EFFECTS OF LANDUSE CHANGE AND FOREST FRAGMENTATION ON THE
BIODIVERSITY AND ECOSYSTEM FUNCTIONING IN THE TROPICAL
LOWLANDS OF SRI LANKA
ENOKA PRIYADARSHANI KUDAVIDANAGE
B.Sc. (Zoology), M.Sc (Environmental Sciences)
A THESIS SUBMITTED FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
DEPARTMENT OF BIOLOGICAL SCIENCES
NATIONAL UNIVERSITY OF SINGAPORE
2011
1
“The forest is a peculiar organism of unlimited kindness and benevolence that makes no demands for its sustenance and extends generously the products of its life activity; it provides
protection to all beings, offering shade even to the man who destroys it.”
— Gautama Buddha
i
Dedicated to my parents
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ACKNOWLEDGEMENTS
I am grateful to late Prof. Navjot Sodhi, my ex-supervisor whose unique combination of intelligence, determination, intimidation and social skills defined my path into the field of conservation biology. I am indebted, to my supervisor Prof. Edward Webb for persuasion and guidance that enabled me to complete the PhD thesis, and to the constant encouragement provided by Prof. Richard Corlett. I extend my gratitude to Prof Sarath Kotagama, my supervisor in Sri Lanka, and mentor for the last 15 years, for his guidance provided throughout my academic life. I thank all examiners for their most valuable and helpful comments that were adopted in the final version of my thesis.
Many thanks to all the past and present staff member and colleagues of the Conservation
Ecology, Applied Plant Ecology laboratories and the terrestrial ecology group including Daniel
Friess; David Bickford; Mary Rose Posa, Nanthinee Jeevanadam, Reuben Clement, Arvin
Diesmos and Lee Wei Kit; the technical and administrative staff including Tommy Tan, Reena
Devi Samayanadan, Priscilla Lee, members of the Biodiversity group and the Raffles Museum for Biodiversity research of the Department of Biological Sciences for all the encouragement assistance and inspiration extended in one-way or another. Special thanks towards Janice Lee and Qie Lan, my fellow dung girls for the comradeship of dung beetles. Prof. David J. Lohman
(Lohman Lab, City College of New York) is kindly acknowledged for extending a generous helping hand in many challenges faced during my PhD study including correcting manuscripts, assisting analytical problems, helpful discussions and providing resources.
I’m grateful to Darren J. Mann, Eleanor Slade and the staff of the Oxford University
Museum of Natural History for patiently guiding me through the intense process of species identification and for the opportunity to use the scarab beetle collection at the American Museum
iii of Natural History. Thanks to the ScarabNet research team including Finbarr Horgan for sharing with me their knowledge about dung beetles. I acknowledge all my collaborators; Thomas
Wanger (Center for Conservation Biology , Stanford University), Neil Collier (Lohman Lab,
City College of New York) for analytical work; Priyadarshan Dharmarajan (Ashoka Trust,
India), Manori Gunetilleke (National Museums, Sri Lanka) and Deepchandi Lekamge
(Sabaragamuwa University) for the taxonomic study.
I acknowledge the Department of Wildlife Conservation and the Forest Department, Sri
Lanka for granting me access to work in the protected area and permission to export specimen for taxonomic work. I thank the Vice- Chancellor of the Sabaragamuwa University of Sri Lanka and the Department of Natural Resources for kindly facilitating my graduate studies. Gratitude extended to Professors Nimal Gunatilleke, Savithri Gunatilleke and Drs. Eben and Uromi
Goodale for expertise knowledge, guidance and helpful discussions. Ravi Amarasinghe,
Chamitha de Alwis, Vimukthi Herath, Krishan and Udeni Ariyasiri, Vishan, Amila Perera,
Nuwan Hegodaaarachchi, the Wildlife Conservation Society of Galle and the local assistants who supported the field work are acknowledged.
The Field research was funded by NAGAO Natural Environmental Foundation, Japan and Joe Grove memorial award, UK, while the taxonomic study was funded by the Oxford
University Museum of Natural History, London. The World Bank sponsored IRQUE project and the National University of Singapore Research Scholarship supported the PhD studies.
Finally, I thank everybody including my family who stood by me extending their constant support, patience and understanding through the graduate studies. I owe all that I am to my parents, who made it their lifetimes’ goal to help me fulfill my dreams. I remember with heartfelt gratitude and warmth, my father who passed away one month before I entered the graduate
iv school of his choice and my mother who stood by me all my life before she passed away seven months ago. She shared my love for nature and all living things, supported my chosen career and enjoyed every one of my field expeditions, participating with great enthusiasm. This thesis is a product of her faith in me.
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Contents ACKNOWLEDGEMENTS ...... iii
THESIS SUMMARY ...... 1
LIST OF FIGURES ...... 4
LIST OF TABLES ...... 5
APPENDICES ...... 6
CHAPTER 1: GENERAL INTRODUCTION ...... 7
CHAPTER 2: STUDY SITE ...... 13
2.1 Sri Lanka and the Lowland Wet Zone: a biodiversity hotspot ...... 13
2.2 From game protection to biodiversity conservation: the history and status of biodiversity
conservation in the wet zone of Sri Lanka ...... 15
2.3 Study sites: Sinharaja, Kanneliya and Kottawa ...... 20
2.3.1 Sinharaja Man and Biosphere Reserve ...... 20
2.3.2 Kanneliya conservation forest ...... 23
2.3.3 Kottawa Kombala conservation forest ...... 24
CHAPTER 3: AMPHIBIAN AND BUTTERFLY DIVERSITY ACROSS A LOWLAND TROPICAL
LAND-USE GRADIENT IN SRI LANKA ...... 28
3.1 Introduction ...... 28
3.2 Materials and methods ...... 29
3.2.1 Study area ...... 29
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3.2.2 Sampling diversity and abundance ...... 30
3.2.3 Environmental variables ...... 31
3.2.4 Analysis of species richness and abundance data ...... 32
3.2.5 Analysis of environmental variables ...... 33
3.3 Results ...... 34
3.3.1 Butterflies ...... 34
3.3.2 Amphibians ...... 35
3.3.3 Environmental determinants ...... 37
3.4 Discussion ...... 38
3.4.1 Butterfly response to land-use change ...... 38
3.4.2 Amphibian response to land-use change ...... 40
3.4.3 Environmental predictors of butterfly and amphibian diversity patterns ...... 42
3.5 Conclusions ...... 43
4.1 Introduction ...... 51
4.2 Dung beetles (Insecta: Coleoptera: Polyphaga: Scarabaeoidea: Scarabaeidae: Scarabaeinae and
Aphodiinae) ...... 51
4.2 Materials and methods ...... 53
4.2.1 Study site ...... 53
4.2.2 Beetle sampling and identification ...... 54
4.3 Results and discussion ...... 55
4.3.1 Species distribution overview ...... 55
4.3.2 Species distribution in contrast to previous records ...... 56
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4.3.4 Possible natural and anthropogenic environmental determinants of observed distribution
patterns of dung beetle species ...... 58
4.3.5 Limitations and future work ...... 62
CHAPTER 5: EFFECTS OF LAND-USE CHANGE ON THE COMMUNITIES OF DUNG BEETLES
IN LOWLAND TROPICS OF SRI LANKA ...... 67
5.1: Introduction ...... 67
5.4 Materials and methods ...... 70
5.4.3 Beetle sampling ...... 71
5.5 Data analysis ...... 72
5.8 Conclusion ...... 84
CHAPTER 6 DUNG BEETLE COMMUNITIES IN LOWLAND FOREST FRAGMENTS...... 90
6.1 Introduction ...... 90
6.2 Study site ...... 94
6.3 Materials and methods ...... 94
6.3.1 Dung beetle sampling...... 94
6.3.2 Environmental variables and fragment characteristics ...... 95
6.3.3 Fragment characteristics ...... 95
6.4 Data analysis ...... 96
6.4.1 Summary statistics and community parameters ...... 96
6.4.2 Comparison of species richness, abundance across the fragments ...... 96
6.5 Results ...... 99
6.5.1 Community comparison ...... 100
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6.5.2 Relating species richness, abundance, fragment characteristics and environmental variables 101
6.6 Discussion ...... 104
6.7 Conclusion ...... 111
CHAPTER 7 HABITAT CHANGE IMPAIRS ECOLOGICAL SERVICES PROVIDED BY
SCARABAEINAE DUNG BEETLES IN THE TROPICAL LOWLANDS OF SRI LANKA ...... 122
7.1 Introduction ...... 122
7.2. Materials and methods ...... 123
7.2.1 Study area ...... 123
7.2.2 Sampling design for dung removal and dung beetle trapping ...... 123
7.2.3 Dung removal experiments ...... 124
7.2.4 Beetle sampling ...... 125
7.2.5 Statistical analysis ...... 125
7.3. Results ...... 126
7.4. Discussion ...... 127
7.4.1 Functional responses of dung beetles ...... 127
7.4.2 Economic implications for conservation ...... 129
7.5 Conclusions ...... 131
CHAPTER 8: GENERAL DISCUSSION – RESEARCH FINDINGS AND THEIR APPLICABILITY
...... 137
8.1 Ecological trends ...... 137
8.2 Conservation implications ...... 143
BIBLIOGRAPHY ...... 150
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APPENDICES ...... 184
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1 THESIS SUMMARY
2 Habitat disturbance caused by the rapid expansion of agriculture and anthropogenic land use
3 severely impact native forest biota. Resulting changes in the environment include altered
4 biotic community composition and ecosystem functions. The present study examines the
5 effects of land use change and forest fragmentation on biodiversity and ecosystem
6 functioning in Sri Lanka using community data. This study was conducted in the lowland wet
7 zone, the most species-rich zone of Sri Lanka, but which has been severely affected due to
8 logging and conversion to agriculture and home gardens. This is the first ecological study to
9 be conducted in the area that encompasses several modified land use types. Throughout this
10 thesis, selected modified land use types; old selectively logged forest, monoculture
11 plantations, home gardens and forest fragments, are compared with primary forests to
12 evaluate the effects of habitat disturbance. Amphibians, butterflies and Scarabaeinae dung
13 beetles were chosen as focal taxa in this study because they are known to be among the best
14 indicators of habitat disturbance due to their sensitivity to habitat changes and cost efficiency
15 of sampling. In the second chapter, I provide background information to Sri Lanka’s
16 conservation history at the study sites. Chapter three examines effects of anthropogenic land
17 use and selective logging by surveying the diversity and community composition of
18 amphibians and butterflies in primary forest and across the chosen land use types. I found
19 that amphibians, specially endemics and direct developing species were more susceptible to
20 habitat modification than butterflies in the lowland wet zone landscape. The environmental
21 determinants of the communities indicated that structural variables of the habitats were more
22 important for amphibians, while butterflies communities were more responsive to climatic
23 variables. In chapter 4, I include a taxonomic update, distribution maps, and a photographic
24 guide to the Sri Lankan Scarabaeinae beetles, including potentially new species and new 1
1 records for Sri Lanka. The reference collection, established from island wide sampling and
2 verified with type specimens, was used to identify specimens gathered during ecological data
3 collection. Analysis of species distributions revealed that dung beetle diversity was correlated
4 with the mammal diversity across the bioclimatic zones of the island. In chapters five and six,
5 I report the diversity and abundance of Scarabaeinae beetles in multiple land use areas over a
6 wide geographic range and twenty forest fragments in the lowland wet zone. I found that
7 diversity and abundance negatively responded to anthropogenic land use in tea plantations
8 and home gardens, primarily through altered abundance and community composition; total
9 species richness was less affected. Communities in more than 70% of forest fragments were
10 significantly different from the primary forest and those differences were best explained by
11 fragment area, area to edge ratio and some abiotic and structural environmental variables (i.e.
12 soil temperature, soil pH, maturity of the forest indicated by DBH profile). Finally, I relate
13 dung beetle species richness and abundance to ecosystem functioning by studying dung
14 removal across the same gradient of land-use change, and then discuss how disturbances can
15 affect dung removal service and nutrient recycling. Dung removal was negatively affected by
16 land use change, primarily through altered abundance and functional group diversity. Further,
17 I discuss the importance of restoring nutrient enrichment of soil through dung removal, and
18 the potential economic benefits for agriculture. This dissertation provides the most coherent
19 picture to date of how amphibians, butterflies, and mostly Scarabaeinae dung beetles are
20 affected by land use change and forest fragmentation in Sri Lanka, and how ecosystem
21 functioning of dung beetles is influenced by habitat modification. In addition, the study
22 surmounts some of the hurdles to tropical conservation research by supplementing the limited
23 knowledge on ecological effects of habitat disturbance in South Asia specifically by
24 highlighting an ecologically little known country in the region. The research findings can be
2
1 used to make scientifically informed recommendations for the conservation of pristine forests
2 and management of anthropogenic land use areas to increase their conservation value.
3
4
5
6
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8
3
1 LIST OF FIGURES
Figure 2.1 Major climatic zones of Sri Lanka
Figure 2.2 Map of Sri Lanka and the study sites
Figure 2.3 Categories of peer reviewed publications from the Sinharaja forest
Figure 3.1 Map of the Sinharaja forest-lowland sector
Figure 3.2 Nonmetric multidimensional scaling ordination of Bray-Curtis similarity indices for butterflies and amphibians Figure. 4.1 Major bio-climatic zones of Sri Lanka and sampling locations of the island wide dung beetle survey Figure 4.2 Dung beetle trap designs used during the study
Figure. 4.3 Distribution of dung beetle species according to body size, across the bio- climatic zones Figure 5.1 Nonmetric multidimensional scaling ordination of Bray-Curtis similarity indices for dung beetles Figure 5.2 Relating the land use and environmental variables to dung beetle richness abundance data using Constrained Correspondence Analysis Figure 5.3 Recursive partitioning analysis of the average species richness in the landscape. Figure 6.1 Locations of the sampled forest fragments
Figure 6.2 Recursive partitioning analysis of the species richness and abundance data vs. fragment characteristics. Figure 6.3 Constrained Correspondence analysis: species richness and abundance data vs. fragment characteristics and environmental variables Figure 7.1 Average percentage of cow dung removal by dung beetles in four habitat types and two seasons Figure 7.2 Correlation of fertilizer use and tea production in Sri Lanka ( 1961 – 2002) 2
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1 LIST OF TABLES
Table 3.1 Summary statistics of butterfly and amphibian sampling
Table 3.2 Environmental determinants of butterfly species richness and abundance
Table 3.3 Environmental determinants of amphibian species richness and abundance Table 5.1 Summary statistics of dung beetle species richness, abundance and biomass of habitat types Table5. 2 Summary of results from the generalized mixed-effects models for the effect of land-use change on species richness Table 5.3 Community similarity among land use types quantified by Morrisita Horn index Table 6.1 Summary statistics of dung beetle species richness and abundance of individual forest fragments. T able 6.2a Summary of community parameters calculated for 20 forest fragments
Table 6.2b Community similarity among fragments compared using two measures
Table 6.3 Generalized linear models: fragment characteristics as determinants of dung beetle species richness. Table 7.1 Dung removal as a function of temporal and spatial parameters in the lowland wet zone of Sri Lanka. Table 7.2 Dung removal as a function of species richness and abundance of different dung beetle guilds 2
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1 APPENDICES
Appendix 3.1 Distribution and endemism of butterfly species in a lowland wet zone landscape, Sri Lanka Appendix 3.2 Species accumulation, population abundance and rarefaction curves for butterflies in sampled habitat types Appendix 3.3 Summary statistic of the multi-response permutation procedure results for community comparison of amphibians and butterflies Appendix 3.4 Distribution, endemism and conservation status of amphibian species in a lowland wet zone landscape, Sri Lanka Appendix 3.5 Species accumulation, population abundance and rarefaction curves for amphibians in sampled habitat types Appendix 3.6 (a) Environmental variables that best describe the species richness and abundance of butterflies and amphibians in all sampled habitats and (b) the correlation matrix. Appendix 4.1 Species checklist of the Scarabaeinae beetles recorded in Sri Lanka
Appendix 4.2 Dung beetle fauna of Sri Lanka (Plates 1-14)
Appendix 4.3 A representative set of the distribution maps of Scarabaeinae beetles in Sri Lanka Appendix 5.1 Species accumulation and population abundance curves for dung beetles in sampled habitat types Appendix 5.2 Dung beetle abundance and seasonal variation across land use types
Appendix 5.3 Proportionate abundance of functional groups of dung beetles across land use types. Appendix 5.4 Environmental and structural variables that best describe the species richness and abundance of dung beetles in all sampled habitats Appendix 5.5 Attributes of Scarabaeinae and Aphodinae dung beetle species recorded from the study sites Appendix 6.1a Species accumulation curves for dung beetles in all sampled fragments
Appendix 6.1b Population abundance curves for dung beetles in all sampled fragments
Appendix 6.2 Influence of fragment characteristics on standardized dung beetle community parameters. Appendix 6.3 Fragment characteristics used for the analysis
Appendix 6.4 Environmental variables used for the analysis
Appendix 8.1 Comparison amphibian, butterfly and dung beetles across forests and home gardens 2
3 6
1 CHAPTER 1: GENERAL INTRODUCTION
2 A century ago, environmental protection was primarily a means to sustain wildlife for
3 scientific and recreational interest. This theme has shifted over time, driven by the
4 exponential growth of human populations and rapid economic development (Sodhi et al.,
5 2007). There is enormous pressure on natural resources in the tropics, particularly fauna and
6 flora (Jha and Bawa, 2006). Wildlife is now encompassed by the term “biodiversity” and is
7 conserved under general legislation instead of specific conservation laws. Sustainable
8 utilization has replaced harvesting, and ecosystem approaches are preferred over species
9 approaches. Conservation has become a participatory process. Adaptive management,
10 monitoring and evaluation have become necessary in regulations. As the severity of global
11 biodiversity loss continues to increase (Reid and Souza, 2005; Sodhi et al. 2007),
12 conservation is no longer a matter of interest but a dynamic, multidisciplinary field and a
13 battle for survival.
14 The current global biodiversity crisis is driven primarily by the clearance and
15 conversion of tropical rainforests, which contain most of the planet’s species (Turner and
16 Corlett, 1996). Unprecedented loss of tropical forest cover is due mainly to the rapid
17 expansion of agriculture and other human-modified habitats (Jang et al,. 1996; Whitmore,
18 1997; Laurance, 1999; Sodhi and Brook, 2006). Tropical forests are either completely cleared
19 and converted to anthropogenic uses or partially cleared, leaving forest patches embedded in
20 a matrix of plantations, home gardens and other anthropogenically modified habitats (Turner,
21 1996). These patches are also interchangeably called as forest fragments or remnants
22 (Lindenmayer and Fischer, 2007). Both types of habitat disturbance severely impact native
23 forest biota. Resulting changes in the environment include altered biotic community
24 composition, behavioural changes in animals, and extinction due to the inability to adjust to
7
1 habitat changes. Disruption of original community structures affects species that are
2 functionally important, thus altering ecosystem functions (Terborgh et al. 2001). The
3 apparent link between biodiversity and ecosystem functioning has accelerated efforts to gain
4 scientific knowledge and implement conservation measures (Loreau et al., 2001). Frequent
5 changes in wildlife and forestry regulations, designation of protected areas, and joining
6 international conventions on conservation are among the political measures adopted by many
7 tropical countries to help curb biodiversity loss. The scientific community is focused on
8 understanding the response of biotic communities to habitat change and on providing
9 empirical frameworks to mitigate the loss (Tilman, 1999; Fahrig, 2003; Balmford and Bond,
10 2005).
11 Scientific evidence necessary to document the vulnerability of species and to identify
12 taxonomic groups most susceptible to disturbance is still scarce in some parts of the tropics
13 (Dunn, 2004b). Biodiversity science faces a number of problems in many South Asian
14 countries (Bawa, 2006) where logistical hurdles delay implementation of management
15 practices suggested by research. Quantification of the effects of anthropogenic modifications
16 on habitat quality and the effectiveness of conservation measures are still inadequate. “Cut
17 and paste” resource management where decisions based on research findings from one
18 country or region are applied elsewhere without suitability trials may be more harmful than
19 inaction. I have observed this in Sri Lanka and it is known to be practiced in other Asian
20 countries. Adopting a “one-size-fits-all” approach is another issue partially arising from lack
21 of data where forest areas are protected without monitoring degradation caused by human
22 impacts or considering the ecological value of habitats is detrimental to the remaining
23 biological communities and a waste of limited resources.
24
8
1 Research on tropical forest fragmentation and modification has come mainly from
2 studies in the Neotropics (Didham et al,. 1998; Terborgh et al,. 2001; Fahrig, 2003;
3 Cushman, 2006); well-replicated studies in South and Southeast Asia are limited. This dearth
4 of research in Asia is disturbing because the Asian tropics are the most imperiled on Earth
5 (Sodhi et al., 2004). Because of limited resources, conservation efforts are prioritized based
6 on the magnitude of threats (Spector and Forsyth, 1998; Spector, 2002). With the exceptions
7 of Australia and tiny Singapore, every country with tropical rain forest is a developing
8 economy and thus poorly endowed to deal with conservation threats. Further, the
9 undervaluation of biodiversity resources by the global market economy provides developing
10 countries with less incentive for conservation (Lapham and Livermore, 2003). Their inability
11 to identify threats to biodiversity often handicaps their ability to act on conservation
12 emergencies within their own borders.
13 Large, connected primary habitats and regions of rapid species turnover are
14 prioritized in planning efforts for their conservation value (Wikramanayake et al., 1998;
15 Foreman et al., 2000; Spector, 2006). In light of this, understanding the habitat value of
16 anthropogenic land-use areas is often neglected. Species richness, abundance and community
17 composition in anthropogenically modified areas varies according to habitat type and quality.
18 Nevertheless, many human-dominated landscapes may prove to be valuable habitats and
19 could be enhanced for their habitat value with suitable management.
20 The present study examines the effects of land use change and forest fragmentation
21 on biodiversity and ecosystem functioning in the tropical lowlands of Sri Lanka. More
22 specifically, the study provides an understanding of how amphibians, butterflies, and
23 Scarabaeinae dung beetles are affected by habitat disturbance in Sri Lanka and how
24 ecosystem functioning of dung beetles is influenced by changing habitats. Sri Lanka is an
9
1 oceanic island in South Asia, with a large network of protected forests (Chapter 2). Despite
2 the extent of these forests, damage wrought by massive deforestation of the last century is
3 poorly understood. This paucity of scientific data hampers the evaluation of existing
4 conservation practices and informed management of forest and modified habitats.
5 Amphibians, butterflies and Scarabaeidae dung beetles are globally known to be
6 among the best indicators of habitat disturbance (Gardner et al, 2008) due to their sensitivity
7 to habitat changes and cost-efficient sampling (Gardner et al, 2008), and were therefore
8 chosen as focal taxa in this study. Effective indicator responses are measurable within
9 limitations of sampling protocols (Gardner, 2010). Variation in the diversity of indicator taxa
10 reflects the link between ecological conditions and management or disturbance. In addition to
11 understanding taxon specific responses to disturbance, studying a known indicator in novel
12 terrain allows assessment of its response variability and utility as an indicator or a
13 “calibration” of its indicator value in a different environment (Gardner, 2010).
14 Few studies have explored the responses of multiple taxa under different management
15 regimes or interventions in the same landscape (Lawton et al., 1998; Schulze et al., 2004;
16 Barlow et al., 2007; Smith et al., 2008). The use of multiple taxa provides a better
17 understanding of ecological consequences of a habitat change as responses by each group
18 may differ (Gardner, 2010).
19 In this study, the choice of habitat types and the bioclimatic zone were based on the
20 outcomes of forest cover loss in Sri Lanka during the past 150 years. The lowland wet zone
21 was the most severely affected due to logging, but it is also the most species-rich (Chapter 2).
22 Throughout this thesis, selected modified land use types, old selectively logged forest,
23 monoculture plantations, and home gardens are compared with primary forests as a reference
24 point to evaluate the effects.
10
1 In the second chapter of this thesis, I provide background information to Sri Lanka’s
2 conservation history at select study sites. The third chapter examines effects of anthropogenic
3 land use and selective logging by surveying the diversity and community composition of
4 amphibians and butterflies in primary forest, old selectively logged forest, monoculture
5 plantation forests and home gardens. I discuss how these two taxa with different ecological
6 requirements are affected by habitat change, and discuss their suitability as habitat indicators
7 in Sri Lanka. In my fourth chapter, I have included a taxonomic update of Sri Lankan
8 Scarabaeinae beetles. Much of Sri Lanka’s invertebrate fauna remains unknown except for
9 work done in early 20th century and published in Fauna of British India. Sri Lanka’s dung
10 beetles were first included in Arrow, (1931), followed by a few subsequent addition of
11 species in Balthasar (1963). I collected dung beetles across the country and prepared a
12 reference collection to identify dung beetles gathered as data. I found that the distribution of
13 dung beetle species across the island has changed in comparison to previous records. In the
14 fifth and sixth chapters, I explore the diversity and abundance of Scarabaeinae and
15 Aphodiinae dung beetles (referred to as dung beetles here after) in multiple land use areas
16 and forest fragments in the lowland wet zone. Dung beetles were selected because of their
17 well-known indicator potential (described in chapter 4), and the feasibility of sampling over
18 such a large landscape, and their link to ecosystem functioning. In my seventh chapter, I
19 relate diversity to ecosystem functioning by studying dung removal by dung beetles across a
20 gradient of land-use change, then discuss how disturbances can affect useful functions
21 through altered community structure. Further, I discuss the importance of restoring
22 ecosystem functions and potential economic benefits.
23 The specific objectives of the study were to assess the responses of three selected taxa
24 to habitat disturbance and to relate altered diversity to function. The general objectives of the
11
1 study surmounts some of the hurdles to tropical conservation research; 1) supplement the
2 limited knowledge on ecological effects of habitat disturbance in South Asia; 2) to explore
3 and highlight an ecologically little known country in the region; 4) document a poorly known
4 taxon in Sri Lanka; and 4) to make scientifically informed recommendations for the
5 conservation of pristine forests and management of anthropogenic land use areas to increase
6 their conservation value.
7
12
1 CHAPTER 2: STUDY SITE
2 2.1 Sri Lanka and the Lowland Wet Zone: a biodiversity hotspot
3 Sri Lanka is a small but biologically rich island of 6,570,134 ha located at the southern point
4 of the Indian sub-continent between 5˚ 54' and 9˚ 52'. A variety of biomes are found in Sri
5 Lanka, and this is in large part responsible for the country’s high species richness.
6 Topographically, a south central massif 2500 m above sea level (asl) is surrounded by broad,
7 lowland plains that reach 75 m asl. Climate varies across the island due to a combination of
8 varying rainfall patterns driven by elevational gradients and monsoons. Three, broad climatic
9 regions are recognized: wet zone, dry zone, and intermediate zone (IUCN, 1993; Fig 2.1a).
10 Whereas the entire dry zone is lowland, the other two zones are further subdivided on
11 the basis of altitude (MENR, 2002). In the wet zone, forests have been categorized as
12 tropical, wet lowland evergreen forests (0-1000 m); wet sub-montane forests (1000-1500 m);
13 and wet montane forests (1500-2500 m). Tropical dry, mixed evergreen forests, riverine
14 vegetation, tropical moist evergreen forest, thorny scrub and mangrove swamps are other
15 types of vegetation found in the island (NBSAP, 1999).
16 Sri Lanka was connected to India during the Pleistocene ages and the fauna is
17 evolutionarily related to the Indian mainland. However, after the sea level rise, isolation and
18 limited migrations allowed a large number of endemic species to evolve over a period of 25
19 million years in the . mid-Paleocene to late Eocene Epochs (Bossuyt et al., 2004). Southwest
20 of Sri Lanka is known as the Highland Complex (IUCN and MENR, 2007) is of considerable
21 biogeographic significance as it holds an exceptional diversity of a relict biota distinct from
22 that of the Western Ghats of India (Ashton & Gunatilleke, 1987). The biota of Sri Lanka's
23 south-western rainforests and the montane forests also show evidence of significant and
24 prolonged isolation from both the island's dry zone and from peninsular India
13
1 (Meegaskumbura et al., 2002). Recent periods of low sea level again created biotic affinities
2 between the mainland and the island in the Western Ghats–Sri Lanka biodiversity hotspot as
3 a result of been frequent migrations (Ashton and Gunatilleke, 1987).
