed sufficiently between the
transect sites to allow habitat comparisons. The sampling sites were uniformly
distributed along pre-marked transects.
208179 Abstract 21
The sampling sites were 100m2 and as high as the canopy reached at that
particular site. The site width stretched 5m either side of the transect path to make up
the 10m. This was the best size to use for the type of vegetation, to take into account
the different fern niches mentioned in the introduction.
Figure 6. A Diagrammatic Representation of the Sample Site. (Not to Scale)
Ferns that were present within the sampling area were recorded and awarded a
measure of abundance. The scale of occurrence used was:
• 4 = Abundant
• 3 = Common
• 2 = Frequent
• 1 = Occasional - Rare (Considered rare if seen once)
• 0 = Not Present
Visual assessments of percent canopy cover, average canopy height, emergent canopy
height, substrate type/geology, amount of soil cover, disturbance and forest type,
208179 Abstract 22
topography (indicating drainage) and general habitat characteristics were noted at
each sampling site at the central point (see diagram).
Specimens found with sori were collected and kept moist in polythene bags for
further analysis and identification. Non-sporulating specimens were either collected or
photographed and logged using a digital camera (aim 1).
Light in the general area was measured next to where ferns occurred in the
sample site using the light meter. Light was measured morning to midday to reduce
the difference between light levels at differing fieldwork times. The light reading was
taken when the sun was in as it was exceptionally responsive to bright light, although
on sunny days this was not always possible. The light readings are therefore not
entirely reliable. The arbitrary units recorded were later converted to Lux (Appendix
1) to allow for the exponential increase between the units, using the statistical
software package, Minitab. Light readings at the ground vegetation level were also
taken if many ground ferns were present.
Soil pH and moisture were taken by inserting the meter probes into the soil
approximately 1m to the left or right of the central point of the sample site or where
soil covering was ample. The circumference breast height of the largest tree within the
sample area was measured to give an indication of forest maturity. It was assumed
that areas where the trees were wider were more mature and not subjected to forestry
type disturbances.
Placing a compass horizontally at eye level and reading the degree of
inclination gave a measurement of slope. Gaps in the overhead canopy were estimated
by using a clear CD case which had a series of dots drawn on to it. When placed
horizontally to the ground at eye-level, the number of dots that were covered by light
208179 Abstract 23
reflection gave a measure of light coming in through the canopy. These measurements
aid the accomplishment of aims 2 and 3.
Kakenauwe grid survey (to accomplish aim 4)
Random number tables (from Minitab) were used to pick random plot numbers within
the Kakenauwe grid. The grid was firstly mapped and a starting point established.
Random numbers chosen between 1 and 10 gave measurements representing numbers
of grid sectors across and down. A random number between 0o and 360o was chosen
for the degree to face once at the site and another (also between 1 and 10) for the
number of metres to walk into the undergrowth.
This is demonstrated in the grid below.
An example
Across- 3
Down- 2
Degrees from north point- 45
Metres in from path- 5
208179 Abstract 24
Figure 7. Demonstration of plotting sampling sites.
After 70 plots were chosen, a route was planned throughout the grid. The presence or
absence of three fern types was recorded in each plot visited. These ferns were
Selaginella ciliaris, Lygodium circinnatum and a species of Dryopteris (awaiting
identification). These ferns occurred frequently throughout the reserve.
The environmental variables assessed at each plot included average canopy
height, the percent of exposed rock substrate, percent of canopy cover, light from
directly overhead and from the largest canopy gap were measured using the CD case,
alongside light of the general area using the light meter, circumference breast height
of the largest tree, distance to nearest path and level of disturbance, using methods
described above.
River Transects and Biodiversity Inventory (to achieve aims 1, 3 and 6)
The river transects followed the same methods as the transects (described
above). The sample site width of 10m was divided into two halves either side of the
208179 Abstract 25
river. The river width was not included as very few if any ferns grow in the actual
river basin. The purpose of the river transects was to identify the fern species that only
inhabit river areas and also to assess the habitat distribution of ferns found previously.
To measure the distance between the sampling points a 50m rope was used. As
some of the river areas are steep limestone tufas, transects were limited to a total
length of 500m with sampling points every 50m. Percentage of canopy cover and the
area habitat were noted at each site.
Aim 5 of this study will be accomplished by comparing data and results from the
transect data, the grid survey and the river transects.
3.2.2 Statistical methods
Correlation matrices using the statistical software package, Minitab, assessed
correlations between the fern occurrence levels and the environmental variables
measured throughout the transects. The significant associations found between
specific fern species and environmental variables were then further analysed using
fitted line plots. (Page 22-43)
Strong correlations between ferns and the variables were also discovered if the
species had low occurrence levels so a binary logistic regression analysis was done
where the fern occurred significantly in the transects. This is explained in further
detail in the results section (pages 45-52).
The Kakenauwe grid survey was also analysed using the binary logistic
regression technique. This analysis allows positive/negative data to be compared.
There were at least 30 positive and 30 negative occurrences of all three fern species
208179 Abstract 26
used within the 70 plots examined, enabling a good comparison of plot habitat where
the ferns occurred and did not occur (Page 52-56).
The fern species discovered in the river surveys and biodiversity inventories
were compared between the three different sites using the Jaccard Index (Magurran,
2004). This analysis technique (otherwise known as Marczewski-Steinhaus distance)
allows the differences between the species found at the different sites to be calculated.
(See Results, pages 57-59).
208179 Abstract 27
4. Results. 4.1 Biodiversity Inventory from Transect Data. A list of all the species discovered in all areas covered. Ferns mentioned here that do
not appear in the transect data were discovered en route or in surrounding areas and
identified. Many other species were found but these were not identifiable.
Identifications from Bogor, Jakarta are still awaited.
Photographs of some of the ferns are in the last part of this section.
• Acrostichum aureum – fern of mangrove swamp undergrowth (Whitten et al
2002).
• Adiantum hemionitis – ground fern found in dry, well lit mature forests.
• Angiopteris evecta – large ground fern found in river
valleys.
• Asplenium nidus – epiphytic fern found across a wide habitat range.
• Cyathea contaminans – tree fern found at altitudes above 200m (Whitten et al
2002).
• Dicranopteris linearis – dominating ground fern of cleared forest areas.
208179 Abstract 28
• Drynaria sparsisora – epiphytes found on ultrabasic
vegetation (Whitten et al 2002).
• Drynaria quercifolia – epiphytic fern found on ultrabasic
vegetation (Whitten et al 2002).
• Lindsaea lucida – ground fern (also possibly epiphytic)
found across a wide forest habitat.
• Lygodium circinnatum – common climbing ground fern.
• Microsorum punctatum – epiphytic fern found across a wide habitat range.
• Nephrolepis biserrata – ground fern that inhabits cleared forest areas.
• Ophioglossum pendulum – epiphyte of palms and other trees.
• Phymatosorus scolopendria – ground fern (also epiphyte) found in relatively
disturbed areas.
• Pteridium aquilinum – dominating ground fern of cleared areas.
• Pteris ensiformis – common ground fern.
• Pteris tripartata – ground fern found on limestone substrate in cleared areas.
• Pteris vittata – ground fern found in cleared areas.
• Pyrossia lanceolata – succulent epiphyte found in more mature forest.
• Pyrossia piloselloides – as above.
• Selaginella ciliaris – very common ground fern ally.
• Tectaria aurita – ground fern of forest and river habitat.
• Teratophyllum aculeatum – tree climbing ground fern.
208179 Abstract 29
4.2 Transect Data.
Matrices are presented from each transect, showing species correlations with the
environmental variables. Fitted line plots for fern species that have these significant
associations and where they have occurred sufficiently, are also included for further
detail. Associations at both 95% (p values less than 0.05, highly significant) and 90%
(p values less than 0.10, moderately significant) confidence levels are both included.
Conclusions will be made using the 95% confidence associations as a priority
over the moderately significant associations. Correlation matrices also correlate fern
species against each other. This information is not included, as it is not required to test
the stated hypothesis.
Presentation of Results
BLUE shaded boxes for the FERNS species recorded.
LILAC shaded boxes for the VARIABLES measured along the transect.
BRIGHT YELLOW boxes to represent HIGHLY SIGNIFICANT associations.
DULL YELLOW boxes to represent MODERATELY SIGNIFICANT associations.
The following abbreviations for the fern species and the environmental variables will
be used throughout the results section:
Table 1. Fern and Fern Ally Abbreviations.
Adian Spp
Adiantum ground fern species
Pteri aqu
Pteridium aquilinum
Ue Unknown fern E
Adian hem
Adiantum hemionitis
Pteri ens
Pteris ensiformis Uf Unknown fern F
Asple nid
Asplenium nidus
Pyrro lan
Pyrrosia lanceolata
Ug Unknown fern G
208179 Abstract 30
Asple spp
Asplenium ground fern species
Pyrro pil
Pyrrosia piloselloides
Uh Unknown fern H
Terat acu B
Bathophyll of Teratophyllum aculeatum
Phyma sco
Phymatosorus scolopendria
Ui Unknown fern I
Cyath con
Cyathea contaminans
Pteri tri
Pteridium tripartata
Uj Unknown fern J
Dicra lin
Dicranopteris linearis
Pteri vit
Pteris vittata Uk Unknown fern K
Dryop pol
Dryopteris Polystichopsis sp.
Selag cil
Selaginella ciliaris Ul Unknown fern L
Dryop spp
Dryopteris ground fern species
Terat acu
Teratophyllum aculeatum
Um Unknown fern M
Dryoa spp
Dryoathyrium ground fern species
Tecta spp
Tectaria (species found in forest)
Un Unknown fern N
Dryna spa
Drynaria sparsisora
Thely spp
Thelypterus ground fern species
Uo Unknown fern O
Lygod cir
Lygodium circinnatum
Ua Unknown fern A
Micro pun
Microsorum punctatum
Ub Unknown fern B
Nephr bis
Nephrolepis biserrata
Uc Unknown fern C
Nephr spp B
Nephrolepis species type B
Ud Unknown fern D
Table 2. Abbreviations used for the Environmental
Variables.
