Assesing the diversity and density of passerine birds in tam dao national park

ACKNOWLEDGEMENTS This thesis was impossible without the support of various professors at Forestry University and Colorado State University I am grateful to my advisor Assoc Professor Vu Tien Thinh for giving me a constant support and enthusiasm guidance during the time of research and writing of this thesis Thanks also go to Professor MacDonald for his support towards my thesis Special thanks to Mr Thanh who actively helped and shared a lot of experience and skill in field survey Finally, I would like to thank staffs in Tam Dao National Park and local people who provide useful information and kind support Ha Noi, November 2015 TABLE OF CONTENTS ACKNOWLEDGEMENTS I INTRODUCTION II GOALS AND OBJECTIVES 2.1 Goals 2.2 Objectives III METHODS 3.1 Study area 3.2 Species descriptions 3.3 Data collection 11 3.3.1 Field survey 11 IV RESULT 17 4.1 Diversity of Passeriformes in TDNP 17 4.2 Density of species 21 4.2.1 Describing survey data 21 4.2.2 Modeling detection probability by distance method 25 4.2.3 Estimating of species density 29 V DISCUSSION 33 VI CONCLUSION 37 REFERENCES 39 LIST OF FIGURES Figure 3.1 Location of TDNP Figure 3.2 Buff-breasted babbler (Pellorneum tickelli) Figure 3.3 Grey-throated babbler (Stachyris nigriceps) Figure 3.4 White-bellied yuhina (Erpornis zantholeuca) 10 Figure 3.5 Grey-cheeked fulvetta (Alcippe morrisonia) 10 Figure 3.6 Simulating objects detected in line transects .11 Figure 3.7 Distance estimation/measurement along transects 12 Figure 3.8 Graph of four standard functions used in Distance method 14 Figure 4.1 Percentage of observation and hearing in Buff-breasted babbler group detection 23 Figure 4.2 Percentage of observation and hearing in Grey-throated babbler group detection24 Figure 4.3 Percentage of observation and hearing in White-bellied yuhina (erpornis) group detection 24 Figure 4.4 Percentage of observation and hearing in Grey-cheeked fulvetta group detection 25 Figure 4.5 Detection probability functions of Buff-breasted babbler 26 Figure 4.6 Detection probability functions of Grey-throated babbler 27 Figure 4.7 Detection probability functions of White-ballied yuhina 28 Figure 4.8 Grey-cheeked fulvetta_Uniform function 29 LIST OF TABLES Table 3.1 Field data sheet used to collect information 13 Table 4.1 Passeriformes diversity in TDNP 17 Table 4.2 Distance group division 22 Table 4.3 Number and percentage of Observation and Hearing in detection species 22 Table 4.4 Buff-breasted babbler_Functions’ parameters 26 Table 4.5 Grey-throated babbler_Functions’ parameters 27 Table 4.6 White-ballied yuhina_Functions’ parameters 28 Table 4.7 Grey-cheeked fulvetta_Functions’ parameters 29 Table 4.8 Transect density of Buff-breasted babbler 30 Table 4.9 Transect density of Grey-throated babbler 30 Table 4.10 Transect density of White-bellied yuhina 31 Table 4.11 Transect density of Grey-cheeked fulvetta 32 Table 4.12 Minimum, maximum and average density of each data set 32 Table 5.1 Detection probability comparison of four Passeriformes 34 ABSTRACT The density and diversity of wild animal including bird species always change, it is necessary to assess the number of these species Wildlife density estimation is an important field that useful for managing and conserving biodiversity However, there is not much research on wildlife density at current time In field survey, not all individuals are detected, especially the one far from transect Therefore detection probability should be concerned before a survey is conducted In this study, density of four Passeriformes species which are Buff-breasted babbler (Pellorneum tickelli), Grey-throated babbler (Stachyris nigriceps), White-bellied yuhina (Erpornis zantholeuca), and Grey-cheeked fulvetta (Alcippe morrisonia) were estimated using DISTANCE program The survey was conducted at Tam Dao National Park (TDNP) from August 26 to September 15, 2015 During the survey 71 Buff-breasted babbler, 53 Grey-throated babbler, 49 White-bellied yuhina and 125 Greycheeked fulvetta individuals were detected With the density of 11.68 birds/ha, Grey-cheeked fulvetta is the most dominant species comparing with three remaining species The density of Buff-breasted babbler, Grey-throated babbler, and White-bellied yuhina respectively are 1.00, 2.25 and 5.23 birds/ha The detection probability and number of bird groups which are detected by observation decrease with the increase of distance, but in some case this trend is not satisfied By density comparison between distances sampling and traditional methods, distance sampling method which use detecting probability indicate its higher accuracy result Therefore adjusting the density by using the detection probability is an effective method in wildlife