1.1 Butterfly species diversity and composition in the tropics
Among all invertebrates, Class Insecta which is commonly known as insects are one of the most abundant organisms in terms of species richness. Under this Class, one of the most well known insect is under the Order Lepidoptera which were divided into two suborders namely, Rhopalocera (butterfly) and Heterocera (moth) which contain some of the most magnificent insects in the world. Suborder Rhopalocera can also be further separated into two superfamilies which are Hesperioidea with family Hesperiidae under it and Papilionoidea with five families that were Papilionidae, Pieridae, Nymphalidae, Lycaenidae (Corbet et al., 1992) and the recently recognize family Riodinidae (Campbell, 2000). There were approximately 1182 butterfly species found in Peninsular Malaysia (Wilson et al., 2015) with more species still recorded up till recent years (see Elliot, 2006) with some has been rearranged into new genus or families or found to be a new species as previous record were still being revised (Elliot, 2006).
For the general public, distinguishing butterflies from moths can sometimes be very difficult. Butterflies can be identified and differentiate easily from moth by its physical features (Gilbert and Singer, 1975; Kristensen et al., 2007). Butterfly antennae are filiform and club-like which the end is thick and match-like. Moth antennae are feather-like and can also be saw-edged as seen under dissecting microscope. From physical morphology, the butterfly was usually brightly coloured but there are some with dull colour for camouflage especially those living in the grassland (Hesperiidae) and some forested species. For moth, most of them are dull coloured with the exceptional of day-flying moth which are usually brightly coloured. Butterflies hold their wings together vertically above their body completely exposing the underside part of the wing while moth hold wings flat on each side of their body forming almost like a triangle shape during resting. The three body parts (head, thorax and abdomen) combined of the butterflies was also thinner and smoother than moth where the body parts of moth were usually thick and fuzzy with hairs. The pupae stage for butterfly is in the form of chrysalis while for a moth is called cocoon.
Butterfly diversity in the tropical ecosystem was well known to be quite diverse comparable to other areas in the world. Due to the intricate habitat characteristics with dynamic ecosystem our forested areas and other type of ecosystem was capable of hosting large numbers of butterfly species as well as other flora and fauna (Basset et al., 2011). The multifaceted configuration of foliage and large habitat range help to increase the diversity of butterflies in the tropical forest (Vu and Vu, 2011). Many protected areas have been established in order to protect our natural ecosystem and conserving biodiversity in response to our diverse number of flora and fauna. However, due to lack of understanding on the general pattern of species distributions, the effectiveness of the protected areas still need to be re-evaluated. In the recent years, researchers had found evidence of shifting in many species, especially in the temperate regions which responded towards the climate change by shifting their habitat range (Klorvuttimontara et al., 2011). This is why research to understand the changes that occur in the long term for any species which in this case butterflies were important for a better insight on the impact of environmental changes to the distribution pattern of our flora and fauna.
1.2 The importance of butterfly communities in our ecosystem
The large area of tropical forest receives adequate precipitation all year round and was rich with flora and fauna and presently facing the ever growing peril of extinction. Many areas of forests, including reserve forest were threatened by deforestation especially land conversion for oil palm plantation or other developments, furthering devastation on our diverse ecosystem. The law which prohibits the conversion of reserve forest for development or others was not properly implemented thus illegal deforestation was still occurring. Insects have always been the quickest animal when compare to vertebrates in detecting and reacting to the changes in their surrounding environments (McGeoch, 1998). This is why identifying insects suitable as an indicator species was important in order for us to better understand on how any disturbance towards the environment could give impact to other living creatures. Given the rapid degradation of our tropical forest ecosystem and global warming, researchers has emphasized the needs of understanding how species responses towards such disturbance. The results would in turn help us making reliable predictions for future impacts of disturbance on biodiversity (Hill and Hamer, 2004).
Butterflies were commonly chosen by researchers as a subject of study due to their property of ease of sampling, easy to be identified, attractive morphological features and they are also fairly large in size with wide taxonomical database (DeVries et al., 1997). However, due to lack of taxonomic knowledge in a few hard to identify species, time and monetary issues the extent of biodiversity appraisal still remains incomplete (Kessler et al., 2009). By being the most well known insects, butterflies bargain great impending insights on understanding the insect’s outline of diversity and the conservation of insects and their habitats. Butterfly was also commonly known to most researchers to exhibit effect on any changes that occur within their surrounding environment for example by being a good indicator for climate change (Devictor et al., 2012). In another findings, Vu (2013) conducted a study looking at the effect of disturbance and altitudes on the diversity of butterflies in a tropical forest of Vietnam found that the rare butterflies or specialist diminished while the widespread species (common species) increases with growing forest habitat disturbance. In another study done by Vu et al. (2015), they have found evidence that the variation in habitat types and human disturbance within the forest area have affected the diversity of butterflies in an intricate ways. This is where the distinct variations exhibit by butterflies distributions in bamboo forest and the road constructions in the forest area had seemingly contribute to the presence of butterflies which are mostly found within the secondary forest.
One of the functions of butterflies within an ecosystem was to act as pollinator which helps in spreading pollens across all flowering species to produce seeds to ensure the continuation of any plant species (Duara and Kalita, 2014). By losing butterfly in an ecosystem, we can say that one of the main pollinating agents will disappear. These will contribute to the loss of diverse flora type within the forest area which will eventually decrease the number of host plant species. Usually when a habitat is disturbed, it was found that some widespread species or generalist which usually has two or more families of host-plant will greatly increase in their number compared to specialist species. Specialist usually have only one host plant families as they have limited resources and this will greatly reduce their abundance with generalist gaining the upper hand in this competition (Warren et al., 2001; Borschig et al., 2013). This will decrease the diversity of forest dwelling butterflies as the specialist was the ones that usually live within the forest canopy dwelling on the forest floor. In a study done by Henderson et al. (2018), they found that higher habitat quality increased the populations of the prairie specialist-specialist butterflies. The maintained practice of burning the habitat had also given an impact in the abundance of the regal fritilliary butterflies where it could increased their populations depending on the length between the burning.
Our forest and everything within it including butterflies needed to be protected from the risk of extinction. For some people who does not involve in doing any ecological studies, they would only think of gaining profits from the forest without looking at the impact of their loss. Instead of looking at the forest just for profits, we should take into account in their aesthetic values, for example butterflies since they were very attractive flying insects which can help in promoting eco-tourism. Conserving the forest areas to protect these little critters alongside other species of flora and fauna would be important not just for us but for future generations. As young children and also a few adults tend to be curious of their surroundings especially those lacking exposure in experiencing what it was like to be in the wild. There are actually many people who would want to pay just to appreciate the presence of wildlife in its natural habitat which was why eco-tourism was now acknowledged as an important industry. A good example was what has been done at the island of Rhodes in Greece which was infamously known as the Valley of Butterflies due to abundant number of Panaxia quadripunctaria and was frequently visited by tourist (Spanou et al., 2012).
1.3 Significance of the study
Research on the butterfly communities has been widely recognised and many research had been done to prove its importance as a biological indicator to detect healthy forest. Thus, within this study, the butterfly within the Sekayu lowland forest was examined in order to further understand the importance of using them as a model for biodiversity assessment for the Sekayu lowland forest. In the second chapter in this thesis, the estimation of species richness was applied to assess the diversity and richness of the butterfly communities in Sekayu. To state the total richness just by using the species abundance data was found to have not been sufficient thus utilising species estimation methods to explain this further was needed (Bruno et al., 2005). As more species were still recorded as sampling effort increases, predictions of the total richness were needed to verify the ability of species accumulation curve to be asymptotic. The previous study done on butterflies in Sekayu was focusing on inventory and species identification and no further than that. By the applications of various estimators, the total richness of butterfly in Sekayu was able to be determined. The findings will also contribute to verify the effectiveness of using selected estimators on the butterfly communities. This could also help to enhance further the results and findings to be used for a proper conservation effort needed to preserve the area.
In the third chapter, the distinct separations of wet and dry seasons in Sekayu were accessed to understand their effect on the distribution and butterfly diversity. Due to the positions of Malaysia within the tropical belt, our country has a clear pattern of the dry and raining seasons. This could be detected by the occurrence of flood throughout the state in Malaysia towards the end until the beginning of the year with less precipitation recorded in the middle of the year (Barzani et al., 2007; Caroline and Wardah, 2012). By obtaining average precipitation data from various locations in Terengganu from previous to recent sampling years, the diversity of butterflies can be compared with the rainfall data. This was done in order to understand the butterfly distributions during periods before and after months of heavy rainfall as whether any rapid increase or decrease in their number of individuals and species has occurred.
Previous study by Pollard (1988) has proved that the weather has an effect to the butterfly distributions with changes occurred in their flight period. The study also concludes that rainfall has indirect benefits in plant growth, which contributes to food availability and also directly affecting butterflies movement. Cerrato et al. (2016) has also establish in their research on the Maculinea butterflies in Northwest Italy where from the outline exhibit by the constant changes in temperature and rainfall, any significant increase in the global temperature will severely affect the populations of the lowland species. The findings from this study is hoped to contribute towards the assessment of environmental factor, i.e., rainfall on the fauna community, which is butterflies. Recently, a distinct shift has occurred in the pattern of rainfall observed on the east coast of Peninsular Malaysia, it is speculated that it has a significant effect on the butterfly distributions which need to be evaluated to understand further.
For the fourth chapter, further study was done to assess the temporal changes that occurred on the butterfly diversity at Sekayu lowland forests. Data were compiled from unpublished materials which contain three years data sampled between 2005 and 2006 against data collected from 2012 to 2014 with a four year gap in between the data sets. Long term studies to assess changes in diversity and species richness of butterflies in Sekayu has never been attempted before due to limited time, lack of sampling effort and inadequate funding. Data from previous study and recent study was obtained from various collectors and was compared to evaluate on how the butterfly community have changed as years passed by. This was important due to the constant activities with additional climate change which have occurred for a long period of time could have a direct effect on the butterfly community. A study was done on butterflies in Korea where Kwon et al. (2013) found out several butterfly species have been declining in number with few common species becomes greatly increased due to the changes occurring in the surrounding environment as they compared the data from previous years to the current study at same site. Thus, within this study in Sekayu, such changes could be expected to occur during the sampling period.
