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

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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.