Av ht Average canopy height (m) Em ht Emergent canopy height (m) Subs % Substrate that was rock % C % Canopy cover CBH Circumference breast height (m) of largest tree L > Light from biggest canopy gap L oh Light from directly overhead Lux Light in general area L v Light at vegetation level D to P Distance to nearest cut path Dist Disturbance (e.g. fallen tree) S. Mst Soil moisture level S. pH Soil pH level Slp Degree of slope
208179 Abstract 31
Talingko to La Bundo Bundo Village, Roadside Transect.
The roadside transect was initially completed to develop fieldwork collection
techniques and to work out the most important aspects of the transect data. This
transect was easy to access and work, allowing the ferns to be surveyed through
a wide variety of forest type, disturbance levels and altitude.
Table 3. Shows the correlation of all species with all the
measured environmental variables measured along the
Roadside Transect.
% Cover Av. C. ht Em. C. ht Light Light Vegn
Substrate
Selag cil
0.287 0.068
-0.201 0.207
-0.290 0.066
-0.231 0.146
-0.385 0.013
-0.009 0.954
Lygod cir
0.033 0.835
0.170 0.288
0.137 0.392
-0.411 0.008
-0.402 0.009
0.010 0.952
Asple nid
0.035 0.827
0.002 0.990
-0.074 0.647
-0.095 0.557
-0.209 0.191
-0.156 0.331
Micro pun
0.074 0.645
0.064 0.691
0.129 0.421
-0.018 0.911
0.095 0.554
0.102 0.526
Ua
0.018 0.911
-0.302 0.055
-0.300 0.057
-0.273 0.084
-0.157 0.326
0.074 0.645
Ub
0.306 0.052
0.150 0.348
0.083 0.607
-0.248 0.119
-0.146 0.361
-0.094 0.558
Uc
-0.172 0.282
-0.185 0.246
0.040 0.804
0.134 0.404
-0.140 0.383
-0.170 0.288
Ud
0.046 0.774
-0.185 0.246
0.040 0.804
-0.251 0.113
-0.077 0.631
0.147 0.359
Ue
0.189 0.238
0.150 0.351
0.289 0.067
0.120 0.455
0.274 0.083
0.016 0.920
Uf
0.155 0.332
0.125 0.437
0.275 0.082
-0.059 0.714
-0.015 0.927
0.147 0.359
Phyma sco
-0.099 0.537
-0.069 0.667
0.062 0.698
-0.025 0.878
-0.037 0.820
0.164 0.306
Dryna spa
0.192 0.230
0.021 0.894
0.228 0.152
-0.038 0.812
0.048 0.766
0.147 0.359
208179 Abstract 32
Asple spp
0.293 0.063
0.415 0.007
0.614 0.000
-0.170 0.287
0.108 0.500
0.246 0.122
Adian spp
0.077 0.632
0.045 0.782
-0.063 0.695
-0.194 0.224
-0.356 0.022
0.306 0.052
Dryop spp
0.191 0.231
0.254 0.109
0.215 0.176
-0.008 0.960
-0.041 0.800
-0.085 0.598
Dryoa spp
-0.050 0.758
-0.139 0.385
-0.121 0.451
-0.023 0.887
0.111 0.488
-0.003 0.985
Pyrro pil
-0.134 0.405
0.069 0.670
0.043 0.788
-0.021 0.896
0.167 0.297
0.261 0.099
Pteri vit
0.006 0.971
0.026 0.871
-0.127 0.429
0.120 0.455
0.013 0.937
-0.102 0.525
Nephr bis
0.010 0.952
-0.237 0.136
-0.148 0.357
-0.098 0.542
0.010 0.949
-0.170 0.288
Pteri tri
-0.092 0.569
-0.148 0.356
-0.067 0.678
0.313 0.046
0.061 0.704
-0.421 0.006
Fitted Line Plots.
Show graphically the significant correlations found
between specific fern species and an environmental
variable.
208179 Abstract 33
Graph 1. Shows the relationship between Selaginella
ciliaris and the light intensity at vegetation level. (95%
Confidence Level)
9876543
4
3
2
1
0
Light Vegn
S.c
.
S = 1.32389 R-Sq = 14.8 % R-Sq(adj) = 12.6 %
S.c. = 4.76291 - 0.426767 Light Vegn
Regression Plot
Graph 2. Shows the relationship between Lygodium circinnatum and light intensity of
the area. (95% Confidence Level)
208179 Abstract 34
12000 7000 2000
2
1
0
light in lux
L.c.
S = 0.462725 R-Sq = 16.9 % R-Sq(adj) = 14.8 %
L.c. = 0.974686 - 0.0001073 light in lux
Regression Plot
Graph 3. Shows the relationship between Lygodium circinnatum and light at
vegetation level. (95% Confidence Level)
9876543
2
1
0
Light Vegn
L.c.
S = 0.464852 R-Sq = 16.1 % R-Sq(adj) = 14.0 %
L.c. = 1.46874 - 0.157600 Light Vegn
Regression Plot
208179 Abstract 35
Graph 4. Shows the relationship between Selaginella ciliaris and percent canopy
cover. (90% Confidence Level)
70605040302010 0
4
3
2
1
0
% Cover
S.c
.
S = 1.37388 R-Sq = 8.3 % R-Sq(adj) = 5.9 %
S.c. = 0.704122 + 0.0185089 % Cover
Regression Plot
Graph 5. Shows the relationship between Selaginella ciliaris and height of the
emergent canopy. (90% Confidence Level)
90807060504030
4
3
2
1
0
Emergent Hgt
S.c
.
S = 1.37266 R-Sq = 8.4 % R-Sq(adj) = 6.1 %
S.c. = 2.85595 - 0.0241112 Emergent Hgt
Regression Plot
208179 Abstract 36
La Pago Grid Transect. Table 4. Shows the correlation of all species with all the
measured environmental variables except soil moisture
and soil pH.
%
Cover Light > gap
Light o.h.
Av. C. ht
Em. C ht
CBH Light Light Vegn
Slope
Selag cil
-0.282 0.071
-0.288 0.064
-0.066 0.678
-0.202 0.201
-0.292 0.060
0.006 0.968
-0.041 0.798
-0.178 0.258
-0.213 0.176
Asple nid
-0.082 0.604
0.017 0.916
-0.019 0.905
0.171 0.280
0.287 0.065
0.145 0.359
-0.049 0.756
0.065 0.681
-0.212 0.178
Micro pun
0.198 0.209
-0.134 0.397
-0.072 0.647
0.031 0.844
-0.140 0.377
0.119 0.454
0.150 0.344
-0.053 0.737
0.332 0.032
Lygod cir
-0.236 0.133
-0.115 0.469
-0.006 0.971
-0.103 0.517
-0.186 0.238
0.141 0.373
-0.327 0.035
-0.342 0.026
-0.495 0.001
Terat acu
-0.194 0.218
-0.149 0.346
0.182 0.249
0.027 0.865
0.046 0.774
0.376 0.014
-0.181 0.252
-0.320 0.039
-0.501 0.001
Pyrro pil
-0.062 0.697
-0.180 0.255
-0.044 0.783
0.195 0.215
0.021 0.895
-0.122 0.440
-0.118 0.457
-0.196 0.213
0.080 0.613
Dryna spa
-0.010 0.949
0.113 0.477
-0.149 0.345
0.212 0.179
0.138 0.385
-0.096 0.546
0.155 0.326
0.033 0.837
0.183 0.245
Pyrro lan
-0.337 0.029
0.347 0.024
-0.004 0.979
0.138 0.382
0.212 0.179
-0.008 0.958
0.185 0.240
0.086 0.590
0.120 0.447
Phyma sco
0.094 0.552
-0.007 0.964
-0.076 0.633
0.138 0.382
0.056 0.726
-0.070 0.657
0.013 0.934
0.064 0.689
0.105 0.508
Ug
-0.078 0.623
0.139 0.380
-0.004 0.979
-0.046 0.772
-0.100 0.528
0.239 0.127
-0.240 0.125
0.064 0.689
-0.142 0.370
Uh
0.094 0.552
0.101 0.526
0.819 0.000
-0.046 0.772
0.056 0.726
0.674 0.000
0.148 0.350
0.043 0.789
-0.142 0.370
Ui
0.181 0.252
0.024 0.882
-0.004 0.979
-0.046 0.772
-0.100 0.528
-0.067 0.672
-0.434 0.004
-0.429 0.005
-0.142 0.370
Uj
-0.078 0.623
0.001 0.997
0.246 0.116
-0.231 0.142
-0.100 0.528
0.314 0.043
0.265 0.090
0.184 0.244
-0.142 0.370
208179 Abstract 37
Fitted Line Plots.
Show graphically the significant correlations found between specific fern species and
an environmental variable.
Graph 6. Shows the relationship between Lygodium circinnatum and gradient of
slope. (95% Confidence Level)
208179 Abstract 38
0 10 20 30 40
0
1
2
3L.
c.
L.c. = 0.939885 - 0.0400912 Slope o
S = 0.730875 R-Sq = 24.5 % R-Sq(adj) = 22.6 %
Regression Plot
Slope
Graph 7. Shows the relationship between Lygodium circinnatum and light at
vegetation level. (95% Confidence Level)
900080007000600050004000300020001000 0
3
2
1
0
Light at veg
L.c.
S = 0.790089 R-Sq = 11.7 % R-Sq(adj) = 9.5 %
L.c. = 1.39399 - 0.0001733 Light at veg
Regression Plot
Graph 8. Shows the relationship between Lygodium circinnatum and light in the
general area. (95% Confidence Level)
208179 Abstract 39
8000700060005000400030002000
3
2
1
0
light in lux
L.c.