1.4 Objectives and general questions of the study
The general aim of this study was to assess in depth and understanding the general patterns of butterflies distribution and species richness in Sekayu lowland forest based on various data sets. Those data allow for species richness to be estimated plus observing the relationship between the monthly butterflies distribution with the general rainfall pattern in Terengganu, specifically. Data from the previous and recent studies for six years was also obtained in order to further access the butterfly species distributions in Sekayu temporally. The summaries of specific objectives were as below:
Assessing the species assemblages and estimating the species richness of butterflies in lowland forest of Sekayu
To determine the butterfly species diversity, richness and evenness.
To estimate the species richness of the butterfly communities.
Examining the patterns of butterfly distributions based from the distinct wet and dry seasons and its relationship with the rainfall pattern in Terengganu
To delineate the species assemblages among wet and dry season.
To determine the relationship between the general rainfall pattern of Terengganu and the butterflies distribution in Sekayu.
Evaluating the changes in the butterfly distribution pattern over time
To assess the species assemblages of butterflies among previously recorded data and the recently recorded data.
To evaluate the changes that occurred among the butterfly communities over time.
SPECIES ASSEMBLAGES AND ESTIMATING BUTTERFLIES SPECIES RICHNESS IN SEKAYU LOWLAND FOREST
Understanding an ecological community requires researchers to obtain great information on the species richness and assemblage structures of the organisms of their choice. Usually people will start to show their long list of observed and collected species. However, the actual total number of species presence in the area was not immediately known. One of the purposes of doing species inventory was to determine how many species actually live within the sampled area. Due to the extensive deforestation of large forest areas especially within the tropics which has caused massive damage to our rich biodiversity, understanding the species diversity is of great importance for us to conserve our nature (Aduse-Poku et al., 2012). The biodiversity hotspots all around the equator which are well known for its lush tropical forest and diverse flora and fauna was the most impacted areas and facing intense pressure from development activities which occurred globally that significantly reducing species richness (Forister et al., 2010, Thom and Seidl, 2015).
Tropical rainforest is considered as one of the richest habitats in the world comprising of many plants and animal species which includes insects such as butterflies. The Malay Peninsula which is one of the 12 biodiversity hotspots (Congo et al., 1997) was located within the earth equatorial belt is rich with dense tropical forest that can accommodate diverse flora and fauna within various habitat types that provide immense host plants to many butterfly species. The butterflies here can be found in various environments such as riparian, forest, forest fringe, garden and grasslands (Otsuka, 2001). In general, butterfly has been grouped as mangrove associated species, lowland open country zone, lowland forest and highland forest species. This is due to the modifications of the butterfly movement is strongly influenced by their responses towards resources (Corbet et al., 1992; Schultz et al., 2012).
Studies to document butterfly diversity in Peninsular Malaysia was probably begun in the late 1700, and that proliferated in 1800 to the next century by various collectors and many new found species were described and reported in the early years (see reference in Corbet et al., 1992). Today, the interest is more on documenting the local species richness (? – diversity) for inventory and conservation purposes since large areas of forest have been converted into many purposes causing destructions in the flora and fauna (Chung et al., 2013). All data obtained from the species inventory were analysed using most basic diversity measurements to determine the value of species diversity (Shannon Simpson indices), richness (Margalef and Menhinick indices) evenness and dominance. These indices were commonly found in most ecological studies as one of the basic analysis in evaluating results from any species abundance data (Magurran, 2007). Those indices base measures as mentioned before are heavily affected by sampling effort and require detection of all species and their abundance to perform well. If this requirement is not met, the result produced will be biased.
Many methods had been developed to support and strengthened the results obtained in order for immediate and precise action to be prepared to conserve nature (Colwell and Coddington, 1994). As common ecological indices only represent ‘values’ obtained from the species abundance data and does not represent the total species richness in the study area (Garcia et al., 2015). Due to the fact that there are undiscovered species in almost every species inventory, using the proposed traditional alpha diversity indices which could only determine results based on obtained data sets is not enough and usually underestimate. Thus applying other techniques in predicting the diversity and richness of species is essential and urgently needed.
2.1.1 Estimation of species richness on butterfly communities
Researchers studying natural communities are always puzzled on how well a sample will represent a community ‘true’ diversity (Hughes et al., 2001). In any scientific expedition most of the taxonomic survey will have many undiscovered species. This is because of the difficulties in completing the list of species for almost every taxonomic survey as it requires a lot of efforts and is almost impossible when it was done in a short period of time. Species diversity and species richness are two terms which has an important attribute in an ecological study (DeVries and Walla, 2001). Differentiating both terms are always difficult and will always cause confusion among researchers on the way of explaining it scientifically and some might define them alike (Spellerberg and Fedor, 2003). In general, species diversity was defined as the number of species to the relative abundance of each species within the community (Gering et al., 2003; Meyer et al., 2011). On the other hand, species richness is defined as the number of different species that represented an ecological community where it is merely a count of species that ignoring the abundant. Magurran (2007) delineates species richness as the number of species in a specified taxon in the selected assemblage which can easily be computed using many well established diversity indices to obtain the value.
Efficient and timely monitoring and management of biological communities requires researchers to have a precise evaluation of species richness (Tsung-Jen et al., 2003). Most measurements of species richness were poorly understood where in most studies the observed number of species (Sobs) usually excludes many rare species and gravely undervalues the true species richness (Brose et al., 2003). As important as it sounds, species richness however is difficult to quantify plus it has become a major topic in many discussions (Colwell et al., 2004; Glowacki and Penczak, 2005; Kery and Schmid, 2006). Since species richness increases incoherently with the number of sample collected or observed, the number of species observed will be inevitably a downward biased estimate of true richness (Chao et al., 2006). The information of species richness is crucial for conservation managers to formulate plan to protect a habitat or establish an area for protection (Uehara-Prado et al., 2007). It is very important to study species diversity nowadays due to local and global habitat destruction lead to species lost other than to understand the functional aspect of biological communities in their environment (Sodhi et al., 2010). Due to the rising importance of understanding species richness for conservation efforts, the species richness estimation which was a theoretical context to explain species richness within a landscape level in stipulations of richness and compositional resemblance were developed (Colwell and Coddington, 1994; Gotelli and Colwell, 2011).
Estimating species richness where not all species occurred within the sample collection is heavily debated by scientist involved in ecology and conservation biology ever since long ago when it was first proposed (Colwell and Coddington, 1994). One of the prime goals in ecological studies is estimating the species richness and sampling effort needed to obtain reliable estimates of richness in a given taxon (or all taxa) occurring in an area (Gotelli and Colwell, 2001). Increasing the sampling area and time will usually produce a graph of accumulated species against sampled areas that rises abruptly during the beginning and then slowly escalating as additional rare species were added (Ugland et al., 2003). However, there are no end results to how many species that truly exist within an ecosystem. Biodiversity sampling data still tend to be under sampled despite of extensive research with long term sampling or cautiously planned studies (Chao et al., 2005). Measurements of diversity which use species abundance and also takes into account number of rare species relentlessly give the result of being underestimates most of the time (Coddington et al., 2009).
2.1.2 Species richness estimators
Species richness estimators were developed from the conceptual framework as proposed by many researchers (see Colwell and Coddington, 1994) and were upgraded and improved since then. Despite many critiques by other researchers questioning the effectiveness of species richness estimation, the software EstimateS (http://viceroy.eeb.uconn.edu/ estimates/) was developed and now have been extensively used in many ecological studies (see Gotelli and Colwell, 2011). From the command line interface, now EstimateS is in the form of fully graphical user interface (GUI) packed with various statistical tools to analyze and interpret data in various forms (Colwell and Elsensohn, 2014). The analyzed data using the EstimateS software will produce a range of results which could be plotted in the form of a line graph for comparing the performances of each estimator chosen. There are many types of estimators that can be chosen to explain the species richness estimation (see Table 2.0). The behaviour and performances of each estimator have always been tested by many scientists within various group of organisms to show which of the estimators are more effective in showing the species richness in the study area. However, results from a different group of organisms generally differ from each other.
In a research done by Rosenzweig et al. (2003) on the butterfly communities, most of the estimators exhibit regions of stability with the most accurate predicted result from the MM equation while ICE in the study failed to perform accurately. This complements the previous study by Longino et al. (2002) where they also applied the concept of species richness estimation within their study on ants in order to appraise the aptitude of each species richness estimator’s performance. In their research, none of the estimators chosen shows any region of stability or reaching the asymptotes although the Michaelis-Menten (MM) equation and Incidence-base Coverage Estimator (ICE) asymptote point fit to the species accumulation curve. They also found that rarity has been one of the main factor that influence the estimators to be unable to perform well. However in a research done by Chao et al. (2009) they have applied the usage of estimators within their study on the butterflies community and found that the two Chao estimators was able to perform well although they found that it is slightly conservative for a small sample size. This result complemented the previous study by Walther and Martin (2001) where they applied species richness estimation method in order to review the method used in an ecological framework by testing the performance of estimators in the bird communities. Their results shows that two Chao estimators have perform well followed by the two Jackknife estimators.
Table 2.1 Nine species richness estimators used to compute or estimate the number of species in a grouping.