4 The southwest section of the island where this study was conducted falls within the
5 wet zone, covering an area of 20,000 km2. Lowland, wet evergreen forest dominant
6 vegetation in the southwest lowlands; (mean annual temperature 28.0˚ C, rainfall 2300 –
7 5000 mm, humidity 75-85%; Pemadasa and Gunatilleke, 1981). Many of the endemic species
8 are concentrated in these scattered fragments of rainforest, which are surrounded by agro-
9 ecosystems and human settlements (IUCN, 1997). The forest is stratified and characterized
10 by a tall, dense canopy of about 30 m with emergents reaching 45 m or more. Vegetation is
11 dominated by tree species in the families; Dipterocarpaceae, Clusiaceae, Myrtaceae and
12 Sapotaceae (Gunatilleke et al., 1996). A broad sub-canopy of 15-30 m and a shrub layer
13 mainly consisting of tree saplings and a sparse herbaceous ground flora complete the forest
14 profile (IUCN and WCMC, 1997).
15 The high degree of endemism has led to southwest Sri Lanka being designated a
16 biodiversity hotspot (Myers et al., 2000). About 75% of all endemic species, including 88%
17 of the flowering plants (Dassanayake et al., 1980–2004), 80% of the fresh water crabs (Bahir
18 et al., 2005), and a major proportion of amphibians (Meegaskumbura et al., 2002;
19 Manamendra-Arachchi and Pethiyagoda, 2005) and land snails (Naggs and Raheem, 2000;
20 Naggs et al., 2005) are recorded in this area. Nearly 95% of the wet zone forests were
21 converted to agriculture or other land uses in the last century (Myers et al., 2000; Mittermeier
22 et al., 2004; Bahir et al., 2005). There is a great demand for land for development as the wet
23 zone contain 67% of the island’s 19 million human inhabitants (Anon., 2003).
24
14
1 2.2 From game protection to biodiversity conservation: the history and status of biodiversity
2 conservation in the wet zone of Sri Lanka
3 Tropical forests are often managed by government departments primarily interested in timber
4 and with minimum concern for the conservation of biodiversity (Sodhi and Ehrlich, 2010).
5 The first recorded estimate of percent forest cover in Sri Lanka is from 1881: 84%. It was
6 44% in 1956 and decreased to 22.4% by 1999 (Bandaratillake, 2003). Over the last 150
7 years, governance in Sri Lanka has moved away from timber extraction towards forest
8 conservation. This transition was driven primarily by public protest for the massive
9 deforestation, intervention by the scientific community, shifting public opinion towards forest
10 conservation, and obligations to international conventions. The concepts of biodiversity
11 conservation and environmental management have been evolving worldwide as natural
12 environments deteriorate and biodiversity declines; policies and regulations have kept pace in
13 Sri Lanka as elsewhere (Kotagama pers. com).
14 Sri Lanka’s protected areas are among the most extensive in the world as a proportion
15 of total land area. Around 15% of the island is protected and only about 18% of protected
16 areas fall within the biologically richest wet zone (Fig 2.1; IUCN and WCMC, 1997). The
17 national government holds the primary jurisdiction for conservation of the country’s
18 biodiversity, managed by two governmental departments: the Forest Department and the
19 Department of Wildlife Conservation. The Department of Forest Conservation administers
20 forest reserves and wilderness areas confined mostly to the wet zone. The Department of
21 Wildlife Conservation (DWLC) is responsible for maintaining national parks, nature
22 reserves, and wildlife in other wilderness areas that occur mainly in the dry zone. Until
23 recently, both of these were administered by the Ministry of Environment and Natural
24 Resources, but recent political changes have prompted the DWLC to now be governed by the
15
1 Ministry of Tourism Development, which has created many recent cases of conflicts of
2 interest. Sri Lanka receives international aid to assist performing its obligations under treaties
3 and conventions such as the Convention on International Trade in Endangered Species
4 (CITES), the Convention on Wetlands of International Importance (RAMSAR Convention)
5 and, most significantly, the Convention on Biological Diversity (IUCN, 1994).
6 Sri Lankan culture has safeguarded wild animal and plant populations for over 2,200
7 years guided by the conservation ethics and beliefs of Buddhism. Mihintale, Anuradhapura,
8 was the world’s first wildlife sanctuary created between 307-266 B.C. (Abeywickrama,
9 1987). Forest protection in Sri Lanka during the modern era was initiated to slow timber
10 felling for export, security, and agriculture. The advent of coffee and tea agriculture in 1835,
11 lead to massive deforestation and hunting, especially of large mammals (Gunawardena, 2005;
12 Jayasuriya et al., 2006). The Wastelands Ordinance of 1840 designated all unoccupied and
13 uncultivated lands (including forests) for the establishment of tea, rubber, coconut and
14 cinnamon plantations—even land previously held by local communities and individuals. The
15 over-exploitation of forests through agreements between contractors and government
16 officials severely degraded the silvicultural value of dry zone forests during this era (NBSAP,
17 1999).
18 During 1882-1889 attempts were made address deforestation and, to promote
19 sustainable management. These include the appointing of a conservator of forest and the
20 establishment of the Forest Department in 1889. While directive of the department was
21 primarily to conserve and manage state forests to maintain a sustainable yield, the actions
22 were clearly utilization-oriented ((NBSAP, 1999; Wijesinghe, 2003). During the same period,
23 concerns over poaching carried by animal traders prompted immediate legislation to forbid
24 the practice. This ordinance readjusted customs duties on firearms, restricted the market of
16
1 animal body parts and regulated hunting (Wijesinghe, 2003). While there were many public
2 protests against the ordinance, those in favour created the first wildlife NGO in Sri Lanka: the
3 Game Protection Society. In order to protect animals, the Forest Department and the Game
4 Protection Society independently established some protected areas to provide sanctuary to
5 animals in areas of high hunting pressure.
6 The first three decades of the 20th century saw more regulations consolidating existing
7 laws relating to mammal, fish, and wild bird protection (Wijesinghe, 2003). These changes
8 also halted all forms of commercial exploitation of wild animals, imposed strict regulations
9 on hunting and increased the protection status of demarcated wilderness areas. The first forest
10 policy was ratified by the State Council in December 1935 to serve several functions: 1)
11 coordinate conservation activities for indigenous fauna and flora; 2) conserve water supplies
12 and prevent erosion; and 3) make the island self-supporting in timber and other essential
13 forest products (MENR, 2002). By this time, the administration of all forests was placed
14 under the Ministry of Agriculture and Lands, and the role of the Forest Department had
15 completely shifted to timber harvesting. 1937 saw the most significant legislation in the
16 conservation history; the Fauna and Flora Protection Ordinance (FFPO) -No.2 of 1937
17 (Chapter 325 of the Legislative Enactments), which was declared to recognize the
18 preservation of indigenous fauna and flora (MENR, 2002). Many national parks, strict nature
19 reserves and sanctuaries were created under the ordinance. Some were challenged because
20 areas designated for animal conservation by the act were earmarked for timber extraction by
21 the Forest Department. Most areas designated as strict nature reserves for animal
22 conservation were of poor timber value in both dry and wet zones. The Department of
23 Wildlife Conservation was established in 1949 and management responsibilities for all
17
1 protected areas declared under the FFPO were transferred to this department, (NBSAP, 1999,
2 Wijesinghe, 2003).
3 The beginning of the Forest Department’s transition from timber supplier to
4 environmental stewardship started with the redefining of the forest policy in 1951(NBSAP,
5 1999). However, logging continued amidst the rapidly developing conservation and scientific
6 interest by local and international parties. A new forest policy was drafted in 1980 to deal
7 with major concerns in environmental conservation for scientific and socio-economic reasons
8 (NBSAP, 1999). It also ensured a supply of wood for agriculture, fuel, and domestic timber
9 uses. Local community participation was encouraged through social forestry (NBSAP, 1999).
10 In 1987, the World Bank launched a controversial project to prepare a forestry sector master
11 plan. The master plan, although supported by the government and intended to support
12 conservation, was primarily concerned with maximizing timber extraction. Out of 278,000 ha
13 of wet zone and montane forests, 159,000 ha were to be protected, while 119,000 ha were to
14 be harvested. However, protection of montane forests under this plan was redundant and
15 unnecessary because these areas were already protected as important watersheds. Forests
16 destined for logging were all lowland rainforest of extreme biodiversity value (Wijesinghe,
17 2003). The Director of the Wildlife Department initiated formulating a National Policy for
18 Wildlife Conservation and in 1990, with the commencement of the National Conservation
19 Review (NCR), a moratorium on logging wet zone forests was enacted and later extended to
20 include to the dry zone (Green and Gunewardene, 1997). The NCR thus completely shifted
21 the role of the Forest Department to biodiversity conservation. When the NCR ended in 2000,
22 the concept of Conservation Forests was established. As legislative changes were forcing the
23 Forest Department to be conservation-oriented, the role of timber harvesting passed to the
24 newly established Sri Lanka timber corporation.
18
1 The new wildlife policy was approved in 1991 to maintain ecological
2 processes and life-sustaining systems, and to ensure sustainable utilization of wildlife
3 resources. Plantation forests were established to supply timber and wood for paper
4 production. To reforest the logged areas, pine trees (Pinus caribea.) were planted, and as a
5 result, Sinharaja rain forest of the wet zone (one of the three main study sites described later)
6 has a belt of Pinus around its periphery separating it from nearby villages. Use of Pinus was
7 challenged eventually, and today, broad-leafed Cassia is recommended instead (Ashton et
8 al., 1997). Since the early 1990s, there have been many national conservation plans such as
9 the National Environmental Action Plan; 1992-1996, and other programs launched by the
10 government and backed by international groups (i.e. The final transformation of the Forest
11 Department into a conservation entity came in 2010 when its name was changed to the
12 Department of Forest Conservation.
13 Sri Lankan culture is predisposed to nature conservation, as indicated in the second
14 Republican Constitution (1978), which states that, “The State shall protect, preserve and
15 improve the environment for the benefit of the community” (Article 27.14) and that “it is the
16 duty of every person in Sri Lanka to protect nature and conserve its riches" (Article 28F;
17 Jayasuriya et al., 2006). When the 30-year civil war ended in 2009, the country opened to
18 rapid development and commercial exploitation. Exploitation of fauna and flora for scientific
19 purposes and for export sale is strictly monitored by the government departments. In contrast,
20 the booming interest in boosting the country’s economy through intensified tourism activities
21 has brought a new set of opportunities, threats, and challenges to biodiversity conservation.
22 Publicity campaigns are attracting global attention to Sri Lankan wildlife, especially the
23 charismatic species, and much infrastructure has been developed in the periphery of protected
24 areas and adjoining landscapes. National parks and other protected areas, which serve as the
19
1 main refuges for wild animals unable to survive in the agricultural matrix surround them, are
2 observed to be subjected to intense tourist activities although no formal study has been
3 conducted to collect data. Recent administrative changes and political decisions have affected
4 the conservation status of many protected areas and taxa. Weak institutions and illegal
5 activities that affect tropical forest management (Kummer and Turner, 1994) are significant
6 issues affecting conservation in Sri Lanka. Many national and international organizations
7 influence the conservation decision-making in the country. Many nongovernmental
8 organizations undertake conservation activities. Some of them are small-scale, localized, and
9 unmonitored, while there are a few conducting effective, long-term programs with lasting
10 effects, particularly to mitigate human-elephant conflict and awareness of this issue. Sri
11 Lankan citizens are, in general, highly informed about conservation, and prevent exploitation,
12 most recently with the aid of media and social networking.
13
14 2.3 Study sites: Sinharaja, Kanneliya and Kottawa
15 The study was conducted in the lowland wet zone landscape around 20 forest fragments of
16 10-200 ha and three large forests: Sinharaja, Kanneliya and Kottawa (Fig 2.2). Only 4.6% of
17 the wet zone (800 km2) now contains natural forest comprising about 140 fragments
18 (Jayasuriya et al., 2006).
19 2.3.1 Sinharaja Man and Biosphere Reserve
20 Sinharaja forest is a fragile and least resilient forest with high species richness, high
21 endemicity, and a unique climax vegetation (Gunetilleke et al., 2004). Sinharaja forest
22 (11,331 ha: elevation 200-1150 m asl) is located in the wet zone in southwest Sri Lanka (6˚
23 21–26’ N, 80˚ 21–34’ E). The mean monthly temperature ranges from 18–27˚ C. The rainfall
24 ranges from 3750–5000 mm, and falls primarily during the southwest monsoon (May–July)
20
1 and northeast monsoon (November–January). A major proportion of the forest is primary
2 vegetation of wet evergreen forest in the northwest and a mix of lowland, wet evergreen and
3 tropical montane forests at higher altitudes in the east (Gunetilleke et al., 2004; Wijesinghe
4 and Brooke, 2005).
5 About 2,200 ha of forest were selectively logged about 40 years ago (IUCN, 1993),
6 but closely resemble the primary forest in some areas today (Wijesinghe and Brooke, 2005).
7 Logging began in 1970 with the use of heavy machines, and was halted in 1978 due to
8 intense public protest. The Decision was mostly driven by the intervention of the scientific
9 community and the public, although there was no scientific evidence to support legislative
10 decision-making at the time (Wijesinghe, 2003). The same year, 8,500 ha of Sinharaja’s
11 lowland forest, comprising 65% primary forest and 34% fern lands/secondary forest, were
12 declared an International Biosphere Reserve. In the 1980s, an additional 2,687 ha of lower
13 montane forests on the eastern end was included in the Sinharaja reserve, raising the total
14 land area to 11,187 ha. In 1985, the Forest Department established a live forest boundary by
15 planting Pinus caribaea. Subsequently, the Sinharaja National Heritage Wilderness Area was
16 declared, and in 1990, UNESCO recognized it as a Natural World Heritage site (MENR,
17 2002).
18 Early research in Sinharaja was limited to gathering herbarium specimens—even as
19 far back as 1855 (Dassanayke and Clayton, 1996). Today, many local and internationally
20 peer reviewed scientific publications have resulted from research at Sinharaja. Fig. 2.3
21 categorizes these peer reviewed international publications from 1985 to date according to the
22 taxa studied. Most of these works, including this study, have been conducted in the western
23 sector of the reserve, accessed via Kudawa. A major proportion of the studies in Sinharaja are
24 on long-term forest dynamics, plant phenology (i.e., Shorea species), studies of non-timber
21
1 forest species (distribution, pollination ecology, reproductive biology, and seed germination)
2 and silvicultural studies of forest species’ responses to various physio-chemical parameters
3 (Gunetilleke et al., 2004). A 50-hectare forest dynamics plot affiliated with the CTFS
4 network (Center for Tropical Forest Science of the Smithsonian Tropical Research Institute)
5 is located within Sinharaja. The National Conservation Review sampled the western, eastern,
6 northern, and southern sectors and recorded 337 species of woody plants in 71 families and
7 191 genera. Of these, 192 species (57%) were endemic (IUCN and WCMC, 1997), and these
8 numbers have increased over the time due to taxonomic changes. One hundred and seventy
9 nine plant species, of which 33% are endemic, are used as non-timber forest products by
10 local people (Gunetilleke et al., 1993).
11 An initial biodiversity survey revealed that Sinharaja harbours 19% of the fish, 53%
12 of the amphibians, 57% of the reptiles, 90% of the birds and 67% of the mammals endemic to
13 Sri Lanka (Kotagama, 1989). While species checklists of numerous taxa exist, few have been
14 studied in detail. A long-term research program on avian flocks by a single team lead by
15 Sarath Kotagama and Eben Goodale (1982-2010) constitutes a major proportion of the peer-
16 reviewed publications of fauna. Sarath Kotagama (1985-1986), and Mayuri Wijesinghe
17 (2004-2006) have documented small mammals of Sinharaja. Amphibian studies mainly
18 focused on revising taxonomy and describing new species. Newly described species include
19 living species and undescribed museum specimens that can no longer be located in the wild
20 and are presumed extinct (Bahir et al., 2005). Invertebrate studies in Sinharaja have focused
21 on mosquitoes (Diptera: Culicidae), tiger beetles (Coleoptera: Cicadellidae), snails
22 (Mollusca: Gastropoda) and most recently on ants (Hymeoptera: Formicidae).
23 The ecological effects of selective logging and land use change in Sinharaja are less
24 well known. Kotagama et al., (1986) explored the effects of selective logging on small
22
1 mammal diversity. Wijesinghe and Brooke (2004, 2005) followed by comparing small
2 mammal distribution patterns and vulnerability of endemics across a land-use gradient from
3 primary forests and logged forest to Pinus plantations and abandoned human land use areas
4 surrounding Sinharaja. Gunewardana et al., (2010) compared ant diversity in primary forest
5 and selectively logged forest.
6 Research on the abiotic environment at Sinharaja include: records of physico-
7 chemical and biological properties of soil across primary and disturbed areas (Maheswaran
8 and Gunetilleke, 1988, Hafeel and Gunatilleke, 1989); endomycorrhiza in forests and Pinus
9 plantations; leaf litter decomposition rates and nitrogen fixation in the forest and fern-land
10 (Maheswaran and Gunetilleke, 1988, 1990); seed bank germination (Uduporuwa et al.,
11 1997). As summarized in Gunetilleke et al., 2004, many of the socio-economic studies are
12 anthropological surveys that focus on the utilization of the forest as a resource by local
13 people, ecological valuation of forest resources, community-based participatory management,
14 and overexploitation through hunting and forest product harvesting and negative
15 environmental consequences of utilization.
16 2.3.2 Kanneliya conservation forest
17 The forest of 6,114.4 ha (6˚ 09-18’ N and 80˚ 19-27’ E) is located in the southwestern Sri
18 Lanka in the southern part of the wet zone. It is part of the Kanneliya-Dediyagala-
19 Nakiyadeniya (KDN) complex. The average annual rainfall is about 4,445 mm and falls
20 mainly during the Southwest monsoon season from mid May to the end of September, and
21 the mean monthly temperature is around 27˚ C (IUCN and WCMC, 1997). Selective logging
22 in Kanneliya was halted by the moratorium on all logging within the wet zone, and the forest
23 was declared an International Biosphere Reserve in 2004 (Jayasuriya and Abayawardana,
24 2008).
23
1 Studies on Kanneliya identify it as the floristically richest area in South Asia (Peeris,
2 1975; Gunatilleke and Gunatilleke, 1990; Singhakumara, 1995). The KDN complex is known
3 to harbor the highest percentage of endemic woody species (60%) of any single wet zone
4 forest (NBSAP 1999). The revised Handbook to the Flora of Ceylon describes the taxonomy
5 of plants from this area. About 85% of the peer-reviewed publications from Kanneliya are on
6 plant phenology, forest dynamics, and floral distribution. Five percent of the studies focus on
7 invertebrates, and the remainder examines species distributions, forest management and
8 conservation plans. The forest is known for high species richness of endemic amphibians
9 (e.g., Nannophrys guentheri, Ramanella palmata and Icthyophis glutinosus), reptiles, and fish
10 (NBSAP 1999). Kanneliya was included in an internationally funded scientific project
11 conducted by the Ministry of Environment and Natural Resources from 2000-2006, to
12 promote protection of the lowland forest ecosystems through community participation.
13 There are about 78 villages surrounding the entire KDN complex. Their primary
14 source of income is from tea and paddy cultivation. More than half the community is of the
15 government identified poor income level. Most home gardens are partially cultivated with
16 food or commercial crops. The locals are still dependent on the non timber forest products
17 from the forest complex although utilization is much low due to the protection status
18 (Bandaratillake, 2003). The KDN management plan of 1995 has outlined the involvement of
19 local people in conservation through community participation.
20
21 2.3.3 Kottawa Kombala conservation forest
22 Kottawa Kombala conservation forest is protected under the Forest Department and is located
23 in Galle District (6˚ 09-18’ N and 80˚ 19-27’ E; ~2000 ha). The forest reserve includes
24 Kottawa Rainforest and Arboretum margined by a main road and Hiyare selectively logged
24
1 forest fringing the Hiyare reservoir. Surrounding matrix consists of densely populated areas
2 and small to large scale tea plantations and other crops. Published studies available from this
3 site include a many species descriptions. Kottawa was widely used in floral studies by
4 Gunatilleke (1978); Pemadasa and Gunatilleke (1981); Gunatilleke and Gunatilleke, (1985);
5 Gunatilleke and Ashton (1987), and participatory forest management studies by Yamamoto
6 (2000). Field observations in Kottawa show the return of some of the canopy and sub-canopy
7 endemic plant species following selective logging. However, it is poorly known how endemic
8 herbs and shrubs recovered after disturbance (Gunatilleke et al., 2004).
9 The earliest known biodiversity research work on Kanneliya and Kottawa was
10 conducted in the early 1980 through the Nation Conservation Review Programme (NCR
11 Data) by the Forest Department with the collaboration of International Union for
12 Conservation of Nature (IUCN) The more recent 5-year survey of the biodiversity of Sri
13 Lankan natural forests established a faunal checklist for Kanneliya and Kottawa. These
14 studies highlighted the importance of the remaining rainforests of the wet zone for the
15 conservation of woody plant, animal diversity and for the protection of watersheds (Green
16 and Gunawardena, 1997). Some of the peer-reviewed publications also include ecological
17 studies examining the effects of forest fragmentation on snails (Raheem et al., 2008, 2009),
18 the distribution of Slender Loris (Nekaris et al., 2005).and species descriptions of fish,
19 amphibians, lizards, mollusks and insects.
20
21 *The above section was written from personnel communication with Prof. S.W. Kotagama,
22 Professor of Environmental Sciences and former Director of the Department of Wildlife
23 Conservation. Except for the cited references, facts in this section are extracted from Prof.
24 Kotagama’s unpublished notes with permission.
25
1 2 Figure 2.1 (a) Major climatic zones of Sri Lanka (Jayasuriya et al., 2006). 3
26
1
2 Figure 2.2: Site map: includes the geographic location of Sri Lanka, the wet zone and the three forest 3 sites that were sampled. Anthropogenic habitats were sampled in the surrounding matrix within 10km 4 range from the forest. Modified from UNEP-WCMC's World Database on Protected Areas. GIS 5 vector data was provided by R Hijmans, UC Davis via www.diva-gis.org. 6
d studie p rou g Taxonomic
7 Number of peer reviewed publications
8 Figure 2.3: Peer reviewed publications, categorized by subject, resulting from research conducted in 9 Sinharaja rainforest (1985-2010). Data were obtained from Web of Science, Google, and Google 10 Scholar searches using the following search terms: Sinharaja, wet zone forest, lowland wet zone, Sri 11 Lanka 27
1 CHAPTER 3: AMPHIBIAN AND BUTTERFLY DIVERSITY ACROSS A LOWLAND
2 TROPICAL LAND-USE GRADIENT IN SRI LANKA
3 3.1 Introduction
4 Megadiverse lowland rainforests are being felled throughout tropical Asia for agricultural
5 expansion, bio-fuel production and urban development (Sodhi et al., 2010). While research
6 findings on the response of biotic communities to land-use change have helped to mitigate
7 tropical biodiversity loss in other parts of the world (Balmford and Bond, 2005), such studies
8 are urgently needed in tropical Asia, which suffer the highest tropical deforestation rates
9 globally (Sodhi and Brook, 2006; Sodhi et al., 2009).
10 Sinharaja Man and Biosphere Reserve (hereafter, Sinharaja), is the largest and one of
11 the most species-rich lowland rainforests remaining in Sri Lanka. Although part of it has
12 been selectively logged (Gunatilleke and Gunatilleke, 1980), the forest has regenerated over
13 several decades. The forest is now protected and surrounded by home gardens, tea
14 plantations, and non-native Pinus caribea (pine) trees acting as a buffer between the forest
15 and the adjoining villages (Ashton et al., 1997).
16 Studying distribution across a land-use gradient allows to understand species
17 responses to disturbance (i.e. Wijesinghe and Brooke, 2005; Koh, 2007, Sodhi et al., 2007;
18 Gardner, 2010), and ecological characteristics that make them adaptable or vulnerable
19 (Connell and Orians 1964). Most faunistic studies on the effects of habitat disturbance focus
20 on a single taxon and disturbance type (Dunn, 2004b). A multi-taxon approach will
21 ultimately lead to a more representative picture of land-use change effects because responses
22 to disturbance may differ between taxa (Gardner, 2010). Further, comparing the effects of
23 multiple types of land-use will allow for more accurate predictions of diversity in
24 increasingly disturbed and fragmented habitat matrices. Examining taxonomically and
28
1 ecologically distinctive taxa simultaneously allows better identification of environmental
2 predictors of abundance and diversity that are common across these groups.
3 Species richness, abundance, and community composition of butterflies and
4 amphibians in primary forests, are compared with other land use types (selectively-logged
5 forests, home gardens, and Pinus caribea plantations) in and around Sinharaja forest.
6 Through this comparison, I aimed to assess the impacts of habitat change, resulting from the
7 conversion of forest to human modified habitats. As there were no temporal (pre-habitat
8 conversion) data, the associations between species richness/abundance/community
9 composition and different land use types were used as responses by each taxon. Best
10 environmental predictors of amphibian and butterfly diversity were determined using
11 Bayesian regression modeling, and compared our results with similar studies from other
12 regions. The research findings are used in Chapter eight, to make scientifically informed
13 recommendations for the conservation of forest habitats and management of anthropogenic
14 land use areas in Sri Lanka.