S = 0.794749 R-Sq = 10.7 % R-Sq(adj) = 8.4 %
L.c. = 1.83032 - 0.0002181 light in lux
Regression Plot
Graph 9. Shows the relationship between Teratophyllum aculeatum and forest
maturity (measured by circumference breast height of largest tree in sampling area).
(95% Confidence Level)
6543210
2
1
0
C.B.H (m)
T.a.
S = 0.493310 R-Sq = 14.1 % R-Sq(adj) = 12.0 %
T.a. = 0.0200331 + 0.199312 C.B.H (m)
Regression Plot
208179 Abstract 40
Graph 10. Shows the relationship between Teratophyllum aculeatum and light at
vegetation level. (95% Confidence Level)
900080007000600050004000300020001000 0
2
1
0
Light at veg
T.a.
S = 0.504312 R-Sq = 10.2 % R-Sq(adj) = 8.0 %
T.a. = 0.819864 - 0.0001025 Light at veg
Regression Plot
Table 5. Shows the correlation of all species with soil moisture and soil pH.
First 30 sample sites only.
N.B Epiphytic ferns have been omitted, as they are not relevant to the variables.
Soil Moist
Soil pH
Selag cil
0.011 0.952
-0.063 0.741
Lygod cir
-0.245 0.192
-0.064 0.736
Terat acu
-0.497 0.005
0.295 0.114
Phyma sco
0.141 0.458
-0.094 0.621
Ug
-0.280 0.134
0.296 0.113
Uh
-0.291 0.119
0.198 0.294
Ui
-0.129 0.498
0.101 0.597
Uj
-0.129 0.498
0.003 0.986
208179 Abstract 41
Graph 11. Shows the relationship between Teratophyllum aculeatum and soil
moisture. (95% Confidence Level)
0 1 2 3 4 5 6 7
0
1
2
T.a.
_2
T.a._2 = 1.06506 - 0.162021 Soil moistur
S = 0.504710 R-Sq = 24.7 % R-Sq(adj) = 22.0 %
Regression Plot
Soil Moisture
La Pago T2 Transect. Table 6. Shows the correlation of all species with all the
measured environmental variables measured in the La
Pago T2 Transect.
% Cover
Av. C. ht Em. C. ht CBH Soil pH
Selag cil
-0.211 0.533
0.075 0.826
0.101 0.768
0.425 0.193
0.631 0.037
Lygod cir
0.509 0.110
-0.821 0.002
-0.773 0.005
0.127 0.709
-0.003 0.993
Asple nid
-0.020 0.954
0.518 0.103
0.603 0.050
-0.127 0.711
0.106 0.756
Uk -0.660 0.385 0.245 -0.218 -0.124
208179 Abstract 42
0.027 0.243 0.468 0.518 0.716 Ul
0.091 0.790
0.459 0.156
0.395 0.229
0.157 0.644
0.164 0.631
Um
-0.285 0.396
0.310 0.353
0.395 0.229
-0.266 0.429
-0.268 0.425
Fitted Line Plots.
Show graphically the significant correlations found between specific fern species and
an environmental variable.
Graph 12. Shows the relationship between Selaginella ciliaris and soil pH. (95%
Confidence Level)
6.56.05.55.0
2
1
0
Soil pH
S.c
.
S = 0.731365 R-Sq = 39.8 % R-Sq(adj) = 33.1 %
S.c. = -6.19623 + 1.22536 Soil pH
Regression Plot
208179 Abstract 43
Graph 13. Shows the relationship between Lygodium circinnatum and average height
of the canopy. (95% Confidence Level)
8070605040302010
2
1
0
Av Canopy Hg
L.c.
S = 0.405887 R-Sq = 67.4 % R-Sq(adj) = 63.8 %
L.c. = 1.85246 - 0.0247723 Av Canopy Hg
Regression Plot
Graph 14. Shows the relationship between Lygodium circinnatum and the height of
the emergent canopy. (95% Confidence Level)
208179 Abstract 44
90807060504030
2
1
0
Emergent Hgt
L.c.
S = 0.450631 R-Sq = 59.8 % R-Sq(adj) = 55.3 %
L.c. = 2.13438 - 0.0235403 Emergent Hgt
Regression Plot
Kakenauwe Grid Transect.
Table 7. Shows the correlation of all species with all the
measured environmental variables measured in
Kakenauwe Grid.
% Cover Av can ht Em can ht CBH Soil moist Soil pH Selag cil -0.008
0.976 0.262 0.310
0.330 0.196
0.126 0.630
0.334 0.191
-0.035 0.895
Lygod cir 0.570 0.017
0.250 0.334
0.022 0.933
-0.092 0.726
0.078 0.766
-0.511 0.036
Asple nid -0.119 0.650
-0.034 0.898
0.281 0.275
-0.022 0.933
-0.370 0.143
0.256 0.322
Micro pun
0.417 0.096
0.171 0.511
-0.089 0.733
0.238 0.358
0.011 0.966
0.575 0.016
Dryna spa 0.034 0.898
0.521 0.032
0.462 0.062
-0.104 0.692
-0.083 0.750
0.150 0.566
Phyma sco
0.417 0.096
0.171 0.511
-0.089 0.733
0.238 0.358
0.011 0.966
0.575 0.016
Terat acu B
0.384 0.128
0.332 0.207
-0.230 0.374
-0.184 0.480
-0.254 0.325
-0.491 0.046
Dryop spp
-0.523 0.031
-0.655 0.004
0.206 0.427
-0.278 0.280
-0.002 0.993
0.082 0.756
208179 Abstract 45
Asple spp -0.237 0.359
-0.291 0.257
0.160 0.540
-0.070 0.790
0.187 0.471
0.235 0.364
Pteri tri -0.062 0.813
0.055 0.834
0.324 0.204
-0.283 0.270
0.162 0.534
0.150 0.566
Fitted Line Plots.
Show graphically the significant correlations found between specific fern species and
an environmental variable.
Graph 15. Shows the relationship between Lygodium circinnatum and percent canopy
cover. (95% Confidence Level)
807060504030
2
1
0
% Cover
L.c.
S = 0.604635 R-Sq = 32.4 % R-Sq(adj) = 27.9 %
L.c. = -1.34416 + 0.0301523 % Cover
Regression Plot
208179 Abstract 46
Graph 16. Shows the relationship between Lygodium circinnatum and soil pH. (95%
Confidence Level)
6.96.86.7
2
1
0
Soil pH
L.c.
S = 0.632456 R-Sq = 26.1 % R-Sq(adj) = 21.2 %
L.c. = 41 - 6 Soil pH
Regression Plot
Graph 17. Shows the relationship between Dryopteris species and percent canopy
cover. (95% Confidence Level)
208179 Abstract 47
807060504030
3
2
1
0
% Cover
Dry
.sp
S = 0.981090 R-Sq = 27.4 % R-Sq(adj) = 22.5 %
Dry.sp = 3.17157 - 0.0433503 % Cover
Regression Plot
Graph 18. Shows the relationship between Dryopteris species and the average height
of the canopy. (95% Confidence Level)
6050403020
3
2
1
0
-1
Av Canopy Hg
Dry
.sp
S = 0.870197 R-Sq = 42.9 % R-Sq(adj) = 39.1 %
Dry.sp = 3.13233 - 0.0660150 Av Canopy Hg
Regression Plot
Anoa T2 Transect.
208179 Abstract 48
Table 8. Shows the correlation of all species with all the
measured environmental variables measured along the
T2 Transect in node camp Anoa.
% Cover Av. C ht Em. C ht CBH Light Selag cil
-0.032 0.864
-0.003 0.989
-0.014 0.942
0.079 0.674
-0.228 0.217
Lygod cir
0.508 0.004
0.327 0.073
0.152 0.413
0.306 0.095
-0.252 0.171
Asple nid
0.095 0.611
-0.267 0.146
0.013 0.946
-0135 0.469
-0.105 0.573
Micro pun
-0.070 0.707
-0.214 0.247
-0.191 0.303
-0.043 0.820
-0.047 0.802
Dryna spa
-0.010 0.958
-0.094 0.616
-0.003 0.989
-0.052 0.780
0.040 0.829
Nephr bis
-0.607 0.000
-0.335 0.066
-0.565 0.001
-0.438 0.014
0.353 0.051
Nephr spp B
0.087 0.642
0.146 0.432
0.102 0.583
0.226 0.222
-0.129 0.489
Ua
-0.112 0.550
0.027 0.885
-0.076 0.683
0.002 0.993
-0.263 0.152
Ub
-0.070 0.707
0.025 0.894
0.242 0.189
0.029 0.875
-0.390 0.030
Adian hem
-0.029 0.877
0.155 0.404
0.146 0.434
0.204 0.272
0.123 0.508
Ue
-0.047 0.800
0.190 0.307
0.062 0.742
0.012 0.948
-0.019 0.919
Uf
-0.047 0.800
0.190 0.307
0.062 0.742
0.012 0.948
-0.019 0.919
Un
0.116 0.535
-0.219 0.237
-0.097 0.602
-0.104 0.576
0.055 0.770
Uo
-0.210 0.256
-0.137 0.462
-0.097 0.602
-0.104 0.576
0.055 0.770
Dryoa spp
-0.645 0.000
-0.464 0.009
-0.654 0.000
-0.490 0.005
0.399 0.026
Cyath con
-0.645 0.000
-0.464 0.009
-0.654 0.000
-0.490 0.005
0.399 0.026
Pteri aqu
-0.047 0.800
0.108 0.563
-0.018 0.924
-0.130 0.487
0.217 0.241
Dicra lin
-0.432 0.015
-0.195 0.293
-0.413 0.021
-0.404 0.024
0.420 0.019
208179 Abstract 49
Fitted Line Plots. Show graphically the significant correlations found between
specific fern species and an environmental variable.
Graph 19. Shows the relationship between Lygodium circinnatum and percent canopy
cover. (95% Confidence Level)
908070605040302010 0
2
1
0
% Cover
L.c.