Chao 1 Abundance-based estimators. Colwell and Coddington, 1994
Chao 2 Incidence-based estimators that relies on the co-occurrence data. Chao, 1987; Colwell and Coddington, 1994
ACE Abundance-based estimators that often found to over estimates richness especially in small sample size. Chao et al., 2005; Barlow et al., 2007
ICE Incidence-based estimators that depends on the co-occurrence data to measure rarity that also often found to over estimates richness when there is smaller sample size. Longino et al., 2002; Ugland et al., 2003; Chao et al., 2005; Colwell et al., 2012
First order jackknife Incidence-based estimators which require presence and absence data to quantify rarity and estimates based on number of unique, duplicates and site sampled. Chao and Tsung-Jen, 2003; Colwell et al., 2012
Second order jackknife Incidence-based estimators which require co-occurrence data to quantify rarity and estimates based on number of unique, duplicates and site sampled. Uehara-Prado et al., 2007; Cola et al., 2014.
Bootstrap Incidence-based estimators that depend on the co-occurrence data to measure rarity that also estimates richness based on the fraction of sites containing each species. Chao and Tsung-Jen, 2003; Barlow, et al., 2007
Michaelis-Menten runs Both Michaelis-Menten utilize analogous equation to extrapolate species accumulation curve but calculate it in a different way from each other and make use of utmost probability to estimate parameter and their variances. Longino et al., 2002; Uehara-Prado et al., 2007; Ramesh et al., 2010
Michaelis-Menten means Make use of maximum probability to estimate parameter and their variances. Longino et al., 2002; Uehara-Prado et al., 2007; Ramesh et al., 2010
EstimateS software developed for the species richness estimation has been widely used by most scientists abroad on various communities. For example, a study was done on estimating the species richness of spider communities in Northern European deciduous forest by Scharff et al. (2003) and the diversity in the fish communities by Tokeshi and Arakaki (2007) in Indonesia. In Malaysia, a study done by Yong et al. (2012) applied the usage of estimators on the data from the butterfly’s communities. In their study, they had applied the usage of six non parametric estimates as suggested by Walther and Moore (2005) and had found that the estimated butterfly species richness almost reaching asymptote. In their research, 58-71% species were able to be detected by their sampling efforts at all sites. But there are no further detailed explanations on how each of the estimators performed. In another study on herpetofauna by Rooijen et al. (2011), they had also applied the method of species richness estimation within their study on the herpetofauna species richness in Pangkor Island. Their results showed that Chao 1 estimator has been performing well alongside with their proposed estimator and they have expected 69 reptile species and 19 amphibians to inhabit the island. However they found that the estimation of species richness for would be downwardly biased due to lack of sampling efforts as small sample size produced estimators which tends to underestimate the true species richness. Assessing the most effective method in giving a better explanation of the diversity within the study area has always been a great debate among researchers. Using multiple types of measurements will help us to better understand the species composition the community of our studies.
Obtaining reliable estimates of species richness, it extensive sampling effort is needed and vital in order to acquire a more accurate result of total species richness for a better planning of conservation effort (Ugland et al., 2003). The first objective within this study was the application of the common alpha diversity analysis such as Shannon, Margalef and Evenness indices to assess the butterfly communities’ assemblages. Despite of the recreational activities since it was minimal towards the river and the forest amenities, the forest area which was also situated within the Hulu Terengganu forest reserve is expected to have high species diversity and richness due to the diverse ecosystem types in the study area. Second objectives were applying the method of estimating species richness on the abundance based data sampled from the butterfly communities in Sekayu lowland forest. This was done to understand how these theoretical methods or the richness estimators works in explaining the total species richness within the study area by observing and comparing the values and performances of each chosen estimator . The species richness was expected to increase as sampling increases. The estimators should be able to perform well and help in explaining the distribution patterns of the butterfly communities within Sekayu.
2.2.1 Study areas
This study was done to assess the butterfly species richness and assemblages in Sekayu lowland forest which was situated in Hulu Terengganu, Terengganu (Figure 2.1). The study site was located within the two areas connected to each other where one is along the Sekayu Recreational Forest while the other is a portion of the forest is within the Sekayu Agricultural Park. Both areas have two separated waterfalls with elevated areas bordering a lowland hill forest. The highest elevation reached for the sampling area is approximately 200m above sea level while the lowest point is less than 40m with length of almost 3km in distance (Figure 2.1).
Some part of the forest reserve of Sekayu which is situated near Kg. Sekayu has been established for recreational purposes in 1974 before being officially opened in 1985. Adjacent to the forest area was the Sekayu Agricultural Park, a developed land for agriculture activities manage by the Department of Agriculture, Terenganu. The Sekayu Recreational Forest is a lowland dipterocarp forest rich with Shorea sp. and Dipterocarpus sp. Within the recreational area, there is a small herb garden and few ornamental and fruit plants such as Durio sp. (durian) and Baccaurea sp. (rambai) planted by the Forestry Department Terengganu. Within the study sites some forested areas have been cleared for development of man-made amenities such as chalet, hall, campsite, public toilet, pavilion and others (FigureS 2.2 and 2.3).
Figure 2.1 The overview of the study area generated using Google Maps showing the sampling areas (red line) and the elevation profile (bottom) of the sampling site at Sekayu lowland forest which is situated in Hulu Terengganu, Terengganu. Inset is the map of Peninsular Malaysia showing the state of Terengganu where the study took place.
Figure 2.2 Man-made paths with public amenities for visitors and ornamental plants, planted by the Forestry Department.
Figure 2.3 Fruit orchards comprising of banana and lime trees in the Sekayu Agricultural Park situated adjacent to the Sekayu Recreational Forest as seen far at the back.
2.2.2 Sampling technique
In order for us to understand the distributions of species, we usually need to measure the abundance of the population over space or time. Since it is almost impossible to obtain the complete list of total count or survey of a particular large population such as butterflies, some kind of estimate of abundance was needed. Non probability or non random sampling method was applied in this study since accessing the entire butterfly populations is hard due to their large population number. The type of non probability sampling applied was the consecutive sampling where researchers were studying a single group of subjects within a chosen time interval (Explorable.com, 2010). This type of sampling gives researchers many alternatives when it comes to sample size and sampling schedule. The sample size can be relatively large or small and the time of sampling is dependent on the researchers giving them an ease of sampling which effectively reducing cost, time and workforce. Conversely, this sampling method could hardly represent the entire populations thus researchers will need to use a large sample size enough to represent a big portion of the population. Some technique to estimate the entire population of study will be needed.
Thus this type of sampling method is applicable for the estimations of butterfly species since it does not need attain the total number of butterfly species to explain the distributions of the entire population of butterflies. Samplings were done from March 2013 until May 2014 from 0900 and 1630 hrs for two days per month using sweep nets. Butterflies were also observed and recorded along a line transect line of c. 500m with the total coverage of approximately 3km (Figure 2.1). By using the sweep netting technique complemented by the point count survey, more butterflies can be recorded than using sweep netting alone. Only one person performed the sampling to avoid bias in species identifications. For the point count survey, the butterfly was observed and identified on the spot. However, any sample that could not be identified was captured using the sweep netting technique. This usually applied to the smaller sized butterflies from families such as Lycaenidae, Hesperiidae and Riodinidae which are hard to be identified in aerial view. The butterflies were stunted by pinching in the thorax part which is usually the middle segment of the body to cause suffocation and placed in the triangular envelope with information prior to the capture for further identification. These samples were brought to the lab for further identification to the lowest possible taxa (Borror and White, 1970). Captured butterflies were identified using keys from Corbet et al. (1992) and also pictures and description from Otsuka (2001). All samples were deposited in the Entomology Museum in Universiti Malaysia Terengganu.
2.2.3 Data analysis
The species recorded were divided into rare (four or less number of individuals per species) and common group (species with more than five individuals) based from methods by Devries et al. (1997). Common diversity measurements such as Shannon diversity index, richness index (Margalef index), dominance and evenness index were calculated using PAST software ver. 3.10 (Hammer et al., 2001). PAST was free software available online for scientific data analysis with many different functions from manipulating data and plotting graphs of various statistical methods including ecological analysis. While the Simpson reciprocal index was computed manually using the formula 1/D. These indices were robust and can be easily explained as it is one of the most basic calculations in any ecological studies which have been used for so long (Whittaker, 1972; Magurran, 2007). These values are calculated based on the average values for each index obtained from the monthly butterfly data. From the butterfly datasets, the species were further divided into groups comprising of singletons (single individuals per species) and doubletons (two individuals per species) which the values will be used in the other statistical method which will be explained later.
Several indices were computed to evaluate the sampling efforts. The ratio of total individuals to species represented by the sampling intensity was measured for the whole data sets (Coddington, 1996). The inventory completeness index which is the percentage of species which are not singletons was computed as referred to the method by Toti et al. (2000) where the total values of observed species (Sobs) minus the singletons was divided by Sobs multiply with 100. Presence of small fractions of singletons within the study indicates that the sampling is thorough enough for estimated value to represent the whole population. Other than that, all the values of species richness estimators were divided with the Sobs in order to attain the values of adjusted estimated range to further emphasize the sampling effort. The ‘inventory completion’ was also calculated as recommended by Scharff et al. (2003). It is an inventory partition as the proportion of observed species richness to the value of Chao 1 richness estimate for that partition. These calculations were done to indicate the efficiency and how complete the sampling was done where the value for coefficient of variation of inventory completion equal or less.
Inventory completeness index, ICI:
y1 = number of species observed (Sobs)
y2 = number of singletons
z = number of species observed (Sobs)
Inventory completion, ?:
y = number of species observed (Sobs)
z = value of Chao 1 richness estimates
Species richness estimation
The estimation of species richness for total species richness was calculated using EstimateS ver. 8.2.0 by using both abundance and incidence data. The software can be obtained for free on the website (http://purl.oclc.org/EstimateS). The daily sampling data matrix was constructed and partitioned into months and converted into .txt format to be further analyzed using the software. This matrix contained the abundance-based data of all samplings since all butterflies came from only one sample size which is the Sekayu lowland forest. The classical abundance-based data was used to produce a smooth graph of estimated species richness with assumptions that there is random integration of individuals (Colwell et al., 2004). Nine species richness estimators comprised of seven non parametric estimators (ACE, ICE, Chao 1, Chao 2, first order jackknife, second order jackknife and bootstrap – see Table 2.0, Toti et al. (2000) and Longino et al. (2002) for further explanation) and two accumulation curve models (MMruns and MMMeans – see Walther and Moore, 2005) was applied for the species richness estimation with calculations performed using the EstimateS software. For all calculations, 50 iterations which is the default value in the EstimateS software were used to randomize the sample to produce a smooth accumulation curves plotted using a line graph on Microsoft Excel 2007.