survey and monitoring I INTRODUCTION Passeriformes which is also called passerine or perching bird is the largest order of bird and the dominant avian group on Earth today With 5,300 species, the order Passeriformes is divided into two suborders: Tyranni and Passeri The first suborder contains about 1,250 species which is considered more primitive and is often grouped informally as the “suboscines” The other suborder’s birds are often grouped as the “oscines” or “songbirds” This group is made up of about 4,500 species (Jonsson et al, 2006) Passeriformes has a worldwide distribution, with representative on all continents except Antarctica The highest density of perching bird is in the tropics Because of high diversity, perching bird is a wide field for researching and studying Passeriformes and all other wildlife density always change under the impact of natural and human pressure So, to reach the best control and management, it is important to estimate the density and diversity of these species However, the importance of wildlife survey was not early recognized This is the reason why the quantitative methods in wildlife survey were introduced later than in flora survey Most of the quantitative methods in surveys and monitoring wildlife were created in the recent four decades (Gilbert et al 1976) Therefore, the number of research or study about wildlife density is still limited In field survey, the density and diversity estimation of fauna is much difficult than flora The biggest challenge is detect the entire individual in study area, almost object far from transect is missed For this reason, ecologists generally have to depend on some kind of estimate of abundance or density There exists a variety of method exist to this job Each kind of wildlife is best suitable with one survey method For example, insects, aquatic organisms, soil organisms can be surveyed by using point, plot survey methods; number of fishes or small mammals are detected by mark and recapture method; etc In case of bird, the best method used is line transect survey method However, it is still impossible to obtain a complete count or census of a natural bird population It mean that the density estimated is smaller than reality and detection probability is smaller than There are two ways to deal with this error The first way is survey in narrow transects, but this way is not efficient in term of time and economic And the rest way is using the data collected from objects in far transect to estimate the detection probability, and then use this number to adjust the density estimate The term “distance sampling” refers to a suite of method that will estimate the absolute density of biological population, based on accurate distance measurements of all objects near a line or point (Buckland et al 1993) The main methods are line-transect sampling and point-transect sampling In both methods, each object detected is recorded the distance from the line or point to this object Not all objects can be observed, but a fundamental assumption of the basic methods is that all the objects on the line or point are detected, and the “detection probability” equal Detection probability is the probability of detecting an object, given that it is at any distance from the random line or point With increasing distance from the line or point, the objects become harder to detect So, the key to distance sampling method is to fit a suitable detection function to the observed distances, and use this function to estimate the proportion of missed objects By this proportion, we can obtain point and interval estimates for the density and diversity of objects in study area Distance sampling is a good method for both flora and fauna survey, but it has not popular applied in Vietnam yet TDNP is a protected area in North Vietnam It was established in 1996, succeeding from the Conservation Forest Tam Dao which was formed in 1977 TDNP is a precious natural resource, where keep high biological diversity with many rare and endemic plants and animals Tam Dao forest also has many species of rare medicinal plants as an useful sources of medicinal Moreover, tourism in TDNP has been becoming a remarkable economic income Birdlife international has ranked Vietnam as one of the leading countries of density and diversity of birds According to many statistic data, Vietnam’s bird population is over 870 species (Nguyen Cu, 1995) Particularly, in TDNP, it has discovered 239 bird species which belong to 140 genus, 50 families The highest species diversity in TDNP is Passeriformes, include 147 species belonging to 73 genus and 26 families In the total 239 bird species, there are endemic species to the North of Vietnam and endemic species to the country In Vietnam, study on bird species is a major field from past to