S = 0.540484 R-Sq = 25.8 % R-Sq(adj) = 23.3 %
L.c. = -0.316042 + 0.0183681 % Cover
Regression Plot
Graph 20. Shows the relationship between Lygodium circinnatum and the average
height of the canopy. (90% Confidence Level)
208179 Abstract 50
40302010 0
2
1
0
Av. Canopy H
L.c.
S = 0.593042 R-Sq = 10.7 % R-Sq(adj) = 7.6 %
L.c. = 0.270113 + 0.0177574 Av. Canopy H
Regression Plot
Graph 21. Shows the relationship between Lygodium circinnatum and forest maturity.
(90% Confidence Level)
1.51.00.50.0
2
1
0
C.B.H (m)
L.c.
S = 0.597487 R-Sq = 9.3 % R-Sq(adj) = 6.2 %
L.c. = 0.276690 + 0.515117 C.B.H (m)
Regression Plot
Anoa T1 Transect.
208179 Abstract 51
Table 9. Shows the correlation of all species with all the
measured environmental variables measured in the T1
transect of node camp Anoa.
% Cover Av C ht Em C ht CBH Light Selag cil
-0.116 0.616
0.131 0.571
-0.026 0.910
-0.270 0.236
-0.014 0.951
Lygod cir
0.310 0.171
0.014 0.951
-0.060 0.796
-0.116 0.617
-0.374 0.095
Micro pun
0.233 0.309
0.177 0.441
0.183 0.428
0.143 0.537
-0.542 0.011
Asple nid
-0.013 0.956
-0.236 0.302
-0.263 0.250
-0.240 0.294
-0.202 0.379
Dryna spa
0.060 0.793
-0.036 0.875
0.018 0.940
0.045 0.847
-0.076 0.744
Dryop pol
0.040 0.865
-0.177 0.442
-0.243 0.289
0.618 0.003
-0.236 0.304
Dryoa spp
-0.293 0.197
-0.037 0.874
-0.243 0.289
-0.075 0.746
0.065 0.780
Thely spp
-0.115 0.620
0.035 0.880
0.004 0.985
-0.054 0.816
-0.063 0.785
Dicra lin
-0.431 0.051
-0.171 0.459
-0.167 0.469
0.118 0.610
-0.162 0.482
Nephr bis
-0.431 0.051
-0.171 0.459
-0.167 0.469
0.118 0.610
-0.162 0.482
Nephr spp B
-0.202 0.380
-0.268 0.241
-0.245 0.284
-0.071 0.759
-0.144 0.534
Tecta spp
0.265 0.246
0.270 0.236
0.392 0.079
-0.107 0.643
-0.297 0.191
Fitted Line Plots.
Show graphically the significant correlations found between specific fern species and
an environmental variable.
Graph 22. Shows the relationship between Microsorum punctatum and light in the
general area. (95% Confidence Level)
208179 Abstract 52
8000700060005000400030002000
3
2
1
0
light in lux
M.p
.S = 0.929415 R-Sq = 29.4 % R-Sq(adj) = 25.7 %
M.p. = 2.33921 - 0.0003396 light in lux
Regression Plot
Graph 23. Shows the relationship between Lygodium circinnatum and light in the
general area. (90% Confidence Level)
208179 Abstract 53
8000700060005000400030002000
2
1
0
light in lux
L.c.
S = 0.731180 R-Sq = 14.0 % R-Sq(adj) = 9.5 %
L.c. = 1.65724 - 0.0001671 light in lux
Regression Plot
End of Transect Analysis. 4.3 Problems encountered. A correlation matrix takes into account all of the fern species recorded and all of the
tested environmental variables. When a significant association is discovered, whether
it is at a 95% or a 90% confidence interval, the non-occurrences of a fern species is
also taken into account. The analysis requires the sample number to be based on the
ferns presence only.
Plots for the significant associations show that the line of best fit allows for
these non-occurrences. The non-occurrences have to be taken into account so that
there is a basis of comparison for the positive occurrences but there is risk that the line
of best fit will be skewed.
An example.
208179 Abstract 54
Lygodium circinnatum is found to have an association with light in the general area.
The fitted line plot (see below) shows a negative correlation using a line of best fit.
The sample sites in which Lygodium did not occur are influencing the line to have a
steeper negative gradient when it should have a more horizontal line running between
the occurrence levels of 1 and 2.
This concludes that the non-occurrences should be ignored when using this type of
analysis.
Graph 24. Example. Shows the relationship between Lygodium circinnatum and light
in the general area.
8000700060005000400030002000
2
1
0
light in lux
L.c.
S = 0.731180 R-Sq = 14.0 % R-Sq(adj) = 9.5 %
L.c. = 1.65724 - 0.0001671 light in lux
Regression Plot
To overcome this, the transect data will be further assessed using Binary Logistic
Regression. The original correlation matrices are very useful as they show the
abundance levels of the ferns at each measured point along the transects. Binary
logistic regression analysis only takes into account the presence or absence of a fern at
a particular point and not its level of abundance.
4.4 Binary Logistic Regression for the transect data.
208179 Abstract 55
Only transects of more than 20 sample sites will be assessed, as the samples
sizes would otherwise not be large enough. Only significant associations found from
the above correlation matrices are used.
The only fern species used in this analysis technique are ones that occurred
sufficiently through the transect lines. A guide of approximately 10 + was used. If
ferns did not have sufficient non-occurrences, then these were also not used, as there
has to be relatively equal positive and negative fern occurrences for this particular
analysis.
Minitab Analyses – Shows any significant associations
between specific fern species and all environmental
variables measured from the Binary Logistic Regression
Analyses.
Confidence Levels
If the p value is lower than 0.05 then the association between the fern or fern ally and
the environmental variable is significant.
If the p value is higher than 0.05, but lower than 0.10, the association is classified as
moderately significant.
The format of the analysis output is explained throughout Figure 8.
Talingko to La Bundo Bundo Roadside Transect.
208179 Abstract 56
Figure 8. Binary Logistic Regression Analysis for Selaginella ciliaris.
Link Function: Logit Response Information Variable Value Count S.c._1 1 26 (Event) 0 15 Total 41
This shows that S. ciliaris occurred 26 times out of 41 possible sites.
Correlation analysis data.
Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 8.263 4.835 1.71 0.087 % Cover 0.07023 0.02825 2.49 0.013 1.07 1.01 1.13 Av Canop -0.04196 0.04777 -0.88 0.380 0.96 0.87 1.05 Emergent -0.03810 0.04255 -0.90 0.371 0.96 0.89 1.05 light in 0.0003063 0.0003410 0.90 0.369 1.00 1.00 1.00 Light Ve -1.1311 0.6290 -1.80 0.072 0.32 0.09 1.11 Rocky Su -0.1630 0.8520 -0.19 0.848 0.85 0.16 4.51
Overall significance Log-Likelihood = -18.390 Test that all slopes are zero: G = 17.070, DF = 6, P-Value = 0.009
Selaginella ciliaris inhabited areas where percent canopy cover is higher and light
intensity at vegetation level is lower.
Figure 9. Binary Logistic Regression Analysis for Lygodium circinnatum. Link Function: Logit Response Information Variable Value Count L.c._1 1 10 (Event) 0 31 Total 41 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 8.095 4.225 1.92 0.055 % Cover -0.05874 0.03333 -1.76 0.078 0.94 0.88 1.01 Av Canop 0.07679 0.03899 1.97 0.049 1.08 1.00 1.17 light in -0.0007509 0.0004207 -1.78 0.074 1.00 1.00 1.00 Light Ve -0.5997 0.5019 -1.19 0.232 0.55 0.21 1.47 Rocky Su -1.297 1.098 -1.18 0.237 0.27 0.03 2.35 Log-Likelihood = -14.981 Test that all slopes are zero: G = 15.593, DF = 5, P-Value = 0.008
208179 Abstract 57
Lygodium circinnatum inhabited areas where canopy cover and light intensity at the
vegetation level are lower and where canopy heights are higher.
Figure 10. Binary Logistic Regression Analysis for unknown ground fern A.
Link Function: Logit Response Information Variable Value Count un 1_1 1 10 (Event) 0 31 Total 41 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 6.347 3.295 1.93 0.054 % Cover 0.01603 0.02429 0.66 0.509 1.02 0.97 1.07 light in -0.0004058 0.0002804 -1.45 0.148 1.00 1.00 1.00 Emergent -0.10909 0.04335 -2.52 0.012 0.90 0.82 0.98 Log-Likelihood = -16.773 Test that all slopes are zero: G = 12.007, DF = 3, P-Value = 0.007
Unknown fern A preferred habitats where the emergent canopy heights were lower.
Figure 11. Binary Logistic Regression Analysis for Asplenium ground fern species. Link Function: Logit Response Information Variable Value Count Asp._1 1 10 (Event) 0 31 Total 41 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant -14.271 5.190 -2.75 0.006 % Cover -0.01412 0.03508 -0.40 0.687 0.99 0.92 1.06 Emergent 0.12851 0.05222 2.46 0.014 1.14 1.03 1.26 Light Ve 0.5507 0.4450 1.24 0.216 1.73 0.73 4.15 Rocky Su 2.362 1.315 1.80 0.072 10.62 0.81 139.64 Log-Likelihood = -12.186 Test that all slopes are zero: G = 21.183, DF = 4, P-Value = 0.000
Asplenium species was discovered more in areas where the emergent canopy height
was taller and where the substrate was rock.
La Pago Grid Transect. Figure 12. Binary Logistic Regression Analysis for Asplenium nidus.
208179 Abstract 58
Link Function: Logit Response Information Variable Value Count A.n._1 1 26 (Event) 0 16 Total 42 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 0.761 3.804 0.20 0.841 % cover -0.07208 0.04192 -1.72 0.086 0.93 0.86 1.01 Av Canop -0.03481 0.05096 -0.68 0.494 0.97 0.87 1.07 Emergent 0.08298 0.04676 1.77 0.076 1.09 0.99 1.19 Light at 0.0001635 0.0002271 0.72 0.472 1.00 1.00 1.00 Log-Likelihood = -23.934 Test that all slopes are zero: G = 7.953, DF = 4, P-Value = 0.093
Asplenium nidus inhabited areas of lower canopy cover and higher emergent canopy
height.