There are also other two parameters that were observed as indicator of inventory completeness which is the number of ‘unique’ species and ‘duplicate’ species where the results were obtained from the EstimateS software (Longino et al., 2002). The ‘unique’ are species that only represented by a single individual in the collection of the entire sample collected while ‘duplicate’ are species that are known from two samples. I assumed that the total species richness of the butterfly population within the study site have been fully sampled when (1) the last 5% values from the accumulation curve plotted were equivalent where it has reached asymptote by exhibiting the horizontal line in the end and (2) the last 50% values of the accumulation curve are contained by the 5% of the concluding value of the accumulation curve (Walther and Morand, 1998).
2.3.1 Butterfly species assemblages in Sekayu
From the total of 15 months sampling done in two days from March 2013 until May 2014, 156 species of butterflies were recorded (Appendix 2). Nymphalidae was found to be the most abundant family for each month with high number of species recorded in March 2013 (14 species). For March 2014, there was a decrease in number of species recorded with two species absent during the sampling. The most number of species collected was also from family Nymphalidae which is 73 species (40.7%) followed by Lycaenidae (27 species, 9.9%), Pieridae (20 species, 26.5%), Papilionidae (19 species, 14.1%), Hesperiidae (14 species, 7.6%), and lastly Riodinidae (three species, 1.2%). Appias lyncida vasava were the most dominant species with 58 individuals. Two species were most common species that occurred for almost every month except January 2014 and May 2014 which are Eurema ada iona and Papilio memnon agenor respectively. The other two most common species are Cupha erymanthis lotis and Pareronia valeria lutescens, 48 individuals and 46 individuals respectively. A total of 34 species which are in the least abundance with only one individual recorded per species for example Allotinus leogoron leogoron, Anthene emolus goberus from Lycaenidae and Atrophaneura nox erebus from Papilionidae.
A few protected species were recorded including Idea lynceus lynceus belonging to the Nymphalidae family and two from Papilionidae, Troides helena cerberus and Troides amphrysus ruficollis. Based from Figure 2.4, there were 76 species with 34 singletons and 21 doubletons and another was three to four individuals recorded per species and this can be considered rare within the study site due to a small number of individuals (less than four) recorded compared to other species. 46 species were considered as common occurring species with more than 11 individuals and the rest were considered as intermediate with a number of individuals recorded between five to 10 individuals.
Figure 2.4 Rank-abundance curve illustrated the distributions of butterflies in Sekayu lowland forest, Hulu Terengganu that shows proportions common (1) and rare (2) species.
The rank abundance distributions also show a relatively higher proportion of uncommon or rare species compared to common species which were evaluated by the number of individuals per species. Approximately 29% of the species abundance was contributed by common species, 22% intermediate abundant level while the rest 49% belongs to the rare species. The graph shows to fit the log-series distributions. The butterfly species assemblages for each month of samplings also differ from each other Figure 2.5. The Shannon diversity index was the highest in March 2014 (4.17) while the lowest was in January 2014. For the Margalef index, the highest value was recorded in March 2013 (15.26) and the least was in January 2014 (2.79). The null hypothesis where there are equal distributions of individuals among species was also rejected since there was differences in the mean distributions of species monthly (df = 14, p = 3.9 x 10-17).
Figure 2.5 Shannon Diversity Index (H’) (blue) and Margalef richness index (red) values based on monthly sampling data from March 2013 until May 2014 showed an almost similar trend throughout the study period.
2.3.2 Species richness estimation of the butterfly communities
The summary of the species richness assemblages and the species richness estimations was given Table 2.1. Based on the table, the value of Shannon diversity index is quite high with 3.49 with the Simpson diversity index value of 0.96 (maximum is 1). There is no dominating species within this study since the dominance index value was very low (0.04). The species is distributed slightly uniform among month since the evenness index value is 0.69 (0 being the lowest and 1 as the maximum). The sampling intensity (9.47) was found to be highly satisfactory (value almost equal to 10). In conjunction, the value for inventory completeness (78.21) shows that the sampling was thorough enough for the total butterfly community due to the small fractions of singletons found within the community.
The percentage of inventory completeness was quite high (85.26%) implying a shortfall in sampling because Chao 1 takes into account number of singleton and doubleton thus this sampling cannot be fully considered complete. However the value of the adjusted estimated range is quite low (0.36) which is less than 0.5 indicating that the sampling done was enough to obtain a good species richness estimation for the butterfly communities. Nevertheless since there are a mix results shown by the calculation for inventory completeness analysis that slightly in disagreement to each other, the value generated by the estimators are very usable for highly diverse species.?
Table 2.2 Summary of species richness estimation analyzed from monthly butterfly data in the Sekayu lowland forest. Randomizations without replacement were done 50 times using EstimateS with classic formulae for Chao 1 and Chao 2 at default (10) value for the coverage-based estimator limit for rare and infrequent species.
Observed species, Sobs 156
No. of samples 15
No. of individuals 1478
Shannon, H’ 3.49
Simpson, 1-D 0.96
Simpson reciprocal, 1/D 9.09
Dominance, D 0.04
Evenness, H’/ln S 0.69
Sampling intensity 9.47
Inventory completeness index 78.21
% inventory completeness 85.26
Adjusted estimate range 0.36
Chao 1 183.52 ± 101.67
Chao 2 232.50**
Jack 1 203.6 ± 95.06
Jack 2 234.12
Note * denote the minimum value while ** denote the maximum value
Despite all estimators to exhibit values that do not reach asymptote, the Michaelis Menten means happens to flatten off rather quickly and almost reaching asymptote when compared to others (Figure 2.5). The latter happens to stabilise gradually as more individuals were added. And somehow all the ‘lower group’ estimators tend to stabilizing and move closer to each other as sampling efforts increased. The other two estimators which are ACE and Chao 1 also appeared to coalesce together towards the end and roughly lined up closed and almost overlapping each other. The same goes for the curve of ICE and Jack 1 which are closely placed together. For the singletons and doubletons, both have shown values which are way below the observed species number with both lines seem to flatten off with no increase or decrease in value as more species were added (Figure 2.7). However both indicators were shown to exhibit an almost linear graph indicating that it has reached the asymptote even if more individuals were added. The number of unique and duplicates also did not change as more individuals were added though the number of duplicates seems to be decreasing at the end (Figure 2.8).
Figure 2.6 Observed and estimated number of butterfly species in SLF versus number of individuals sampled based on 50 randomised samples. The first value of each line was removed to improve the visibility of the graph.
Figure 2.7 The figure shows values singletons and doubletons in SLF versus the number of individuals sampled based on 50 randomised samples.
Figure 2.8 The figure shows observed and unique and duplicates in SLF versus the number of individuals sampled based on 50 randomised samples.
The composition of butterflies in Peninsular Malaysia is extraordinarily rich despite of its small land masses compared to other countries with close to 1200 species recorded (Wilson et al., 2015). In Sekayu lowland forest, all six butterfly families were recorded in this study with Nymphalidae as the highest recorded family and the most abundant but the least recorded with low abundant was Riodinidae. This was not surprising at all because Nymphalidae was the largest families of butterfly (Corbet et al., 1993) that had been recorded in Peninsular Malaysia and this family being mostly taxonomically recognized and has a broader range of species description (DeVries, 1997) compared to other families.
The diversity and richness of butterfly species in Sekayu was also high. The species were distributed quite evenly among monthly samplings with only few dominating species for example Appias lyncida vasava with the most abundant species. Based on the species abundance distributions, the Sekayu lowland forest butterfly communities are mostly recognized with a large proportion of rare species in the overall sample. The observed local dissimilarity in species abundance distributions might be due to their larval host plant density, habitat preference; food, quantity and courtship areas (Andrew et al., 2011; DeVries et al, 2012) and other factors which are not directly measured in this study.
2.4.1 Butterfly assemblage at Sekayu lowland forest
All diversity indices (Shannon, Simpson, Simpson reciprocal) indicate high diversity of species at the study area. The evenness values among monthly samplings were also quite high indicating that the butterfly species were distributed evenly for every month. There are only a small number of species (12 species) seems to be dominating the area of study with individuals more than 30 while the rest was less than that thus resulting in the low value of dominance index. This study showed that butterfly community in Sekayu is dominated mostly by rare species as the high number of singletons and doubletons recorded. Approximately 15% from the total 1031 butterfly species in Peninsular Malaysia (Corbet et al., 1992) or 13.2% based on 1182 species as reported by Wilson et al. (2015) could be found here in Sekayu. The findings are equivalent to the previous study such as in Ulu Muda Forest Reserve in the state of Kedah where a total of 151 species of butterflies had been recorded by Zaidi et al. (2005) represent almost 13% species of currently known butterflies in Peninsular Malaysia. The diverse habitat type in Sekayu based from the observations shows that it was capable of hosting large number of butterfly species despite of the anthropogenic disturbance due to its status as a recreational forest and closely situated to Sekayu Agriculture Park that has many fruit plant species. The high diversity of butterfly species in Sekayu was contributed by the complex vegetation structure and habitat type based from observation in the study area.