15
16 3.2 Materials and methods
17 3.2.1 Study area
18 The present study was conducted in the lowland section of the Sinharaja Man and Biosphere
19 Reserve (Figure 3.1; see Chapter two for details of the study site). Butterflies and amphibians
20 were sampled in primary forests (PF), selectively-logged forests (SLF), home gardens (HG),
21 and Pinus caribea plantations (PP). The two forest habitats were collectively termed “forest”
22 and the other two habitats of human origin were termed “anthropogenic habitats” in some
23 parts of the discussion for the interpretation of data. The land use gradient is PF< SLF<
24 PP 29 1 located as a large area close to the boundary embedded in the primary forest. Home gardens 2 at places have encroached into the Pinus plantations which are supposed to be surrounding 3 the forest as a buffer. The PF has a closed canopy, but the SLF has more canopy openings 4 and thicker undergrowth. Vegetation in some previously logged areas has re-grown to closely 5 resemble the unlogged areas. The PPs’ were grown as a method of rapidly reforesting 6 previously logged areas and creating a buffer zone in the periphery of the forest. The HG and 7 PP sampled were within 5 km of the forest boundary. 8 9 3.2.2 Sampling diversity and abundance 10 In each habitat type, I sampled amphibians in twenty-five 150 m x 10 m transects and 11 butterflies in thirty-two 100 m x 5 m x 5 m transects between 20 April 2007 and 30 June 12 2008. Sampling was carried out in periods of relatively wet weather (average monthly rainfall 13 393.6 mm) and relatively dry weather (average monthly rainfall 239.2 mm). I used a 14 modification of the line transect count method (Pollard and Yates, 1993; Posa and Sodhi, 15 2006) to determine butterfly richness and abundance. Butterflies were sampled between 0800 16 to 1030 h, where a 100 m transect was traversed once at a uniform pace for 1.5 h, and all 17 individuals that come within an imaginary 5 x 5 x 5 m³ box in front and above of the observer 18 were recorded. Butterflies were sampled during the early morning period of peak activity as 19 observed during the preliminary field surveys. Butterflies were captured using a handnet 20 when required for identification and was photographed and released at the same location. A 21 visual encounter surveys was used to sample frogs (Heyer et al., 1994) during the period 22 which corresponds with the greatest anuran vocalization and activity (Heyer et al., 1994; 23 Marsh and Pearman, 1997). It was between 1930 hours and 2300 hours for this study site as 24 per preliminary survey. Amphibians were collected in sampling bags where necessary to be 30 1 brought back for identification and were released at the same location, the following day. The 2 time of each transect walk was constrained to 1.5 h that coincide within the specified 3 sampling time and there was no significant difference in abundance and diversity based on 4 transect starting time. Sampling effort (time) was the same for all habitat types and both taxa. 5 All transects were at least 500 m from the boundary of a habitat and from other transects, in 6 an attempt to assure statistical independence of spatial samples. The minimum distance 7 between two different habitat types was 1 km. 8 9 3.2.3 Environmental variables 10 Butterflies and amphibians were sampled in the same localities. To characterize the habitats 11 ecologically, a set of 14 climatic and structural environmental variables within a 5 m radius 12 plot from the central point of each transect were measured. The variables measured; 13 percentage of canopy cover (measured with a circular densitometer), percent of shrub cover 14 (visually estimated), diameter at breast height (dbh) of the closest 10 trees with dbh > 5 cm, 15 number of dead trees, number of fruiting and flowering plants, average litter depth (cm) 16 calculated from five randomly selected points, percent soil moisture and pH (both measured 17 with a Kelway® Soil pH and Moisture Meter, Kel Instruments Co., Inc, Wyckoff, New Jersey 18 ); soil temperature; ambient temperature; humidity; and atmospheric pressure (these last four 19 variables were measured with a Kestrel 2000 weather meter, Nielsen-Kellerman, Creek 20 Circle, Boothwyn, PA). The extent of human modification and disturbance was also 21 estimated by counting the number of buildings within 25 m of each transect. 22 31 1 3.2.4 Analysis of species richness and abundance data 2 The statistical approach used for the data analysis was based on methods used by previous 3 researcher in similar studies. Data analysis consists of three main sections; comparison of 4 richness abundance data, quantifying community differences and relating environmental 5 variables to richness abundance data. Species richness and abundance data were analysed for 6 each taxon to be compared across habitat types. Sample-based rarefaction curves (species 7 accumulation) were calculated, using EstimateS Version 7.5.2 (Gotelli and Colwell, 2001; 8 Colwell, 2006). The number of individuals vs. the number of samples was plotted to compare 9 population densities across habitats. Nine nonparametric species richness estimators were 10 averaged to a single estimated value and sampling completeness of each taxon in each habitat 11 was calculated as the percentage proportion of observed species richness over estimated 12 species richness (Soberón et al., 2000; Magurran, 2004; Posa and Sodhi, 2006). Mean species 13 richness and abundance from each habitat type were compared with Kruskal- Wallis one-way 14 analysis of variance (ANOVA), with Tukey Test for pair wise multiple comparison using 15 SPSS 17.0 (http://www.spss.com). Indirect gradient analysis with Non-metric Multi- 16 dimensional Scaling (NMS; Kruskal, 1964) of amphibians and butterflies were used to 17 ordinate individual sampling localities, revealing faunal similarities among sites. Outliers 18 were identified in an outlier analysis (Tabachnik and Fidell, 1989). This method of ordination 19 graphically represents the community relationships in selected habitats. Axes in an ordination 20 plot are synthetic variables or dimensions that are created by summarizing multiple variables 21 that control complex relationships. A table of species absence-presence matrices was used to 22 compute Sorensen (Bray-Curtis) similarity indices (McCune and Grace, 2002) for each of the 23 two taxa. The NMS algorithm uses the resulting matrix of similarity indices to ordinate the 24 sample units (sampling localities) in species space along a pre-determined number of axes. 32 1 Species scores were plotted in the same ordination plot in sample space. Sample localities are 2 represented as points in which the distance between points in the ordination is approximately 3 proportional to the dissimilarity between the entities. The correlation coefficient was 4 calculated by comparing positions of the sample units on the ordination axes with the 5 abundance of species. The squared values of each correlation coefficient express the 6 proportion of variation in an ordination axis that is explained by the variable in question. 7 Multi-response permutation procedures (Mielke and Berry, 2001; Clements et al., 2006) were 8 used to differentiate the butterfly and amphibian distribution among the four habitats. It is a 9 non-parametric multivariate test that quantifies differences in species compositions based on 10 a rank-transformed distance matrix that adopts the same distance measure (i.e., Sorensen 11 distance). The outlier analysis and the non-parametric analyses were performed using PC- 12 ORD Version 4.33; McCune and Mefford, 1999). 13 14 3.2.5 Analysis of environmental variables 15 For both amphibians and butterflies, I chose a priori model sets that incorporated various 16 combinations of habitat structural complexity variables and climatic variables as explanatory 17 factors. For butterflies in particular, I used subsets of models incorporating different 18 vegetation strata (i.e., leaf litter, shrubs, and trees) and adult food sources (flowers and 19 fruiting trees) as explanatory variables. Hypotheses from other studies that have been shown 20 to drive species richness and abundance patterns (e.g., Wanger et al., 2009, 2010; Koh, 2007) 21 were incorporated. Candidate models were challenged with the data in an evidence-based 22 Bayesian Multi-Model Inference approach. The choice of a Bayesian over an Information 23 Theoretic modelling approach was out of personal preference. The model that best fit the data 24 was selected according to the Deviance Information Criterion (DIC), DIC weights, evidence 33 1 ratios, and percent deviance explained (see Gelman et al., 2004). I used linear regression 2 modelling and fitted a poisson error structure and a log link to both the species richness of 3 amphibians and butterfly species. For amphibian and butterfly abundance, Gaussian error 4 structure provided a considerably better fit than the Poisson error structure. Butterfly 5 abundance data was square-root transformed to improve normality. All analyses were 6 performed in R with the package R2WinBUGS (Sturtz et al., 2005) and WinBUGS (Lunn et 7 al., 2000). 8 9 3.3 Results 10 3.3.1 Butterflies 11 I recorded 120 species of butterflies including 12 of the 20 Sri Lankan endemic species 12 (Table 3.1; Appendix 3.1). Species richness was highest in home gardens (HG) followed by 13 selectively -logged forest (SLF), then the Pinus plantations (PP) (Table 3.1; Appendix 3.2). 14 Primary forest (PF) had the lowest butterfly species richness and abundance. Estimated and 15 observed numbers of species (Table 3.1) were similar in rank order abundance among sites. 16 Butterfly surveys were estimated to have sampled 70-87% of species across all habitats. The 17 mean species richness and abundance of the two forest habitats were significantly different 18 from those of the HGs and PPs (H (3) = 54.37, P <0.05; H (3) = 40.15, P < 0.05). Significant 19 differences indicated by ANOVA concurred with the overlap/non-overlap of the true CIs 20 generated by EstimateS. Only the mean abundance was significantly different between the 21 two forest habitats. Sixty eight percent of the butterfly species in PF were found in SLF. 22 Sixteen species were restricted to forest habitats and 39 to anthropogenic habitats. 23 Nonparametric multi-dimensional scaling produced a three-dimensional ordination plot 24 (Figure 3.2a) as the best graphical representation of the community structure of butterflies. 34 1 The three axes explained 69.8% of the total variance and independently accounted for 30.5%, 2 17.9% and 21.4% of the total variance. The final ordination had a stress value of 20.6. 3 Sample scores (i.e., 124 transects after data modification: McCune and Grace, 2002) were 4 plotted in species space (i.e., 120 species) on the first and third axes, which represented the 5 highest variance. As the distance between points (transects) in the ordination are 6 approximately proportional to the dissimilarities between the entities, clusters indicate 7 transects of similar species composition. A graphical overlay of habitat types on the 8 ordination displayed that the two forested habitat types vaguely formed a cluster, as did 9 samples from home gardens. Most of the species were distributed in the modified habitats 10 (Fig. 3.2a). The clusters formed by butterfly transects were not visually prominent. However 11 the differences between butterfly communities of various habitats were distinctive 12 significantly as quantified by the multi-response permutation procedure (Appendix 3.3). 13 Habitats that are most dissimilar are indicated by high negative T values in pair wise 14 comparisons. Primary forest with home gardens yielded the highest negative value (-31.186) 15 and the two forest habitats yielded the least (-7.807). 16 17 3.3.2 Amphibians 18 I sampled 32 species of amphibians including 21 endemic species (Table 3.1; Appendix 3.4). 19 Species richness estimates were similar in rank order to the observed number of species and 20 the sampling completeness ranged from a minimum of 85% (SLF) to a maximum of 99% (PF 21 and HG). A total of 24 amphibian species were recorded in forest habitats, while 18 were 22 encountered in non-forest habitats. The highest species richness was found in SLF, followed 23 by HG and PF respectively (Appendix 3.5). Only a single species, Pseudophilautus folicola 24 (endemic leaf dwelling shrub frog; Manamendra-Arachchi and Pethiyagoda, 2005) was 35 1 recorded within the plantations during the study. Mean species richness of the two forest 2 habitats was not significantly different, whereas the PP was significantly different (H (3) = 3 59. 08, P < 0.05) from all the other habitats. The abundance per transect followed the same 4 trend for the PP (H (3) = 56.74, P < 0.05). The overlap/non-overlap of the true CIs generated 5 by EstimateS for observed species richness and abundance concurred with ANOVA results. 6 Ninety four percent of PF species were found in SLF. The species overlap between the forest 7 and disturbed habitats was only 6.5%. Two dimensions were used in the NMS analyses of 8 amphibian transect (Fig. 3.2) after comparing stress values in relation to dimensionality. The 9 final ordination had a stress value of 22.5, which falls within a range of satisfactory values 10 found for most ecological community data sets (McCune and Grace, 2002). The two axes 11 explained 66.5% of the total variance and independently accounted for 33.2% and 33.3% of 12 the variance. Sample scores (i.e., 91 transects after data modification: McCune and Grace, 13 2002), were plotted in species space (i.e., 32 species) on the two axes. A graphical overlay of 14 habitat types on the ordination, shows a distinct clustering of transects in forests habitats, 15 reflecting their species compositional similarities. The anthropogenic habitats formed a 16 separate cluster. The results of the multi-response permutation procedure (Appendix 3.3), 17 confirms the biotic distinctness of the two forest habitats from the two anthropogenic 18 habitats. The pair wise comparisons of T values from SLF with PP yielded the highest 19 negative (-26.228). Comparison between the two forest habitats yielded the least negative T 20 value (-0.535) indicating their similarity. 21 As graphically represented by the ordination, species assemblages differed across the 22 habitats. Fourteen amphibian species including four unidentified species were restricted to 23 forest transects. All identified species in this group were endemic, majority were endangered 24 and12 were direct developing species that lack a tadpole stage. Most of the forest species are 36 1 listed as either vulnerable or endangered in the IUCN Red List (IUCN, 2010). The eight 2 species only found in HGs were mostly large-bodied species of least concern (LC; Appendix 3 3.4). Pseudophilautus folicola was the only species found across all habitats and was more 4 abundant in forest habitats. 5 6 3.3.3 Environmental determinants 7 The Bayesian model selection revealed that butterfly species richness was determined 8 primarily by climatic variables such as temperature and humidity (Table 3.2; Appendix 3.6). 9 Models representing structural complexity of the habitats were poorly supported by the data 10 (i.e., ΔDIC values and evidence ratios in particular increase rapidly [>23 and >106, 11 respectively]). Butterfly abundance patterns were similar, with the climatic models being 12 superior to the structural complexity models. However, differences were not as distinct as for 13 species richness (ΔDIC values and evidence ratios were > 9 and > 92, respectively). In 14 comparison with butterfly species richness, where the best models explained 10.1% of the 15 total variance, the abundance models only explain 3.7%. 16 Amphibian species richness was driven mainly by habitat structural variables (Table 17 5.3). Models with climatic variables were consistently less probable than models with 18 structural variables. This is evident in the large differences in DIC values (<2 for the first 19 four models vs. >63 for the climatic models), and evidence ratios (for the climatic models > 20 106). Whereas four models explained patterns in species richness with equal likelihood, the 21 amphibian abundance patterns were best explained by a single model: SPR ~ CANCOV + 22 SHCO + LLT + WATER. All other models clearly show elevated ΔDIC values and evidence 23 ratios. Overall, the models give strong support that structural complexity determines 24 amphibian species richness and abundance. There is very little support for models containing 37 1 only climatic variables (i.e., large ΔDIC values and very high evidence ratios [>63 and >106, 2 respectively]). While climate variables were most important in explaining butterfly diversity 3 patterns, amphibian diversity patterns were best explained by structural habitat parameters. 4 5 3.4 Discussion 6 3.4.1 Butterfly response to land-use change 7 Closed canopy primary forests (PF) harbor forest-dwelling butterfly species that are often 8 associated with specific microhabitat conditions (i.e., soil, light; Vasconcellos-Neto, 1991). 9 The more heterogeneous conditions of intermediately disturbed habitats support species that 10 are adapted to open habitats (Koh and Sodhi, 2004). Further, patchily-distributed resources 11 common in open areas attract butterflies from adjoining forests (Cleary and Genner, 2006). 12 The proximity of home gardens (HG) and Pinus plantations (PP) to the forest, the presence of 13 many open areas in selectively-logged forest (SLF) and the abundance of flowering plants 14 and undergrowth in home gardens might explain the observed high species richness and 15 abundance of the butterflies in these respective habitats. The proximity of modified habitats 16 to old growth forests is an important determinant of species richness as it allows frequent re- 17 colonization (Schulze et al., 2004). 18 Although most endemic species are known to prefer closed forest canopies (Spitzer et 19 al., 1997, Hill et al., 2001) and are known to predict vulnerability to human disturbance (Posa 20 and Sodhi 2006), I found that the limited number of endemics (12) recorded were evenly 21 distributed in all habitat types. Species restricted to the forest (16) in Sinharaja were mostly 22 small butterflies (Hesperidae and Nymphalidae) associated with shady vegetation. Schulze et 23 al., (2004) documented that the number of endemics in a site is closely related to its species 38 1 richness. In contrast, our data shows that the number of endemics is not necessary an 2 indicator of the species richness. 3 Butterfly responses to land-use change are complex and regionally variable, as both 4 increased and decreased diversity have been reported in association with disturbance 5 (Kremen, 1992; Lawton et al., 1998; Koh, 2007). Some of these disparate results may be due 6 to sampling, as butterflies are more active in open sunlight (Dennis, 2004), and thus more 7 likely to be sampled in open habitats than in dense closed canopy habitats where detection is 8 difficult regardless of true abundance (Posa and Sodhi 2006). Vertical stratification of 9 butterfly distribution, and the stratification of tropical forest with varying degrees of 10 accessibility, can also affect measures of diversity (DeVries, 1998, Bonebrake et al., 2010). 11 Additionally, butterfly larvae are often host-specific, and many host plants are likely confined 12 to forests. Thus, the observed high diversity in disturbed habitats may be short-lived in areas 13 without adjacent intact forests (Barlow et al., 2007), or may represent vagrants and transients 14 from nearby forest habitat (Posa and Sodhi, 2006). Butterfly sampling was conducted during 15 the period of maximum butterfly activity as observed during the preliminary surveys for this 16 site. However, it is understood that there may be certain butterflies that come out during the 17 other times of the day and may not have been sampled due to adhering to a morning sampling 18 period. This is a possible caveat. 19 Some studies have shown that logging cause a decrease of butterfly diversity, (e.g., 20 Lawton et al., 1998; Cleary, 2003), but few other studies have reported that such disturbances 21 have no significant effect (Spitzer et al., 1997; Willott et al., 2000). Pristine forest and old 22 secondary forest are known to be important to maintain the diversity of tropical butterflies 23 (e.g. Koh, 2007). In Sulawesi, butterfly diversity was high in agro-forestry systems, but 70% 24 of forest-restricted species were absent from these habitats (Schulze et al., 2004). In this 39 1 study, the communities in the disturbed habitats were more diverse than those of the forest 2 habitats. This however does not imply that more disturbed habitats should be created for 3 butterfly conservation. While many species were observed in both forest and anthropogenic 4 areas, there were some species restricted to forest. Proximity of forest and presence of host 5 plants are two of the most important determinants of butterfly presence in a habitat (Koh and 6 Sodhi, 2004). Whether the communities found in HGs’ and PPs’ around Sinharaja can 7 survive in isolation, has to be linked to a detailed study on host plants. 8 Regardless of the importance of butterflies as an indicator (Gardner et al., 2008), this 9 site-specific variation of species’ responses and the complexity of interpreting results give 10 rise to difficulties for sound conservation planning using the taxon. Underlying habitat- 11 specific mechanisms of species responses must be determined before strategic application of 12 research findings (Koh, 2007). 13 14 3.4.2 Amphibian response to land-use change 15 Amphibian species richness was highest in SLF, although the mean species richness was not 16 significantly different to PF. The same pattern was found in birds, mammals (Wijesinghe and 17 Brooke, 2005) and also ants (Gunawardene et al., 2010) in Sinharaja. Decades of natural 18 regeneration after logging may have facilitated re-colonization of native species. The thick 19 and moist litter cover of the SLF may provide suitable microhabitats for litter dwelling frogs 20 (e.g. Philautus species). In addition, selectively- logged forests may harbor common species 21 from both pristine forest and structurally diverse adjoining habitats (Gardner, 2010). 22 The amphibian communities of PP and HG were notably different from forest 23 communities. Although few studies have addressed the effects of land-use change on 24 amphibians throughout tropical Asia, this is a pattern that has been observed before (e.g. 40 1 Wanger et al., 2010). While anthropogenic habitats are of conservation value to many 2 species, some forest species in Sinharaja are seemingly incapable of adjusting to the altered 3 conditions of adjacent habitats. These community changes may be due to their requirement 4 for specialized habitat conditions, which makes some species more vulnerable to disturbance 5 than the others. 6 Although suitably structured plantation habitats are known to be useful for 7 biodiversity conservation elsewhere (Hartley, 2002; Carnus et al., 2006; Gardner, 2010), PP 8 do not seem to fulfill this role for the amphibians in Sri Lanka’s lowland forests. Species of 9 the genus Philautus that are direct developers (no tadpole stage in the lifecycle), require 10 moist leaf litter or soil to lay eggs (Bahir et al., 2005). These conditions were not found in 11 PP, where pine needles form a thick layer of dry loose litter. This could be among the factors 12 that have contributed to the low species richness in PP. Further, low species richness in PP 13 around Sinharaja cautions against planting pine trees as a mean of restoring deforested land 14 or boundary demarcation, as this habitat may act as barrier to amphibian dispersal between 15 large pristine forests and surrounding fragments. 16 Amphibian species found in HG were mostly common and widespread. Unlike in 17 butterflies, all identified amphibian species restricted to the two forest habitats were 18 endemics. Endemics are known to be less adaptable to habitat disturbance than the non- 19 endemics (Sodhi and Brook, 2006). Habitat disturbance has been identified as a threat to 20 most of the endemic amphibians of Sri Lanka due to their restricted ranges and sensitivity to 21 microhabitat conditions (Erdelen, 1988; Pethiyagoda and Manamendra-Arachchi, 1998). 22 Similar trends have been reported in birds and mammals of Sinharaja: endemics were most 23 abundant in forest habitats, while non-endemics were found in forests and in surrounding 24 disturbed areas (Wijesinghe and Brooke, 2004). 41 1 3.4.3 Environmental predictors of butterfly and amphibian diversity patterns 2 Understanding the environmental predictors of diversity is crucial with the vulnerability of 3 tropical rainforests and their inhabitant to changing climatic conditions (Corlett, 2011). 4 Butterfly diversity was determined primarily by two climatic variables (temperature and 5 humidity) and one structural variable, canopy cover. Butterflies are poikilothermic and seek 6 well-lit, warm areas to attain the appropriate temperature for flight (Dennis, 2004). 7 Heterogeneous HGs may provide steep temperature and humidity gradients within the 8 habitat, thereby enhancing the capacity attract more species. Many studies relate canopy 9 cover and light intensity to the diversity and species composition of tropical butterflies (e.g., 10 Hill et al., 2001, Laurance et al., 2002, Hamer et al., 2003, Koh and Sodhi, 2004). Canopy 11 cover affects humidity and other microclimatic conditions, and the persistence of particular 12 host plants, and is therefore an important explanatory variable in butterfly distribution (Blau, 13 1980, Basset et al., 2001, Koh and Sodhi, 2004). Understanding environmental drivers 14 behind species responses to land-use change is important to explain the observed diversity 15 patterns in different habitats (Koh, 2007). 16 The high diversity found in disturbed areas may also be artefactual. Butterfly fauna of 17 many forests is vertically stratified and standard ground surveys only sample a fraction of the 18 total fauna. Since canopy height is significantly lower in disturbed areas, these vertical strata 19 are sometimes compressed toward the ground, thereby inflating estimates of diversity in 20 disturbed areas (DeVries, 1988, Schulze et al., 2001, Fermon et al., 2005, Koh, 2007). 21 As explained by the best-fit model, amphibian species richness and abundance in the 22 sampled habitats were driven by habitat structure (i.e., leaf litter thickness, canopy cover, 23 shrub cover, and percentage of available water). Previous studies in Southeast Asia have 24 highlighted the importance of canopy cover and leaf litter thickness for amphibians (e.g., 42 1 Inger and Colwell, 1977; Wanger et al., 2009, Wanger et al., 2010). Litter and shrub cover 2 are important habitat components for shrub frogs in the genus Philautus, which includes 3 many shrub- and litter-dwelling endemics. The availability of water is a concern for large and 4 non-direct developing frogs which were found more in modified habitats. 5 Conservation implications of this study will be discussed in the last chapter. 6 7 3.5 Conclusions 8 Amphibian and butterfly communities in and around Sinharaja forests in Sri Lanka respond 9 differently to the impacts of selective logging and other land-use change. Amphibian 10 communities differ markedly in different disturbance regimes, whereas butterfly communities 11 at the same sites are relatively less distinctive between forest and anthropogenic habitats. 12 Susceptibility of endemic amphibians to land-use change makes the conservation of primary 13 and regenerating selectively logged forests a priority. However, structurally enhanced and 14 sustainably managed HG and agro-forestry systems will play integral part in future tropical 15 biodiversity conservation. Enhancing the quality of these habitats when in proximity to 16 pristine forests should be prioritized because they can provide habitats to forest species and 17 some non-forest species. More studies using different taxonomic groups are urgently needed 18 to explore the responses of Sri Lankan biodiversity to various land-use changes. Using 19 amphibians or butterflies independently in an assessment may create contrasting pictures of 20 habitat disturbance and they are not predictable of the diversity of each other in this 21 landscape. Different environmental predictors for butterflies and amphibians (climatic and 22 structural factors, respectively) further underscore the importance of including multiple 23 indicator taxa in surveys that are intended to inform sensible habitat management. 24 43 1 A modified version of this chapter has been published 2 Kudavidanage E.P., Wanger T.C., de Alwis C., Sanjeewa S. & Kotagama S.W. (2011). 3 Amphibian and butterfly diversity across a tropical land-use gradient in Sri Lanka; 4 implications for conservation decision making. Animal Conservation DOI: 10.1111/j.1469- 5 1795.2011.00507.x 6 44 1 Table 3.1: Summary statistics of butterfly and amphibian sampling. Given values are means (± standard error). The percentages of species detected in surveys 2 (a) were calculated from observed species richness and the average asymptotic species richness estimated from the nonparametric species richness estimators. 3 (ACE, ICE, Chao1, Chao2, Jack1, Jack2, Bootstrap, MMRuns, MMMeans; for details of these estimators see Magurran 2004) 4 precede 5 Butterflies Primary forest Logged forest Home gardens Pinus plantations No. of transect walks 32 32 32 32 6 Estimated species richness 61.23 ± 3.74 85.06 ± 3.23 108.89 ± 1.70 66.08 ± 1.15 Species detected (% of total) 43 (35.83) 66 (55.00) 95 (79.17) 57 (47.50) % detected by the survey 70.22 77.59 87.24 86.26 Number of individuals 463 783 1413 762 Mean no. spp./transect ± SE 6.28 ± 0.66 8.71 ± 1.08 19.44 ± 1.52 12.34 ± 0.84 Mean no. individuals/transect ± SE 14.5 ± 2.26 24.47 ± 3.78 44.11 ± 3.69 23.81 ± 1.54 Amphibians Number of transect walks 25 25 25 25 Estimated species richness 17.09 ± 0.19 26.89 ± 0.62 18.64 ± 0.23 1.00 ± 0.005 Species detected (% of total) 17 (53.13) 23 (71.88) 18 (56.25) 1(3.13) (a) % detected by surveys 99.47 85.54 96.56 99.89 Number of individuals 1317 1247 1276 30 Mean no. spp./transect ± SE 6.6 ± 0.55 7.84 ± 0.57 8.2 ± 0.48 0.64 ± 0.09 Mean no. individuals/transect ± SE 52.68 ± 3.4 49.92 ± 3.39 51.04 ± 5.44 1.20 ± 0.21 45 1 Table 3.2: Environmental determinants of butterfly species richness (SPR) and abundance (ABD). 2 aStatistical variables: Dhat = point estimate of the deviance; DIC = Deviance Information Criterion; 3 ΔDIC = difference between the DIC of the best model and the model of interest; pD = number of 4 effective parameters; %DEV = percent deviance explained; wDIC = DIC weights; EvRat = Evidence 5 Ratio. The best fitting models (ΔDIC ≤ 2) are indicated in bold italics. Note that the best models 6 contain mainly climatic variables. Variables are Leaf litter thickness (LLT), canopy cover 7 (CANCOV), the number of flowering trees (FLTR), the number of fruiting trees (FRTR), temperature 8 (TEMP) and humidity (HUM) on the transect at the time of sampling, and shrub cover (SHCO). 9 10 Predictors of butterfly species richness (SPR) Model Dhata DIC ΔDIC pD %Dev wDIC EvRat ~ Temperature + humidity 456.9 463.0 0.0 3.0 10.0 0.6 1.0 ~ Temperature + humidity + canopy cover 455.7 463.6 0.6 4.0 10.1 0.4 1.4 ~ Fruiting trees + flowering trees+ canopy cover 481.3 489.2 26.3 4.0 5.1 0.0 502574.9 ~ Fruiting trees + flowering trees 485.9 491.8 28.8 3.0 4.4 0.0 106 ~ Fruiting trees + flowering trees+ litter cover + shrub cover + canopy cover 479.1 492.6 29.6 6.8 5.0 0.0 106 ~ Litter cover + shrub cover + canopy cover 495.0 503.0 40.0 4.0 2.4 0.0 106 ~ Canopy cover 503.2 507.1 44.2 2.0 1.2 0.0 106 Null model 510.3 512.3 49.3 1.0 0.0 0.0 106 Predictors of butterfly abundance (ABD) ~ Temperature + humidity 267.1 275.1 0.0 4.0 3.7 0.7 1.0 ~ Temperature + humidity + canopy cover 267.0 277.1 2.0 5.0 3.3 0.3 2.7 Null model 279.4 283.3 8.2 2.0 0.0 0.0 61.6 ~ Fruiting trees + flowering trees 276.1 284.2 9.1 4.0 0.4 0.0 92.9 ~ Canopy cover 278.5 284.5 9.4 3.0 0.0 0.0 109.7 ~ Fruiting trees + flowering trees+ canopy cover 275.2 285.4 10.3 5.1 0.4 0.0 169.6 ~ Litter cover + shrub cover + canopy cover 277.7 287.7 12.6 5.0 -0.5 0.0 552.8 ~ Fruiting trees + flowering trees+ litter cover + shrub cover + canopy cover 275.1 289.1 14.1 7.0 -0.3 0.0 1127.2 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 46 1 Table 3.3 Environmental determinants of amphibian species richness (SPR) and abundance (ABD). 2 a See Table 3. 