current time Before 1945, almost of bird research project were done by foreign scientist In this period, the most famous is two French scientists are Delacour and Jabuille From 1945 to 1954, because of war, all researches were interrupted After that, bird research was started again in 1957 The most remarkable projects belong to authors like Vo Quy (1962-1966), Tran Gia Huan (19601961), Do Ngoc Quang (1965), Vo Quy and Anorova N.C (1967) Generally, scientist had focused on classification In 1971, professor Vo Quy had summarized his research from year before and published the book: “Biology of common bird in Vietnam” Then, when Vietnam formally became independent, “Bird Vietnam” and “Morphology and classification” were introduced and these books also were the first books about bird morphology and classification in Vietnam Then, in the years after that, the area of forest was reduced rapidly because of increasing of population and economic development As the result, plant and animal decreased seriously in term of number, bird species is one To deal with this problem, Vietnam Government has established a system which includes 87 forest areas (Area is about 1690 km2) But almost these areas did not promote efficiently At that moment, the book “The list of Vietnam bird” of Vo Quy –Nguyen Cu (1995) was published The list contains 19 orders, 81 families and 828 species of Vietnam bird The book focuses much on status and distribution of each species In recent years, there are many biodiversity preservation projects of foreign Government invest to Vietnam such as Netherlands, Germany, Australia, NGOs, Bird Life International, IUCN, WWF, etc From that biodiversity preservation in Vietnam started to develop and “Bird Vietnam” book published by Nguyen Cu, Le Trong Trai, Karen Phillips (2000) This book introduces about over 500 species in 850 recorded species in term of description, distribution, status and color picture attached Fauna which includes birds in Tam Dao was studied by some French professor like J.Delacouri (1931), Osgood (1932), Bourret (1943),… Investigation in Tam Dao has strongly developed from 1954, include projections of University students From 1990-1992, Forest Inventory and Planning Institute has conducted researches on fauna in this area They counted for 281 wild life species which include 58 mammals, 46 reptiles, 19 amphibians, and 158 birds species In 1995, base on Vo Quy and Nguyen Cu research, Tam Dao had 239 bird species Although, up to now there are number of research about birds in TDNP, most of them are general study, and there is not much study which point out the density and diversity of a particular order Therefore, in this study, the density and diversity of some species in the highest diversity order – Passeriformes are assessed using the distance sampling method II GOALS AND OBJECTIVES 2.1 Goals The goal of this research is to provide basic information on the diversity and density of Passerine birds which can contribute to the management and conservation of biodiversity in TDNP 2.2 Objectives - To assess the diversity of Passeriformes in TDNP - To estimate the detection probability of four Passeriformes in TDNP by using Distance sampling method - To estimate the density of four Passeriformes in TDNP Buff-breasted babbler: Figure 4.5 Detection probability functions of Buff-breasted babbler Table 4.4 Buff-breasted babbler_Functions’ parameters Detection Functional form AICc Value Density CV (%) probability (Birds/ha) Half-normal 186.48 0.45 21.07 1.00 Uniform 186.62 0.42 21.23 1.08 Negative exponential 186.93 0.31 23.83 1.50 Hazard-rate 187.05 0.46 23.15 0.98 (AIC- Akaike’s Information Criteria; CV- Coefficient of variation) With the smallest AIC value (186.48), Half-normal function is the best fit model which can well show the detection probability of Buff-breasted babbler density The next orders are Uniform, Negative exponential, and Hazard-rate which have higher AIC values And, χ2 values, degree of freedom, and p-value are 0.22, and 0.89 (>0.05) These are good parameters to model the fluctuation of detection probability by distance 26 Grey-throated babbler: Figure 4.6 Detection probability functions of Grey-throated babbler Table 4.5 Grey-throated babbler_Functions’ parameters Detection Functional form AICc Value Density CV (%) probability ( Birds/ha) Half-normal 117.03 0.28 25.66 2.25 Negative exponential 117.32 0.22 28.39 3.19 Hazard-rate 117.74 0.34 27.6 1.71 Uniform 118.59 0.30 27.76 2.09 With the smallest AIC value (117.03), Half-normal function is the best fit model which can well show the detection probability of Grey-throated babbler density The next orders are Negative exponential, Hazard-rate and Uniform which have higher AIC values And, χ2 values, degree of freedom, and p-value are 1.74, and 