Habitat characteristics were proven to give effect to the butterfly species composition at a particular chosen habitat which in this case Sekayu. An intricate habitat condition and higher resource heterogeneity were able to sustain the high species diversity (Ramesh et al., 2010; Bhardwaj et al., 2012). Barlow et al. (2007) supported the idea that habitat type and vegetation structure play an important role compared to the landscape structure in influencing the butterfly species. Base on the observations made during the sampling, the visited area that comprised of downstream, forest edge and adjacent agricultural areas able to support huge butterfly species. The observed high species diversity and richness might also be due to vegetation structure and higher resource availability (Kocher and Williams, 2000) as an effect of maintaining the surrounding areas by planting ornamental plant species and fruit plants from the adjacent area. Odegaard (2006) in his research found that the higher species diversity in certain places were also due to the diverse habitat features which makes it suitable for diverse species to inhabit the area increasing the species assemblages within the beetles community. Similarly, this reason could also be comparable to the diverse butterfly population in Sekayu as proven by the richness and diversity results where high value of diversity and richness recorded.
More than half of the butterfly population in the Sekayu lowland forest were comprised of rare species (67.9%) due to low number of individuals recorded per sampling (less than 10 individuals). While the rest, 50 species were considered common due to the high number of individuals recorded (more than 10 individuals per species). This pattern of distribution was common in nature where a large proportion of the captured butterflies were regarded as rare (DeVries et al., 1997) due to their low abundance. The observed local dissimilarity in species abundance distributions within the study site might be due to their larval host plant density, habitat preference; food, quantity and courtship areas (Andrew et al., 2011) and other factors which are not measured. In general, the butterfly community is dynamic and changes of faunal diversity measures over time suggest the variation of diversity indices recorded in this study (DeVries et al., 2012). Authenticating the abundance distributions and other factors contributing to the diversity of species might help in explaining further the habitat heterogeneity among the butterfly communities.
2.4.2 Estimation of species richness in the butterfly community of Sekayu
Coddington et al. (1991) guessed that 10:1 ratio of individuals to species in a diverse tropical community would yield sufficiently accurate estimates of species richness. In this study the ratio of individuals to species or the sampling intensity for the butterfly communities was nearly 10:1 (9.47) complements previous study as mentioned thus indicating it was enough to be a good representative of the butterfly communities of the whole Sekayu. However, Coddington et al. (1996) in their studies on spiders stated that the ratio given before was still high since they found their ratio of 18:1 of individuals to species, meaning more individuals to be obtained for a species of spiders to be recorded. The distributions of spiders and butterflies differs based on their survival strategies such as life cycle, food preferences, behaviours and others thus different results would be anticipated. It is typical for such values of sampling intensities to fall below 10 due to many factors such as seasonal, sampling length and others as referred to Scharff et al. (2003) in his study on spiders in beech forest where the sampling intensity value was 30. Despite that, they suggested that the minimum sampling intensity was adequate enough to yield a persuasive asymptotic richness estimates.
The performance of each estimator has always been tested and compared to find which would be the best among all available estimators (Walter and Morand, 1998). Despite none of the estimators values happens reached asymptote, the sampling efforts could be considered thorough enough based from the values of inventory completeness and adjusted estimated range. Toti et al. (2000) on their study one on spiders found that lower value of adjusted estimated range in heath grass bald (0.33) compared to Appalachian grass (0.59) indicates that the sampling was nearly complete in heath grass bald. In this study on the butterflies of Sekayu, the values of estimated range (ratio of the range of all estimators except Chao and Lee divided by observed species richness) fall below 0.5 indicating that the sampling was also nearly completed as campared to the results from the previous study. The inventory completeness was approximately 85% within the study area are able to support the sampling to be enough. This was in agreement with Cardoso (2009), where at 80% inventory completeness the sampling was considered to be comprehensive for arthropod inventories.
Despite of the sampling effort was indicated to be adequate enough to represent the butterfly communities in Sekayu, the estimators still fail to exhibit any asymptotic line. However the best performances among all nine estimators was MMMeans as it stabilizes more quickly as more individuals were added (Figure 2.6) so it could be considered as good estimators. Fermon et al. (2005) has chosen MMMeans based on its effectiveness value to describe the species richness in fruit feeding butterflies of Sulawesi and found that the estimator computed values which are higher than observed species richness however there are no further explanations to which the estimator performs well or not. Ramesh et al. (2010) also applied MMMeans in their studies as it gives a well described species accumulation records as sampling intensity increases and found that rarity has affected the performance of the estimators to change where as the number of singletons increased so does the greater the difference between observed and estimated species richness. So in this study on butterfly community of Sekayu, almost 1/3rd of the total 156 species observed was comprised of singletons and doubletons which could be the reason why the estimators were performing in such ways and unable to exhibit asymptotic level.
Most of the estimators do not exhibit any region of stability as the lines continue to increase when more species were recorded. This was the same as in a research done by Longino et al. (2002) on the ant community where none of their chosen richness estimators happen to reach an asymptote though the regions of stability happens to occur. This also happens in this research on butterflies of Sekayu where no region of stability were exhibited by any estimators. Jack 2 and Chao 2 estimators (Figure 2.6) produced a steep line and recorded the highest estimated values of species richness. Both estimators were not significant towards each other as both lines appear to overlap if more species were recorded. The undersampling bias would be the caused for the estimators fail to perform well as the high number of rare species still present during this study. Yong et al. (2012) however in their study on butterflies in land bridge islands, in Tasik Kenyir, Terengganu found that most estimators chosen have exhibited asymptotic curves. Since butterflies on the island have a limited number of species and usually low in abundance this could produce such results which differ than this study in Sekayu where unlimited resources and diverse habitat types that influenced the diversity of butterflies.
The number of observed species will always be below the estimated species richness whenever the sampling was not exhaustive (Walther and Morand, 1998). The final inventory of this study indicates that approximately 67% butterfly species from the total estimated species richness have been sampled. In most studies, whether previously or recently on various organisms always noted that the overall richness estimators were influenced by the numbers of rare species present (O’hara, 2005). The number of singletons and doubletons recorded in Sekayu was approximately 22% and 14% respectively from the total 156 species recorded. Both Sobs (number of observed species) and estimated species richness would be likely to deviate significantly if the ratio of singletons to doubletons (uniques to duplicates) is greatly skewed. The species accumulation curve (Sobs) and the estimators plotted on the line graph (Figure 2.6) shows how each estimator exhibit values all above the Sobs with three distinct groups present ‘higher group’, ‘medium group’ and ‘lower group’ estimators. Eventhough no asymptotic lines were produced by each plotted estimators, the estimation tools have proven to be useful and could be a very prevailing method available in generating a reliable estimate of species richness in an ecological study.
This study examined the Rhopalocera or butterfly species assemblages in Sekayu lowland forest. The applications of estimating species richness which have been increasingly recognized worldwide by researchers were successfully applied in this study. The butterflies in the study area are quite diverse with large numbers of butterflies species were able to be recorded and they all come from the six families of butterflies including the recently recognized family Riodinidae (Campbell et al., 2000) exist in Malaysia. The communities of butterflies in Sekayu were dominated by the high number of rare species with 35% singletons and doubletons from the total 156 species were recorded. It was predicted that they are more species to be collected if the sampling effort were to be increased. This was based on the values species richness estimation, which has been proven to be a good tool to help in explaining ‘true species richness’ despite of the non asymptotic curves produced in this study.
Common alpha measurements such as Shannon and Simpson diversity index, Margalef richness and Evenness indices were strictly dependant on number of species and individuals collected. It was essential in many studies but insufficient to reveal the overall community richness. As they only computed data based from the total number of observed species, the hidden species which are not recorded were unable to be determined. To completely sample all species were almost impossible, which gives more reasons to why applying other measures that could reflect the ‘true’ species richness in a study area is greatly needed. Due to the presence of a high number of unique and duplicates within this study, the estimators were unable to perform well to reflect the species richness in SRF. Lack of sampling effort within this study produced estimators which are not asymptotic as the abundance number increases. Despite all that, based from this research the inability for the estimators to perform accordingly still shows how powerful they are as a tool for ecological studies as it was able to detect the ‘hidden’ species richness which is immeasurable by the alpha diversity measurements.
Application of species richness estimation to support the results from the common ecological indices would be needed as to completely sample the whole community was almost impossible. Since they will always be a sparse number of data collected due to limited funding, not enough time or sampling effort needed plus the environmental factors that also influences the presence of butterflies, absence of species due to undersampling bias will still able to be distinguished. By observing and comparing performances of each estimator, species richness estimation has been proven to be able to predict the total richness within the study area. The predicted number of species richness enables researchers to decide whether the sampling effort was enough or not, to reflect the whole community which were studied. Based on this butterfly study in Sekayu, it can be concluded that more species were still unable to be detected during the sampling length. More species were able to be recorded if more sampling effort were to be given equally during the study.
Tropical rainforest has been highly threatened recently by development, logging and other anthropogenic activities which could severely affect the diversity and distributions of butterflies in particular area. As different species have different ability of adapting in the ever changing environment, some sensitive species mainly specialist which are usually rare butterflies species with certain strict habitat preferences and is susceptible to the changes due to their smaller distribution size will be extinct. This would cause decreasing species richness since large area will be conquered by the widespread generalist with a broader range of niche causing high competition for survival where the weak will usually disappear and finally facing annihilation. Although most conservation attempts usually focused on preserving pristine areas of permanent forest, we must not neglect the significance of preserving other types of forest and fragmented landscapes which also contributed towards species richness. The results from this study were hope to help in understanding our tropical rainforest for the future conservation planning and protection of diverse flora and fauna for the generations to come.
DOES RAINFALL INFLUENCING THE BUTTERFLY DIVERSITY AND SPECIES RICHNESS AT SEKAYU LOWLAND FOREST?
Tropical forest resides within the Earth equatorial belt and it is well known for its two distinct seasons of wet and dry which differs from the forest in the temperate region with its four cycles of summer, spring, autumn and winter. The tropical rainforest receives an unequal amount of precipitation annually as the highest amount of precipitation was usually recorded during the wet season while the lowest being in the dry seasons. In any scientific inventories, just studying the species diversity and richness in one season was not sufficient enough to explain why the distribution of any chosen species happens in such ways. Figuring out the external factors which influence the patterns of diversity was important for a proper and efficient conservation method to be applied. Studying the effects of such seasonal cycle on insects abundance and diversity was important for us to understand how precipitation have an influence on insect diversity and distributions. Insects such as butterflies are known to be delicate creatures in nature in which their bodies can be easily damaged by heavy rainfalls.