2 for a description of statistical variables.Models were ranked according to their 3 corresponding ΔDIC (best models are with a ΔDIC < 2 and are indicated in bold italics; for statistical 4 variable definition see footnote). Note that most variables in the best models for explaining amphibian 5 species richness and abundance quantify habitat complexity. Variables are leaf litter thickness (LLT), 6 canopy cover (CANCOV), shrub cover (SHCO), percentage of available water bodies (WATER), 7 average temperature (TEMP) and humidity (HUM), and soil temperature (S.TEMP). Models should 8 be compared to the saturated (SAT) and the null (NULL) model. Predictors of Amphibian species richness Model Dhata DIC ΔDIC pD %Dev wDIC EvRat ~ Leaf Litter Thickness 380.5 384.4 0.0 2.0 19.1 0.4 1.0 ~ Canopy Cover + Shrub Cover + Leaf Litter 374.8 384.8 0.4 5.0 19.7 0.3 1.2 ~ Leaf Litter Thickness + Temperature 379.5 385.5 1.1 3.0 19.1 0.2 1.7 ~ Shrub Cover + Leaf Litter Thickness 380.2 386.2 1.8 3.0 18.9 0.1 2.5 ~ Temperature + Humidity + Soil Temperature 438.5 448.2 63.8 4.8 6.2 0.0 >106 ~ Soil Temperature + Water 446.6 452.4 68.0 2.9 4.9 0.0 >106 ~ Temperature + Humidity 446.7 452.8 68.4 3.0 4.8 0.0 >106 Null model 471.7 473.8 89.4 1.0 0.0 0.0 >106 Predictors of Amphibian abundance ~ Canopy Cover + Shrub Cover + Leaf Litter 645.2 657.3 0.0 6.0 7.9 0.9 1.0 ~ Leaf Litter Thickness 656.7 662.8 5.5 3.1 6.7 0.1 15.6 ~ Leaf Litter Thickness + Temperature 655.5 663.6 6.3 4.0 6.7 0.0 23.3 ~ Shrub Cover + Leaf Litter Thickness 656.2 664.3 7.0 4.0 6.6 0.0 33.1 ~ Temperature + Humidity +Soil Temperature 676.6 688.6 31.3 6.0 3.5 0.0 >106 ~ Soil Temperature + Water 682.6 690.7 33.4 4.0 2.9 0.0 >106 ~ Temperature + Humidity 683.7 691.8 34.5 4.0 2.8 0.0 >106 Null Model 705.3 709.3 52.0 2.0 0.0 0.0 >106 9 47 1 2 Figure 3.1: Map of the Sinharaja forest (lowland sector) showing the primary forest, the selectively 3 logged areas and the Forest Dynamic Plot (FDP; Modified with permission from Gunatilleke et al. 4 2004). Symbols indicate the approximate centers of transect clusters that spreads around each point: 5 Pinus caribea plantations (P); home gardens (H); Primary forest (solid circle); Selectively-logged 6 forest (shaded circle). 7 48 1 2 (a) 3 Figure 3.2a: Nonmetric multidimensional scaling ordination of Bray-Curtis similarity indices for 4 butterflies. The ordination is based on presence/absence data for both groups sampled in all four 5 habitat types. Outlined symbols represent samples (transects) categorized by land-use type. Solid 6 squares indicate the distribution of species across the land uses. The clustering of transects indicates 7 similar species compositions. Forest and anthropogenic habitats are separated into discrete clusters 8 with a more distinct pattern in amphibians than butterflies. 9 10 49 1 (b) 2 3 Figure 3.2b: Nonmetric multidimensional scaling ordination of Bray-Curtis similarity indices for 4 amphibians. The ordination is based on presence/absence data for both groups sampled in all four 5 habitat types. Outlined symbols represent samples (transects) categorized by land-use type: (1) PF; (2) 6 SLF; (3) HG; (4) PP. Solid squares indicate the distribution of species across the land uses. The 7 clustering of transects indicates similar species compositions. Forest and anthropogenic habitats are 8 separated into discrete clusters with a more distinct pattern in amphibians than butterfli 50 1 CHAPTER 4 DUNG BEETLE FAUNA OF SRI LANKA 2 A collaborative study with Dr. Darren J. Mann (Oxford University Museum of Natural History, UK) 3 and D. Lekamge ( Sabaragamuwa University of Sri Lanka) 4 4.1 Introduction 5 Accurate identification of species is crucial for ecological and biological research, but 6 specimen identification remains a challenge for invertebrate biologists, particularly in 7 tropical countries where species richness is staggering. Many tropical invertebrates are less 8 known, revised taxonomic keys are rarely available, and reference collections with type 9 specimens are primarily found in European museums. On the Indian subcontinent, for 10 example, the taxonomy of most invertebrate groups has not been revised since the publication 11 of the Fauna of British India series in the mid-1900s. Some authors argue that the problem of 12 species identification should be placed in the context of the current global biodiversity crisis 13 (Wheeler et al., 2004). 14 15 4.2 Dung beetles (Insecta: Coleoptera: Polyphaga: Scarabaeoidea: Scarabaeidae: 16 Scarabaeinae and Aphodiinae) 17 Dung beetles are in the family Scarabaeidae, subfamilies Scarabaeinae and Aphodiinae, and 18 include the largest members of the insect order Coleoptera. The Scarabaeinae comprises 19 about 4,500 described species worldwide (Ratcliffe and Jamisson, 2000). Since most species 20 of Scarabaeinae feed exclusively on feces, this subfamily is known as true dung beetles. 21 There are dung-feeding beetles in other families, such as the Geotrupidae (the earth-boring 22 dung beetles) that are also included in this guild. Aphodiinae beetles are relatively small and 23 comprise the bulk of dung dwellers (Halffter and Edmonds, 1982). Scarabaeinae are more 24 common in tropical regions, but in northern latitudes Aphodiinae tend to dominate the dung 25 beetle assemblage (Crowson, 1981, Hanski and Camberfort, 1991). Dung beetles are 51 1 scavengers that feed on mammalian dung, carrion, decaying fungi, or litter, and females lay 2 their eggs in dung (Halffter and Edmonds, 1982). Non-Scarabeidae beetle families are also 3 found on dung and carrion, including Histeridae (hister beetles, which are predators of other 4 insects), Hydrophilidae (water beetles, which “swim” in dung to feed and breed), 5 Staphylinidae (rove beetles, both predators and dung feeders), and Leiodidae (small carrion 6 beetles). Dung beetles mainly use herbivore and omnivore dung, particularly from mammals, 7 but occasionally from birds and reptiles (Howden and Young 1981; Hanski and Camberfort 8 1991; Estrada and Coates–Estrada 2002; Krell et al., 2003). In tropical forests in particular, 9 many dung beetles are attracted to both dung and carrion, (Hanski 1987). 10 Dung beetle species of the subfamily Scarabaeinae in Sri Lanka have been recorded 11 in two classic, regional volumes covering parts of South Asia: Arrow 1931 and Balthasar 12 1963. These volumes cover the diversity of lamellicorn beetles in a wide geographical region 13 from Sri Lanka, India, China and Java and Arabia. Most of the sampling in both these of 14 publications was haphazard and restricted to selected locations within Sri Lanka. The 15 Monograph of the Scarabaeidae and Aphodiidae of the Palaearctic and Oriental region 16 Coleoptera: Lamellicornia by Vladimir Balthasar (1963) includes fifty dung beetle species 17 found in Sri Lanka, whereas the whole volume describes the distribution of dung beetles in 18 Europe, Central, South and Southeast Asia. Prior to Arrow’s (1931) relatively comprehensive 19 publication on the Sri Lankan lamellicorn fauna (beetles with lamellate terminal segments in 20 the antennae; includes the scarabaeids and stag beetles), a single paper briefly discussed some 21 of the lamellicorns collected in Sri Lanka by Bugnion (Gillet 1924). It records 47 species 22 from three subfamilies: Coprinae, Aphodiiane and Troginae. 23 Analysis of ecological data of dung beetles (Chapters 5, 6 and 7) required 24 identification of dung beetle specimens to the species level. For this purpose, we aimed to 52 1 make a reference collection of Sri Lanka’s dung beetles (Coleoptera: Scarabaeidae; 2 Scarabaeinae and Aphodiinae) by sampling more than 100 locations island-wide. Using our 3 reference collection and specimens available at the National Museum of Sri Lanka, we aimed 4 to establishe a field identification key. For final verification of names, we compared the 5 reference collection with collections in India and with the available type specimens at the 6 Oxford University Museum of Natural History and the British Museum of Natural History. 7 We also compared the geographic distribution of dung beetles sampled in our study with 8 distributions indicated by Arrow (1931) to identify possible local extinctions. In so doing, we 9 documented the species distributions of dung beetle across the bio-climatic zones of Sri 10 Lanka. This chapter presents the taxonomic findings and distribution information but not the 11 quantitative data as what is relevant for this thesis is species identification for the ecological 12 study in the wet zone. However the wealth of quantitative data will be analyzed for future 13 publications 14 15 4.2 Materials and methods 16 4.2.1 Study site 17 We sampled in all of the six bio-climatic zones of Sri Lanka designated by Wijesinghe et al., 18 (1993): low- and mid- country wet zone; montane wet zone; low- and mid- country 19 intermediate zone; montane intermediate zone; dry zone; and arid zone (Figure 4.1a). Forest 20 types in these zones range from dry monsoon forest in the dry coastal lowlands to tropical 21 montane cloud forest reaching a maximum altitude of 2,524 m in the central highlands 22 (Wijesinghe et al., 1993). 23 53 1 4.2.2 Beetle sampling and identification 2 Dung beetles were sampled across all bioclimatic zones of Sri Lanka (Fig. 4.2b) in 3 various habitat types ranging from pristine forests to degraded scrublands and 4 anthropogenically modified areas from 2008 to 2011. This includes systematic sampling 5 conducted in the ecological studies and the random sampling conducted island wide. 6 Sampling locations were determined on historical records and also to include different 7 habitats. Sampling efforts were random not predetermined for each bioclimatic zone and are 8 not proportionate to the area of the zone as this is a preliminary taxonomic survey. Sampling 9 effort for the wet zone is higher as the ecological study was conducted there. Pitfall traps 10 baited with cow/human dung were used (detailed description given in chapter 5). 11 Additionally, flight intercept traps and sand traps were used to collect large nocturnal beetles, 12 dweller, and tunneler beetles. Sand traps are plastic containers filled two-thirds full with the 13 soil substrate of the specific sampling area topped up with cow dung, buried in the soil up to 14 the rim of the container exposing the dung layer for 24 hours (Figure 4.2). Dwellers could be 15 extracted by washing the dung layer, whereas sieving the soil allowed the tunnellers to be 16 extracted. We designed this novel trap, which is more suitable for arid climates where dung 17 would desiccate too rapidly and where sandy soil allows deep burial of dung. 18 Dung beetle species were categorized according to the upper limits of body lengths, 19 and the size classes were taken as follows; Small Class2: 2 – 4mm: Small Class 1: 5 – 8mm ; 20 Medium: 9 -15mm:: Large Class 1; 16 -25mm Large Class 2: >25. This categorization was 21 done only for the preliminary taxonomic survey in order to observe the relationship between 22 distribution and body sizes across bioclimatic zones. 23 Collected beetles were initially preserved in 95% ethanol, and were identified using 24 Arrows (1931), Balthasar (1963) and the collections at the National Museums of Sri Lanka 54 1 and Asoka Trust for Research in Ecology and Environment, India. A representative set of 2 specimens were dry preserved for identification and photographed using SYNCROSCOPY 3 digital optical microscope at the Oxford University Museum of Natural History (OUMNH). 4 Identifications were verified by Darren J. Mann at the OUMNH. Some of the collected 5 specimens were compared with available type specimens at the Natural History Museum, 6 London. We labeled potential new species and/or species that cannot be identified using a 7 letter code UNK (i.e. Onthophagus unk 1). Unidentified species that looked similar to an 8 existing species were coded with “N” (i.e Onthophagus amphinasus N). 9 10 4.3 Results and discussion 11 4.3.1 Species distribution overview 12 A species checklist of all Scrabaeinae beetles recorded in Sri Lanka is given in Appendix 4.1. 13 We excluded Aphodiinae, Geotrupidae and other dung-dwelling beetles due to the difficulty 14 of identification. Appendix 4.1 includes species recorded by Arrow (1931), Balthasar (1963), 15 ScarabNet. the ScarabNet Global Taxon Database Version 1.5,(www.scarabnet.org), and 16 specimen collections at the National Museum of Sri Lanka, Oxford University Museum of 17 Natural History, British Museum of Natural History, and all species recorded in three years 18 of field surveys, including undescribed/unidentifiable species (2008-2011). 19 Our surveys found a total of 92 species of Scarabeinae dung beetles. Including this, 20 the entire checklist now consists of 117 species of 33 genera. This includes species 21 previously recorded from Sri Lanka, new records and unidentified species from this study. 22 Twelve morphospecies of Aphodiinae were recorded, but not identified to species level. 23 Aphodius species 1-3 were verified as three distinct species, as they were the dweller species 24 found in the ecological sampling of the lowland wet zone and were important components of 55 1 the analysis. In addition, 11 Hystaridae, one hemipteran Cyanidae, two Elataridae, one 2 Chrysomalidae, one Trogidae, one Bostrichidae and four Scirtidae were found dwelling in 3 dung. 4 Genus Onthophagus had the highest number of species: 76. Appendix 4.3 illustrates a 5 representative set of maps constructed to highlight individual species distribution, island 6 wide. Similar maps were constructed for a majority of the species recorded. For example, 7 P.setosus was found only in the low-country wet zone and C. ultor, C. lilliputanus, O. ludio 8 only in the low-country intermediate zone. The number of species recorded from the montane 9 zone clearly was less than that of the lowland climatic zones. More intense sampling is 10 required with multiple bait types and sampling methods to establish the final geographic 11 range of each species. 12 13 4.3.2 Species distribution in contrast to previous records 14 All genera previously recorded from the island were found during our surveys, but this 15 wasn’t true of all previously recorded species. Several species from the genera Scarabaeus, 16 Gymnopleurus, Caccobius, Onthophagus and Panelus were not recorded during the survey. 17 Some of these unidentified specimens might be new species or genera. Eleven of the 18 identifiable species were recorded in Sri Lanka for the first time by us. Our surveys suggest 19 that 61% of the species in Sri Lanka are from the Indo-Sri Lankan region, 21% are endemics 20 and the rest show wide distribution. This excludes the unidentified species and may change 21 once the taxonomy is fully verified. Some of the endemics are previously only known from 22 the type specimen (i.e., Sysiphus tarantula, which we collected from the same location as the 23 type). 56 1 Only one species in the genus Scarabaeus, S. gangeticus, was found in the present 2 study, but two additional Scarabaeus species were recorded by Arrow (1931). Scarabaeus 3 gangeticus is the largest crepuscular roller beetle in Sri Lanka, and has a clear habitat 4 restriction to the dry and arid zones (sandy soil) where cattle and other large mammals are 5 abundant. An individual S. gangeticus in the arid zone of Sri Lanka can carry an average 6 mass of 52.2 ± 6.6 g of cattle dung in a ball and travel a minimum distance of 11 cm to a 7 maximum of 16.8 m from the edge of the dung source before burying the dung ball. The 8 other two species that were known to occur in Sri Lanka (Arrow 1931), Scarabaeus erichsoni 9 and Scarabaeus sanctus (the former is said to be an inland species), were not recorded during 10 our extensive surveys. Sri Lanka has undergone massive loss of forest cover in the last 11 century (Pethiyagoda, 2005), which has altered many habitats. With few exceptions, the 12 effects of these changes on the insect fauna are virtually unknown. 13 We found five species of the six Gymnopleurus species recorded by Arrow (1931); G. 14 smaragdifer was missing from our samples. Out of the five Gymnopleurus species that we 15 found, only G. melanarius (Paragymnopleurus melanarius) was found in the low- and mid- 16 country wet zone, whereas Arrow (1931) documented three Gymnopleurus species from wet 17 lowlands (P. melanarius, G. miliaris and G. koenegi). Previously, P. melanarius was widely 18 distributed across the wet zone, but our sampling shows clear habitat restriction to relatively 19 undisturbed areas and good forests. Along with the two other congeners (G. parvus and G. 20 gemmatus), G. miliaris and G. koenigi are found preferably in the dry and arid regions, and to 21 a lesser extent in the low- and mid-country intermediate zone. The complete absence of G. 22 smaragdifer is significant as it may indicate possible extinction. The distribution of 23 Caccobius species that we recorded differed from previous locality records. For example, 24 Caccobius unicornis which was from the wet zone Arrow (1931), was also found the in dry 57 1 zone locations. We recorded 61 species of Onthophagus, including several unidentified 2 species. Onthophagus amphinasus (N) and O. pusillus were restricted to the low country wet 3 zone but the two species were found in different locations. Onthophagus fuscopunctatus was 4 restricted to coastal areas (sandy soils) in dry and arid zones. Several species of Onthophagus 5 had non-uniform but island-wide distributions, Onthophagus igneus (LMIZ, DZ); 6 O.troglodyta (LMIZ); O.lilliputanus (LMIZ); O. solidus (LMWZ, LMIZ); O. bifasciatus 7 (LMWZ, DZ; AZ, MWZ); O.ludio (LMIZ) are among the first time records of occurrence for 8 Sri Lanka. 9 Genus Ochicanthon had four species in the island and two of them (O. cingalense and 10 O. triste) were only recorded in the montane zone above 1200 m asl. Paraliatongus, 11 Euoniticellus, Drepanocerus and Panelus were represented by single species; E. pallipes (dry 12 zone) and Panelus. setosus (low- and mid-country wet zone) had not been previously 13 recorded. Onitis philemon and Onitis. subopacus were recorded from Arrow’s (1931) 14 original sites, Scarabaues erichsoni, Scarabaues sanctus, Gymnopleurus smaragdifer, 15 Onthophagus regalis and O. questus are among the previously recorded species that were not 16 found during this study. 17 18 4.3.4 Possible natural and anthropogenic environmental determinants of observed 19 distribution patterns of dung beetle species 20 The island-wide distribution of Scarabaeinae beetle species can be attributed to species- 21 specific habitat preferences interacting with climatic/geographic variation across the island 22 and anthropogenic modifications of habitats. Spatial diversity is influenced primarily by 23 environmental variation such in soil, vegetation, and dung type (Davis et al., 2001). 24 Distributions are strongly influenced by forest modification (Nummelin and Hanski 1989) 58 1 and deforestation, as well (Howden and Nealis 1975; Klein 1989; Halffter et al., 1992; 2 Estrada et al., 1998; Estrada and Coates-Estrada, 2002). Loss of favorable environmental 3 conditions, increased predation by natural enemies, and loss of mammals providing dung 4 resources affects dung beetle community structure in anthropogenic habitats (Andresen and 5 Laurance, 2007; Nichols et al., 2007). 6 Both natural and anthropogenic differences between the zones may play an important 7 role in shaping dung beetle communities in Sri Lanka. Wet zone forests are dense with more 8 undergrowth and humid, moist conditions. Dry zone forests are more open with less 9 undergrowth and humidity. Mammals are the major suppliers of dung for beetles. There are 10 91 species of indigenous mammals in Sri Lanka including 16 endemic species and 12 11 introduced species (Weerakoon and Goonatilake 2006). About 70% of these are small 12 mammals. Although diversity and endemism of the mammals are high in the wet and 13 montane zones, the dry zone forest contains most of the large mammals (Kotagama 14 unpublished data) that are the primary suppliers of dung for beetles (Nichols et al., 2007). 15 Riverine marshes, flood plains, and swamp forests in the dry zone are said to support the 16 greatest density of large mammals in Sri Lanka, including Elephas maximus maximus 17 (Elephant), Panthera pardus kotiya (Leopard), Axis axis (Spotted Deer), Rusa unicolor 18 unicolor (Sambar Deer) and Bubalus bubalis(Buffalo). Massive deforestation in the wet zone 19 during the 19th century caused many large mammals to move to the dry zone. With the 20 exception of one small population, most elephants (Elephas maximus) occupy dry zone 21 forests in large numbers, particularly the ecotones created by shifting cultivation (Kotagama 22 pers. Com). Domestic cattle are abundant in anthropogenic habitats of the dry zone and some 23 feral cattle occupy the forest. Large mammals in the wet zone are mostly restricted to forests 24 and exist in small populations. Cattle farming is not common in the wet zone. For these 59 1 reasons, dung and dung beetles are more common in the dry zone. Association of species rich 2 dung beetles communities with large mammal diversity has been recorded in Africa and 3 South America (Ridshill-Smith and Edwards, 2011) 4 An interesting pattern emerges in the distribution of roller beetles within Sri Lanka. 5 All but two species of roller beetle are found in the dry zone, and most rollers have relatively 6 large populations (Kudavidanage unpublished data). The wet zone is primarily dominated by 7 the rollers Paragymnopleurus melanarious and Sysiphus spp. We found that the largest roller 8 beetle in the wet zone, P. melrnarius, is vulnerable to loss of forest cover. Population sizes of 9 this species decrease rapidly in open, anthropogenically modified areas (Chapter 4). 10 Although P. melanarious is also found in the dry zone, Scrabaeus gangeticus is more 11 predominant, and extends its distribution into the arid zone, where it is the most abundant 12 species. Large dung beetles are more susceptible to the loss of forest cover (Larsen et al., 13 2008), but S. gangeticus may be able to tolerate habitat disturbance to take advantage of the 14 surfeit of dung and paucity of competitors in the dry zone. Extrapolating from these 15 observations, it is clear that the absence of large rollers from human habitats in the wet zone 16 maybe in part be due to a dearth of dung and not just a lack of canopy cover. 17 Changes in the distribution of Paragymnopleurus melanarius and several other 18 species of Onthophagus (e.g., O. hystrix, O.pusillus) in the low- and mid-country wet zone 19 are consistent with previous studies showing that native, forest-inhabiting dung beetle species 20 undergo local extinction in areas where tree cover has disappeared. In these areas, they are 21 replaced by open area species (Halffter and Arellano, 2002). Arrow (1931) recorded the 22 presence of these species in Colombo and surrounding areas—the areas that have undergone 23 massive alteration in the forest cover and environment during the course of the last eighty 24 years (MENR, 2002). The two roller species S. erichsoni and G. koenigi, which were 60 1 recorded by Arrow in 1931 from Colombo, were not recorded there during our field survey. 2 Onthophagus gazella was earlier recorded in wet-zone, whereas the species was only 3 recorded from dry and arid zones during our survey. Onthophagus laevigatus is another 4 species that shows similar habitat restriction and deviation; it was recorded from low country 5 wet zone by Arrow (1931) but was only found in dry and arid zones during our survey. At a 6 more local level, we observed that even within the same geographic range in the wet zone, P. 7 melanarious was a forest dweller and Catharsius molossues was abundant in the forest and 8 anthropogenically modified areas. 9 Dung beetle species distributions are influenced by climate, elevation, soil, and litter 10 properties (Davis et al., 2001). Sri Lanka’s bio-climatic zones have distinctive vegetation, 11 soil, and climatic variations and elevation differences. Microhabitat preference is also 12 determined by the functional groups which, in turn, are affected by variables such as tree 13 canopy cover, shrub canopy cover, and ground herb cover (Bartholomew et al., 2000). Soil 14 moisture (Hölldobler and Wilson, 1990) leaf litter, logs, rocks, and debris are thought to 15 affect predation and foraging of ground-dwelling beetles (Lassau et al., 2005). Chapters five 16 and six discuss the importance of environmental parameters (i.e., temperature, leaf litter, soil 17 conditions) as predictors of dung beetle distributions in the lowlands. 18 The climate of insular Sri Lanka is marked by two monsoonal periods bringing rain 19 throughout the year. The species richness of dung beetles is known to increase with rainfall 20 and decrease with temperature (Davis et al., 2001). Increased activity of dung beetles during 21 the rainy season (Gill, 1991), owing to favorable soil conditions (Nyeko 2009), and results in 22 higher rates of their ecological functions (Amezquita and Favila, 2010). Dung beetles are also 23 known to reproduce during rainy reasons, leading to more dung burial for egg laying 24 (Halffter and Edmonds, 1982). Chapter 7 discusses the significance of rainfall in dung beetle 61 1 ecosystem functioning. Sri Lanka receives two types of monsoonal rains, and the impact of 2 this climatic factor has a significant impact on the distribution of dung beetle species on a 3 large scale. 4 Community structure, abundance and diversity, are often negatively correlated with 5 elevation (Rahbek, 1995). We observed less species in the montane zones (Fig 4.3). Lower 6 productivity at higher elevations is suggested as one of the mechanism for this decline 7 (Lawton et al., 1987). Body size of a species is considered to be a function of environmental 8 productivity (Geist 1987). We noted that smaller species dominate montane zones whereas 9 large and medium sized species were most abundant in all other zones. 10 The long time lapse between the past and present records and the habitat change that 11 occurred during the period may explain the absence of certain species from their original 12 locations. Studies from other regions verify the impact of climate change on dung beetle 13 diversity, both directly on the taxa and indirectly through the impact on mammal diversity 14 (Kerr and Packer, 1997). Similar patterns of shifting species distributions have been observed 15 in Sri Lankan avifauna (Kotagama, unpublished data). 16 17 4.3.5 Limitations and future work 18 Limited access to reference specimens and lack of taxonomic expertise were the two most 19 limiting constraints encountered in initial stages of this study. At a later stage, we benefited 20 from resources and collaboration provided by OUMNH, SCARABNET and its members, 21 which has facilitates the exploration of dung beetles as a focal taxon throughout the world. 22 The need for databased collections and electronic access to taxonomic publications is 23 recognized globally, and this infrastructure requires attention at a national level (Shimura, 24 2003). 62 1 In order to complete the dung beetle checklist, establishing the status of certain 2 species and describing the new species will require more sampling effort. We are expanding 3 the sampling into more geographical locations, more varied habitat and microclimatic 4 conditions, and in more seasons, especially those areas and zones that have been minimally 5 covered before. Although species identification was carried out, abundance data was not 6 calculated from the island wide sampling as it is not part of the ecological study and the time 7 was limited. For future work, abundance data will be calculated and more systematic 8 sampling will be conducted with species accumulation curves constructed to check the 9 adequacy of sampling. This is crucial as some of the previously recorded species were not 10 recorded during the study. More sampling is required to confirm if the absence of previously 11 recorded species during the study implies extinction. The vertical stratification of dung 12 beetles in the tropics (Tregidgo et al., 2010) also suggests the need to look for canopy dung 13 beetles. We noted clear morphological variation within certain species (i.e., Catharsius. 14 molossues, Onthophagus spinifex) which may suggest that these are species complexes in 15 need of taxonomic revision. 16 As recognized by the Convention on Biological Diversity (CBD) along with the 17 Global Taxonomy Initiative (GTI; Shimura, 2003), it is crucial to document biodiversity in 18 all countries, and particularly in ecosystems that are under severe threat. The GTI emphasizes 19 the taxonomy of indicator taxa and how the “imbalances in numbers of collections, experts, 20 and species” is a problem facing the Asian region; Sri Lanka is no exception. Our study 21 contributes to broadening knowledge of global dung beetle fauna. Apart from the ecological 22 aspects of this research, it will also be a taxonomic resource for many future studies through 23 the provision of reference specimens and resulting publications (i.e. field guide to 24 Scarabaeinae beetles of Sri Lanka; description of new species. 63 1 2 (a) (b) 3 4 Fig. 4.1(a) Major bio-climatic zones of Sri Lanka (Wijesinghe et al, 1993): A. Low- and mid-country wet zone. Dry zone, C. Low- and mid-country 5 intermediate zone. D. Montane wet zone, E. Montane intermediate zone, F. Arid zone: (b) locations sampled throughout the island from 2007-2011 indicated 6 by points. 64 1 2 (a) (b) 3 4 (c) 5 Fig 4.2 (a) Cow dung baited pitfall trap; (b) Sand trap for tunnellers and dwellers; (c) Baited 6 pitfall trap – lateral view (Photo courtesy for (c) - Janice Lee) 65 1 2 3 Fig. 4.3. Distribution of dung beetle species according to body size, across the bio-climatic zones. 4 Species were categorized according to the upper limits of body lengths, and the size classes were 5 taken as follows; Small Class2: 2 – 4mm: Small Class 1: 5 – 8mm ; Medium: 9 -15mm:: Large Class 6 1; 16 -25mm Large Class 2: >25: 7 66 CHAPTER 5: EFFECTS OF LAND-USE CHANGE ON THE COMMUNITIES OF DUNG BEETLES IN LOWLAND TROPICS OF SRI LANKA 5.1: Introduction Lowland tropical rain forests are affected by their high accessibility to increasing human population. These diverse ecosystems are rapidly converted into logging concessions, agricultural and urban areas (Kummer and Turner 1994, Sodhi et al., 2007). Land use change which is the direct alteration or modification of a habitat as a result of human activities, is one of the major types of disturbances that changes tropical habitats (Tilman, 1999; Fahrig, 2003). Species level data such as distribution, richness, endemism are often used to understand the ecological response of species to habitat change (Spector, 2006). Invertebrates are often less concerned in disturbance studies and biodiversity monitoring over more charismatic taxa despite their strong and rapid responses to landscape changes (Samways, 1993; Dunn, 2004a, Nichols et al., 2007). Insects specifically have a major potential to be information surrogates for a boarder range of biodiversity and aid to refine conservation strategies to answer questions in biological research (Spector, 2006). Scarab dung beetles of the sub families Scarabaeinae and Aphoidinae are gaining a rapid global recognition as ideal focal organisms for studying the effects of anthropogenic disturbance on diversity and ecosystem function in tropical forests (Finn, 2001; Halffter and Favila, 1993; Spector, 2006). Studies on dung beetle responses to forest fragmentation and disturbance have increased over the years in the Neotropics and recently SE Asia (e.g. Davis et al., 2001; Scheffler, 2005; Shahabuddin et al., 2005; Slade et al., 2007. 2010; Lee et al., 2009 and Qie et al., 2010). South Asia still remains unknown in this context. Exploring new geographic areas with different environmental conditions, disturbance history and land use types can provide 67 important empirical data on tropical forests. In the local context, the study will cater to the dearth of ecological knowledge on habitat disturbance in the lowland tropics in addition to documenting a less known taxon. My research findings will provide valuable insights on land-use change and its impact on biodiversity of the tropical forests of Sri Lanka which will be much useful for conservation and management decision making 5.2 Dung beetles Dung beetles are characterized by their use of dung, and in some cases other organic debris as food and nesting resource (Hanski and Cambefort, 1991). Worldwide there are more than 5,000 dung beetle species, and the tropics are the more diverse than warm temperate (Hanski and Cambefort, 1991). The majority of them fall in the subfamily Scarabaeinae (Hanski, 1991), which is the dominant group in tropical regions. They provide several ecological services such as waste removal, secondary seed dispersal and vertebrate parasite suppression (Mathison and Ditrich, 1999; Andresen and Feer, 2005; Horgan, 2005). Dung beetles are also characterized by how they process dung. These groups are called guilds or clades of which there are four: rollers (telecoprid nesters), tunnelers (paracoprid nesters), and dwellers (endocoprid nesters; Hanski and Camberfort, 1991) and kleptoparasites. The rollers (telocoprid nesters) separate dung from the dung pad to form it into a ball, and roll it away to be buried (Hanski and Cambefort, 1991). The telecoprids include the long-legged ball-rollers. Kleptoparasites use dung resources of other scarabs, and frequently associate with social insects such as termites. The distance they travel before burying depends on the species, terrain features and soil condition. They also have well- understood ecological roles (Hanski and Cambefort, 1991) and a relatively stable taxonomy (Philips et al., 2004). Importantly, simple, standardized trapping methods and simple field 68 manipulation that enable the assessment of community structure and functioning (Larsen and Forsyth, 2005) permits efficient comparative evaluation of different habitats and disturbance regimes. Further, dung beetles can be sampled more cost-effectively than many other rainforest taxa, and are effective indicators of landscape change (Gardner et al., 2008). Dung beetle diversity and abundance is positively associated with habitat area (Klein, 1989; Favila and Halffter, 1997; Andresen, 2003; Feer and Hingrat, 2005). Species composition is known to changes distinctly across habitat types, and complete species turnover has been observed across a natural ecotone less than 100m (Spector and Ayzama, 2003). In general, dung beetles are identified as one of the best indicator species of habitat disturbance, satisfying the three criteria of selecting an indicators; viability, reliability and interpretability (Nichols and Gardners, 2011). 