0.19 (>0.05) These are good parameters to model the fluctuation of detection probability by distance 27 White-bellied yuhina: Figure 4.7 Detection probability functions of White-ballied yuhina Table 4.6 White-ballied yuhina_Functions’ parameters Detection Functional form AICc Value Density CV (%) probability (Birds/ha) Negative exponential 107.84 0.22 35.61 5.23 Half-normal 108.77 0.28 33.43 2.80 Hazard-rate 109.31 0.30 39.17 2.59 Uniform 110.81 0.30 33.58 2.58 With the smallest AIC value (107.84), Negative exponential function is the best fit model which can well show the detection probability of White-bellied yuhina density The next orders are Half-normal, Hazard-rate and Uniform which have higher AIC values And, χ2 values, degree of freedom, and p-value are 1.25, and 0.54 (>0.05) These are also good parameters to model the fluctuation of detection probability by distance 28 Grey-cheeked fulvetta Figure 4.8 Grey-cheeked fulvetta_Uniform function Table 4.7 Grey-cheeked fulvetta_Functions’ parameters Detection Functional form AICc Value Density CV (%) probability ( Birds/ha) Negative exponential 260.77 0.21 20.26 11.68 Hazard-rate 261.25 0.32 19.98 6.52 Half-normal 261.42 0.28 19.04 7.76 Uniform 263.20 0.30 18.85 7.29 With the smallest AIC value (260.77), Negative exponential function is the best fit model which can well show the detection probability of Grey-cheeked fulvetta density The next orders are Hazard-rate, Half-normal, and Uniform which have higher AIC values And, χ2 values, degree of freedom, and p-value are 1.51, and 0.47 (>0.05) These are also good parameters to model the fluctuation of detection probability by distance 4.2.3 Estimating of species density The below tables show transects’ density of each data set of each species: 29 Table 4.8 Transect density of Buff-breasted babbler Transect Density (Birds/ha) LB UB 0.51 0.41 0.62 1.01 0.82 1.25 0.67 0.55 0.83 1.18 0.96 1.46 0.84 0.68 1.04 0.67 0.55 0.83 2.02 1.64 2.50 0.17 0.14 0.21 0.67 0.55 0.83 10 1.18 0.96 1.46 11 2.36 1.91 2.92 12 0.67 0.55 0.83 Mean 1.00 0.66 1.50 Table 4.9 Transect density of Grey-throated babbler Transect Density (Birds/ha) LB UB 2.33 1.73 3.15 6.53 4.85 8.81 2.33 1.73 3.15 0.93 0.69 1.26 0.93 0.69 1.26 2.80 2.08 3.77 30 1.87 1.39 2.52 1.87 1.39 2.52 1.40 1.04 1.89 10 2.33 1.73 3.15 11 1.40 1.04 1.89 12 No detection No detection No detection Mean 2.25 1.37 3.69 Table 4.10 Transect density of White-bellied yuhina Transect Density (Birds/ha) LB UB 5.13 3.33 7.89 3.85 2.50 5.92 2.56 1.67 3.95 5.13 3.33 7.89 20.51 13.33 31.56 1.28 0.83 1.97 5.13 3.33 7.89 6.41 4.16 9.86 3.85 2.50 5.92 10 5.12 3.33 7.89 11 1.28 0.83 1.97 12 2.56 1.67 3.95 Mean 5.23 2.66 10.30 31 Table 4.11 Transect density of Grey-cheeked fulvetta Transect Density (Birds/ha) LB UB 13.46 10.50 17.25 1.12 0.88 1.44 4.49 3.50 5.75 19.07 14.88 24.44 14.58 11.38 18.69 1.12 0.88 1.44 10.09 7.88 12.94 19.07 14.88 24.44 10.09 7.88 12.94 10 16.82 13.13 31.56 11 15.70 12.25 20.13 12 14.58 11.38 18.69 Mean 11.68 7.89 17.31 LB: Lower Bound; UB: Upper Bound And, the following table includes minimum, maximum and average density of each data set Table 4.12 Minimum, maximum and average density of each data set Species D_min D_max D_average Buff-breasted babbler 0.17 2.36 1.00 Grey-throated babbler 0.93 6.53 2.25 White-bellied yuhina 1.28 20.51 5.23 Grey-cheeked fulvetta 1.12 19.07 11.68 D_min: Minimum Density (Bird/ha); D_max: Maximum Density (Bird/ha); D_average: Average Density (Bird/ha) There are big differences between minimum and maximum density of transects That means all four species are not evenly distribution over study region 32 V DISCUSSION The change of distance can lead to the changes of detection probability and observation/hearing proportion as well The number of detected groups goes down when the distance go up It makes the detection probability decrease gradually However, this trend is not always true In case of Buff-breasted babbler, there is no change in number of detected groups when distance increase from interval 36-48 to 48-60 meters (Figure 4.8) It is similar with the case of Grey-cheeked fulvetta (Figure 4.12) Especially, when distance increase from interval 24-36 to 36-48 meters, number of discovered Grey-throated babbler groups increase instead of decrease (Figure 4.12) The cause of this error may be the lacking skill of surveyor or the barrier in survey transects (such as: trees) which can lead to wrong distance estimation Due to the small size of all four bird species (11-15cm), and the high density of tree (primary forests), surveyors cannot see the bird at far distance It means the proportion of bird groups discovered by observation decrease when the distance increase, and bird groups tend to be detected by their sound at far distance Because the song of