Figure 13. Binary Logistic Regression Analysis for Lygodium circinnatum. Link Function: Logit Response Information Variable Value Count L.c._1 1 17 (Event) 0 25 Total 42 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 12.533 7.443 1.68 0.092 % cover -0.08858 0.06839 -1.30 0.195 0.92 0.80 1.05 Light > -0.00342 0.03173 -0.11 0.914 1.00 0.94 1.06 Light o. -0.1724 0.2339 -0.74 0.461 0.84 0.53 1.33 Av Canop 0.1368 0.1006 1.36 0.174 1.15 0.94 1.40 Emergent -0.15495 0.09333 -1.66 0.097 0.86 0.71 1.03 C.B.H (m 2.370 1.167 2.03 0.042 10.70 1.09 105.46 light in -0.0011206 0.0008747 -1.28 0.200 1.00 1.00 1.00 Light at 0.0000915 0.0005162 0.18 0.859 1.00 1.00 1.00 Slope o -0.12732 0.05700 -2.23 0.026 0.88 0.79 0.98 Log-Likelihood = -15.353 Test that all slopes are zero: G = 25.985, DF = 9, P-Value = 0.002
Lygodium circinnatum preferred flat, more mature forest habitats where emergent
canopy height was lower.
Figure 14. Binary Logistic Regression Analysis for Teratophyllum aculeatum. Link Function: Logit Response Information
208179 Abstract 59
Variable Value Count T.a._1 1 13 (Event) 0 29 Total 42 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 4.828 5.925 0.81 0.415 % cover -0.03369 0.06388 -0.53 0.598 0.97 0.85 1.10 Light > -0.02702 0.03503 -0.77 0.441 0.97 0.91 1.04 Light o. 0.0791 0.2345 0.34 0.736 1.08 0.68 1.71 Av Canop 0.05970 0.06897 0.87 0.387 1.06 0.93 1.22 Emergent -0.09526 0.06607 -1.44 0.149 0.91 0.80 1.03 C.B.H (m 1.3278 0.8973 1.48 0.139 3.77 0.65 21.90 light in -0.0000373 0.0006145 -0.06 0.952 1.00 1.00 1.00 Light at -0.0002147 0.0004392 -0.49 0.625 1.00 1.00 1.00 Slope o -0.15599 0.07156 -2.18 0.029 0.86 0.74 0.98 Log-Likelihood = -14.725 Test that all slopes are zero: G = 22.522, DF = 9, P-Value = 0.007
Teratophyllum aculeatum inhabited areas where topography was more flat. Anoa T2 Transect. Figure 15. Binary Logistic Regression Analysis for Lygodium circinnatum. Link Function: Logit Response Information Variable Value Count L.c._1 1 21 (Event) 0 10 Total 31 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant -4.842 5.042 -0.96 0.337 % Cover 0.10002 0.05258 1.90 0.057 1.11 1.00 1.23 Av. Cano -0.03440 0.07988 -0.43 0.667 0.97 0.83 1.13 Emergent -0.02679 0.08462 -0.32 0.752 0.97 0.82 1.15 C.B.H (m 2.602 2.304 1.13 0.259 13.50 0.15 1235.49 Light in -0.0001120 0.0003189 -0.35 0.725 1.00 1.00 1.00 Log-Likelihood = -13.031 Test that all slopes are zero: G = 12.924, DF = 5, P-Value = 0.024
Lygodium circinnatum inhabited forest where there was more canopy cover.
Figure 16. Binary Logistic Regression Analysis for Selaginella ciliaris. Link Function: Logit Response Information Variable Value Count S.c._1 1 24 (Event) 0 7 Total 31 Logistic Regression Table
208179 Abstract 60
Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 8.200 3.610 2.27 0.023 Light in -0.0007028 0.0003220 -2.18 0.029 1.00 1.00 1.00 Emergent -0.07033 0.04936 -1.42 0.154 0.93 0.85 1.03 Log-Likelihood = -13.258 Test that all slopes are zero: G = 6.603, DF = 2, P-Value = 0.037
Selaginella ciliaris preferred lower light intensities.
Figure 17. Binary Logistic Regression Analysis for Asplenium nidus. Link Function: Logit Response Information Variable Value Count A.n._1 1 18 (Event) 0 13 Total 31 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 3.284 3.670 0.89 0.371 % Cover 0.01114 0.04250 0.26 0.793 1.01 0.93 1.10 Av. Cano -0.23053 0.09556 -2.41 0.016 0.79 0.66 0.96 Emergent 0.15633 0.08147 1.92 0.055 1.17 1.00 1.37 C.B.H (m -0.742 1.762 -0.42 0.674 0.48 0.02 15.05 Light in -0.0004963 0.0002821 -1.76 0.079 1.00 1.00 1.00 Log-Likelihood = -14.587 Test that all slopes are zero: G = 12.992, DF = 5, P-Value = 0.023
Asplenium nidus inhabited areas where average canopy height was lower but
emergent heights were taller. This epiphyte preferred more shady areas.
No significant associations found for Microsorum punctatum. None found for Drynaria sparsisora. Anoa T1 Transect. Figure 18. Binary Logistic Regression Analysis for Lygodium circinnatum. Link Function: Logit Response Information Variable Value Count L.c._1 1 14 (Event) 0 7 Total 21 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 3.268 1.682 1.94 0.052 light in -0.0005450 0.0003274 -1.66 0.096 1.00 1.00 1.00
208179 Abstract 61
Log-Likelihood = -11.704 Test that all slopes are zero: G = 3.325, DF = 1, P-Value = 0.068
Lygodium circinnatum inhabited more shaded areas.
No significant associations found for Microsorum punctatum.
Table 10. Summary of significant p values for transect data from binary logistic
regression analysis.
The values in the cells are the significant p values found to associate the
environmental variables (down left hand column) with the ferns (next to the p
value). These will be in the column of the transect where the significant
association occurred.
If variables that had very insignificant p values were removed then the p values from
the last binary logistic regression analyses were used in this table.
Key. S.c = Selaginella ciliaris L.c = Lygodium circinnatum A.n = Asplenium nidus T.a = Teratophyllum aculeatum Ua = Unknown ground fern A Asp = Asplenium species.
La Pago Grid Roadside
Transect Anoa T2 Anoa T1
Overall p Value
0.093 A.n 0.002 L.c 0.007 T.a
0.009 S.c 0.008 L.c 0.007 Ua 0.000 Asp
0.037 S.c 0.024 L.c 0.023 A.n
0.068 L.c
% Canopy Cover
0.086 A.n 0.013 S.c 0.078 L.c
0.057 L.c
Average Canopy Height
0.049 L.c 0.016 A.n
Emergent Canopy Height
0.076 A.n 0.097 L.c
0.012 Ua 0.014 Asp
0.055 A.n
208179 Abstract 62
C.B.H
0.042 L.c
Light at Vegetation
0.072 S.c
Light in General Area
0.074 L.c 0.029 S.c 0.079 A.n
0.096 L.c
Slope
0.026 L.c 0.029 T.a
Rocky Substrate
0.072 Asp
The binary logistic regression analysis was very useful in establishing any significant
associations between the fern species and the environmental variables in the transect
data sets. This technique was very highly regarded so a survey based on this type of
analysis was carried out in the Kakenauwe grid, (See methods).
Binary Logistic Regression allows a comparison of sites where specified fern species
are present, to sites where it does not occur. The analysis takes into consideration
what environmental variables are influencing the ferns and also the environment
conditions where the fern does not occur. Highly significant correlations show up the
variables that most greatly influence the presence or absence of a particular fern
species.
4.5 Binary Logistic Regression Survey on Kakenauwe Grid. Kakenauwe Grid. 70 sites were measured to assess factors influencing the presence or
absence of Selaginella ciliaris, Lygodium circinnatum and Dryopteris species at
random plots.
Figure 19. Binary Logistic Regression Analysis for
Selaginella ciliaris. (Explained throughout to
demonstrate process of analysis)
208179 Abstract 63
Link Function: Logit Response Information Variable Value Count S.c. 1 34 (Event) 0 36 Total 70
Indicates that there are 34 random sites where Selaginella occurred (1) and 36 random sites where Selaginella did not occur (0). Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 3.065 3.170 0.97 0.334 Av Canop 0.01817 0.03490 0.52 0.603 1.02 0.95 1.09 Substrat -0.02135 0.01354 -1.58 0.115 0.98 0.95 1.01 % Cover -0.01510 0.03426 -0.44 0.660 0.99 0.92 1.05 Light > -0.007657 0.008691 -0.88 0.378 0.99 0.98 1.01 Light Ov 0.03130 0.02581 1.21 0.225 1.03 0.98 1.09 Lux 0.0000302 0.0001707 0.18 0.860 1.00 1.00 1.00 C.B.H (m -0.2000 0.4256 -0.47 0.638 0.82 0.36 1.89 Distance -0.2479 0.1483 -1.67 0.095 0.78 0.58 1.04 Disturba -1.9595 0.7316 -2.68 0.007 0.14 0.03 0.59 Log-Likelihood = -40.653 Test that all slopes are zero: G = 15.677, DF = 9, P-Value = 0.074 larger than 0.05 so stats stops here.
Alpha level = 0.05 (confidence level of 95%). Analysis is repeated after the most insignificant variable is removed. This was light
from the largest canopy gap. This increases the significance of the overall p value,
from moderately significant to significant, making the analysis more reliable.