Previous study conducted some times ago by Pollard (1988) has demonstrated that a high correlation exists between weather and the butterfly abundance. There was an evidence of differences in preferable seasons for the insects to occur where some species could be very rare in certain months while being abundant in the others. DeVries and Walla (2001) also found that in general the pinnacle of insect abundance generally occurs during the wettest part of the year where high number of insect abundance occurs in the month after heavy rainfalls during the wet seasons. This result however would be different for each insect community. In this study in Sekayu, the diversity, richness and abundance of the butterflies within the wet and dry seasons was assessed to better understand on how the rainfall influence distributions of butterflies within the study periods. The effects of rainfall pattern on the butterfly communities were determined by applying multiple methods of alpha and beta diversity measurements. This was to give a better insight and a much reliable results to reflect the differences and any hidden aspects of diversity.
3.1.1 Rainfall and the butterfly communities
The life cycles and dispersals of any insect communities across the selected area of study would be affected by many external factors which among others, the seasonal rainfall pattern. Certain types of butterflies have limited number of environmental factors which they favour while some could occur almost every month regarding of the season. Grotan et al. (2012) have investigated the seasonal cycles of species diversity and resemblance in a tropical butterfly community in Amazonian Ecuador. They used the long term sampling data of 10 years to examine whether the recent and historical temporal rainfall and temperature pattern have any effect on the distinct seasonal cycles in species distributions. Among the butterfly community in their study area, they discovered that there was a clear annual cycle in species assemblages and community similarity. There were also distinct changes occurring between wet and dry seasons among the butterfly community composition. Another study by Grotan et al. (2014) found that the butterfly communities in the Central American rainforest happen to exhibit a different type of biannual cycles in species diversity. The community similarity was the greatest during the driest month in the annual cycle concluding that there was a distinct separation of the occurrence butterfly during wet and dry seasons.
There was a lack of long term studies in understanding the effects of rainfall pattern on the butterfly communities especially in Malaysia. Many studies have focusing on species inventory or resolving the morphology or taxonomic identification of the organisms. In Peninsular Malaysia, most studies on butterflies were focusing on the species inventory and the impact of forest conversion on butterfly communities. Yong et al. (2012) studying the butterfly communities in land bridge islands in Peninsular Malaysia to assess the species diversity and richness at Tasik Kenyir, Terengganu was probably an only such study exemplifying on measuring diversity but did not focusing on the influence of rainfall on the diversity itself. Another example was by Chung et al. (2013) where they conducted an insect survey to assess the diversity of multiple insect communities such as butterflies, dragonflies and others in Bukit Hampuan Forest Reserve, Sabah. Doing species inventory such as this was important to assess the existence of any endemic or rare species to enhance the need of a proper conservation act (see Chapter 2). Another study was also done on the Family Papilionidae to determine the identities of Graphium butterflies whether it was morphologically or genetically different between each other by Wilson et al. (2014). It was important to determine whether each species within a particular genus was the same or not for a proper identification. Determining the correlations between the wet and dry seasons and the distributions of butterflies was also important as to prove and give some reasoning of why such results was obtained other than just understanding the diversity and differences of species among all the families.
Nonetheless there were a few studies in Borneo that were done to analyse the impact of rainfall or drought seasons on the butterfly communities. Hill et al. (2005) was examining the effects of rainfall on the distribution of the satyrine butterfly Ragadia makuta and was comparing the differences between selectively logged and unlogged forest in Borneo. They found that heavy rainfall in a month prior to the surveys will significantly reduce the butterfly abundance. However there were no differences occurred between selectively logged or unlogged forest in either monthly or annual samplings for the butterfly abundance and distribution. Their results also confirmed that rainfall and severe drought reduce the Ragadia makuta abundance but the impacts was short lived and not affected by habitat disturbance. This complements the previous study done by Hill (1999). Even so this result would be different for other butterfly species since butterflies was known to have different habitat preferences. Since the insect larval stage is also usually host plant associated, the production of food during different season usually varies and this could also change the butterfly distributions.
3.1.2 Butterflies community structure and assemblages
Using multiple types of diversity measurements could help to alleviate and improve the results of species abundance and distributions. As different analysis applied on a given dataset can yield deeper insights and uncover the hidden value of diversity as the data was manipulated and interpreted differently for each analysis. However, using too many measurements does not mean that we will get the best results. Thus, more effort should be spent in applying the best tool (Peres-Neto, 1999). There are many methods that can be applied to analyse diversity of species. One of them is the rarefaction method which unambiguously distinguished the non-linearity of the correlation between area and species number (Koellner et al., 2004). There are two types of rarefaction which is individual-based or sampled-based. The individual based rarefaction is restricted to only one sample where it calculates the expected number of species where the individuals are drawn at random from a single representative sample from an assemblage. While for the sampled-based rarefaction, it calculates the expected number of species to illustrate the average number of accumulated species as the sample number increases (Crist and Veech, 2006).
Lately, other measurements of biodiversity which takes into account the higher taxon has become increasingly accepted. The ability of measurements which is less biased towards sampling efforts has always been searched. Ecologist have been separating diversity into local (?) and also regional (?) conventionally with the two related by the degree to which species composition diverges over an area (Lande, 1996; Magurran, 2007; Terlizzi et al., 2008). As introduced by Whittaker (1960), ? diversity is defined as the average species diversity at the within-locale scale whilst ? diversity is the total species diversity at the geographical scale. Beta (?) diversity with so many definitions was commonly described as changes in species composition among different habitats (Whittaker, 1972; Wilson & Shmida, 1984).
3.1.3 Average taxonomic distinctness (?+) and variations of taxonomic distinctness (?*) as a measures of beta diversity
Study in the tropic using butterfly as a model for beta diversity has not been fully explored. For example, many studies on butterfly fauna in the Peninsular Malaysia is more focused on species diversity which was conducted in several sampling site in Malaysia. Some examples of such studies were done at Ulu Muda Forest Reserve in the state of Kedah (Zaidi et al., 2005), Sungai Ber, Kelantan (Jalil and Sharifah, 2008) and a few sites in Cameron Highlands, in the state of Pahang (Norela et al., 2011). However in Sabah, Beck and Chey (2007) used the measurements of beta diversity on geometrid moth samples to quantify the spatial turnover of moth ensembles across northern Borneo, by considering the environment- and sampling-related consequences on ensemble composition, and assessed the remaining spatial patterns in the data. Their study showed that wide-ranging data set that geometrid samples from northern Borneo differ with altitude, human-caused habitat alteration and the precipitation at (and prior to) sampling. However, taxonomic similarities using average taxonomic distinctness to delineate communities and temporal turnover were never been greatly investigated.
Warwick and Clarke (1995) proposed taxonomic diversity/distinctness (?) where it was a concept of beta diversity to measure the average level of which individuals in a group are coexisting to each other and used the species presence or absence data instead of abundance data (Heino et al., 2007). Based on the quantitative species abundance and incidence (presence/absence) data, Clarke and Warwick (1998) tested the statistical sampling properties which the average taxonomic distinctness (?+) have a significant benefit over species richness measure and gained an appearance for its hypothetical sampling variance due to its characteristic of being independent towards sample size or sampling effort (Clarke & Warwick 1998; 1999; Zintzen et al., 2011). In a study done by Ellingsen et al. (2005) at Norwegian continental shelf, the information on the soft sediment macrobenthos was used to examine the use of average taxonomic distinctness as a diversity measure. The findings was that there was an irregularity in ?+ because the taxonomic categorization system vary among dissimilar phyla and taxonomically associated biodiversity measures might be more eloquently applied to a single phylum than to all taxa combined. The author also found where the dominant phyla displayed dissimilar patterns of ?+ and therefore one taxonomic group could not be taken to represent the others in terms of taxonomic distinctness.
Butterflies were recognized by researchers to be very vulnerable to changes in their environment for example the ecosystem structure, weather and others (Heikkinen et al., 2005). This was why in this current study, an attempt is made to, 1) the butterfly’s species assemblages among wet and dry seasons will be assessed and 2) the effect of seasonal pattern on the butterfly diversity distributions was also examined to delineate the differences between seasons. Studying the distinct wet and dry season due to the northeast monsoon and southwest monsoon that influence the abundance of insect was essential. The absence and presence of butterflies monthly differ and reasons to why this occurs was needed to be comprehended in order for a better understanding on their diversity. So within this study the rainfall data obtained from the Department of Irrigation and Drainage of Terengganu will be used alongside the butterfly data to further proof that precipitation does affect the butterfly diversity in the Sekayu lowland forest. Different type of diversity measurements will be applied in order to understand better the relationship between butterfly species distribution and the seasonal rainfall pattern in Sekayu.
3.2.1 Study site
The study site where the data of butterfly community were collected are similar as in Chapter 2.
3.2.2 Sampling technique
Mode of data acquirement was similar to as in Chapter 2.
3.2.3 Data analysis
The rainfall data were obtained from the Department of Irrigation and Drainage of Terengganu for an average of five years (2009 to 2013). The daily rainfall data were pooled up together to obtain a series of average rainfall pattern for five years. Rainfall data obtained were considered as the general rainfall pattern which was acquired from several rainfall collection sites in the state of Terengganu. We were unable to use the data from the nearest rainfall collection site in Kg. Sekayu due to the insufficient and incomplete data with few missing days due to reasons not known to us. The data will be used to delineate the month of heavy rainfall and dry season where the volume of precipitation of over 300mm per month was fixed as the wet season.
Common diversity measurements such as Shannon diversity index, richness indices (Margalef index) and evenness index were calculated using PAST software ver. 2.15 (Hammer et al., 2001). Statistical analysis for taxonomic distinctness was performed using Primer6 software (Clarke ; Gorley, 2006) using the incidence based data with selected taxonomical hierarchy data. Average taxonomic distinctness (?+) and variation of taxonomic distinctness (?+) among monthly data were measured and differentiated based on the master species list. Correlation analysis was also computed by using Microsoft Excel 2007.