5.3 Aims I investigated how habitat disturbance affect the richness, diversity and community composition of dung beetles in tropical forests in the lowlands of Sri Lanka. I hypothesized that the disturbance of forest habitats and converting them to non forest land uses decrease species richness and abundance and alters community compositions in the lowlands. In the Chapter three of the thesis, I explored how butterflies and amphibian diversity and community structures are affected across the same habitat gradient in the same landscape at a small scale. By sampling forest and modified habitats across the entire lowland wet zone in this study, I examine how similar types of land use changes across a large geographic scale can affect a taxon that is known to be highly responsive to habitat change. 69 5.4 Materials and methods 5.4.1 Study area The study was conducted in and around three protected forest reserve sites; Sinharaja Man and Biosphere Reserve, Kanneliya and Kottawa and Kottawa-Kombala lowland tropical rainforests (see Chapter 2 for more details on study sites). I estimated dung beetle richness and abundance and measured rates of dung removal ecological function across a land-use gradient consisting of four land-use types in each site; primary and selectively logged forests within the reserve and tea plantations and home gardens in surrounding matrix within 10 km of forest boundaries. I equated each land-use to a habitat type for this study. Since, there are no temporal (pre-post habitat conversion) data on diversity or ecosystem functioning in the wet zone, the primary forest is used as a spatial control to discuss the responses of dung beetles to habitat disturbance in adjacent land use types. Primary forests and selectively logged forests are pooled commonly under “forest” and the other two habitats under “anthropogenic” categories where necessary in the interpretation. 5.4.2 Sampling design for dung beetle trapping For this study, I collected data on species richness and abundance. Sampling was conducted from 15 January 2008 to 30 January 2010. In each of the four habitats within the three sites, three rounds of sampling were conducted, one during the dry season from January – April and the other two during the rainy season from May – October. This produces a total of nine sampling sessions for each habitat type (i.e. primary forest) from three sites and three rounds of sampling. Each sampling session consisted of five transects with five sampling points, each located 75-100 70 m apart. The sampling design produced a total of 900 traps for this study. Deep slopes, extreme open areas, areas adjoining water bodies, or areas with large piles of dung or decomposing material were avoided. A minimum distance of 500 m from the boundary of a habitat and from other transects was maintained for each transects in an attempt to achieve statistical independence of spatial samples. The minimum distance between two different habitat types was 1 km. The sampling design followed the protocol established by ScarabNet . The distance between the traps is 50m according to the protocol, and I maintained a distance of 75-100m to ensure maximum independence. 5.4.3 Beetle sampling Dung beetle sampling adopted the standard baited pitfall methods (Larsen and Forsyth, 2005). A plastic cup (10 cm dia.) was buried in the forest floor with the rim flush with soil and filled with ca. 30 ml NaCl solution with a few drops of detergent as a surfactant. The trap was baited with 20 g of human faeces wrapped in 2 mm mesh and protected by a rain cover. Traps were left in the field for 48 hrs before beetles were collected from the cups. I used human faeces as baits because it attracted more beetle species and individuals than cow dung within 48 hrs at our study sites (E. Kudavidanage, unpublished data). Similar observations had been made by previous authors at different sites in SE Asia (Lee et al., 2009, Qie et al., 2010). However, as cow dung was used for the dung removal experiment described in a following chapter, we pruned from the dataset beetle species found only on human dung to strengthen comparisons between the two sampling methods. 5.4.4 Vegetation sampling 71 I measured a set of climatic and structural environmental variables within a 5 m radius plot originating from each trapping location to describe the habitats. The variables measured during the study were percentage of canopy cover measured with a circular densitometer, percent of shrub cover, diameter at breast height (dbh) of the closest 10 trees with dbh > 5 cm, number of dead trees, average litter depth (cm) calculated from five randomly selected points, percent soil moisture and pH; soil temperature; ambient temperature; humidity; and atmospheric pressure. A detailed description of the vegetation parameters are given in chapter 3. The measured vegetation parameters were compiled from two similar ecological studies on land-use change; Clements et al., 2006; Posa and Sodhi, 2006. 5.5 Data analysis Summary statistics of richness and abundance data were calculated for each habitat type in individual forest. Sample-based rarefaction (species accumulation) curves and individual based rarefaction curves and density curves were constructed to assess the sampling adequacy and compare the population data. Sampling completeness in each habitat was calculated as the percentage proportion of observed species richness over estimated species richness (Soberon et al., 2000; Magurran, 2004). EstimateS Version 8.2.0 (Gotelli and Colwell, 2001; Colwell, 2006) was used for the analysis. To compare the species richness, I tested the hypothesis that disturbance of primary forest, by logging or land use change for agriculture, reduces the average species richness this landscape. Generalized linear mixed effects modelling (family = poisson, link = log) in the lme4 package, implemented in R version 2.13.1 (R Development core team 2011) was used for the analysis. The model tested for species richness impacts and had one fixed effect 'Land' (land 72 uses) with four levels: (1) Primary forest (2) selectively logged forest (3) tea plantation and (4) home garden. Transects (within each site) and sites (Sinharaja, Kanneliya and Kottawa) were included as random effects to predict the true effects of land-use change across the entire lowland wet zone without being confounded by any transect or site effects. To study the community change across habitat the following set of standardized community parameters were created (sensu Nichols et al., 2007). Parameters include (1) total species richness (Stotal); total abundance (Ntotal); intact species richness (Sintact) ;intact abundance (Nintact); total biomass of individuals in each habitat (Btotal); proportionate biomass of the intact community assemblages in modified habitats (Bintact); the abundance-weighted similarity of species composition of a modified habitat type(CMH) measured by the Morisita- Horn similarity index (Magurran, 1988) and the evenness of species’ abundance distributions in a modified habitat type (EH) measured by the Shannon evenness index(Magurran, 1988). Intact species are those recorded in primary forest communities that were also captured in that study’s modified habitat types. It is an indication of the presence of forest community in modified habitat types. For intact richness abundance measures, community parameters calculated for each modified habitat type were standardized relative to values calculated for the primary forest, such that the primary forest value was scaled to 1.0, and every modified habitat type supported some proportion of the primary forest value (Dunn, 2004a; Nichols et al., 2007). Indirect gradient analysis with Non-metric Multi-dimensional Scaling (NMS; Kruskal, 1964) of the effects of land use type on dung beetle community composition was conducted using non-metric multidimensional scaling (NMDS) in the PCORD software package Version 4.33; (McCune and Mefford, 1999). Outliers were identified in an outlier analysis (Tabachnik and Fidell, 1989). Multi-response permutation procedures (Mielke and Berry, 2001; Clements et 73 al., 2006) were used to differentiate the dung beetle distribution among the four habitats. Details of this analysis are given in chapter 3. A multivariate analysis of the effects of land use type on dung beetle community composition was conducted using non-metric multidimensional scaling (NMDS) in the Primer software package. A distance matrix was calculated on the species abundance matrix, using the Bray-Curtis dissimilarity method (and a dummy variable of one to account for zero distances between points in the distance matrix (zero distances might occur when two or more rows in the data matrix have equal species abundances). I then investigated the relationship between the dung beetle communities (species abundance matrices) and the environmental characteristics of the different land uses using constrained correspondence analysis (CCA; Ter Braak, 1986). I used the CCA package (Baccini et al., 2008) within R version 2.13.1 (R development core team, 2011) to run these analyses. The abundance data in the species matrix were log transformed before analysis. Initially, a CCA was run on the full model that included all environmental correlates (predictors). I used an analysis of variance (ANOVA) permutation test (no. iterations = 500, max. iterations = 5000), within the CCA package, to determine which environmental correlates had statistically significant effects. The permutation tests provide a robust method of selecting the most appropriate model to explain the relationship between the response matrix (species abundances) and the environmental predictor matrix. The statistically significant environmental correlates were included in the final reduced model. The three forest areas were differentiated in the data matrix to observe the variation among the replicates. I examined the effects of environmental correlates on dung beetle species richness using recursive partitioning analysis using the rpart package and implemented in R version 2.13.1 74 (2011). The model used to construct the first tree with ANOVA and a clipping value = 0.0001included seven predictor variables: soil temperature (TS), soil pH (PHS), litter cover (CL), litter depth (DL), shrub cover (CS), canopy cover (CC) and mean diameter at breast height of trees (MDBH). I then pruned this initial tree to avoid over-fitting the model to the data. To do this I selected the complexity parameter value that minimized the cross-validated error from the complexity parameter table. The full tree was pruned using a complexity pruning value of 0.0144. I further examined the forest and non forest affinity of individual species using proportionate abundance as a measure. As there was no significant difference between the two forest habitats and between the two highly modified habitats (see results), I pooled abundance data for individual species under two classes; forest and non forest. Proportionate abundance in each habitat class was used as a measure of habitat affinity. Selected ecological and physical characteristics were correlated with the habitat affinity to observe relationships. The product of elytra width and body length measured from the anterior margin of the pronotum to the pygidium was used as a measure of body size. Body size is collinear with other traits, such as body mass and wing loading (Larsen et al., 2008) hence can be used as a surrogate for mass and flight ability related traits. 5.6 Results Thirty six species of Scarabaeinae and Aphodinae beetles were recorded from the entire landscape which consists of thirty seven percent of the dung beetle species recorded for Sri Lanka. This consists mainly of medium to small bodied beetle species. Species accumulation curves of all fragments indicated adequate sampling (Appendix 5.1). Rarefaction curves for the 75 two highly modified habitats showed a rapid species accumulation gradient within a given number of individuals. Sampling completeness ranged from 89.4-94.2 %. The maximum number of species was detected from the selectively logged forest and tea plantations and the minimum was from the primary forest (Table 5.1). The total species richness in modified habitats was slightly higher but not significantly different from that of forest habitats. Contrastingly, the mean number of species per sampling point was highest in the primary forest (4.87±0.14) and the least in home gardens (2.07±0.12; Table 5.1). The generalized mixed effect modelling compared mean species richness of modified habitats with the primary forest. The mean dung beetle species richness was significantly lower (P<<2e-16) in gardens and tea plantations but not in selectively logged forest (Table 5.2). Compared with primary forest habitat the mean beetle richness was 47% lower in tea plantations and 56% lower in gardens. The random effects of transect (Variance = 0.079, S.D. = 0.28) and forest replicates (Variance = 0.017, S.D. = 0.13) had very little overall effect compared to the fixed effects for species richness comparison. Total and average abundance in primary forests (5687; 26.075±1.39) were almost four times higher than in the home gardens indicating decreased abundance with anthropogenic land use modification (Table 5.1). The total biomass and the intact biomass showed followed the same trend as abundance by prominently decreasing with habitat modification. Community comparison using nonparametric multi-dimensional scaling produced a three-dimensional ordination (Figure 5.1), as the best graphical representation of the community structure of dung beetles in four habitat types. The three axes explained 61.7% of the total variance and independently accounted for 29.9%, 19.6% and 12.1% of the total variance. The final ordination of sample scores was plotted on the first and second axes, which represented the highest variance and had a stress value of 23.19. Transects of two forest habitat types formed a 76 cluster, as did samples from home gardens and tea plantations in graphical overlay of habitat types on the ordination. As the proximity of transects to each other indicated similarity of the community structure, the clustering of transects in NMS graph showed that the two forest communities separated from the communities of the two highly modified habitats. Community evenness as indicated by the Shannon index was similar between the two forest habitats (Table 5.3). Morrisita Horn index (CMH) compared community similarity among the forest and land use types (Table 5.3). Primary forest and the selectively logged forest communities were 93% similar. Only 26% similarity was found between the primary forest and tea plantation communities and 30% between the primary forest and the home gardens. Nichols et al., (2007) suggested 85% community similarity cut off limits to indicate if a community has significantly changed from an original forest community. Results from both NMS and the CMH community comparison shows that dung beetle communities of the home garden and tea plantation clearly differ from that of the primary forest but not between each other. Figure 5.1 indicates the distribution of species. Each point indicates the position of a species within the sample (transect) scores. If a species point overlaps the position of transect cluster for primary forest, that species is likely to be found in a forest habitat. For example, the largest tunneller, Catharsius mollossus was associated with non-forest habitats while the largest roller, Paragymnopleurus mlenarious was found in forest habitats. Proportionate abundance of the species found in modified habitats that were also found in the forest was extremely less indicating low frequency of occurrence (Appendix 5.5). Majority of the dung beetle species on every habitat consist of tunnellers. The proportion of rollers decreased with the increasing habitat modification (Appendix 5.3). The functional importance of this decline is discussed in chapter 6. Environmental variables (Appendix 5.4) were related to species richness and community 77 structure using regression trees and CCA. The final pruned tree model (Figure 5.3) from recursive partitioning explained approximately 23% of the variation in mean dung beetle species richness in relation to the environmental predictors. Canopy cover, litter cover, shrub cover, soil pH and the soil temperature jointly contributed to explain the observed species richness patterns. Soil temperature was the most important factor in determining average species richness. Each node in the tree is a determining point of environmental variable which splits the species richness accordingly. For example, temperature above 21.75˚C and litter cover below 27.5mm produced a mean species richness of 1.8 dung beetles. At higher CL, it is more complex with shrub cover influencing species richness. The numbers on the ends of the branches is the mean species richness produced by the combination of conditional factors. At lower soil temperatures the number of large trees is important for species richness. Also, if temperature is high and litter cover is low, then there is a large reduction in species richness. The CCA model that best described the relationship between dung beetle community composition (species richness and abundance) and environmental variables included the predictor terms Forest, Habitat, Time, soil pH and Shrub cover (Figure 5.2). However, this relationship is not very strong as the model explained only 13% of the total constrained variation. Different environmental variables were important in different habitats. Soil pH was positively correlated with forest habitats while shrub cover was strongly positively correlated with tea plantations and home gardens. Communities of the two forest habitats were closely correlated as well as the communities of the two highly modified habitats. Study sites two (Kanneliya) and three (Kottawa) have relatively more similar community structures than that of site 1(Sinharaja). Separation of the wet and dry sampling sessions in the model indicated that there is also a temporal variation among the communities. This is discussed in detail in chapter 6 78 5.7 Discussion Conversion of forest land to agricultural areas and human habitats place the lowland tropical rainforest among the most threatened (Kummer and Turner, 1994). All the modified lowland habitats included in this study have been created through such intense deforestation of the last century. Understanding species responses o to such habitat disturbance is necessary to determine the general patterns and underlying causes. Dung beetles of the lowland wet zone landscape of Sri Lanka responded to habitat conversion to anthropogenic land uses, by decreased species richness and abundance and altered community structures. Forests held more dung beetles than the modified habitats indicating the importance of primary forests and old selectively logged forests as habitats. Dung beetle communities of the selectively logged forests did not significantly vary from those of the primary forest. Same pattern was observed for butterflies and amphibians in the same habitats in my previous study. Intensity of logging, time since logging, proximity to the nearest primary forest, degree of interference for the recovery are among the many factors that determine the community composition in a logged forest (Gardner, 2010). Selectively logged forests in this study are few decades old, partially resemble the primary forest in vegetation structure (Gunewardene et al., 2010), protected from intense human activities and are in close proximity with the primary forest; conditions that favour re-colonization and possible merging of communities. Dung beetles can recover from perturbations more quickly than other arthropod groups due to their relatively high mobility (Quintero and Roslin, 2005). For example, Amazonian dung beetle assemblages recovered to a near original state following fragmentation within 15 years (Dunn, 2004a; Quintero and Roslin, 2005).The close proximity to the primary forest may also allow dung supplying mammals to re-colonize. As the study compared the two 79 forest habitats across a large geographic range in the lowland wet zone, it is safe to assume that the forest dung beetle communities of the lowlands may have significantly recovered from any effect that may have occurred from selective logging. Since there is no post-logging data, the extent the initial communities were affected and to what degree recovery was necessary cannot be determined. Several previous studies on dung beetles have found undisturbed and selectively logged forests having similarities in beetle biomass and abundance (Davis et al., 2000; Davis and Philips, 2005; Nichols et al., 2007; Shahabuddin et al., 2010). Such similarities are found mostly in land-uses retaining a high degree of forest cover such as selectively logged forest, secondary and agroforests (Pineda and Halfter, 2005; Vulinec et al., 2006). High intensity logging can results in long-term detrimental effects on dung beetle diversity, composition and function (Slade et al., 2010). However, much caution is required in comparing studies on faunal responses to a given selective logging as the species responses are molded by site specific environmental variations. Dung beetles responded negatively to the anthropogenic land use in home gardens and tea plantations by decreased mean richness, abundance and altered community companion. However, there was no effective discrimination between the communities of the two anthropogenic land use types. The low availability of dung resources, unfavorable environmental conditions and altered predator prey dynamics are key factors driving species and population declines (Nichols et al., 2007, 2008). Environmental variables are important in structuring the dung beetle assemblages (Escobar, 2004; Hanski and Cambefort, 1991) due to their susceptibility to changes in abiotic conditions caused by disturbance (i.e. Osberg et al., 1994; Davis et al., 2000; Duncan and Byrne, 2000). 80 Several environmental variables including canopy cover, shrub cover, and soil temperature and soil pH were important in determining the species richness and community structure. Microclimatic conditions important for dung beetles are much dependent on vegetation structure (i. e radiant heat; Halffter et al., 1992; light intensity and air and soil temperature and humidity; Davis; 2002). Such that the loss of canopy cover and/or shrub cover may indirectly affect other variables. Forest and anthropogenic communities were primarily affected by different environmental factors. Soil pH was important in forest habitats where the overall canopy cover is high. Shrub cover was the most important in anthropogenic habitats where canopy cover was less. The total numbers of species present in plantations and home gardens however were higher than that of the primary forest. Most of the species were present in low abundance raising the possibility that some maybe either vagrants visitors from nearby forest areas seeking patchy resources or individuals from forest habitats migrating through the matrix. This is further supported by the fact that the communities of anthropogenic habitat are more similar to that of the selectively logged forest than the primary forest. Species could also exist in low populations in patches of suitable habitats within the home garden tea plantation matrix. In comparison to many previous studies on fragmentation and land use change, combined matrix of tea and home gardens is not intensely contrasting from the forest due to the presence of many small areas (1 -5 ha) with shade trees and also clusters of bushed. The demarcation of home gardens and tea plantations as land uses are anthropogenic. For dung beetle communities favourable vegetation conditions in the combined matrix can function as stepping stones (Fischer and Lindenmayer, 2006) that allows intermixing of the populations land use types. This could be one possible explanation for the high community similarities between tea plantations and home gardens. 81 There was a prominent pattern in the distribution of large roller and tunneller functional groups (Appendix 5.3). The largest roller species P. melanarious was predominantly a forest dweller (Appendix 5.4). The largest tunneller species C. molossus in contrast was dominant in anthropogenic habitats where P. melanarious was rarely presents creating a clear niche separation. As P. melanarious comprises a major proportion of roller beetles in the wet zone, this distribution patter creates a significantly altered functional group diversity (discussed in chapter 6). Similar patterns of species loss and replacement with increasing modification of tropical forest have been documented by some authors (Scott et al., 2006; Nichols et al., 2007). Decline in forest species richness and abundance with increasing habitat modification is often complemented by an increase in the abundance and richness of species characteristic of more open habitats (i.e. hyperabundance of small bodied beetles; Nichols et al., 2007). This phenomenon known as the density compensation phenomena is distinct in more open and managed fields and also dependent on landscape context (Howden and Nealis, 1975; Davis et al., 2000). Highly modified habitats of this study showed no density compensation in comparison to the primary forest shows a decline in biomass. Similar findings for lack of density compensation and decreased biomass have been presented previously in other regions (i.e Klein, 1989; Larsen et al., 2005; Vulinec, 2002; Scheffler, 2005; Gardner et al., 2008 and Slade et al., 2010). Further, although large bodied dung beetles in general are known to be vulnerable to habitat disturbance and are more susceptible to population declines (Larsen et al., 2008), only large rollers followed the trend in this study. This is primarily because most of the wet zone dung beetles with the exception of the two above are small to medium size beetles and large bodied beetles are more abundant in the dry zone. The wet zone only recorded 34 species out of a total of 92 recorded for Sri Lanka during this study (Chapter 2). Large dung beetles are sensitive to high temperature and 82 relatively dry environmental conditions in disturbed areas (Chown 2001, Lee et al. 2009). While large bodied P. melanarious extirpated in anthropogenic habitats, equally or large sized tunneller C. molossus adapted to increase populations sizes. Individuals of this species have thick cuticles, multiple projections in the head and shows freezing behaviour with legs retracted when touched (Kudavidanage pers. Obs.). Some of these traits may have helped in surviving in anthropogenic habitats although it is yet to be confirmed scientifically. C. molossus in home gardens however were heavily infested with parasites in comparison to those in tea plantations. This is an observation made in several tea plantations between Kanneliya and Kottawa forests and requires future investigation. Species richness by itself is not an adequate measure to study the difference in forest and anthropogenic land use areas. Combining community evenness, abundance and measures of community similarity create a more accurate picture. Using just species richness as a measure to evaluate habitat quality may inflate the actual value of the habitat through the incorporation of vagrant and visiting species (Gardner, 2010). Although dung beetle abundance is not considered as a successful measure of land use change (Nichols et al., 2007), I find that it is highly significant and correlates with the declining trend of biomass with modification. Biomass indicates the response to the total available resource base and may decline with disturbance even as abundance increases (Horgan, 2005; Larsen et al., 2005). Acknowledging some of the limitations of the study, I did not include site specific variations as the study was planned for the entire landscape assuming homogeneity within given land use types. However it was noted that forest site one (Sinharaja) was different in terms of community structure in comparison to the other two sites, but it did not affect the overall conclusion as trends for species richness; abundance and community composition between land 83 uses remain constant for all three sites. Replicating anthropogenic habitats using distance from the forest as a variable while controlling for the geographic variation can provide more complete picture for land use change, as it will isolate a given land-use type from the effect of the proximity to the forests. More detailed analysis of habitat and biogeographic affinities of species can provide an insight to species specific responses (Davis et al., 2000) that are masked by the overall community effects. For dung beetle sampling, the use of multiple bait type and multiple trapping methods are advisable which were not permissible in this study. 5.8 Conclusion Thirty seven percent of the dung beetle species recorded for Sri Lanka was found in the lowland wet zone consisting mainly of medium to small bodied beetles. Tropical forest dwelling dung beetle communities responded negatively to the conversion of forest land. The protected selectively logged forests imply a recovery of dung beetle communities several decades after logging. Tea plantations and home gardens had an overall low mean species richness, abundance and biomass. Communities of these two highly modified habitats were much similar indicating interaction of landscape features and possible merging of communities. Community changes reflected prominently in altered functional groups mediated by the loss of large rollers, which make them the most vulnerable to habitat change. Understanding of how different environmental variables are important in different habitats is useful in improving the habitat quality of anthropogenic habitats for dung beetles. Findings of this study corroborate the general trend of dung beetles negatively responding to land use change, but there are many specific characteristics of the landscape that creates differences in the nature of responses. 84 Table 5.1: Summary statistics of dung beetle species richness, abundance and biomass of habitat types. Given values are means (± standard error). The percentages of species detected in surveys were calculated from observed species richness and the estimated species richness calculated from the nonparametric species richness estimators. (ACE, ICE, Chao1, Chao2, Jack1, Jack2, Bootstrap, MMRuns, MMMeans; Magurran 2004); Standardized community parameters include: Sintact (richness of the intact forests species), Nintact (abundance of the intact forest species) and Bintact (biomass of intact forest species) Primary forest Selectively logged Home gardens Tea plantations Estimated species richness 26.8± 0.87 27.98± 0.28 27.50±0.76 28.65±0.42 Species detected 24 27 25 27 % detected by surveys 89.4 96.5 90.9 94.2 Mean no. spp./sampling point 4.87±0.14 4.57±0.15 2.07±0.12 2.40±0.14 Total abundance 5687 4989 1826 2106 Mean #. individuals/sampling point 26.075±1.39 22.17±1.67 8.12±0.82 8.68±1.48 Intact species richness (Sintact) 0.74 0.88 0.81 Intact abundance (Nintact) 0.98 0.31 0.33 Total biomass(Btotal) 40.8 x 104 32.2 x 104 12.0 x 104 14.9 x 104 Intact biomass (Bintact) 0.78 0.29 0.36 Shannon Index 2.34 2.34 2.06 2.23 85 Table5. 2: Summary of results from the generalized mixed-effects models for the effect of land-use change on species richness. The generalized mixed-effects model fit by the Laplace approximation describing the effects of land use change on dung beetle species richness. Primary forest and selectively logged Forest are not significantly different. Random effects (study site and transects within habitats) were not significant. Fixed effects Estimate SE z-value Pr(>|z|) Primary forest 1.549 0.090 17.039 <0.001 Home garden -0.873 0.081 -10.775 <0.001 Logged forest -0.079 0.073 -1.089 0.276 Tea plantation -0.730 0.074 -9.767 <0.001 Table 5.3: Community similarity among land use types quantified by Morrisita Horn index .Marginal value of 0.85 is used to detect the alteration of a community from the primary forest (sensu Nichols et al., 2007). Plantations and home gardens are more similar to the logged forest than the primary forest. Morisita-Horn index of community similarity Land use1 Land use2 Primary forest Selectively logged forest 0.932 Primary forest Home gardens 0.296 Primary forest Tea plantations 0.262 Selectively logged forest Home gardens 0.412 Selectively logged forest Tea plantations 0.363 Home gardens Tea plantations 0.969 86 Figure 5.1: Nonmetric multidimensional scaling ordination of Bray-Curtis similarity indices for dung beetles. The ordination is based on richness abundance matrix sampled in all four land use types. Symbols represent sampling points within the ordination space clustered according to their similarity. Two forest and two highly modified habitats are separated in discrete clusters. Similarity among the clusters were quantified with the Multi Response Permutation Procedure (MRPP, Mielke and Berry, 2001); PF vs SLF= -15.56, PF vs HG =-93.22, PF vs TEA =-123.88, HG vs TEA =-4.51. The highest negative value indicates the most similar habitats. 