the species is not loud, only small numbers of groups were detected by acoustic signal at far distance The density of Buff-breasted babbler, Grey-throated babbler, White-bellied yuhina and Grey-cheeked fulvetta respectively are 1.0, 2.25, 5.23 and 11.68 birds/ hectare So, Greycheeked fulvetta is more abundant than the other three species A common point of all four species is unevenly distribution This is evidenced by the big different between transects’ density (Table 4.7-4.11) A strongly example is the differences between minimum and maximum transects’ density of White-bellied yuhina and Grey-cheeked fulvetta are 16 and 17 times The considered reasons are position and natural condition of transect lines If the transect lines are established near human residents and be affected by human activities, the number of detected groups could be smaller than transect lines in restricted regions In addition, a transect line which has thin tree density can help surveyor more easily in 33 detection And there will be more detection in a transect has good condition for bird living (food, shelter,…) The effect of volume of bird sound on detection probability is quite obvious (Table 5.1) Most of Buff-breasted babbler birds were detected by their songs/calls (69%), and the detection probability of this species is higher than other three species In addition, Halfnormal function model (Figure 4.5) shows the gradually decrease of detection probability respect to increase of distance That means their calls are easy to be recognized and loud enough to be heard at far distances In contrast, almost birds are also detected by hearing but the calls of Grey-throated babbler, White-bellied yuhina, and Grey-cheeked fulvetta are quite small and hard to be heard That limits ability to recognize of surveyor at far distance and causes low detection probability Table 5.1 Detection probability comparison of four Passeriformes %Observation %Hearing Body Cluster size Detection (%) (%) Size (cm) (individual) Probability Species Buff-breasted 31.0 69.0 13-15 1.41 0.45 43.4 56.6 12-15 2.17 0.28 38.8 61.2 11-13 2.88 0.22 35.2 64.8 12.5-14 3.34 0.21 babbler Grey-throated babbler White-bellied yuhina Grey-cheeked fulvetta 34 An advantage of distance sampling surveys is can be performed at any time during the year because it is not dependent on the variation of vision Table 5.2 below shows the comparison between the estimation results using traditional and distance sampling method: Table 5.2 Comparing density estimation results using traditional and distance sampling method Number of Number of Width Species Method birds Area Density (ha) (Birds/ha) excluded (m) recorded birds 60 100 216 1.00 breasted 60 100 216 0.46 babbler 50 99 180 0.55 Don’t account detecting 40 95 144 0.66 probability 30 88 12 108 0.81 20 76 24 72 1.05 10 43 57 36 1.19 Using detection probability 60 115 216 2.25 throated Don’t account detecting 60 115 216 0.53 babbler probability 50 115 180 0.64 40 112 144 0.78 30 107 108 0.99 20 105 10 72 1.50 10 69 46 36 1.92 Buff- Grey- Using detection probability White- Using detection probability 60 141 216 5.23 bellied Don’t account detecting 60 141 216 0.65 35 erpornis 50 140 180 0.78 40 130 10 144 0.90 30 130 10 108 1.20 20 123 18 72 1.71 10 96 45 36 2.67 Using detection probability 60 417 216 11.68 cheeked Don’t account detecting 60 417 216 1.93 fulvetta probability 50 414 180 2.30 40 403 13 144 2.80 30 396 21 108 3.67 20 372 45 72 5.16 10 276 141 36 7.67 Grey- probability Except the circumstance of Buff-breasted babbler, all three remain cases are excellent evidences which show the advantage of distance sampling method Comparing with traditional method, density result estimated by distance sampling method is always higher The density results using traditional method increase when the width of transect decrease In other word, when apply this method, to approach more exactly result surveyor should survey in only small areas However, it wastes time and costs Therefore, distance sampling should be popular use instead of traditional method Although distance sampling method is a great way used to estimate detection probability and density of bird, bias are still occurred Therefore, in order to avoid significant bias and get high accurate data, when field investigations are conducted, surveyor should be more thoughtful in counting and distance estimating 36 VI CONCLUSION In total 298 detected groups, there are 71 Buff-breasted babbler, 53 Grey-throated babbler, 49 White-bellied yuhina and 125 Grey-cheeked fulvetta groups The number of detected groups decrease when the distance increase, it make the detection probability decline At far distance, groups are found out by acoustic signal instead of observation It is reason why proportion of observation decrease