Rarefaction analyses was used to evaluate the observed butterfly species richness. The data were separated into two sets which are the wet and dry season. The rarefaction curves were plotted from a smooth species accumulation curved obtained from randomly re-sampling data using the software Ecosim. The comparisons were made for species richness of all seasons with each curve analogous to the number of individuals between months of lowest sampling effort. This analysis was performed using the EcoSims software that can be obtained freely online using 1000 iterations. The dissimilarity in species diversity within monthly samplings were shown via the examination on the 95% confidence intervals for individual based rarefaction curves.
Taxonomic distinctness measurement was calculated based on species presence or absence data (Heino et al., 2007). Assessment of diversity using average taxonomic distinctness (?+) was found to give a significant advantage over species richness measure and gained an appearance for its hypothetical sampling variance due to its characteristic of being autonomous towards sample size or sampling effort (Clarke ; Warwick 1998; 1999; Zintzen et al., 2011). This formula is used to explain and compare the diversity of butterfly species according to the wet and dry months where the step lengths among taxonomical levels or hierarchy was manipulated as compared to the master species list to obtain the funnel plot of average taxonomic distinctness. The master species lists from a range of different seasons in the Sekayu lowland forest and its surrounding area was constructed based on cladistic principle. The taxonomic groups used were: species, genus, subfamily, family, superfamily and suborder.
Average taxonomic distinctness (?+)
?+ = ??i 300mm) (Figure 3.1). For the rest of the month between April and September was considered as a dry month. It can be concluded that three months at the end of a year will be wet and prolong until the first three months in the beginning of a year with dry seasons in between.
Figure 3.1 Distribution of monthly rainfall data for an average of five years in Terengganu. Bar graphs represent the wet and dry season (white denotes dry season while black as wet season). Red line mark the 300mm rainfall amount that was considered to represent wet month.
Somehow in the recent years distinct changes happen to the rainfall data collected from the similar stations between Mar 2013 and May 2014 (Figure 3.2). There were only three heavy rainfall months (Oct 2013 to Dec 2013) sandwiches between drier months of Mar 2013 until Sep 2013 and Jan 2014 and May 2014. This pattern seems to be significantly difference from the pattern observed from five years rainfall data.
Figure 3.2 Distribution of monthly rainfall data for Mar 2013 until May 2014 where sampling took place. Bar graphs represent the wet and dry season (white denotes dry season, while black as wet season). Red line marks the 300mm rainfall amount that was considered to represent wet month.
When the total rainfall data were spread according to sampling month (beginning Mar and ending May) the results from the five years rainfall data shows that there was a four months of heavy rainfall (precipitation volume ; 300mm based from the average of total volume of five years rainfall data from 2009 until 2014) and they were assumed as wet season (Figure 3.3). Another 11 month of less rainfall was assumed as dry seasons. The number of butterfly species recorded during the study period was plotted onto the graph to observe the pattern of butterfly distribution. The same was done for the actual sampling months between 2013 to 2014 (Figure 3.4). Based from the two figures, there was a distinct separation between wet and dry seasons of butterfly species richness.
The distribution pattern of the butterflies community within Sekayu using the dataset from year 2013 until 2014 showed an interesting pattern – it follows both five years average rainfall and current rainfall volume recorded throughout the study period (Figures 3.3 and 3.4). High species richness was observed in Mar 2013, which can be said as month after the wet season, and species richness gradually decreased as rainfall decreased. The number of species recorded plummeted further and at the lowest in Jan 2014, coincide with the first month after heavy rainfall. The following month despite less rainfall amount recorded, the number of species richness of butterflies surge drastically in March 2014 (78 species) which was comparable to the previous years species richness (80 species).
Figure 3.3 Monthly rainfall data for an average of five years in Terengganu. The blue line indicates the distributions of butterfly species based from datasets collected from Mar 2013 until May 2014. Bar graphs represent the wet and dry season (white denotes dry season while black as wet season).
Figure 3.4 Monthly rainfall data for the length of sampling period from March 2013 to May 2014. Black bars denote the wet season while the rest is dry season. The blue line indicates the distributions of butterfly species based from datasets collected from Mar 2013 until May 2014.
The volume of rainfall was clearly shown to be related to the number of species of butterflies and this was proven by the positive correlation between rainfalls and species abundance (Figure 3.5). Conversely, it was only a weak negative linear relationship between rainfall and species richness with R2 = 0.14. Despite of the low correlation value, the butterfly species recorded was still capable of exhibiting results to show how the rainfall pattern influences their distributions. The results yield from one-way ANOVA also prove that there was a statistically significant difference of rainfall and butterfly distributions relationship (p300mm) while the rest were considered as dry (see Figure 3.2). 149 butterfly species was recorded for the dry season and only 67 species was recorded for the wet season. Nymphalidae was found to have the highest number of species and was the most abundant family recorded during dry season (70 species, 531 individuals) and the same family for the wet season (34 species, 70 individuals; Appendix 3 Table A3.1). The least recorded species was the recently recognized family Riodinidae where only three species (15 individuals) were recorded during the dry season and one species (three individuals) during the wet season. Appias lyncida vasava (57 individuals) was found to be the most abundant species recorded during dry season while for the wet season the most abundant species was Papilio nephelus sunatus (nine individuals). The other most common species during the dry season was Cupha erymanthis lotis (44 individuals) and for wet season Papilio memnon agenor (eight individuals).
The rank abundance curves exhibit a log normal distribution as shown in Figure 3.9 there was a distinct separation between the distributions of butterflies where the lower number of species and individuals recorded during wet season when compared to the dry season with most of them are rare species (individuals less than five) recorded. The number of rare species was very high for both dry (79 species) and wet (60 species) seasons. From the total 156 species recorded, there are 33 singletons and 21 doubletons that were recorded during the sampling in dry season. While for the wet season, 25 singletons and 14 doubletons were recorded. The high number of rare species during wet season was mostly made up from some common species which occur in less abundant due to other external factors that influence their distributions when compared with the dry season.
The diversity, richness and evenness indices for monthly samplings were found to vary among each other (Table 3.1). The values of the Shannon, Simpson, Menhinick, Margalef, Equitability and Evenness indices showed the similar pattern which all values decreasing as the sampling progressing towards the wet season (year end) and registered the lowest values in January 2014 before levitating again during the dry season (post wet season) (Figure 3.10). However, dominance index demonstrated the contrasting pattern which the highest value peaking in January 2014, where species-individual ratio was equal. The pattern of species richness (taxa diversity), individual collected, and diversity indices plotted from March 2013 until May 2014 showed a distinct pinnacle at early dry seasons (March/April 2014) but decreasing again soon after for the species richness and individual collected (Figure 3.10a) and Menhinick and Margalef indices (Figure 3.10c). The other indices relatively consistent (Figures 3.10b and 3.10d).
Figure 3.9 Rank abundance curves for the wet (red) and dry (black) seasons for butterflies in Sekayu lowland forest for sampling from March 2013 to May 2014.
Table 3.1 Number of species, individual and diversity indices values of butterflies recorded in Sekayu lowland forest, Terengganu.
The data recorded clearly showed a strong positive relationship between number of individual and number of species collected (Figure 3.11). The relationship also recorded a very small p-value (2.8 x 10-7) indicating that an increased in the number of individuals significantly produce more species recorded during the study.
Figure 3.10. The pattern of species richness (taxa diversity), individual collected, and diversity indices plotted from March 2013 until May 2014 at Sekayu lowland forest
Figure 3.11. The relationship between species richness and abundance of butterflies collected at Sekayu lowland forest, Hulu Terengganu.?
There was a distinct separation of seasonal rainfall in between dry and wet seasons in Sekayu Recreational Forest. Based from the data obtained, the number of precipitations becomes lesser as the data were separated into smaller sets. For five years (2009 until 2013) rainfall data collections there used to be six months of heavy rain. After partitioning the data of five years into the length of sampling, there was only four months of heavy rainfall excluding February as it was in between dry months. While for the rainfall distributions during the sampling duration from March 2013 until May 2014, only three months of heavy rain were recorded while the rest being dry months. The rainfall data were obtained from several places in Terengganu so there might be changes that occur during the day of sample collection which are not known to us. For example the rainfall samples were collected during dry days or being affected by other factors since weather can never be controlled.
For the distributions of the butterfly species, higher number of species was recorded with family Nymphalidae having the highest number of species for both seasons while the least being Riodinidae. The rank abundance for both seasons’ shows to exhibit log normal distributions with higher number of rare species recorded during the wet season compared to the dry season as they have the least abundance number for each species. Most of the rare species found in wet season were comprised of the common species which can be found during the dry seasons as they occur in less abundance due to the weather change during the wet season. The species assemblages in both seasons also differ greatly as the diversity and richness were recorded higher in dry compared to the wet months of samplings. The dry month of March 2013 and 2014 was found to exhibit almost similar diversity and richness with only a slight difference in values for Shannon diversity and Margalef richness indices. The beginning of dry month after rainfalls in January 2014 recorded the lowest number of species diversity and richness.
3.4.1 The effect of seasonal rainfall pattern on the butterfly communities
Hypothetically, when the volume of precipitation increases, the numbers of butterfly species will also decrease. Tiple and Khurad (2009) found that due to the higher availability of food resources during favorable climate circumstances, there will be higher abundance of insect diversity. This was shown in Sekayu where after two months of low occurrence From January 2014, there was an increase in the number of butterfly species until it reaches the peak in March 2014. The effect of the wet and dry seasons on the butterfly community was found to be highly correlated. Eventhough there is only a weak correlation between the distributions of butterflies and the rainfall pattern there was still a positive relation in between them making it reliable to say that rainfall does influence the number of species observed in every month of sampling. Grotan et al. (2014) have also reported within his study that temporal changes in the environment has influenced the patterns of butterfly distributions. The effect of the wet and dry seasons on the butterfly community was highly correlated in their study.