87 Figure 5.2: Relating the land use and environmental variables to dung beetle richness abundance data using CCA. The significant model explains 13% of the constrained variation. Habitat 1= primary forest; 2 =SL forest; 3= home gardens; 4= tea plantations. Forest 1= Sinharaja; Forest 2= Kanneliya; Forest 3= Kottawa. Time 2= Dry season; Time 3= Wet season. Vectors represent the environmental variables and the length of the vector indicates the variation. Soil pH was the best explanatory variable for the primary forest while shrub cover was important for tea plantations and home gardens. Communities of the two forest habitats are closely associated as well as the communities of the two highly modified habitats. Study sites 2 and 3 have relatively more similar community structure than that of site (forest 1). There is also a temporal variation in the communities. 88 Figure 5.3: The recursive partitioning analysis of the average species richness in the landscape.Predictor variables: soil temperature (TS), soil pH (PHS), litter cover (CL), litter depth (DL), shrub cover (CS), canopy cover (CC) and mean diameter at breast height of trees (MDBH); complexity pruning value of 0.0144 The final pruned tree model explain approximately 23% of the variation in dung beetle species richness in relation to the environmental predictors canopy cover, litter cover, soil pH and the soil temperature were the main determinants of species richness. Soil temperature is the most important factor. (i.e.: at temperature above 21.75˚C and litter cover below 27.5mm produce a mean species richness of 1.8 dung beetles) 89 CHAPTER 6 DUNG BEETLE COMMUNITIES IN LOWLAND FOREST FRAGMENTS 6.1 Introduction Fragmentation breaks apart continuous forest habitats into smaller, isolated fragments surrounded by a matrix of anthropogenically modified land (Wilcove et al., 1986; Tilman, 1999; Fahrig, 2003; Wright, 2005, Sodhi et al., 2007). Original forest inhabitants are lost through the reduction of the original vegetation cover, and there is a cascade of negative effects: increase of forest edges; degradation of microclimatic conditions; changes in community interactions; and, ultimately, population declines and local extinction of forest species (Fahrig, 2003, Koh and Menge, 2006). Responses of many taxonomic groups to fragmentation have been studied in detail; they include birds and mammals (Andrén, 1994; Laurence et al., 2002), reptiles (Gibbons et al., 2000), amphibians (Stuartet al., 2004, Bickford et al., 2004), invertebrates (Didham et al., 1996; Nichols et al., 2007) and plants (Hobbs and Yates, 2003). The consequences of forest fragmentation are more severe in the tropics than in other regions (Fahrig, 2003), and although the ecological effects of forest fragmentation are well-studied over the past two decades (Wilcove et al., 1986, Laurance, 1991, Saunders et al., 1991, Turner, 1996; Fahrig, 2003), empirical evidence is still relatively poor in the Asian tropics (Laurance and Bierregaard, 1997, Sodhi et al., 2007). Recent work on invertebrates in forest fragments in South and Southeast Asia includes work by Koh et al., (2002); Qie et al., (2010) and Raheem et al., (2008). Many conceptual models have been used to study forest fragments and their surrounding matrix. The species-oriented approach, which examines the response of individual species, and the pattern-oriented approach, in which landscape changes are more concerned, is commonly employed. The species approach is often hindered by the need to study as many species as 90 possible in the landscape (Fischer and Lindenmayer, 2006). The island biogeography model (IBG; MacArthur and Wilson, 1967), the patch–matrix–corridor model (Forman, 1995) and the variegation model (McIntyre et al., 1992) are also frequently used conceptual models. The species-area relationship (SAR) (Arrhenius 1921) is commonly used to explain the decrease in species richness in habitat fragments. Species area curve (Arrhenius, 1921; Gleason, 1922) is based on the log-log model (log S = c + z log A, where c is the intercept and z the slope). In the classic SAR, species richness declines with fragment area with an approximately linear relationship on a log scale (Gleason 1922). This is one of the most general and universally important theoretical bases for the research on forest fragmentation (Rosenzweig 1995, Qie, 2010). However, Lomolino and Weiser (2001) argued, however, that there is also a small island effect (SIE) where below a certain fragment area limit stochastic events have stronger effects than area (Lomolino & Weiser 2001). As many of the remaining lowland tropical forest fragments are small (Turner & Corlett 1996) it is important to understand the role of fragment size in determining the number of species. Most research on fragmentation adopts the IBG model, which likens fragments to patches or oceanic islands in a uniform, ecologically neutral matrix and predicts species richness using fragment area and isolation (MacArthur and Wilson, 1967). However, in reality, terrestrial habitat fragments are not surrounded by an impenetrable matrix analogous to water surrounding oceanic islands. The applicability of IBG to habitat fragmentation studies is therefore often challenged (Gilpin and Diamond, 1980; Sodhi et al., 2007) since dispersal is more likely through degraded matrix than over seawater (http://www.umass.edu/landeco/index.html). The type of habitat in the surrounding matrix it is an important factor influencing the fauna and flora in fragments (Saunders et al., 1991; Ricketts 2001). The patch matrix corridor 91 model identifies the matrix as a homogenous land-use type surrounding forest fragments and corridors between the fragments (Forman and Godron, 1986). The variegation and landscape mosaic models (McIntyre and Hobbs, 1999) both consider the entire landscape to be a spatially heterogeneous and complex assemblage of patch types that should be viewed from the perspective of the organism (variegation) or the ecological process (landscape mosaic) of interest. Metapopulation theory models a patchwork of suitable habitats amid unsuitable matrix, and models extinction in fragments, as well as subsequent re-colonization from source populations in (usually) large fragments (Harris, 1984). However, the poor dispersal capacity of many tropical taxa may limit the applicability of this model to highly diverse study systems (Sodhi et al., 2004). Similarly, source-sink theory suggests that large areas of continuous forests act as sources and small patches in fragmented landscapes with poor habitat conditions for reproduction can act as sinks, (Pulliam, 1988). This study focuses on characteristics of forest fragments, and different landuse types in a common matrix and their correlation with species responses to understanding the effects of fragmentation in the lowland wet zone of Sri Lanka. This is a simple and an effective pattern- oriented method for studying fragmentation (Fischer and Lindenmayer, 2007). As large forests are fragmented over time, providing empirical evidence on the effect of fragmentation on communities in these modified landscapes becomes necessary. Massive deforestation during the past century has reduced the lowland tropical rainforests of Sri Lanka into a few large forest areas and a multitude of fragments embedded in a matrix of human modified landscape (IUCN, 1993; MENR, 2002). Combinations of anthropogenic activities and conservation measures have provided this landscape with unique environmental and social attributes. Most intact forest 92 fragments remain in areas that are not amenable to human habitation, such as steep slopes. These fragments are not sharply separated from the surrounding matrix and are subject to frequent human disturbance in comparison to undisturbed forests as observed during the study period. Disturbances include hunting, harvesting timber wood and other forest products, clearance of patches for cultivation, waste dumping, using for illegal alcohol brewing etc. The matrix is not a checkerboard of rigidly contrasting patches, but a more subtly varied mosaic of vegetation including home gardens and plantations. Dung beetles respond in a predictable way to tropical forest fragmentation and are therefore useful indicators of disturbance (Halffter and Arellano, 2002; Davis and Philips, 2005; Nichols et al., 2007). I selected 20 forest fragments between Kanneliya and Kottawa forests that are embedded in a landscape matrix of agriculture and home gardens (Chapter 2). I first investigated how dung beetle communities vary among these fragments and with communities in intact forests. I compared species richness, diversity, and several other community parameters. Then, using several analytical methods and a stepwise procedure, I related environmental variables and fragment characteristics with my biotic measures to determine the best predictors of species richness, abundance, and community structure. In the previous chapter I examined the effects of land use change on dung beetle communities. Here, I explore forest fragmentation in the same landscape using similar analytical methods to produce a more coherent picture of habitat modification in the lowland wet zone. Fragment size, habitat type, habitat quality, fragment shape, land use adjacent to the fragment, and the extent to which the wider landscape isolates populations are considered as factors influencing the presence of species in fragments (Sodhi and Ehrlich, 2010). 93 6.2 Study sites The study was conducted in the south lowlands of Sri Lanka (Chapter 3). Twenty forest fragments (Fig. 6.1) ranging from 10-200 ha were selected for sampling. Each forest fragment was at the summit of a small hill and was surrounded by a matrix consisting primarily (> 75% collectively) of tea plantations and home gardens. To draw comparisons among habitats, the two closest, large forests (>4000ha) served as positive spatial control for the community comparison. Each forest fragment is legally protected but utilized by local people to varying degrees. In this study, I considered hilltop fragments to be separate from the matrix. The forest edges are not clearly demarcated, and was marked by the end of the large tree line for this study. If a series of densely vegetated home gardens and fallow land existed between two fragments, I consider it to be a forest corridor and included the corridor as a separate matrix characteristic in the analysis. The matrix was not assumed to be homogenous but, for analytical purposes, was categorized into one of two land uses: home gardens or agriculture (primarily tea plantations). The tea plantations were further categorised on the intensity of cultivation (domestic or large-scale). 6.3 Materials and methods 6.3.1 Dung beetle sampling I sampled dung beetles twice between January 2010 and December 2010 and pooled the data for analysis. I used the same baited pitfall sampling method adopted in previous studies (Chapter 5). Traps were a minimum of 100 m from the forest edge, and each trap point was sampled only once. The number of traps was proportional to the area of each fragment; the smallest fragment had 10 traps and the largest had 30 traps. Traps were never placed less than 50 m from a Buddhist temple, which were sometimes present in fragments. 94 6.3.2 Environmental variables and fragment characteristics To describe the habitats, we measured a set of climatic and forest structural environmental variables within a 5 m radius of each trapping location. Details on variables measured are given in Chapter 4. 6.3.3 Fragment characteristics To describe the forest fragments and the surrounding matrix, I measured the following set of quantitative and categorical variables: fragment area (ha) and circumference, area/edge ratio, elevation (m), category of matrix (categorical); vegetation coverage (categorical), soil quality (categorical), intensity of utilization by people (categorical), presence of temples, evidence of encroachment, distance to the nearest home garden from the forest edge (average of 10 home gardens), distance to the nearest tea plantation from the edge of the forest (m), distance to the nearest road from the forest edge, percent coverage of land use (home gardens, tea, oil palm, other) in a 1 km belt from the forest edge), maturity of the nearest tea plantation, intensity of tea growth, quality of the nearest 10 home gardens, number of forest fragments within 1 km from the forest edge, distance to the nearest forest fragment within 1 km of the forest edge, nearest other fragment studied, distance to nearest other fragment studied, nearest large forest, distance to nearest large forest (m), presence of prominent forest corridors between the fragment studied and the nearest fragment, isolation index, age of the oldest home gardens, specific observations. Only some of these variables were used in an analysis to predict species richness and abundance. Distance was measured as the straight-line distance between a fragment and the nearest intact forest or large fragment, rather than a metric of ‘effective’ distance (Winfree et al., 2005). 95 Distance measures were obtained using Garmin map sources version 10.2.1 and Google Earth associated shareware GE path 1.4.4a (website). GPS data was obtained for ground-truthing using a Garmin 60 CSx GPS. 6.4 Data analysis 6.4.1 Summary statistics and community parameters I constructed species accumulation curves, calculated summary statistics of species richness and abundance, and assessed sampling completeness against averaged nonparametric species richness estimators (Magurran, 2004) using EstimateS Version 8.2.0 (Gotelli and Colwell, 2001; Colwell, 2006). I calculated the following set of standardized community parameters, which were modified from Nichols et al., 2007 and used in the previous chapter for the land use change study;: total species richness (Stotal); intact species richness (Sintact); modified species richness (Smod); abundance per sampling point (Ntotal); intact abundance per sampling point (Nintact); Community similarity (CMH) measured by the Morisita-Horn similarity index (Magurran, 1988); Community evenness (EH) measured by the Shannon evenness index (Magurran, 1988).Measured community parameters were independently correlated with fragment area (AREA), area-to-edge ratio (RAE) and the distance to the nearest large forest (DNF). 6.4.2 Comparison of species richness, abundance across the fragments I used three methods; generalised linear modelling (GLM); constrained correspondence analysis (CCA), and regression trees to relate species richness and abundance of forest fragments to fragment characteristics and environmental variables. Generalised linear modelling (GLM) was used to determine which fragment characters best explain the observed species richness and 96 abundance. Subsequently, a regression tree was used to identify the contribution of each significant variable in determining species richness and abundance. A final Constraint Correspondence Analysis (CCA) was used to plot the direction of each significant fragment characteristic and environmental variables in explaining the observed richness abundance pattern. I used generalized linear mixed-effects modelling implemented in the lme4 package in R version 2.9.1 (R development core team, 2009). A set of 84 a priori models were used to investigate the factors that best predicted dung beetle species richness in this fragmented landscape. The models followed three analytical themes (sensu Sodhi et al., 2007): connectivity; fragment shape; and land use in the surrounding matrix. The connectivity theme included three predictor variables: the presence of a forest corridor (ForCorr), the distance to the nearest fragment (DNFrag), and the distance to the nearest continuous undisturbed forest (DNF). The shape theme included two predictor variables: the size of the forest fragment in hectares (AREA), and the ratio of area-to-edge of the forest fragment (RAE). Theland-use theme included two predictor variables: the intensity of tea cultivation around the forest fragment (TEA) classified as domestic or intensive, and the dominant land-use type in the landscape matrix (MATRIX). The predictor variables, distance to the nearest fragment and the distance to the nearest forest were re-scaled by dividing by 100 and 1000, respectively. This transformation was necessary to avoid error messages that are typical of the lme4 package because of its inability to deal with variables that have high ranges. The models included a random intercept ‘Site’ (lme4 notation (1|Site)) to account for the unbalanced replication of the sampling design (Zuur et al., 2010). I investigated the relationship between the dung beetle species richness, and abundance 97 and the environmental characteristics of the forest fragments using constrained correspondence analysis (CCA; ter Braak1986). Constrained correspondence analysis links a community data matrix with the corresponding constraining matrix (environmental variables / landscape characteristics) and provides an automated interpretation of the ordination axes (ter Braak, 1986). I used the CCA package (Baccini et al., 2008) within R version 2.13.1 (R development core team, 2011) for these analyses. Abundance data in the species matrix were log transformed before analysis. I analysed the beetle species-abundance data from the 20 forest fragments in relation to three secondary matrices: (1) fragment characteristics (2) fragment environmental variables and (3) fragment characteristics and environmental variables combined. Constrained Correspondence Analysis was run on the full models, including all explanatory variables. I then ran ANOVA permutation tests to decide which explanatory variables to include in the final model. Accordingly, soil temperature (TS), soil pH (PHS), litter cover (CL) and maturity of the forest (DBH5: indicated by number of trees with DBH > 5) were included in the final analysis. The permutation tests are a robust method of selecting explanatory variables that are significantly affecting the relationship between the beetle communities and the environmental characteristics of the study sites. Reducing the number of variables in this way avoids over- parameterization of the model. I built a regression using the rpart library within the R (version 2.9.0) programming environment to examine fragment characteristics that predict beetle species richness data collected from the 20 forest fragments. The variables were selected from the CCA and the first six models with the highest model weight. When a data set is complicated, a single linear regression model is not adequate to explain the entire data space. An alternative approach is to subdivide the data space into smaller regions of data space to which simple models can be fitted. 98 Branches of regression tree represent such recursive partitioning. Each of the terminal nodes of the tree represents a cell of the partition, and has attached to it a simple model that applies to that cell only. Individual cells are separated at the root nodes of the tree (Ciampi 1991). An initial complexity pruning value of 0.001 was used for calculating the full regression trees that examined the relationships between beetle species richness and (1) abundance and (2) the characteristics of the habitat fragments. I selected the final complexity pruning values of 0.01 and 0.0131, respectively, using the methods of Breiman et al., (1984) as reviewed in Merkle and Shaffer (2011). 6.5 Results I found twenty-six species of dung beetles in total across all fragments and species richness of all fragments were less than that of the primary forest. Sampling completeness as determined by the ratio of observed to estimated species (see Chapter 3), was above 75% for all fragments (Table 6.1). The fragment Dolahena (120 ha) had the highest total number of species (20) and individuals (1,684), followed by Darakulkanda (233 ha), which had a total of 18 species and 503 individuals. The mean number of species per transect and abundance per transect were compared and was also found to be highest for Dolahena (120 ha). Only 11 species were sampled from Paththara (196 ha) and Waththahena (193.5 ha), which are both larger than Dolahena; however, beetles were particularly abundant at both sites. The fewest number of species (6) was found in Muhudubenkanda (10 ha), which is among the smallest fragments (9.98 ha). The smallest fragment Usbimjanapadaya (5.21 ha) that had a dense growth of forest and was located close to another large forest, and had 12 species and many individuals (Table 6.1). Species accumulation curves indicated that all fragments except Yakgala kanda (10.5 ha) were adequately sampled 99 (Appendix 6.1). In general, dung beetles in the roller functional group were more abundant in large, densely forested fragments than elsewhere. They require specific microclimatic conditions, making these forest dwellers more vulnerable to fragmentation. Observations of individual species show that five primary forest species were not found in any of the fragments. All of them were found in low numbers, even in the forest, and they may be naturally rare. One species was found only in a single fragment of of the 20 surveyed. 6.5.1 Community comparison I compared dung beetle communities in forest fragments to communities in intact forest using the Morrisita Horn index (CMH; Table 6.2a). The index value which is 1.0 for the pristine forest ranged from a minimum of 0.43 to a maximum of 0.94 among the fragments. The dung beetle community in the largest fragment of Darakulkanda (233 ha) was the most similar to communities in primary forest, while Dolahena (120 ha), the fragment with the highest species richness, abundance, and area/edge ratio, was 85% similar. The same two fragments had the highest community evenness as measured by the Shannon diversity index. Budapanagama (8.3 ha), the second smallest fragment with a degraded forest, had the community least similar to the primary forest (40%). Intact species richness and abundance explains the proportion of forest species in fragmented habitats. The largest forest fragment had the highest intact species richness and abundance. Several densely forested fragments (i.e., Dolahena; 120 ha, Horamadulla; 86.7 ha) had high proportions of intact species (0.81) in low proportion of intact abundance (0.29, 0.31). I used the cut of points for Morrisita Horn index ,CMH = 0.85 as an index for comparing communities (following Nichols et al., 2007), and Sintact= 0.85 as measures of biologically significant community and species change, (sensu Nichols et al., 2007). I found that community 100 structure and species richness were significantly altered in all forest fragments except the largest fragment (Darakulkanda) and the fragment with the highest species richness and abundance (Dolahena). Log area and the community similarity index were not significantly correlated (R2; 0.0419, P >0.05). Many fragments had beetles species also found in home gardens, and Darakulkanda and Dolahena had more species shared with these habitats than any other fragment, indicating that the matrix is utilized by many dung beetle species. Similarity among fragments was compared using CMH and shared species richness (Table 6.2b). Total abundance and species richness declined as area and area-to-edge ratio declined. Simple linear regression analysis revealed that the total number of species was positively correlated with log fragment area (R2 = 0.189, P = 0.05) and the log area-to-edge ratio (R2 = 0.203, P < 0.05). Average abundance was also positively correlated with both log area (R2 = 0.203, P < 0.001), and with log area-to-edge ratio (R2 = 0.809, P < 0.001; Appendix 6.2). I was not able to detect any significant correlation between species richness/abundance parameters and any of the fragment characteristics using linear regression models. 6.5.2 Relating species richness, abundance, fragment characteristics and environmental variables I tested and found no evidence for spatial autocorrelation among sites when examining differences in species richness across sites (Obs. = -0.064, Exp. = -0.055; P = 0.87). GLMs combined several fragment characteristics to explain the observed species richness (Table 6.3). The species richness of dung beetles in forest fragments was best explained by the saturated model that explained only 3% of the variance (AICc = 576.86; wi = 0.10; %DE = 3.01). The best-ranked unsaturated model contained the terms dominant matrix type (MATRIX) and area to 101 edge ratio; RAE; wi = 0.08, % DE = 1.66). The best ranked single term model included the shape predictor area-to-edge ratio (RAE ; model 22, rank = 22; AICc = 581.73; wi < 0.01; %DE = 0.91). The best-ranked connectivity model included the presence of a forest corridor, distance to the nearest fragment and the matrix type; rank = 44; AICc = 581.73; wi < 0.01; %DE = 0.91). Constrained correspondence analysis related fragment and matrix characteristics and environmental variables to the dung beetle community in separate models and then in a combined model. The bi-plots (Figure 6.3 a-c) show the ordination of dung beetle richness abundance data and environmental variables/fragment characteristics. All of the explanatory variables that are fragment characteristics (Appendix 6.3) were included in the final model based on the results of the permutation tests. This is consistent with the GLM results for fragment characteristics as predictors of species richness. The final model explained 9.4% of constrained variance. There is high variation within the richness and abundance data of fragments, as indicated by the distance among sampling points. Distance to the nearest large forest, and nearest fragment, number of fragments nearby (F2), area to edge ratio and fragment area are the variable having strongest effect on the community structure. The two fragment characteristics area and area to edge ratio explained most of the variance in fragments dominated by agricultural matrix, while matrix characteristics distance to the nearest forest, number of adjoining fragments and the distance to the nearest fragment were important in the fragments having a dominant home garden matrix. This suggests that home gardens with large trees are more effective habitat corridors than tea plantations. The number of fragments (F2) and the distance to the nearest fragment were closely associated vectors indicating that there predictive powers overlapped. Area and area/edge ratio shows the same association. In the analysis of the environmental variables within the forest fragments, soil 102 temperature (TS), soil pH (PHS), litter cover (CL) and maturity of the forest (DBH5: indicated by number of trees with DBH > 5) were included in the final analysis (Appendix 6.4), based on the results of the permutation tests. Soil pH varied largely within fewer fragments and Soil temperature, the number of trees with dbh>5, and litter cover explained more variance in many fragments. Litter cover and the number of trees with dbh>5 were closely associated as predictors, indicating that fragments with large trees may have more litter cover. Many environmental variables were included in the final model, and many were important to only a few fragments and in different magnitudes, as indicated by the CCA. This may have reduced the explanatory power of the model. All fragment characteristics, soil pH, and tree maturity were in the final combined model of environmental variables and fragment characteristics. The final combined model explained 13.09% of constrained variation. The recursive partitioning analysis of the species richness data produced a tree with four nodes that included the explanatory variables area to edge ratio and fragment area (Figure 6.2a) providing a clear picture of the influence of these two fragment characteristics. The first node was separated at area to edge ratio (RAE) = 281.1, providing the steepest gradient and indicating that the primary determinant of species richness is large area-to-edge ratio. For fragments with RAE less than that, another separation marks fragment area =79.51, indicating that fragment area is the main determinant of species richness at lower area-to-edge ratios. The next two nodes for areas less than 79.51 again indicate that the amount of core area and edge is important in smaller fragments. The recursive partitioning analysis of the species abundance data produced a tree with two nodes that included only one explanatory variable area to edge ratio (Figure 6.2b). 