Link Function: Logit Response Information Variable Value Count S.c. 1 34 (Event) 0 36 Total 70 Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 3.339 2.769 1.21 0.228 Av Canop 0.01770 0.03488 0.51 0.612 1.02 0.95 1.09 Substrat -0.02150 0.01353 -1.59 0.112 0.98 0.95 1.01 % Cover -0.01657 0.03320 -0.50 0.618 0.98 0.92 1.05 Light > -0.007877 0.008590 -0.92 0.359 0.99 0.98 1.01 Light Ov 0.03186 0.02550 1.25 0.212 1.03 0.98 1.09 C.B.H (m -0.1962 0.4250 -0.46 0.644 0.82 0.36 1.89 Distance -0.2445 0.1469 -1.66 0.096 0.78 0.59 1.04 Disturba -1.9612 0.7324 -2.68 0.007 0.14 0.03 0.59 Log-Likelihood = -40.669 Test that all slopes are zero: G = 15.646, DF = 8, P-Value = 0.048
208179 Abstract 64
The survey data suggests that Selaginella ciliaris
inhabited relatively undisturbed areas that were closer to
access paths.
Figure 20. Binary Logistic Regression Analysis for Lygodium circinnatum.
Link Function: Logit Response Information Variable Value Count L.c. 1 30 (Event) 0 40 Total 70
This shows that there were 30 sites where Lygodium was present and 40 sites where it did not occur. Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 4.436 3.711 1.20 0.232 Av Canop -0.03372 0.03771 -0.89 0.371 0.97 0.90 1.04 Substrat -0.07342 0.02345 -3.13 0.002 0.93 0.89 0.97 % Cover -0.01414 0.03856 -0.37 0.714 0.99 0.91 1.06 Light > 0.00859 0.01000 0.86 0.391 1.01 0.99 1.03 Light Ov -0.03422 0.02801 -1.22 0.222 0.97 0.91 1.02 Lux -0.0001556 0.0002103 -0.74 0.459 1.00 1.00 1.00 C.B.H (m -0.5500 0.6322 -0.87 0.384 0.58 0.17 1.99 Distance -0.0802 0.1401 -0.57 0.567 0.92 0.70 1.21 Disturba -0.7279 0.7330 -0.99 0.321 0.48 0.11 2.03 Log-Likelihood = -33.663 Test that all slopes are zero: G = 28.280, DF = 9, P-Value = 0.001
The survey data has shown that Lygodiun circinnatum
prefers less rocky areas where there is more soil
covering.
Figure 21. Binary Logistic Regression Analysis for Dryopteris species.
208179 Abstract 65
Link Function: Logit Response Information Variable Value Count Dryop 1 34 (Event) 0 36 Total 70
Indicates 34 random sites where Dryopteris was present and 36 sites where it did not occur. Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant 4.849 3.232 1.50 0.134 Av Canop -0.01088 0.03321 -0.33 0.743 0.99 0.93 1.06 Substrat 0.01657 0.01245 1.33 0.183 1.02 0.99 1.04 % Cover -0.05076 0.03442 -1.47 0.140 0.95 0.89 1.02 Light > -0.01910 0.01182 -1.62 0.106 0.98 0.96 1.00 Light Ov 0.00581 0.02108 0.28 0.783 1.01 0.97 1.05 Lux -0.0000910 0.0001732 -0.53 0.599 1.00 1.00 1.00 C.B.H (m -0.1910 0.4035 -0.47 0.636 0.83 0.37 1.82 Distance 0.1045 0.1336 0.78 0.434 1.11 0.85 1.44 Disturba -0.6811 0.6465 -1.05 0.292 0.51 0.14 1.80 Log-Likelihood = -44.329 Test that all slopes are zero: G = 8.325, DF = 9, P-Value = 0.502
Many attempts to highlight influencing variables for the occurrence of Dryopteris
were done by eliminating various insignificant environmental variables in the Binary
Logistic Regression analysis. The overall p value still remained above 0.05 and 0.10,
so even a confidence level of 90% could not be achieved.
A correlation matrix was established to understand what variables have the strongest
correlations with the occurrence of Dryopteris. The results from this show no
significant associations between Dryopteris and any of the measured environmental
variables.
Table 11. Correlations between Dryopteris species and environmental variables
measured in the survey.
Av ht Subs % C L > L o.h Lux CBH D to P Dist
208179 Abstract 66
Dryop
-0.028 0.816
0.118 0.329
0.061 0.618
-0.184 0.127
-0.032 0.792
-0.020 0.867
-0.073 0.550
0.114 0.346
-0.172 0.154
Fitted Line Plots.
Show graphically the significant correlations found between specific fern species and
an environmental variable.
Graph 25. Shows the relationship between Lygodium circinnatum and the percent of
rocky substrate. (95% Confidence Level)
208179 Abstract 67
908070605040302010 0
1.0
0.5
0.0
Substrate %
L.c.
S = 0.431229 R-Sq = 26.2 % R-Sq(adj) = 25.2 %
L.c. = 0.674874 - 0.0107088 Substrate %
Regression Plot
Graph 26. Shows the relationship between Selaginella ciliaris and distance to access
path. (90% Confidence Level)
0 1 2 3 4 5 6 7 8 9 10
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
S.c
.
S.c. = 0.512722 - 0.0109281 Distance to
S = 0.506454 R-Sq = 0.3 % R-Sq(adj) = 0.0 %
Regression Plot
Distance to Path
4.6 River Surveys and Biodiversity Inventory.
208179 Abstract 68
New fern species found alongside previously discovered species along the river
transects and in the surrounding alluvial deposits were:
• Angiopteris evecta
• Ophioglossum pendulum
• Tectaria aurita
• Unknown W- common ground fern found only near rivers
• Unknown X- ground fern, larger biomass. Found on cliffs and embankments
• Unknown Y- ground fern, described to have overlapping asymmetric pinnules
• Unknown Z- in shallow, running water on rocky substrate. (Thought to be
Tectaria rheophitica)
(All unknowns awaiting identification from Bogor, Jakarta.) Other ferns found throughout the river environments were Selaginella ciliaris,
Lygodium circinnatum and epiphytic ferns.
Table 12. Shows where the different fern species were found in the three different
river locations. (* Indicates fern presence).
River Anoa La Bundo La Pago Area
Flat and open river valley. Alluvial deposits. Tufa limestone. Varying canopy height. Altitude 500m
Well established. Limestone substrate. Cliffs present. Varying canopy cover, generally well lit. Altitude 50m
Flat limestone basin. Steep banks with soil substrate. Area well lit. Disturbance due to fallen tree. Altitude 50-100m
FERNS Selaginella ciliaris
* * *
Lygodium circinnatum
* * *
Asplenium nidus * * *
208179 Abstract 69
Microsorum punctatum
* *
Phymatosorus scolopendria
*
Nephrolepis species type B
*
Drynaria sparsisora
* *
Ophioglossum pendulum
*
Pteris vittata
*
Tectaria aurita
*
Pyrrosia piloselloides
*
Pteridium tripartata
*
Unknown A
*
Unknown B
*
Unknown w
* *
Unknown x
*
Unknown y
*
Unknown z
* * *
To compare the presence of species that occurred in the three different areas, the
Jaccard index is used. This statistical analysis (also known as Marczewski-Steinhaus
distance index) is used as it analyses data in a presence/absence format.
The equation:
Cj = a / (b+c-a), compares two of the sites together at one time. All three sites will be
compared with each other.
a = number of species found in both sites
b = total number of species in site 1
208179 Abstract 70
c = total number of species in site 2
Table 13. Results from the Jaccard Index to compare the
similarity of species between the three different rivers.
Anoa and La Pago Anoa and La Bundo La Pago and La Bundo
Cj = 5 / (10+10-5)
Cj = 5 / 15
Cj = 5 / (10+9-5)
Cj = 5 / 14
Cj = 5 / (10+9-5)
Cj = 5 / 14
Cj = 0.33 Cj = 0.36 Cj = 0.36
If Cj is 1 then there is no difference in species diversity between the two sites.
If Cj is 0 then the species diversity of the two sites is completely different.
Analysis of results will be covered in the discussion.
208179 Abstract 71
4.7 Specimen Images.
Table 14.
Shows images taken of some of the ferns and fern allies during fieldwork.
Forest Species
Sori on the lower leaf epidermis.
Adiantum hemionitus.
Cyathea contaminans.
Cyathea contaminans amongst the canopy.
Cyathea contaminans. This specimen reached
approximately 6m in height.
208179 Abstract 72
Asplenium nidus. Epiphyte using a mature tree as substrate. The rhizome catches falling leaves and debris for nutrients.
Dryopteris. Awaiting species identification.
Fallen leaf of Drynaria sparsisora.
Lindsaea lucida.
A mature Lygodium circinnatum ground fern.
Fertile fronds of Lygodium circinnatum.
208179 Abstract 73
Nephrolepis biserrata.
The epiphytic Pyrrosia piloselloides. The elongated fronds show brown sori.
Selaginella ciliaris. Mature and juvenile fronds of S.ciliaris.
Teratophyllum aculeatum climbing a tree trunk.
A ground fern species of Tectaria. Identification yet to be received.
208179 Abstract 74
A sporulating species of Tectaria. Awaiting species identification.
Lecanopteris ant fern. The swollen rhizome of this species provides a home for ants.
River Species
Angiopteris evecta commonly known as the elephant fern due to its substantial size.
Tectaria aurita. The fertile fronds can be seen coming up out of the centre of the vegetative fronds.
208179 Abstract 75
Unknown Y. Distinctive due to overlapping pinnules.
The sori distribution on Unknown Y.
Rheophytic fern found in shallow streams and on stream banks. Thought to be a species of Tectaria. Awaiting species identification.
Rheophytic Tectaria inhabiting a stream bank.
208179 Abstract 76
5. Discussion.
The composition of fern communities in the Lambusanga Reserve is
influenced and determined by both individual and interacting environmental factors.
Some of these factors or variables were measured to assess how much influence they
had on the fern community composition. The results show statistically, the
significance of these associations.
Selaginella ciliaris.