The dendogram (Figure 3.6) displays results of the similarity among butterfly species distributions seasonally where they can be grouped into three different clusters. The distance of likeness in between the months of dry and wet appears to be quite similar between each other with it is in close proximity, especially in the dry month of September and wet month of October 2013. This shows that the distribution of species for both months was almost similar to each other despite of the large difference in the number of precipitations received. There was only a slight decrease in the number of species recorded in October 2013. However the similarity among the number of species recorded then continues to decrease as the distance among each month increases when comparing September 2013 to November 2013 despite being in the same cluster. This shows that there was a change that occurs in the butterfly species recorded for each month of dry and wet seasons. The numbers continue to shift in between month as the butterfly species recorded decreases when volume of rainfall increases.
Insect species which include butterflies each have their own specific diapauses termination where it is mediated by photoperiod and humidity. This was due to among some seasonal species; their active time was very dependent on the slight changes in rainfall pattern. For example in a previous study long before in a beetle species Stenotarsus rotundus which can be found in Panama their diapauses length occurs every rainy season and during the entire dry season as they will remain inactive until the first rains of the rainy season activates them (Wolda, 1988). Grotan et al. (2012), discovers that, the discrete seasonal cycles in species diversity and community likeness are affected by temporal dissimilarity in current and past rainfall and temperature with time lags up to three months on the butterfly communities. They also mentioned the great difference in temperature and rainfall between wet and dry season will cause most species to develop seasonal dormancy, diapauses or hibernation and having seasonal reproduction. This can be seen in Sekayu where some butterfly species were found to be inactive during the wet seasons.
The rarefaction curves for the butterflies in Sekayu shown to exhibit a higher number of expected species richness and abundance in the dry season when compared to the wet season (Figure 3.7). This shows how the increase in the number of rainfalls affects the richness of butterflies during each season as the number of active species and the abundance was reduced during the wet seasons. A study done by Badik et al. (2015) shows that prolonged periods of precipitation will result in the high cloud cover and low temperature which will reduce the adult butterflies activities. Other than that, due to the wet conditions and decrease in temperature will also result in a colder weather which in turn caused higher mortality among the larval stage for butterflies. The unsuitable weather conditions and increased in amount of flash floods and risk of larva being washed away by heavy rain will result in a decrease in number of butterflies. This can be seen in Sekayu where lowest number of butterflies recorded in January 2014 after the wettest month of December 2013.
In the Figure 3.6, distinctness values of habitat type were plotted on the confidence funnel created from the area species list, to test for significant removals from the null hypothesis. As the values of average taxonomic distinctness increases, the beta diversity will decrease and vice versa. The scattered points as shows that there were less number of shared species among both seasons as the results was computed based on cladistic principles and absence and presence instead of abundance. Most points of wet and dry season for ?+ were positioned within the 95% simulated confidence funnel for respective taxonomic distinctness. This indicated that the sampling during both seasons has values which are almost equal to the whole butterfly dataset. The butterfly distributions among seasons in the sampling area may already naturally demonstrate average values of average taxonomic distinctness when all points are within the funnel. With exception of one point of dry season that falls below the 95% simulated limit indicating it as less diverse than the other months. The decrease in the taxonomic diversity will decline the values of average taxonomic distinctness although it does not mean that species richness were also reduced (Clarke and Warwick, 2001a).
Increased in the light intensity, especially during dry season where lower number of rainfalls recorded indicate fewer amounts of cloud cover. This will increase the activity of butterflies and in turn results in an increase in butterfly diversity (Hamer et al., 2003). Butterfly diversity is known to be affected by changes in weather condition. This is due to the rapid larval development during dry season compared to cold or wet season (Pollard, 1988). The increase number of rainfalls during wet season will increase availability of food resources after months of heavy rains thus providing a much suitable conditions for larval development. This can be concluded that dry and wet seasons are both important for either the adult or larval stage developments in the life cycle of a butterfly. However prolonged months of dry seasons or wet seasons will result in drought or heavy floods which gave a great impact on the diversity of butterflies. The decreasing number of rainfalls as observed would either greatly reduced or induced the diversity of butterfly species depending on their preferable habitat conditions. As there was evidence in changes of patterns of rainfall mentioned before, something must be done to prevent any disappearance of species without knowing the reasons why does it happen. Studying other factors affecting the diversity would also be important for a better understanding and explanation on how the distributions of butterflies were being affected for future references.
3.4.2 Seasonal butterfly diversities in Sekayu lowland forest
In a long term study done on butterflies for 10 years by DeVries et al. (2012), they found that the seasonal cycle of rainfall was proven to influence the butterfly species distribution. The highest activity of butterflies was recorded mostly during the dry season for every year of data collection. This was recorded to be the same within this study in Sekayu Recreational Forest where lots of butterfly species were recorded during dry season. Family Nymphalidae was found with the highest number of species and abundance for both seasons. In Malaysia, a study by Syazwany and Ahmad (2014) founds that Nymphalidae has the largest number of species recorded in Kuala Lompat in Krau, Pahang. Sengupta et al. (2014) also found within his study that Nymphalidae is the most dominant species collected in upper Neora Valley National Park, a sub-tropical broad leaved hill forest in the eastern Himalayan landscape, West Bengal, India.
Favourable weather conditions, increased in host-plant availability and food production has influenced certain species of butterflies to escalate. The other factors influencing the local dissimilarity in species abundance distributions was availability of larval host plant density, habitat preference; food, quantity and courtship and other factors which are not measured (Andrew et al., 2011). Appias lyncida was the most dominant species recorded during dry season with 38% from the total 149 species. While for the wet season, most species have less number of individuals with the highest from species Papilio nephelus with only nine individuals recorded. Most butterflies which were active in the wet season are large ones and not smaller sized ones. In the drier months, most grassland type butterflies would be more active and occurs in large numbers especially from the family Pieridae such as from the genus Appias sp. and Eurema sp. Papilio nephelus was larger sized butterflies with bigger wings compare to Appias lyncida with smaller body and wings. There was also evidence showing that some butterflies undergo polyphenism or changes to their physical bodies such as colour or size according to season in a study by Brakefield (2008). He found that Melanitis leda species changes its form and sized where it has smaller size and the form are more cryptic for camouflage during dry season and darker in colour with smaller spots and larger wings and tails in the wet season.
Within this study in Sekayu, most species during wet and dry season could be considered rare due to lack of abundance with 65% of dry season and 43% of the wet season from the total 156 species recorded with individuals fewer than 10. This pattern of distribution is common in nature where a large proportion of the captured butterflies were regarded as rare within the study area due to their low abundance (DeVries et al., 1997). Some butterfly species have a very specific niche to which habitat or conditions they preferred the most while others could have various type of niche and can exist in any conditions. Those with limited preferences were called specialist which are usually rare in nature while the rest as generalist that could be found almost everywhere. In any condition, generalist would be the most successful species as they were able to adapt to various conditions unlike specialist with specific needs.
However within this study on butterfly community in Sekayu, the sampling length was still not enough to reflect the true seasonal effect on the butterfly community. It will take a long term research to fully observe the changes in the gap between dry and wet seasons. Some researchers would also use data recorded from previous study and pooled them up to make a long list to observe and compare the changes that occurred previously and in recent years. Casner et al. (2014) have compiled data of 18 to 22 years to study the effects of urban expansion and changing climate on the butterfly fauna. They found that an increase in temperature has caused certain types of butterfly species to disappear in recent years. Temperature was influenced by the amount of cloud cover and rainfalls received in a year. Less amount of rainfall will caused prolonged months of dry seasons with drought thus caused the rise in temperature which in turn affects few butterfly species which could not adapt to the changes.
Understanding how the butterfly distributions affected by the seasonal rainfall pattern would be an important subject in ecological studies. Due to lack of long term samplings and insufficient environmental data, much information on changes in environment and the way they affect the organisms in our tropical forest was unable to be fully understood. Such studies were important especially nowadays since our usual pattern of seasonal rainfall was now being affected by climate change. Despite of the insufficient data, we can still observe how the usual average rainfall pattern of six months for five years in early and end of the year differs from the recent years. Where in 2013 until 2014 there were only three months of heavy rainfall at the end of the year. Within this study, studying the rainfall pattern shed some lights on understanding the distribution patterns of butterflies in the Sekayu lowland forest. The variation in the butterfly species distribution pattern monthly is in conjunction with the seasonal rainfall pattern especially during the sampling length. The increase in the volume of precipitation cause a decline in the number of butterfly species recorded. Despite of the low correlation between rainfall and butterfly distributions, it can still be concluded that environmental factor such as rainfall plays an important role in influencing such distributions patterns to occur.
Just relying on traditional ecological indices was not enough to figure out the patterns of the dispersals in the butterflies communities. In this study, the use of multiple methods such as correlation, rarefaction analysis and taxonomic distinctness was able to relate the changes in the pattern of butterflies’ species number among different seasons. By applying multiple analyses, the gap in ecological indices were able to be reduced thus supporting and improving the efficiency of the results. This can be seen where correlation able to relate how the increase in number of rainfalls will significantly reduced the number of butterfly species. Other than that rarefaction analysis shows how species richness differs among seasons with taxonomic distinctness able to confirm the distinctness in diversity among month of samplings in different conditions whether wet or dry. Eventhough this study was done only in a year with extra three months of samplings, it can be seen there were some outline exhibit by the butterfly distributions as the pattern of rainfall changes across the years. This is why long term studies were needed to further emphasize the influence of the seasonal rainfall in the tropics on the butterflies distribution for a better view of what actually happened within our butterfly community. The finding in this study was necessary to enhance the baseline knowledge of fauna presence especially at the study area. The result from this study also may help in planning conservation program and managing the area to safeguard its flora and fauna for future generation.