103 6.6 Discussion The composition of animal communities in forest fragments are determined by natural phenomena, human alterations, and species-specific responses by animals to anthropogenic change. For these reasons, results from many fragmentation studies are ambiguous (Haila, 2002), and explaining the effects of forest fragmentation on species richness, abundance, and community structure of dung beetles in lowland tropical Sri Lanka was therefore the most aspect of the research undertaken in this thesis. The twenty forest fragments surveyed in this study each varied in species richness, abundance, and community structure. The dung beetle community in the largest forest fragment was more similar to that of intact forests, followed by Dolahena (120 ha), a fragment of medium size with a large undisturbed area. No single variable was significantly correlated with community similarity index values. However, most of the fragments with community similarity index values similar to those of primary forests were of good forest quality (see categorical index of fragment quality; Appendix 6.3). All fragments were still above home gardens and tea plantations in the community similarity scale which had 0.29 and 0.26 similarity with primary forest community respectively (Chapter 5). The fragment and matrix characteristics measured in this study were selected from those commonly used in previous studies: the amount and structure of native vegetation; the prevalence of manmade forest edges; the degree of landscape connectivity, and the structure and heterogeneity of modified areas (Fischer and Lindenmayer, 2006). To improve upon the simple analyses used in previous studies, I used a combination of more sensitive non-parametric and parametric methods to find predictors of species richness, abundance, and community structure. Fragment area and area/edge ratio were the most significant predictors of total species richness 104 and abundance in simple linear regression models and regression tree models, None of the matrix characteristics were significant in the regression models. The generalized linear models (GLM) found that the saturated model of fragment and matrix characteristics best explains species richness, but had low predictive power. In Constrained Correspondence Analysis (CCA) models, I assessed fragment characteristics, matrix characteristics, and environmental variables separately before finally combining them all. All fragment and matrix characteristics used in the previous analyses were also used in this analysis. In the first CCA plot, the community composition was explained by the connectivity measures of the matrix (distance to the nearest fragment, number of fragments nearby, and distance to the nearest large forest in addition to the fragment area and area to edge ratio. The CCA on environmental variables found soil temperature, pH, forest maturity indicated by the DBH profile (DBH5), and litter cover are important. When all variables were combined, all matrix characteristics and environmental characteristics in the previous two models were found except soil temperature. The combined model environmental variables and fragment characteristics had more predictive power of the richness abundance pattern than individual models of fragment characteristics and environmental variables. The fragment characteristics area and area to edge ratio better explained the composition of dung beetle communities in fragments than did any characteristics of the surrounding matrix. Only the non-parametric CCA found any significant relationships between environmental characteristics (of forest or matrix) and the composition of beetle communities in the fragments. One possible reason for the poor explanatory power of matrix characteristics could be that many matrix characteristics, including those not measured by this study, collectively influence species richness and abundance. Alternatively, the matrix could be inhabited by many species of dung beetles that dilutes the impact. However, the overlapping of 105 matrix, fragment and environmental characteristics with the type of dominant matrix was a key finding. Figure 6.3 demonstrates that when a forest fragment is surrounded by tea plantations, area, area to edge ratio, litter cover and dbh profile explain most of the variation in dung beetle community composition, but when the matrix is home gardens, The distance to the nearest fragment and forest, number of nearby fragment, soil pH and the presence of large trees are the most important factors. Area and isolation were important determinants of dung beetle communities, and large areas of forest were often associated with high total and intact species richness (Nichols et al., 2007). This study suggests that area is strongly positively associated with species richness. There are several reasons why bigger fragments might host most species: higher colonisation rate of species from other fragments and fewer extinctions (MacArthur and Wilson, 1967); large fragments are more likely to contain areas with specialized microhabitats (Harris,1984); large areas are more likely to have a variety of different habitat conditions and are therefore more liable to host a greater variety of species with specialized habitat requirements (Connor and McCoy, 1979; Fischer and Lindenmayer 2007) . Many previous studies conclude that forest area is a strong predictor of species richness. The fauna assessed in these studies include birds in Singapore (Castelletta et al., 2005); arboreal forest marsupials in northern Australia (Laurance et al., 1991); invertebrates, understory birds and mammals in the Amazon (Laurance et al., 2002); mammal species richness in Peninsular Malaysia (Laidlaw, 2000); dung and carrion beetles in Mexico (Estrada et al., 1998); and dung beetles in South America (Andresen; 2003). Smaller forest fragments generally have low populations (Bender et al., 1998) due to resource limitations (Zanetteet al., 2000). This argument on forest area is challenged by the observation that dung beetles are highly mobile, which 106 enables individuals to move freely among fragments, particularly if intervening matrix habitats are favourable. In the present study, there were a few small fragments with high species richness and abundance. It is also possible that more small mammals were using these small forest fragments as reserves, thereby providing the beetles’ necessary dung resources. The area-to-edge ratio is generally a simple measure of fragment shape and core area (Rutledge 2003). It also should be noted that since I used unweighted area-to-edge ratios, which give large fragments higher area-to-edge indices in comparison to small fragments of the same shape (Rutledge 2003). Therefore, it is more accurate to state that the area-to-edge ratio in this study is a function of both area and edge, allowing more realistic comparison across fragments. For example, Dolahena, which is half the size of the largest fragment, has the highest area-to- edge ratio. It also had the highest species richness and abundance. When fragments of nearby areas of similar size are compared, those with higher area to edge ratio have higher species richness and abundance. Fragmentation increases the edge-to-interior ratio of forest fragments. Edges have hotter and drier microclimates; increasing this habitat then alters the structure and dynamics of communities (Fischer and Lindenmayer, 2006). The shape of small fragments is usually assumed to be more important than for large fragments (Saunders et al., 1991). Contrastingly, I found that a forest fragment’s shape is important in determining dung beetle species richness in fragments of all sizes (Figure 6.2a). Area-weighted indices seem to more appropriate for landscapes dominated by large forest fragments. Half of the seven studies and 109 fragments examined by Nichols et al., 2007 for the effects of forest fragmentation on dung beetles, show that species richness declines with increasing spatial isolation from large forests, whereas total abundance typically decreases significantly with increasing distance of the fragment from large, intact forests (Nichols et al., 107 2007). However, species-specific traits such as morphology, physiology, behaviour, and habitat choice determine a species’ ability to penetrate less-than-suitable matrix habitats, and the type of matrix further affects each species’ ability to pass through the matrix (Stouffer and Bierregaard, 1995; Fischer and Lindenmayer, 2006, Sodhi and Ehrlich, 2010). Some studies have found that dung- and carrion-feeding beetles can be isolated by just 100 m of open habitat between fragments (Klein, 1989). Open areas act as physical barriers to many species, but vegetation regeneration in the matrix can facilitate movements among fragments. Most home gardens surrounding forest fragments have clusters of trees and shrubs whereas. These areas may act as stepping stones for beetles to penetrate into the matrix and reach other nearby forest areas. In Chapter 5, I discussed that some of the beetles found in the tea plantation and home garden habitats might be from nearby forest habitats and are moving through the modified areas. Large-scale tea plantations only have occasional trees that provide shade. Small-scale and domestic tea plantation areas are interspersed with clumps of large trees and shrubs, and may provide more favourable conditions if adjoining a forest. A few studies on dung beetles have found that the matrix is important in determining the beetles’ response to fragmentation (Klein, 1989; Andresen, 2003; Quintero and Roslin, 2005). However, these findings should be considered in the context of the quality of the matrix, which may range from inhospitable to highly inhabitable. The matrix can be of high quality for many species and also provide passage for migration between fragments and large forest areas (Sodhi et al., 2007). The significance of the effect of the matrix is determined by the degree of penetration of effects into the fragment. Matrix-tolerant species are generally better able to move among fragments and are therefore better buffered against the detrimental effects of the matrix than forest specialists (Laurance et al., 2000; 2002). For many organisms, the detrimental effects 108 of isolation are partially reduced enhanced connectivity of fragments (Saunders and Hobbs, 1991; Bennett, 1999). Tropical species that persist in the face of habitat degradation are known to rely on their ability to move through modified habitats (Gascon et al., 1999; Sekercioglu, 2002). The CCA identified several environmental variables that partially explain variation in species richness and abundance among dung beetle communities in forest fragments. Variation in environmental variables among fragments was low. Combined with matrix and fragment characteristics, the environmental variables are masked in importance by the fragment characteristics (Figure 6.3c). Previous studies have shown that the effect of high temperature in surrounding matrix may penetrate through the edges (Kapos, 1989). Many environmental variables are correlated, and the alteration of one may have cascading effects on others. For example, fragments with many trees DBH > 5 cm also had more litter. The loss of canopy may alter soil temperature and consistency; soil properties were important in my investigations of land use change and fragmentation. Dung beetles are susceptible to changes in soil conditions (Nichols et al., 2008). Increased soil temperature may affect litter decomposition and moisture retention. Soil temperature is related to dung beetle activity (Klein, 1989, Davis et al., 2004, Nyeko, 2009) and soil pH is known to positively affect dung beetle communities (Bertone, 2004) Five primary forest species of low abundance were not found in any of the fragments, and they may be naturally rare. This suggests that rare species are particularly vulnerable to fragmentation. Copris signatus and C. sodalis were the most common species found in forest fragments. Surprisingly, C. sodalis prefers intact forests (Chapter 4). Onthophagus occulatus, O. bifaciatus, O. martialis, and O. militaris previously recorded in secondary forests (Chapter 5) were also recorded in fragments. Onthophagus centricornis and O. cervus, which were not previously recorded in forest habitats, were found in fragments and in the matrix. Onthophagus 109 unifaciatus, which was rare in forests, was found in 18 of the 20 fragments. The large roller Paragymnopleurus melanarious, which is a forest dweller (Chapter 5), was found in low numbers in many fragments except in Dolahena and Kandewattegoda, where it was common. Forest dwellers like large roller beetles require specific microclimatic conditions, making these forest dwellers more vulnerable to fragmentation. Undetected human impacts and natural anomalies might bias these data (i.e., previous fires, use of agrochemicals in tea plantations). The degree and nature of human activity within fragments may have a significant effect. For example, many forest fragments with temples were trampled by humans in the areas where the temples are located, but are less affected by hunting and timber harvesting than temple-free fragments. Some species seem to move among fragments frequently, and some species that were not found might only be active during certain periods that were not sampled. Information on seasonality is unavailable for most tropical insects, including the dung beetles examined in this study, so these factors cannot be taken into consideration. Information on dung availability or mammal density was not be sampled, although we had checklists of mammals generally present in each fragment. Scarabaeine beetles depend on mammal dung for food and nesting (Hanski and Cambefort, 1991). Some previous studies consider only primate density as a measure of resource availability (Nichols et al., 2007), but ungulate and small mammal density would have to be considered in our study sites, as they comprise most of the mammals in this landscape. It unknown whether forest fragments harbour stable populations. All fragments in this study were formed about 50-70 years ago, but they are subjected to continued pressure by the intensifying human activities in the matrix and factors affecting the habitat quality may change. It is known that a fragment is likely to harbour more species than it is capable of maintaining 110 once isolated and some of these species are eventually lost through ‘species relaxation’ (Fischer and Lindenmayer, 2006). 6.7 Conclusion Forest fragments provide habitat for species that cannot live in the anthropogenically modified landscapes surrounding them. Species may use modified habitats for foraging, but still may need specific microhabitat conditions found in forest areas for reproduction and nesting. Understanding the ecology of forest fragments therefore is necessary. Most of the forest fragments examined in this study had dung beetle communities with significantly fewer species than primary forest. This loss was primarily due to reduced habitat size and increased edge interface. Many characteristics of the surrounding matrix helped explain diversity of fragment dung beetle communities, but more refined methods of data collection and analysis may help identify key predictors and their importance. Finding that different land use types influence other matrix characteristics (i.e., distance to the nearest fragment is explanatory of richness and abundance in a fragment surrounded by home gardens is a key finding that may have practical applications in landscape management. Conserving forest fragments without monitoring degradation caused by human impacts or considering the ecological value of habitats is detrimental to the remaining biological communities and a waste of limited resources 111 Table 6.1: Summary statistics of dung beetle species richness and abundance of individual forest fragments. Given values are means (± standard error). The percentages of species detected in surveys were calculated from observed species richness and the average asymptotic species richness estimated from the nonparametric species richness estimators. (ACE, ICE, Chao1, Chao2, Jack1, Jack2, Bootstrap, MMRuns, MMMeans; for details of these estimators see Magurran 2004). Variable Number Estimated Species % Mean # of Mean #. of traps species detected detected no. individuals individuals/trap richness (% of by spp./trap total) surveys Usbimjanapadaya 10 13.1±0.8 12(46.2) 91.6 4.4±0.9 199 19.9±6.5 Budapanagama 10 9.78±0.4 8(30.8) 81.8 2.8±0.6 89 8.9±2.42 Aguruwalakanda 10 13.5±1.6 12(46.2) 88.9 3.6±1.2 137 13.7±6.2 Muhudubenkanda 10 6.1±0.1 6(23. 1) 98 4.1±0.5 71 7.1±1.4 Yakgalakanda 10 11.7±0.8 9(34.6) 76.6 3.1±0.6 64 6.4±1.3 Seethaladola 15 13.2±0.6 12(46.2) 90.9 4.1±0.1 278 18.5±5.1 Nidangala 15 13.9±0.3 13(50.0) 93.6 5.1±0.7 383 25.5±4.9 Kabaragala 15 11.4±0.3 11(42.3) 96.8 4.5±0.8 340 22.7±8.6 Atamassa 20 12.8±0.3 12(46.2) 93.39 3.1±0.4 274 13.7±2.0 Nabadawa 20 13.4±0.5 13(50.0) 96.8 3.8±0.6 304 15.2±4.2 Godahenkanda 20 11.1±0.3 10(38.5) 89.6 2.6±0.3 152 7.6±1.35 Bandungala 20 14.5±1.2 12(46.2) 82.5 2.4±0.6 145 7.25±2.9 Horamadulla 20 20.3±0.8 17(65.4) 83.7 4.8±0.6 431 21.5±5.0 Nerugalkanda 25 13.3±0.3 13(50.0) 97.9 4.8±0.4 554 22.2±2.5 Dolahena 25 21.5±0.4 20(76.9) 93.1 6.8±0.9 1684 67.4±14. Kandewattagoda 25 11.2±0.1 11(42.3) 98.1 5 ±0.4 498 19.9±2.2 Polgahawila 30 9.1±0.1 9(34.6) 98.4 4.1±0.3 399 13.3± 1.9 Waththahena 30 11.2±0.2 11(42.3) 98.3 3.7±0.4 578 19.37±4.0 Paththara 30 11.2±0.1 11(42.3) 98.1 4.7±0.2 571 19.03±1.6 Darakulkanda 30 18.8±0.3 18(69.2) 95.6 5.6±0.5 503 16.8±1.8 112 Table 6.2a: Summary of community parameters calculated for 20 forest fragments Standardized community parameters include: Sintact (richness of the intact forests species), Nintact (abundance of the intact forest species), CMH (Morisita Horn Index of community similarity relative to intact forest) Unstandardized community parameter include Stotal *(total species richness), Ntotal* (total abundance) and EH (Shannon evenness index);*in table 5.1 Fragment Fragment Number of species Morisita-Horn Intact species Intact Shannon area(ha) shared with intact index (CMH) richness abundance index forest (Sintact) Nintact Usbimjanapadaya 5.21 9 0.789 0.63 0.13 1.84±0.03 Budapanagama 8.27 11 0.431 0.38 0.06 1.42±0.07 Aguruwalakanda 9.89 10 0.524 0.56 0.09 1.94±0.03 Muhudubenkanda 9.98 7.5 0.537 0.31 0.04 1.55±0.03 Yakgalakanda 10.5 15.5 0.651 0.44 0.04 1.83±0.08 Seethaladola 13.26 15 0.824 0.63 0.19 1.93±0.03 Nidangala 23.85 9 0.58 0.69 0.23 2.17±0.03 Kabaragala 24.91 14 0.599 0.63 0.19 2.16±0.02 Atamassa 54.17 10.5 0.792 0.69 0.18 1.96±0.02 Nabadawa 54.95 10.5 0.529 0.56 0.16 2.2±0.01 Godahenkanda 62.44 5.5 0.466 0.56 0.1 2.0±0.03 Bandungala 72.37 9.5 0.567 0.5 0.08 1.93±0.06 Horamadulla 86.65 11.5 0.709 0.81 0.29 1.94±0.04 Nerugalkanda 109.72 11.5 0.436 0.63 0.34 1.8±0.02 Dolahena 120 9.5 0.855 0.81 0.3 2.4±0.02 Kandewattagoda 127.89 8 0.759 0.56 0.31 2.04±0.01 Polgahawila 170 11 0.424 0.44 0.25 1.6±0.02 Waththahena 193.48 11.5 0.682 0.5 0.37 1.68±0.02 Paththara 196 9.5 0.736 0.5 0.34 2.04±0.01 Darakulkanda 233.41 8.5 0.939 0.88 0.72 2.59±0.01 Table 6.2b: Community similarity among fragments compared using two measures. Morrisita Horn index and percentage shared species among fragments. The first six pairs of fragments with the highest similarity values are given. Fragment 1 Fragment 2 Morisita-Horn Fragment 1 Fragment 2 Shared species (%) Budapanagama Polgahawila 0.975 Dolahena Darakulkanda 61.54 Paththara Usbimjanapadaya 0.96 Dolahena Horamadulla 61.54 Usbimjanapadaya Waththahena 0.959 Darakulkanada Horamadulla 53.85 Atamassa Usbimjanapadaya 0.958 Dolahena Nidangala 50.00 Kandewattagoda Paththara 0.949 Darakulkanda Nerugalkanda 50.00 Bandungaka Horamadulla 0.948 Darakulkanda Nidangala 50.00 114 Table 6.3: Generalized linear models: fragment characteristics as determinants of dung beetle species richness. A summary set of six genearalized linear models explaining the species richness of dung beetles in forest fragments in Sri Lanka is given. Statistical variables: k = number of estimated model parameters, AICc = Akaike Information Criterion corrected for sample size, logLik = the negative log-likelihood, dAIC = the difference of AIC and AICc, wi = the model weight, %DE = the percentage of the deviance explained by the model. Models were ranked according to their corresponding wi = the model weight. Predictors included in the models are (AREA); size of the fragment, (MATRIX) Category (matrix) based on the dominant land use within 1km belt from the fragment, (RAE) unweighted area to edge ratio, (The distance to the nearest fragment ) distance to the nearest fragment within 1 km (m) and (DNF) distance to the nearest large primary forest (m). The best fitting model is given in bold italics. Notation n k logLik AIC AICc dAICc RL Rlsum wi ~Saturated (all terms) 388 9 -283.2 586.4 576.86 0.00 1.000 10.415 0.10 ~Matrix type + Area to Edge Ratio 388 3 -287.2 582.3 578.36 0.36 0.836 10.417 0.08 ~The distance to the nearest fragment + Matrix type + 388 4 -286.4 582.9 578.00 1.14 0.566 10.417 0.05 Area to Edge Ratio ~The distance to the nearest fragment +Distance to the 388 5 -285.9 583.9 578.05 1.19 0.551 10.417 0.05 nearest forest + Matrix type + Area to Edge Ratio ~Distance to the nearest forest + Matrix type + Area to 388 4 -286.6 583.2 578.30 1.44 0.487 10.417 0.05 Edge Ratio ~The distance to the nearest fragment + Matrix type + 388 4 -286.7 583.3 578.40 1.54 0.463 10.417 0.04 Area to Edge Ratio Figure 6.1: Map of the sampled forest fragments The map ncludes the three forest areas that were sampled (Modified from UNEP-WCMC's World Database on Protected Areas. GIS vector data was provided by R Hijmans, UC Davis via www.diva-gis.org) and the forest fragments located between two large forests Kottawa and Kanneliya (Garmin map source 6.10.2). Fragmets:1.Usbimjanapadaya, 2. Budapanagama, 3.Anguruwela, 4. Muhudubenkanda, 5. Seethaladola, 6. Yakgala kanda,7.Nidangala, 8. Kabaragala, 9. Atamassa, 10. Nabadawa11. Godahenkanda, 12. Bandungala, 13. Horamadulla, 14. Nerugalkanda, 15. Dolahena, 16. Kandewattagoda, 17. Polgahawila, 18. Wattahena, 19. Paththara, 20. Darakulkanda. 116 (a) Figure 6.2a: The recursive partitioning analysis of the species richness vs. fragment characteristics, Area = AREA; Area to edge ratio = RAE; n = the number of sampling points. According to the regression tree, area to edge ration of 281.2 ha marks the first splitting point and 19 sampling points in fragments with the ratio more than that have the highest average number of species of 9.9 each. The next node at 79.5 ha of area shows 118 sampling points and an average species richness of 5.18 in islands larger than that. 117 (b) Figure 6.2b: The recursive partitioning analysis of the abundance data vs. fragment characteristics. Area to edge ratio (RAE) is the key determinant of abundance. See materials and methods for all variable used in this models. 118 (Figure 6.3 a) 119 (Figure 6.3b) 120 (Figure 6.3c) Figure 6.3: Constrained Correspondence analysis: species richness and abundance data vs. fragment characteristics and environmental variables. Bi-plots present ordination results of dung beetle richness abundance data with (a) Fragment characteristics (b) fragment environmental variables and (c) the fragment characteristics and environmental variables combined. The horizontal axis is the CCA 1 (coefficient 1) and the vertical axis is CCA 2. Dots indicate sampling points and vectors represent fragment characteristics / environmental variables. Final explanatory variable included in the model are decided by ANOVA permutation tests. The variables included: the presence of a forest corridor (ForCorr), the distance to the nearest fragment (DNFrag), and the distance to the nearest continuous undisturbed forest (DNF); the size of the forest fragment in hectares (AREA), and the ratio of area-to- edge of the forest fragment (RAE). the intensity of tea cultivation around the forest fragment (TEA) classified as domestic or intensive, and the dominant land-use type in the landscape matrix (MATRIX), soil temperature (TS), soil pH (PHS), litter cover (CL) and maturity of the forest (DBH5: indicated by number of trees with DBH > 5) 121 CHAPTER 7 HABITAT CHANGE IMPAIRS ECOLOGICAL SERVICES PROVIDED BY SCARABAEINAE DUNG BEETLES IN THE TROPICAL LOWLANDS OF SRI LANKA 7.1 Introduction Predicting species loss is a burgeoning field, but predicting disruptions of tropical ecosystem functions and services in response to habitat change is poorly explored (e.g. MEA 2005, Larsen et al., 2005, Nichols et al., 2008). Species richness and abundance of dung beetles have been correlated with measurable ecological functions such as dung removal, nutrient recycling, and secondary seed dispersal (Andresen, 2003, Andresen, 2005; Horgan, 2005, Slade et al., 2007, Nichols et al., 2008, Lee et al., 2009, Qie et al., 2010 Amezquita and Favila 2010). Dung burial contributes to natural soil fertilization ecosystem service (Nichols et al., 2008) by increased crop yield and plantation productivity through soil conditioning and nutrient recycling (Miranda et al., 2000, Nichols et al., 2008).In this study, I developed a research approach to explore how various spatial characteristics of lowland tropical landscapes (i.e., site and habitat), temporal scales (e.g. seasonal differences caused by rainfall pattern) and community composition (richness and abundance) can influence dung removal ecological function performed by dung beetles. The rate of dung removal by dung beetles was experimentally evaluated across a land use gradient in a spatially and temporally replicated study in Sri Lanka. Although the main aim of this chapter is to look at how dung removal function by dung beetles is affected by land use change, I related crop yield and fertilizer cost to address one key hurdle in the restoration of ecosystem functions, which is assigning financial values to ecosystem services. The cost of fertilizer along with data on nutrient recycling and soil conditioning in different types of agricultural lands can be a useful tool for economic analyses of ecosystem services. It allows monetizing the benefits of restoring ecosystem functions and using them as incentives to encourage environmentally friendly agriculture. Here, I examine the hypothesis that increasing habitat disturbance reduces dung removal by beetles. Dung beetles are categorized into three functional guilds with different nesting strategies and dung removal performance (tunneller, roller and dweller; Halffter and Edmonds 1982, Slade et al., 2010), and I evaluated the role of each guild in dung removal as a measure of their functional potential. Finally, I highlight how the value of ecosystem services can be used as an economic incentive for conservation in lowland tropical landscape. As supplementation of soil nutrients through enhanced ecosystem services may reduce fertilizer costs, the potential of dung beetles in reducing agricultural costs and pollution while increasing crop yields in Sri Lanka is discussed. 7.2. Materials and methods 7.2.1 Study area The study was conducted in the same landscape. Rates of dung removal was measured prior to sampling dung beetle richness and abundance in four habitat types in each site; primary and selectively logged forests. Similar to the previous chapters, the primary forest was used as a spatial control to discuss the functional responses of dung beetles to habitat disturbance in adjacent land use types. 7.2.2 Sampling design for dung removal and dung beetle trapping I collected data on dung removal simultaneous to the species richness and abundance data collection for the land use change study (Chapter 5). In each of the four habitats within the three 123 sites, two rounds of sampling were conducted, one during the dry season from January – April and the other during the rainy season from May – October. This produced a total of six sampling sessions for each habitat type (i.e. primary forest) from three sites and two rounds of sampling. Each sampling session consisted of five transects with five sampling points. I used cow dung for the dung removal experiment because it is commonly found in anthropogenic habitats in Sri Lanka and is used as a fertilizer, and can be directly related to ecosystem services. Cow dung is the best available surrogate to naturally occurring herbivore dung found in Sri Lankan rainforests, such as Sambar deer, Rusa unicolor unicolor, and Purple faced leaf monkeys, Trachypithecus vetulus (E. Kudavidanage, pers. obs.),and could be obtained in the large quantities required for this study. 7.2.3 Dung removal experiments Dung removal was sampled at the first, third and fifth location of each transect. At each sampling point, two piles of homogenized fresh cow dung weighing 100 g and free of beetles and other insects were placed individually on pieces of waterproof paper that can be penetrated by dung beetles. One of the piles served as a control and was enclosed in mesh bag to exclude dung beetles and account for the weight loss by sources other than dung beetles. The treatment remained exposed (See Lee et al., 2009). Dung remaining in the treatment and control piles was collected after 24 hrs, dung beetles (tunnellers and dwellers) in the dung pile removed, dried to constant weight, and weighed on a digital scale with an accuracy of 0.1 g. Immediately after these experimental dung piles were removed, dung beetle fauna was at the same sites using procedures described in chapter 4.The percentage of dung removal was calculated using the following equation: 124 Effective dung removed [%] = [{(a-b) – (a-c)} / a]*100 with a = dry weight of 100g fresh dung bait; b = dry weight of the remaining dung after 24 hrs; c = dry weight of the control after 24 hrs. 7.2.4 Beetle sampling Dung beetle traps were positioned near each removal point immediately after dung removal was measured. This provides a corresponding dung beetle trap for each removal point and a total of 360 traps. Each habitat was sampled twice, once in the relatively dry period between and once in the relatively wet period, using different transects in different seasons. Dung beetle sampling adopted the standard baited pitfall methods (Larsen and Forsyth, 2005) as described in chapter 5. Predicting removal of cow dung with dung beetle diversity measures from human faeces was a potential caveat. This discrepancy of dung types was unavoidable because data on dung beetle community composition was required for an additional, related study. However, to strengthen comparisons between the two sampling methods, I pruned from the dataset beetle species found only on human dung. 7.2.5 Statistical analysis I used an information theoretic multi-model inferential approach to analyse our data (Burnham and Anderson 2004). I used a binomial error structure with a log-log link function in linear regression modelling to assess spatial (site, habitat, and transect) and temporal (replicate) predictors of the rates of dung removal. The saturated model included all individual parameters and all interactions. I tested candidate models with individual and interacting spatial predictors 125 without and in combination with the temporal predictors. The best model had the highest AIC model weight, which is the probability of a given model to be the best model in the set. I used mixed-effects modeling with the same error structure and link function on dung beetle guild-specific species richness and abundance to predict dung removal. The predictors of the best model from the scale prediction approach were included as random effects to predict the true richness and abundance without being confounded by scale issues. To assess the relationship between fertilizer use and tea production in Sri Lanka, I used data from the FAOSTAT database (FAO 2010). A regression model where fertilizer use is predicted by tea production was compared to a null model to test predictive strength. 7.3. Results As habitat perturbation increased, dung removal activity decreased. Average percentages of dung removal: primary forest (74.57 ±2.93.), selectively logged forest (73.09± 2.90), home gardens (24.72 ± 2.78) and tea plantations (27.24±2.91). The average rate of dung removal was significantly high (P < 0.05) in forest habitats in comparison to anthropogenically modified areas (Fig. 7.1). This was further confirmed by the best model, which predicts that habitat and replicate (wet or dry season) are the best determinants of dung removal (Table 7.1). The rate of removal was significantly higher in the rainy season in primary forests and home gardens. Dung removal activity increased as the guild of dung rollers increased in species richness and abundance. The analysis on ecosystem function potentials of specific dung beetle functional guilds revealed that the species richness and abundance of the roller beetle guild best predicted dung removal rates (Table7. 2). 126 I also found that fertilizer use was a positive predictor of tea production in Sri Lanka (coefficient = 1.2; d.AIC = 19.9; w.AIC =1; Fig. 7.2). Thus, fertilizer cost can be used as a gross monetary measure of the loss of natural soil nutrient enrichment. 7.4. Discussion 7.4.1 Functional responses of dung beetles The rates of dung removal in the lowland tropics of Sri Lanka was influenced by spatial variation created by habitat types of different degree of disturbances and temporal variation created by wet and dry seasons. Primary and selectively logged forests with the highest rates of dung removal did not different from each other significantly. There are two possible explanations to the observed similarity. The proximity of primary forests to logged forests and/or three decades of regeneration following logging may have favoured re-colonisation by beetles. The presence of a healthy population of dung beetles is important for recovering logged forests because they enrich soils, disperse seeds, and thus may promote tree growth and replacement (Nichols et al., 2008). As there is no pre- logging data, the second possibility is that the disturbance cause by selective logging may not have resulted in significant extirpation of dung beetle communities. There was no differences among the three sampled forest sites, thus, our results seem to reflect patterns throughout the lowland wet zone and not specific conditions of a single forest. Significantly decreased rates of dung removal in the anthropogenic habitats in the tropical lowlands indicate that dung beetle communities respond negatively to habitat modification. Previous researchers working in other regions have reported similar trends (e.g. Larsen et al., 2005, Lee et al., 2009). Despite of their structural complexity relative to tea plantations, home gardens did not support a higher rate of dung removal. Future studies may illuminate ways that 127 home gardens could become more suitable habitats for dung beetles. Anthropogenic land use conversion alters community structure and decreases functional group diversity. These changes are wrought by changes in vegetation structure (Gardner et al., 2008), loss of other favourable environmental conditions (i.e. high soil temperature; Klein 1989), increased predation by the beetles’ natural enemies (Lan et al., 2010) and altered mammal communities which provide the dung (Andresen and Laurance, 2007; Nichols et al., 2007, 2009). There were clear differences among the beetle communities in forest and anthropogenic areas. A community comparison in the previous study (Chapter 5) shows that the primary and selectively logged forests shared 93.2% community similarity, while the home gardens and tea plantations shared only 39. 6% and 26.2 % respectively with the primary forest. However, for this study, I related the performance to the three functional groups, and found that high richness and abundance of roller beetles increased dung removal. Roller beetles are known for their ability to remove of large quantities of dung (e.g., Horgan 2001, Larsen et al., 2005, Slade et al., 2010). I found that the abundance of the most common large roller in the forest (Gymnoplerus melanarius; Fig. 7.1) declines with forest disturbance. Only 2% of the individuals recorded during the study were from tea plantations and home gardens (Appendix 5.5; chapter 5). A major proportion of the roller beetle abundance consisted of P. melanarious .Therefore decline of this species that could be A potential indicator of ecological disturbances, singlehandedly can cause significant declines of dung removal. High susceptibility of large dung beetles to habitat disturbance has also been reported in other regions (Larsen et al., 2005, Scheffler 2005, Shahabuddin et al., 2010). The degradation of forest leads to the loss of large roller species, which decreases dung removal, thereby reducing nutrient cycling and soil bioturbation provided 128 by these beetles. Although large tunnellers are abundant in home gardens and plantations, they may not functionally compensate for the lack of large rollers in the same habitats. The seasonal influence indicated in the model is caused by the increased rate of dung removal in the wet season. The difference was significant for the primary forest and the home gardens but did not change the Seasonal differences of dung removal are caused by increased activity of dung beetles during the rainy season (Gill, 1991), owing to favourable soil conditions (Nyeko, 2009), resulting in higher rate of cow dung removal (Amezquita and Favila, 2010). Dung beetles reproduce during the rainy season, leading to more dung burial for egg laying (Halfter and Edmonds, 1982). Thus, seasonal variation should be carefully considered when studying and interpreting results of ecosystem functioning, especially when comparing habitats sampled in different seasons. Including various scales in the analysis enables the actual effects of habitat modification to be distinguished from spatial and temporal variation. 