and proportion of hearing increase with the rise of distance The detection probability of Buff-breasted babbler, Grey-throated babbler, Whitebellied yuhina and Grey-cheeked fulvetta respectively are 0.45, 0.28, 0.22 and 0.21 These values are affected by distance and the call volume of birds By using distance sampling method, best fit models for each species data are determined With the best fit model is Half-normal, the density of Buff-breasted babbler and Grey-throated babbler estimated is 1.00 (CI: 0.66-1.50) and 2.25 (CI: 1.37-3.69) In case of White-bellied yuhina and Grey-cheeked fulvetta, the best fit function is Negative exponential, and density respectively are 5.23 (CI: 2.66-10.30) and 11.68 (CI: 7.89-17.31) Because of highest density, Grey-cheeked fulveta is the most abundant species comparing to remaining three species With the big difference of density among transect lines, all four studied species are concluded as unevenly distribution species It is caused by the differences in position and natural conditions of each transect line Because of higher density estimated results, distance sampling method shows its higher accuracy than traditional method This is the reason why distance sampling should be more applied in bird and other fauna surveys The result in density survey of bird species positively contribute to bird conservation in TDNP Bird’s abundance data allow us to measure changes in population size and hence 37 gauge the impact of habitat loss, pollution or harvesting and to assess whether or not isolated populations are viable However, there is not much specific study on bird species in TDNP at current time In conclude, more research should be conducted in TDNP to assess the diversity and density of Passeriformes in specifically and all other avian species in general, especially threatened bird species 38 REFERENCES Anderson, D R., K P Burnham, G C White, and D L Otis (1983): Density estimation of small-mammal populations using a trapping web and distance sampling methods Ecology Barraclough, Rosemary K (2000) Distance sampling: a discussion document produced for the Department of Conservation Wellington, N.Z : Dept of Conservation Birdlife International Buckland, S.T.; Anderson, D.R.; Burnham, K.P.; Laake, J.L (1993) Distance sampling: Estimating abundance of biological populations Chapman and Hall, London, UK Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L and Thomas, L (2001): Introduction to Distance Sampling: Estimating Abundance of Biological Populations Oxford University Press, Oxford, UK Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L and Thomas, L (Editors) (2004): Advanced Distance Sampling Oxford University Press, Oxford, UK Buckland, S.T Estimating bird abundance: making methods work Centre for Research into Ecological and Environmental Modelling, University of St Andrews Gilbert, W., (1976), Starting and stopping sequences for the RNA polymerase, in R Losick and ]IdL Chamberlin (Ed&), RNA Polymerase, Cold Spring Harbor, NY IUCN.redlist 10 Jønsson, K.A., and Fjeldså, J (2006), “A phylogenetic supertree of oscine passerine birds (Aves: Passeri).” Zoologica Scripta 11 Nguyen Cu, Le Trong Trai, Karen Phillips (2009): Bird Vietnam 12 Nguyen Hai Tuat, Tran Quang Bao, Vu Tien Thinh (?): Application of quantitative method in forest ecology research 39 13 Thomas, L., S.T Buckland, E.A Rexstad, J.L Laake, S Strindberg, S.L Hedley, J.R.B Bishop & T.A Marques (2010) Distance software: design and analysis of distance sampling surveys for estimating population size Journal of Applied Ecology 14 Tordoff, A W ed (2002) Directory of important bird areas in Vietnam: key sites for conservation Hanoi: BirdLife International in Indochina and the Institute of Ecology and Biological Resources 15 Source: Orientalbirdimages.org 16 Robson, Craig, (2005) Birds of Southeast Asia Princeton University Press, Princeton, New Jersey 17 Vo Quy and Nguyen Cu (1995): Danh luc chim Viet Nam [The list of bird in Vietnam] Thanh Pho publishing house Ha Noi 18 Vo Quy (1993): A catalogue of the birds of Vietnam Thanh Pho publishing house Ha Noi 19 Vo Quy (1981): Chim Viet Nam [Birds of Vietnam] Thanh Pho publishing house Ha Noi 40 ... basic information on the diversity and density of Passerine birds which can contribute to the management and conservation of biodiversity in TDNP 2.2 Objectives - To assess the diversity of Passeriformes... object of interest is estimated/measured) or 2) estimate the distance between the observer and the bird and the angle of the sighting away from the line (yi is calculated using ri and the sighting... now there are number of research about birds in TDNP, most of them are general study, and there is not much study which point out the density and diversity of a particular order Therefore, in