Selaginella ciliaris was discovered in all of the transects. This fern ally was
the most abundant of the pteridophytes seen and had the broadest habitat range. This
habitat generalist had high abundance and population success and therefore cannot be
used as an indicator of specific forest habitat. Selaginella ciliaris was found to prefer
forest where height of emergent canopy was lower. Areas like this are generally more
shaded but with some light penetration, which S.ciliaris seemed to prefer.
The abundance of S. ciliaris also increased as the amount of light measured at
the vegetation level decreased and as soil pH and percent of canopy cover increased.
Data from other transects showed no conflict with these correlations. Due to this and
the sheer abundance of this species, these results are considered to be moderately
reliable.
The associations mentioned above were tested for their significance and
reliability using a binary logistic regression analysis. This analysis takes into account
the presence or absence of Selaginella in the sample sites allowing a comparison of
suitable and non-suitable habitat for the species in question. Results from the transect
binary logistic regression analysis are reliable as insignificant variables were removed
until the overall p value became significant. Results supported the previous
correlation matrices.
208179 Abstract 77
Selaginella ciliaris was also used in the binary logistic regression survey in
Kakenauwe grid. The results do not dispute the above conclusions made, but they also
do not support them. The results show that Selaginella occurred less in areas that were
disturbed by tree fall and forestry, but more in areas closer to access paths. These data
is slightly conflicting, but from general observation, it seemed that Selaginella
appeared almost everywhere in disturbed and undisturbed sites. Whitten et al (2002)
documents Selaginella as a species of forest gaps. Fieldwork found S.ciliaris in forest
locations where light penetrated through the canopy but not in open clearings.
The habitat preferences stated above for Selaginella are similar to, and
therefore supported by, habitat data in Park Sabah in Peninsular Malaysia. The
unidentified terrestrial species of Selaginella discovered there, inhabited lowland
rainforest areas that were well shaded (Bidin and Jaman, 1999). The data also agree
with the findings of Beukeman and Noordwijk, (2004), which suggested that fern
allies from the Selaginellaceae family require shaded areas. This habitat requirement
of pteridophytes is a general assumption, as they need a certain degree of moisture for
reproduction purposes.
Lygodium circinnatum.
Lygodium circinnatum is also a habitat generalist. It was also found in the
majority of the fieldwork sites. Due to its wide habitat range, it cannot be used as a
biological indicator of different forest habitat. This fern was not as abundant as
Selaginella ciliaris and mainly occurred as solitary individuals and not as a
population. This fern seemed to be influenced by all of the variables at some stage
throughout the different transects, with variation across the fieldwork locations.
Negative correlations with variables in La Pago grid, showed the preference of
lower light intensities and flat gradients. La Pago was relatively undisturbed forest.
208179 Abstract 78
The data from this transect is reliable due to multiple sample sites. Associations
discovered were significant and Lygodium had high occurrence levels in relation to
other ferns throughout this section of forest. The T2 transect also completed in the La
Pago area showed that Lygodium was present in areas where both the overall and
emergent canopy heights were lower. This conclusion is considered less reliable than
the association discovered between this fern and environmental variables in the La
Pago grid as this transect had fewer sampling sites.
The roadside transect showed L.circinnatum to occur more where light
intensity was lower. This fern was found in the shade from surrounding small trees
and plants. This area was highly disturbed by road traffic and people. To support this,
Lygodium was also found to prefer habitats where canopy cover was higher. This
association was discovered in Kakenauwe and the relatively undisturbed Anoa forest
areas.
By assessing the entire data set for Lygodium circinnatum, it would be fair to
conclude that this fern prefers well-established forest habitat, where topography is
flat, light intensity is low and thus more canopy cover. The canopy height is not
entirely relevant if enough shade is present. Bidin and Jaman, (1999) support this.
They determined a relatively open habitat of light shade that had enough surrounding
vegetation for protection and shade.
Results from the binary logistic regression survey (see Results page 53-54)
showed that Lygodium circinnatum occurred in areas where the substrate was less
rocky and there was more soil present. This suggests that this fern prefers more moist
environments and not dry, rocky environments, as it also inhabited riverbanks and
alluvial deposits. This is also supported by Dyer, (1979) who states that Lygodium
occurs on streamsides in response to humidity change, ground water conditions and to
208179 Abstract 79
open, less stable habitats. Climbing Lygodium may also frequently inhabit open,
swampy forest (Dyer 1979). Although the survey does not support the conclusions
drawn from the transect data, it does not conflict with them either.
Limiting factors.
Ground ferns such as Lygodium circinnatum, Selaginella ciliaris, Nephrolepis
biserrata, Dicranopteris linearis, Dryopteris species and Thelypteris species, could be
strongly influenced by environmental and anthropogenic factors that could not be
measured during fieldwork. These include soil pH and moisture (not measured in
every forest location due to equipment failure), soil temperature, composition and
nutrient content, air temperature and humidity, annual precipitation, and interactions
with other species including competition and herbivory.
The epiphytes.
Asplenium nidus and Microsorum punctatum are both epiphytic ferns, which
depend upon large forest trees as substrate. Both of these ferns were discovered
throughout the reserve. Asplenium nidus was found where the emergent canopy
heights were taller in the La Pago sites, as supported by Dyer (1979) who classifies
A.nidus as a fern of higher, drier locations. These ferns are assumed to be
uninfluenced by any soil, substrate or topographical factors, but only by light
intensities, canopy heights and other unmeasured variables. In Kakenauwe grid,
Microsorum punctatum occurred more in shady areas where canopy cover was
thicker. This is supported by the preference of lower light intensities in the mature
Anoa forest areas. This is not entirely reliable as it was not discovered anywhere else.
Microsorum punctatum has been found on rocky cliff substrata within rainforest
habitats (Copeland 1947). Habitat data from Bidin and Jaman (1999), states that these
epiphytes prefer lightly shaded areas of lowland rainforests.
208179 Abstract 80
Drynaria sparsisora was also abundant throughout the reserve. Drynaria
sparsiora was found more in areas where both the average and emergent canopy
height was taller. This relationship with canopy height was only relevant to data
collected from Kakenauwe grid. Dyer (1979) classifies Drynaria as species of higher
and drier locations alongside A.nidus. Studies in Ujung Pandang, Sulawesi, show that
D. sparsisora inhabits all parts of trees as its able to withstand dryer conditions, but P.
piloselloides only inhabits the main trunk where water and nutrients remain the
longest (Whitten et al 2002).
Other epiphytic ferns such as Drynaria quercifolia (found at Kakenauwe
Beach); Pyrossia piloselloides, Pyrossia lanceolata, Ophioglossum pendulum and
Nephrolepis were influenced by the variables associated with epiphytic ferns. If
associations were found, e.g. Pyrossia lanceolata preferred less dense canopy and
higher light intensities from large canopy gaps in the La Pago area; these can only be
used as indicators of habitat preference due to unreliable single occurrence levels.
Ophioglossum pendulum has been found to prefer habitats in light shade (Bidin and
Jaman, 1999) but as this was only found once, conclusions made from this study are
not reliable.
The lack of significant associations found between the epiphytes and the
measured variables, suggests the influence of non-measured variables. These could
include humidity, precipitation, temperature, herbivore predation and wind speed. If
winds are too strong then the ferns cannot build up organic matter in their rhizomes
(Dyer 1979).
Teratophyllum aculeatum.
Teratophyllum aculeatum is a rainforest climber (Dyer 1979) found only in
well-developed tropical rainforest (Holttum, 1932, 1954), whose rhizome is based in
208179 Abstract 81
the soil. This fern depends on attachment to other, well-established plants (mainly
trees) to grow up towards the light and progress from its juvenile bathophyll stage to a
mature adult. Considerable morphological change in rhizome structure and frond form
is undergone as it climbs towards the canopy (Holttum, 1938 and Walker, 1972). The
adult of this fern was only discovered in the secondary forests of La Pago, but it
occurred significantly to gain an idea of habitat preference. Its climbing capabilities,
enables Teratophyllum to achieve efficient spore dispersal whilst taking moisture
from the ground soil (Dyer 1979). This fern occurred where trees were larger and
where the topography was flat. This may be due to higher nutrient and water
requirements, which is characteristic of soils on flatter gradients.
The bathophyll of this fern, discovered in Kakenauwe grid, preferred slightly
acidic soils and lower light intensities.
Phymatosorus scolopendria.
The ground fern, Phymatosorus scolopendria was found in the moderately
disturbed forest of Kakenauwe grid and along the disturbed roadside from Talingko to
La Bundo Bundo. This fern occurred more in less acidic substrate and where canopy
cover was dense, although the latter association was only moderately significant. As
soil pH was only measured in Kakenauwe and not along the roadside transect, it is not
possible to say whether this strongly influences P.scolopendria distribution.
Dryopteris species.
The Dryopteris species used for the binary logistic regression survey in
Kakenauwe grid also inhabited the roadside (which runs along the grid border). It
mainly occurred in areas where the canopy covering was less dense and where the
average canopy height was lower. The transect data suggests that Dryopteris prefers
well-lit areas but this was not supported by statistical analysis from the binary logistic
208179 Abstract 82
regression survey. Page (1976) describes Dryopteris as a weed of open roadside
cuttings. The significant associations found in Kakenauwe grid were not found in the
roadside transect, so this cannot be fully relied upon. Ferns in the Dryopteridaceae
family have been found to prefer more shaded forest areas (Beukeman and
Noordwijk, 2004), which is not supported by findings of this study.
Results from the binary logistic regression survey did not show any significant
associations with any of the variables measured. A correlation matrix was also
completed to check this conclusion, which supported it. Dryopteris is therefore also
influenced by variables not measured in this study. Examples of these are mentioned
previously.
Nephrolepis biserrata.
The ground fern, Nephrolepis biserrata was found along the disturbed
roadside edges and also in the relatively undisturbed area of Anoa. This fern preferred
less-mature forest areas where the canopy was lower and less dense. This suggests
that Nephrolepis biserrata requires higher light intensities, which was also shown by
the data. This concurs with earlier literature that states that N.biserrata grows in open
sites that have some degree of direct sunlight (Wee 1997).