7.4.2 Economic implications for conservation I report important functional consequences of converting forests to agricultural areas, which represents an important contribution to the limited body of existing work on ecosystem services in Sri Lanka. Our findings highlight the need to conserve the pristine forests and manage rapidly growing anthropogenic land use areas to increase their habitability to a range of species. Striking a balance between biodiversity maintenance and crop production in agro-ecosystems is challenging but beneficial to farmers and the rest of humankind (see Steffan-Dewenter et al., 2007, Tscharntke et al., 2005, Tscharntke 2010). Sri Lanka is one of the main global producers of tea. Tea production contributed US$831 million to the country’s economy in 2008, a value that more than doubled from 2002 (Fig. 7.2). I 129 analysed FAO data shows that fertilizer use is a strong predictor of tea production in Sri Lanka. Therefore increasing tea production is linked with increased use of fertilizers. N supplements consist of a major proportion of fertilizers used by Sri Lankan farmers of which, 92% of are imported. Nitrogen is usually supplied to tea plantations in the form of Ammonium Sulphate, which can lower soil pH or acidity and reduce microbial activity when used excessively (UNESCAP, 2003), which makes the soil more dependent on fertilizer and continuously imposing heavy costs on farmers and the country. Thus, the cost of fertilizer use in tea plantations can be a crude financial indicator of the loss of soil productivity or an index of potential financial savings from restoration of natural ecosystem services which includes dung burial. Having said that, it is understood that tea is one of the many crop that consumes fertilizer imports in Sri Lanka. Further, there are many other processes and organisms that contribute to the nutritional state of agricultural soil. However, the relationship of increasing fertilizer use with the increasing crop production was highlighted as a crude indication of all ecosystem services that contribute to enriching soil, partially which is performed by dung beetles. As the intensity of agriculture increases in terms of land area cultivated more fertilizer has to be purchased. Further, as the soil quality decreases with time, more fertilizer might be required to get the same amount of crop harvest from a given land area. With this increasing environmental and monetary costs of fertilizer and growing consumer preferences for organic products, cow dung represents a favorable alternative source of N, which is essential for intensely harvested crops. Although there is great demand for organic tea, very few studies have discussed the potential of cow dung as a source of nutrients for tea saplings in South Asia (i.e., Sundriyal et al., 1994; Naturland, 2000; Gunathilaka et al., 2005). However, cow dung was popular among Asian farmers in the past and is still used as a fertilizer for various other crops 130 and in home gardens (e.g. Hossain, 2001, Sundriyal et al., 1994). Farmers will likely benefit from increasing habitat complexity of agricultural areas, using cow dung as a fertilizer supplement and maintaining healthy dung beetle populations in agro-ecosystems. A minimum of near-natural and complex habitats is required to support biodiversity and associated ecosystem services in managed landscapes (Tscharntke et al., 2005). Home gardens and plantations in the buffer zones and peripheral areas of the forests will benefit more from using cow dung due to possible rapid re-colonization of dung beetles from forest habitats. A wide range of studies has been conducted on dung beetles in different agricultural landscape, however actual management recommendations for practically implementing conservation is scarce. Further studies on dung removal rates in other agricultural areas (i.e., vegetable farms) under different fertilizer and pesticide regimes and studies on the contribution of other dung relocating organisms for soil nutrient supplementation (e.g. flies, termites) would be beneficial. How to improve the habitat conditions to benefit dung beetle populations (i.e. soil quality that increase larval survival rate; Halffter and Edmonds 1982) should be documented in management techniques to improve the diversity and abundance of beneficial fauna in agricultural landscape. In many countries, sustainable farming practice that benefited from ecosystem services were traditionally used (i.e. cascade-based agriculture system in Sri Lanka) and may provide valuable insights for current management practices. 7.5 Conclusions I report depressed rates of dung removal in anthropogenic land use areas in the lowland wet zone of Sri Lanka at different spatial and temporal scales and demonstrate how these changes are affected by the impact on different functional groups of dung beetles. I highlight the functional 131 importance of the roller guild including a key forest species that may possibly become threatened as forest cover dwindles. Restoring dung beetle mediated ecosystem services in the agricultural landscape will be potentially rewarded by reduces fertilizer costs. Management-oriented research on habitat requirements, management techniques that could improve dung beetle species diversity and valuating ecosystem services in different agricultural systems are crucially required. I suggest conservation of pristine forests and managing agro-ecosystems and home gardens to sustain the diversity and ecological functions of the dung beetle fauna of Sri Lanka. 132 Table 7.1 – Dung removal as a function of temporal and spatial parameters in the lowland wet zone of Sri Lanka. Predictor parameter: habitat (primary, selectively logged, home gardens, tea plantations); region (Sinharaja, Kanneliya and Kottawa Kombala forests) and replicate (wet and dry season sampling sessions). The Akaike Information Criterion (AIC) is a measure of relative model ranking; k = number of effective parameters; % Dev = percent deviance explained (structural adequacy of model); AICc = AIC values corrected for sample size; ΔAIC = difference between the best model fit and the model of interest; w = AIC model weight is the probability of a given model to be the best model in the set; Null model = the mean (intercept) model. Model habitat region replicate Transect k %dev AIC AICc ΔAIC w Dung removal ~ habitat + replicate -0.5906 0.1872 3 160.4 403 403.1 0.0 0.2 Dung removal ~ habitat -0.5867 2 161.8 404.6 404.7 1.6 0.1 Dung removal ~ replicate 0.1396 2 229.1 500.8 500.8 97.7 0.0 Null model 1 229.9 501.1 501.1 98.0 0.0 Dung removal ~ transect -0.006688 2 229.9 503 503.1 100.0 0.0 Dung removal ~ region -0.0634 2 229.4 503.5 503.6 100.4 0.0 Table 7.2 – Dung removal as a function of species richness (spr) and abundance (abd) of different dung beetle guilds. Predictor parameters: Functional guilds (Dwellers = dw; Rollers = ro; Tunnellers = tu). Predictor parameter: habitat (primary, selectively logged, home gardens, tea plantations); region (Sinharaja, Kanneliya and Kottawa Kombala forests) and replicate (wet and dry season sampling sessions). The Akaike Information Criterion (AIC) is a measure of relative model ranking; k = number of effective parameters; % Dev = percent deviance explained (structural adequacy of model); AICc = AIC values corrected for sample size; ΔAIC = difference between the best model fit and the model of interest; w = AIC model weight is the probability of a given model to be the best model in the set; Null model = the mean (intercept) model. Model abd.dw abd.ro abd.tu spr.dw Spr.ro spr.tu k %dev AIC AICc ΔAIC w Dung removal ~ spr.ro 0.4821 4 155.1 163.1 163 0.0 0.1 Dung removal ~ spr.ro + abd.ro 0.0268 0.376 5 154 164 164 1.0 0.1 Dung removal ~ abd.ro 0.0486 4 156.9 164.9 165 1.8 0.1 Dung removal ~ spr.ro + spr.tu 0.4589 0.02 5 154.9 164.9 165 1.9 0.1 Dung removal ~ spr.ro + abd.dw 0.0033 0.4625 5 155 165 165 1.9 0.0 Null model 3 161.5 167.5 168 4.4 0.0 (a) (b) Figure 7.1a – The average percentage of cow dung removal by dung beetles (per trap per 24 hrs) in four habitat types and two seasons Dung removal by dung beetles is more rapid in forests than in anthropogenically modified habitats. A) The average percentage of cow dung removal by dung beetles (per trap per 24 hrs) in four habitat types and two seasons (± standard error). PF= primary forest; SLF= selectively logged forest, HG= home gardens, TP= tea plantations. * = significant difference from the PF (df = 3, P < 0.05). 135 Figure 7.2– Correlating fertilizer consumption with the tea production in Sri Lanka. Each point in this figure represent a single year from 1961 – 2002. Data obtained from FAO 2010. Tea is one of the major consumers of fertilizer in Sri Lanka. Increasing tea production requires importing large quantities of fertilizer 136 CHAPTER 8: GENERAL DISCUSSION – RESEARCH FINDINGS AND THEIR APPLICABILITY 8.1 Ecological trends Sri Lanka is part of the Western Ghats and is a biodiversity hotspot; a globally representative area of biodiversity that requires prioritizing in conservation (Myers et al., 2000). Over the last 150 years, a large proportion of natural habitat, especially in the lowland and montane wet zone in Sri Lanka has been lost due to agriculture and timber extraction. The growing human population (currently ca. 20 million) exerts a tremendous pressure on many natural habitats. In the wet zone watershed areas, human population density exceeds the national average (MENR, 2008). Most of the remaining wet zone forests exist in fragments (IUCN and MENR, 2007). Reduction in forest area often leads to losses in habitat diversity and resources, and declines in populations that make species more vulnerable to extinction (Brook et al., 2003; Sodhi et al., 2007; Sodhi and Ehlrich, 2009). Demarcating a large network of protected areas was implemented by the government to conserve the remaining biodiversity. This directive was primarily moulded by the past intervention of the scientific community and the general public (Chapter 2). In addition to the conservation of biodiversity, the protected areas support the country’s economy through the lucrative trade of eco-tourism. Species diversity and distribution patterns of selected faunal groups recorded by researchers and avid naturalists have served the purpose of conservation decision making in the past. However, there is still a dearth of ecological and biological data available to assess the conservation status of existing protected areas. There is an imperative call to comprehend the effects of habitat modification on species and to amend existing management measures. I categorized the existing peer reviewed publications of the past 30 years on lowland rainforest and found that the majority of research has focused on flora. Faunal studies are mostly concerned with animal distribution and behaviour, with the exception of Wijesinghe and Brooke, (2004, 2005), Raheem et al., (2008), and Gunewardena et al., (2010). This study provides the first important set of information on species distribution of amphibians and butterflies in Sinharaja MAB reserve and dung beetles in the entire country. This included a record of 34 amphibians, and 120 butterfly species for lowland Sinharaja which is the highest number recorded so far for this habitat. A revised checklist of the dung beetles fauna is provided for the entire country (Chapter 4). The butterflies included 34 nationally threatened species and rediscovery of several species believed to be extinct. These include Brown Onyx (Horaga albimacula) recorded after 80 years, the Banded Redeye (Gangara lebadea subfasciata) and the Lesser Gull (Cepora nadina) recorded after 65 years. The Scarabaeinae dung beetle survey was conducted in order to identify species for the ecological study. Nevertheless, the final output of this minimally known and ecologically important taxon itself is a major contribution to the biodiversity database of the country, the global Scarabaeinae database, and provides an important foundation for future studies. Butterflies, amphibians and dung beetles vary in their nutritional requirements and mobility. There is limited overlapping between their ecological niches and the vertical distribution in habitats. Through this study, I managed to construct a coherent picture of the responses of these three taxa to land use change in the same lowland forest landscape and highlight the similarities and differences of taxon specific responses. Further, selectively logged forest, forest fragments and two different land 138 use types around the periphery of the existing large lowland forest were compared quantitatively for their habitat value for the same fauna. This is a pioneer ecological study of tropical rainforests biodiversity of Sri Lanka in terms of scope and magnitude as it covers a vast geographic range, multiple land uses and multiple taxa in a single study. This study was conducted with the assumption that the primary forest is the most suitable habitat for all targeted fauna. As pristine forest vegetation is important for many species, ‘optimal habitat’ has been equated with ‘primary forest vegetation’ (see Andrén, 1994; Gibson et al., 2011). Conversion of a forest habitat to a different habitat is equivalent to total habitat loss only for forest species with a new set of habitat conditions that may be occupied by a community that is different from the forest community consisting more of generalists (Lindenmayer and Fischer, 2007). However, the major concerns arising from this change are the loss of diversity and the ecological services (Lindenmayer and Fischer, 2007; Sodhi et al., 2007). All three taxa in this study responded to land use change and fragmentation by altered species richness, abundance and community structures. Functional responses measured for dung beetles were most significantly altered between forest (primary and selectively logged) and anthropogenic (home garden and plantation) habitats. Different magnitudes of responses and some contrasting observations in taxon-specific trends highlight the need for multi taxon studies for habitat assessment. It also attempts to identify faunal groups that are most vulnerable and requires prioritizing in conservation measures. Of the taxa studied, amphibians were most vulnerable to the conversion of forest habitats to human land uses. Amphibian communities showed the minimum variation between the forest habitats and maximum variation between the primary 139 forest and the two anthropogenic habitats (Appendix 8.1). Sri Lanka is a global amphibian hotspot, with a high proportion of endemic species (Pethiyagoda and Manamendra-Arachchi, 1998; Meegaskumbura et al., 2002). The observed vulnerability of endemic and endangered amphibian species to land-use change makes the protection of forest habitats, a priority for amphibian conservation. Although there were 16 butterfly species restricted to the forest, more species were found to associate Pinus plantations and home gardens. Forest butterflies are known to visit peripheral areas seeking resources although their host plants that are often in forests (Koh, 2007). It is highly unlikely that the observed rich diversity in modified habitats may sustain without a substantial amount of host plants or a forest nearby (Posa and Sodhi, 2006; Koh, 2007). Conserving forest habitats is essential for forest communities. However landscape level management that integrates the adjoining anthropogenic habitats is essential for butterfly conservation in general. Dung beetles may be the best potential indicator of all three taxa to be used island-wide. Dung beetles’ sensitivity to environmental perturbations—especially the loss of forest cover, land use change, and forest fragmentation makes them one of the most suitable biotic indicators of habitat disturbance (Nichols et al., 2007). The absence of many dung beetle species previously recorded (i.e., rollers Scarabaeus. erichsoni, S. sanctus and Gymnopleurus. smaragdifer), suggests possible local extinctions, although more sampling is needed before a full conclusion is made. All endemic species recorded in the present study were found in the wet zone, and most were rare. This could be attributed either to natural rarity or to endemics being more vulnerable to disturbances in this landscape (Wijesinghe and Brooke, 2004). Shifting distribution of species ranges were observed by comparing current records with the published locations in Arrow, (1931), and the locations of specimens from multiple 140 museums. These patterns should be interpreted in the light of changing natural habitats, climate change and resource availability. The impacts of deforestation are heavily impinging on mammals (Weerakoon and Goonatilake ,2006) among other fauna, thereby limiting dung resources for dung beetles. The discovery of many potentially new species and new records within one taxon indicates an express need to research the rest of the much unknown invertebrate fauna of Sri Lanka. The recent inclusion of dung beetles in the global IUCN database marked a turning point in dung beetles conservation (Baillie et al., 2008). Globally, more than 12% of dung beetle species are threatened, and most have a restricted range, or are rare forest-dwelling species (Nichols and Gardner, 2011). Data from this study will be incorporated into the next IUCN red data list for Sri Lanka. Butterflies, amphibians and dung beetles are known globally as indicator taxa due to their sensitivity and quantifiable responses to habitat disturbance (Gardner et al., 2008). Although the general pattern of response to habitat disturbance remains the same as previous research elsewhere, there were many site-specific variations leading to differential responses (i.e. time lapse since selective logging, degree of disturbance and habitat quality of the matrix). For example, it was found that total species richness (a commonly used measure of habitat quality) did not create a coherent picture of the effects of land use change on any of these taxa. Despite the altered community structures, all anthropogenic habitats except the Pinus plantations and a majority of the forest fragments had considerably high species richness. One possible explanation could be that due to their high mobility, dung beetles and butterflies are capable of incorporating several land uses in the areas peripheral to the forests their daily activities, although they may or may not inhabit those (Sodhi et al., 2007). Recent studies have found that the dispersal ability of dung beetles in the tropical 141 habitats is similar to that of the butterflies (Roslin and Viljanen, 2011). The other possibility is that the surrounding land might be recolonized from the forest, but it acts as a sink for populations as indicated by the low abundance noted for dung beetles. A more accurate understanding could be provided only through incorporating one or many of the other measures, such as abundance, biomass, community parameters, proportionate abundance of forest species and most preferably functional performances. Endemism and threatened status pinpointed amphibian responses that were not possible through species richness or abundance. Forest species were replaced by a more generalist species in home gardens which resulted in near similar species richness between home gardens and forests. Where community data was helpful to detect responses at forest and anthropogenic habitat level, species approach at a fine scale is suggested to detect responses between individual anthropogenic habitat types. For example information on the dispersal capacity of species relative to their landscape or habitat type is a key determinant of the population dynamics of dung beetles (Moilanen and Hanski, 2001). Species that can move through a given matrix are less vulnerable to habitat disturbance (Lindenmayer and Fischer, 2007). Primary forests in the lowlands sustained high species diversity, harbour forest communities of amphibians, butterflies and dung beetles and may facilitate the recolonization the selectively logged forests. The selectively logged forests of Sinharaja had faunal communities similar to those of primary forest; the magnitude of the similarity varying among the three taxa (Appendix 8.1). Short distances to a pristine forest and the minimum human intervention assured by protection status can expedite the recolonization of a disturbed habitat. In addition to my research findings on butterflies, amphibians and dung beetles, previous researchers have reported similar findings of birds; small mammals and ants in the same pristine and selectively 142 logged habitats of Sinharaja (see Chapter 3). There is no pre-logging data to evaluate the extent of damage incurred on the faunal communities by selective logging. Given that there were significant alterations caused by selective logging, and the above taxa are representative of the faunal diversity of Sinharaja, it is safe to assume that the forest has recovered 30 years after the selective logging and is of great conservation value. The extrapolation of this conclusion to the entire lowland wet zone can be suggested through research findings on dung beetles. 8.2 Conservation implications There were clear differences between the forest and anthropogenic communities across all three taxa. As all three taxa do known indicators, it is possible to assume that they represent at least several other taxonomic groups inhabit the same landscape. All primary forests and old selectively logged forests of the lowlands of Sri Lanka are protected from clearing under the jurisdiction of the forest department with the assurance that they may remain intact. This is an appreciative conservation measure that places large lowland rainforests and the fauna inhabiting them above many unprotected and rapidly cleared rainforests in the region (see Sodhi et al., 2007). The existing protection status can benefit from further strengthening as hunting, illegal encroaching and intense land use for agriculture in the peripheral areas threaten all reserves. Facilitating eco-tourism is as an essential component of the management of protected areas. However, visitor activities and the necessary infrastructure development must be monitored and regulated carefully. The status of the forest fragments surveyed is different from the large forests as they are under less surveillance, and thus are affected to a greater degree by illegal logging and encroachment, which are common problems in protected areas of the 143 tropics (www.fao.org/forestry; DeFries et al., 2005). I observed forest clearance for agriculture and occasionally for the brewing of illegal alcohol in the core areas of the forest fragments which cannot be observed from the periphery of the reserves, Although all forest fragments above 10 ha in the lowland wet zone are also protected by the government under a common moratorium (IUCN, 1993), implementation of conservation legislation is not satisfactory. Hunting causes local extinction of selected species in heavily fragmented tropical areas (Peres, 2001). I observed signs of hunting using trap guns, wire traps and snares set for mammals and jungle fowls in most of the fragments. Further, I discovered animal body parts (i.e. skins, antlers and meat of Sambar deer; Rusa unicolor unicolor, wild boar; Sus scrofa; and Porcupine; Hystrix indica) in some houses adjoining fragments. Interviews with locals confirmed hunting and the reduction of mammals over the years. The presence of a temple within the fragment reduced hunting pressure due to the intervention of the monks. Decreases in mammal populations are a major concern for dung beetles as they are primarily dependent on mammal dung (Estrada et al., 1999, Andresen and Laurance, 2008; Nichols et al., 2009). Biodiversity conservation in Sri Lanka primarily relies on protected area management. Conservation in human land uses compatible with conserving at least a proportion of biodiversity is becoming widely accepted method of integrating biodiversity conservation with human activities (Chazdon et al., 2009). Research findings on how such modified landscapes can support native biodiversity and ecological processes becomes important in this context (Nichols and Gardner, 2011). The research findings of this study call for scientifically informed conservation management plans that reach beyond the protected areas, to include the entire landscape. 144 Different land use types in the matrix is known to alter the ecosystem processes and vegetation characteristics in the forest habitat they surround (Jules and Shahani, 2003). Managing anthropogenic habitat mosaic margining protected areas for both biodiversity conservation and its integral purposes is crucial in this context. Although, I could not directly relate the matrix land use type to significant differences in the diversity of dung beetles, the land use types were indirectly affected through other matrix characters (Chapter 6). All the anthropogenic habitats used for this study falls within a distance of 10 km from the forest. This includes the unique landscape or an eco-tone where forest and anthropogenic habitat communities merge (Bawa et al., 2007). Such human land use areas adjoining forests is critical for both conservation of biodiversity and the livelihood of people living in them. Sustainable land use practices that favour biodiversity can extend the boundaries of the natural habitats of species contained in these habitats (Rosenzweig, 2003; Daily et al., 2001, Bawa et al., 2007). This is often included in management plans for protected areas as buffer zone management to safe guard forests from intense land use areas beyond. Most of the surveyed home gardens and plantations in Sinharaja fall within an established buffer zone that is not strictly monitored but should be an integral part of protected area management. Most buffer zone management attempts in the past have focused more on livelihood management and less on integrating conservation within the buffer zone (Jayasuriya and Abeywardana, 2008). Pinus plantations in the buffer zone of Sinharaja were found to be unfavourable for amphibians. Selective replacement of Pinus with native species (i.e. Shorea, Rattan and Coscinium spp) in Sri Lanka to increase structural and floristic complexity was success in experimental trials (Ashton et al., 1998; Wijesooriya and 145 Gunatilleke, 2003). Such replacement will create favourable microhabitat conditions and increase resource availability. Home gardens are found across the entire wet zone landscape adjoining forest habitats. Home gardens with favourable habitat complexity, will play a complementary role in tropical biodiversity conservation although they cannot be considered a substitute for forest habitats (Webb and Kabir, 2009). The conservation potential of lowland tropical home gardens in Sri Lanka has been discussed by Gunatilleke et al., (2005) and Raheem et al., (2008) for plants and land snails prior to this study. Agriculture is the other dominant land use type in the matrix surrounding forest habitats. In this study, tea plantations were selected and investigated as a land use type because it is commonly found associated with forest habitats and were found to harbor many dung beetle species but in extremely low abundance in comparison to the forest. Many native species, especially generalists, can be conserved in crop production landscapes if well managed (Daily, 2001; Lindenmayer and Franklin, 2002). The success depends on finding the right balance of the environmental structure that satisfies conservation and human land-use (Steffan-Dewenter et al., 2007; Tscharntke et al., 2010). Communities of fragments may benefit from increased area and connectivity as shown in Chapter 5. Enlarging small fragments is possible through the reforestation of abandoned plantations and/or scrub land surrounding them. Having more large tree in the matrix at landscape level will provide more tree cover which in turn is favourable for forest species (Corlett, 2009). In a landscape-level approach, structurally enhanced home gardens and plantations can act as buffers and connectivity corridors between forest fragments. 146 Habitat values of anthropogenic land-use areas can be improved through sustainable management recommendations to support the maximum viable number of taxa. A highly disturbed home garden will have a different community from a home garden with near natural habitat conditions for a taxon. In this study, several favourable structural and climatic environmental variables were identified for each taxon. Most of those were related to the vertical strata each taxa occupy in their habitats. Patchy habitats with high relatively temperature and more shrub cover were preferred by butterflies while shrub and litter cover favoured amphibians. Dung beetle diversity across different land use types and fragments were best explained by soil properties, litter cover and the maturity of the forest which in turn affects the quality of the soil. Identification of common environmental variables that favour multiple taxa is important in landscape management. For example, the need for litter and shrub cover for amphibians, dung beetles and host plants for butterflies can all be addressed through increasing home garden vegetation using suitable plants. Maintaining a healthy litter cover in gardens instead of clearing can be coupled with the incentives for chemical free home based agriculture. Species that can coexist with human land uses are those that survive in modified landscape. By increasing the habitats quality, this range of species could be widened. When habitat requirements are contrasting species need urgent attention should be prioritized. Fertilizers and pesticides from surrounding agricultural areas can affect forest biodiversity (Murcia, 1995). Overuse of pesticides and fertilizer is another known threat for a wide array of biodiversity (Novacek and Cleland, 2001) including dung beetles (Nichols et al., 2007). Sri Lanka is among the largest per-capita consumers of pesticides in Asia (Kudavidanage et al.,, 2006). Use of pesticides and other chemicals in intensely cultivated areas adjoining large forests (Gunetilleke et al., 2006) and 147 fragments as observed may have detrimental effects on fauna inhabiting forest and the matrix. There are no studies available to confirm this assumption in Sri Lanka. Relating altered community structures to functional responses of dung beetles was an important component of this study. Easily measurable functional responses such as dung removal can be effectively used in studies on habitat disturbance. Importance and vulnerability of roller functional group to habitat disturbance was detected. A habitat rich in species may still be functionally retarded unless the key species performing ecological functions are present. Significantly altered dung removal in the anthropogenic land use areas and the susceptibility of large roller beetles to land use change raises much concern. Impact of habitat disturbance on ecological services performed by other fauna critically requires assessment. Pollination by bees and butterflies; mosquito vector control by amphibians, soil fertilization by soil inhabiting fauna are some examples. While strengthening law enforcement is necessary to curb hunting, illegal forest clearing, forest burning and encroaching, community participation and education efforts can greatly contribute to the management of the peripheral areas. Awareness development can reduce negative impacts through human activities and facilitate more biodiversity friendly anthropogenic habitats. Wanton destruction of dung beetles is an issue infrequently discussed. We noted that Catharsius molossus was often burned due to a mythological belief among tea plantations workers that it is an evil spirit. Collecting and killing of Scarabaeus. gangaticus was observed in the arid agricultural areas. Like many other insect groups, importance of dung beetles is not well known and understood by the local communities. As they are different from the charismatic species such as elephants and leopards in Sri Lanka, attracting public interest is challenging but feasible. Large roller beetles and colorful beetles have been 148 used as suitable candidates for such campaigns and were proven to be successful among students, farmers, plantation workers, and wildlife officers as experienced by me during the study period. Public awareness of their functional importance may even more helpful. Recovering ecological services in anthropogenic land use areas can be easily promoted if linked with financial incentives for the inhabitants of those lands as explained in chapter 7. Creating a socially attractive image, providing economic arguments (Chapter 7) and awareness development are among the measure that can be adopted to conserve dung beetles that can also benefit the other fauna inhabiting the land. Awareness development is more effective in South Asian region when couple with direct incentives, livelihood and social development (Bawa et al., 2007) This study provides the foundation for many future ecological studies and collaboration in Sri Lanka. Simultaneously, many data gaps were highlighted. Decision makers should work closely with researchers to identify and fill individual species’ data requirements necessary for making decisions related to conservation. Administrators could liaise with local and international researchers to facilitate collaboration, permit granting for field research and taxonomic work. These efforts will remove some of the primary obstacles that deter ecological research in Sri Lanka and the region. 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