Pioneer species.
Dicranopteris linearis was only discovered alongside Bracken fern, Pteridium
aquilinum in very well lit, open areas where there is little surrounding vegetation and
no shade from tall trees or canopy cover. This is supported by Wee (1997).
Dicranopteris linearis, one of the most geographically wide-ranging species
(Holttum, 1968) grows in thick masses (Bidin and Jaman, 1999) that cover areas of
disturbance such as an erosion scar. These ferns are pioneer species that bind soils
with their persistent underground rhizomes (Fletcher and Kirkwood 1979) until other
208179 Abstract 83
species become established. These ferns are dominant of areas considered too dry for
other ferns and are inhibited by anaerobic, waterlogged soils due to lack of oxygen
(Poel, 1951, 1961). The patch, found in Anoa on the T2 transect, was cleared by local
people. Dicranopteris linearis occurs in habitats of high exposure and frequently on
acid peat soil and thrives due to little competition (Page, 1979). Soil pH is assumed to
be a limiting factor for this fern’s habitat.
Pteridium aquilinum is thought to have one of the widest habitat ranges of all
vascular plants (Fletcher and Kirkwood 1979). It can thrive on soils too dry for most
other ferns, although fronds are sparse and short (Watt, 1964). It thrives best when
water and oxygen supplies are adequate (Fletcher and Kirkwood 1979). Soil studies in
Idaho have shown that this fern is associated with high levels of potassium and also
exists in areas where the soil is acidic and has a high nutrient content (Rose, 1989).
Habitat specialists such as P.aquilinum and D.linearis are good indicators of
their specific habitats (e.g. light and open clearings), but when discovered, the habitat
type was apparent anyway as the area was cleared and there was no canopy. This
makes the use of ferns as biological indicators somewhat questionable.
Asplenium and Adiantum ground fern species.
Species of the ground fern Asplenium and Adiantum both had low
occurrence levels along the roadside transect. This makes suggestions of their specific
habitat requirements from the significant associations discovered unreliable, due to
the small sample size.
The data suggests that the Asplenium species prefers a higher, denser canopy.
Adiantum only occurred four times in areas where the majority of the substrate was
rock. Data from Bidin and Jaman (1999) also showed that terrestrial species of
Asplenium found in Malaysia preferred rocky substrata. Terrestrial species of both
208179 Abstract 84
these ferns are found to inhabit stream banks and are characteristic of rocky patches
and rocky soils (Page 1979).
Tree ferns.
The only tree ferns discovered during fieldwork were in the higher altitude of
Anoa, which is between 300 and 400m above sea level (Carlisle, 2002). Two species
were found, one was thought to be Cyathea contaminans (awaiting identification).
Cyathea contaminans is common throughout Sulawesi from altitudes of 200-1600m
(Whitten et al 2002). A sample size of one i.e. a single occurrence cannot give a true
habitat preference for the tree fern population. The area was relatively exposed on a
steep gradient. There was a deep soil covering and was well lit due to a low canopy.
Cyathea species generally inhabit cool and moist forests (Page 1979). Studies
in Sumatra, Indonesia, used tree ferns as indicators of lowland forest that was open
and well lit or only lightly shaded. This study suggested that tree ferns also inhabited
areas other than forest such as forest edges and forest gaps (Beukeman and
Noordwijk, 2004).
River species.
Data from the river surveys found new ferns that only inhabited the moist river
embankments and the alluvial deposits alongside the limestone tufa. (These ferns and
the habitat characteristics in which they were found are listed on pages 57 and 58).
The analysis of the species found at the three different river locations showed they
were moderately different. This suggests that the presence of alluvial deposits (at
Anoa), the topography, gradient and substrate of the banks, light intensities and
altitude all influence the species composition of ferns in river locations. Other
environmental variables could also be acting on the ferns including humidity,
208179 Abstract 85
direction and speed of river flow (especially on rheophytes), soil pH, soil moisture
and precipitation.
Problems of this study
• The identifications of some of the ferns are uncertain.
• Official identifications not yet received.
• Some unknown species were not sporulating so identification could not
be achieved to species level.
• Some variables were not measured consistently throughout due to
equipment failure.
• Some transect distances were too far to travel in a day and camping
was not an available option.
• The weather was extremely variable (typical of tropics) which could
affect conclusions of fern microhabitats.
• The light meter was very responsive so results differed depending on
cloud cover as well as general shade from other vegetation.
• Specialist ferns (in terms of habitat) occurred less than generalist
species as they are limited by more environmental variables. The
specialists are regarded as better indicators of habitat than generalist
species but reliability is questionable due to low sample sizes.
Future work.
More species need to be identified to species level (results from Jakarta are being
processed). Larger distances need to be covered to gain higher sample numbers of the
rare species and to find undiscovered species. Better measures of the environmental
208179 Abstract 86
variables need to be taken, as visual estimates are not entirely accurate. This has been
highlighted by the transect fieldwork completed for this study.
This study has highlighted limitations of the fieldwork required and has shown what
can and cannot be achieved within the field. This can now be used as a pilot study,
allowing a more complex investigation into the ferns of Sulawesi and their habitats to
be performed.
208179 Abstract 87
6. Conclusion.
Specimens collected throughout the fieldwork have allowed identification of some
common ferns found on Buton Island. Overall, 23 were identified to species level.
Samples of some of these ferns alongside any unidentified sporulating specimens
were dried and pressed for official identification. Results are yet to be received from
Jakarta. Identification of the ferns discovered has enabled a species list to be created
(aim 1).
The data has found many associations between the measured environmental
variables and different fern species, thus achieving the second aim of the study. The
influential significance of these variables on individual fern species has been
discussed and compared to previous work in lowland rainforest ecosystems (see
discussion). Associations that were supported by previous studies are concluded to be
more reliable.
Significant associations were found between individual fern species and
certain environmental variables, which have allowed a primary definition of habitat
requirements for these ferns. Habitat specialists such as P.aquilinum and D.linearis
occurred strictly in their habitat so influential environmental variables were more
easily established (aim 3) than ferns occurring across a wider habitat range. Ferns that
were found not to have any correlations with the variables at all are influenced by
unknown factors that were not measured in this study.
The binary logistic regression survey enabled the specific microhabitat of
Selaginella ciliaris, Lygodium circinnatum and the species of Dryopteris to be
assessed (see discussion). Although Dryopteris did not have any significant
correlations with the variables measured in the survey, suggestions as to which factors
limit its distribution were made using the data collected for this fern from the transects
208179 Abstract 88
(aim 4). The survey compared the habitats in which these ferns were present and not
present, highlighting limiting factors on the distribution of these species. The habitats
concluded are reliable as results were compared to and supported by previous
literature and the transect data.
Habitat specialists (e.g. P.aquilinum) would be better to use as biological
indicators (aim 5) of forest disturbance than habitat generalists such as ground ferns
(e.g. S.ciliaris and L.circinnatum), epiphytes (e.g. A.nidus) or climbing ferns (e.g.
T.aculeatum) due to their stricter habitat requirements. The need for disturbance
indicators remains questionable, as when habitat specialists are discovered, the habitat
type they occur in is already obvious to the observer.
Different fern species were present between the three forest sites and a high
diversity was established along the very disturbed roadside transect. A general
conclusion was that the further the distance travelled, the more different fern species
were discovered.
The river transects and the biodiversity assessments completed throughout the
reserve compared fern species across a wide range of microclimates (aim 6). It is clear
from the discussion that fern diversity on Buton Island is very high, with many more
species yet to be identified and discovered within the Lambusanga Reserve. Future
work needed to elaborate this study further is stated in the discussion.
208179 Abstract 89
7. Acknowledgements.
I would like to thank Dr. Andrew Powling, University of Portsmouth, for his advice
and assistance during the fieldwork planning, data collection and analysis of this
project.
I would also like to thank Dr. Bruce Carlisle, University of Northumbria regarding the
GIS data and mapping of Buton Island, and to the staff and scientists of Operation
Wallacea for their assistance with fieldwork for this project.
208179 Abstract 90
9. Appendix 9.1 Light Conversions to Units of Lux.
A graphical representation of the correlation between arbitrary light units and units of Lux.
0 1 2 3 4 5 6 7 8 9 10
2.5
3.5
4.5
meter
logt
logt = 2.58174 + 0.115977 meter - 0.0124040 meter**2 + 0.0018935 meter**3
S = 0.0338229 R-Sq = 99.6 % R-Sq(adj) = 99.5 %
Regression Plot
Lux conversions for observed light readings. Light Reading
Lux Light Reading
Lux Light Reading
Lux
4 1028 6.1 2457 8.2 5876 4.1 1071 6.2 2561 8.3 6125 4.2 1116 6.3 2670 8.4 6384 4.3 1164 6.4 2783 8.5 6656 4.4 1213 6.5 2901 8.6 6937 4.5 1264 6.6 3024 8.7 7231 4.6 1318 6.7 3152 8.8 7537 4.7 1374 6.8 3285 8.9 7858 4.8 1432 6.9 3425 9 8190 4.9 1493 7 3570 9.1 8537 5 1556 7.1 3721 9.2 8900
208179 Abstract 91
5.1 1622 7.2 3880 9.3 9277 5.2 1691 7.3 4044 9.4 9669 5.3 1763 7.4 4215 9.5 10081 5.4 1837 7.5 4393 9.6 10508 5.5 1915 7.6 4580 9.7 10952 5.6 1997 7.7 4774 9.8 11418 5.7 2081 7.8 4976 9.9 11902 5.8 2169 7.9 5188 10 12405 5.9 2262 8 5408 1 296 6 2357 8.1 5636 3.2 737
208179 Abstract 4
Note from Opwall: This has been assembled from various small files; as such we take no responsibilities for changes or inaccuracies such as incorrect page numbering.
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