INTRODUCTION
Passeriformes, commonly known as passerine or perching birds, represent the largest and most dominant order of birds on Earth, encompassing approximately 5,300 species This order is divided into two suborders: Tyranni, which includes about 1,250 more primitive species often referred to as "suboscines," and Passeri, which consists of the more advanced "oscines."
Songbirds, belonging to the Passeriformes order, encompass approximately 4,500 species and are found globally, with the exception of Antarctica The highest concentration of these perching birds occurs in tropical regions, making them a rich area for research and study due to their remarkable diversity.
The density and diversity of Passeriformes and other wildlife fluctuate due to natural and human influences, making it essential to estimate these factors for effective management and control Unfortunately, the significance of wildlife surveys was not acknowledged early on, leading to the delayed introduction of quantitative methods compared to flora surveys Most quantitative techniques for wildlife surveys and monitoring have been developed in the last four decades, resulting in a limited body of research on wildlife density.
Estimating the density and diversity of fauna in field surveys poses greater challenges compared to flora, primarily due to difficulties in detecting all individuals within the study area, leading to potential omissions of distant objects from transects Consequently, ecologists often rely on various estimation methods for abundance or density, with each wildlife type suited to specific survey techniques For instance, point and plot survey methods are effective for insects, aquatic organisms, and soil organisms, while the mark and recapture method is utilized for fish and small mammals In the case of birds, the line transect survey method is considered the most effective Despite these methodologies, achieving complete accuracy in fauna detection remains a significant challenge.
A complete count or census of a natural bird population often underestimates density due to a detection probability of less than one To address this error, one method involves surveying narrow transects; however, this approach is not time-efficient or cost-effective Alternatively, researchers can utilize data from distant transects to estimate detection probability, which can then be applied to adjust density estimates accurately.
Distance sampling is a method used to estimate the absolute density of biological populations by accurately measuring the distance of objects near a line or point (Buckland et al 1993) The primary techniques involved are line-transect sampling and point-transect sampling, where the distance from the line or point to each detected object is recorded A critical assumption of these methods is that all objects on the line or point are detected, with a detection probability of 1 However, as the distance from the line or point increases, detecting objects becomes more challenging Therefore, the effectiveness of distance sampling relies on fitting an appropriate detection function to the observed distances, which helps estimate the proportion of missed objects This proportion allows researchers to derive point and interval estimates for the density and diversity of organisms in the study area While distance sampling is effective for surveying both flora and fauna, its application remains limited in Vietnam.
Tam Dao National Park (TDNP), established in 1996 and originating from the Tam Dao Conservation Forest formed in 1977, is a protected area in North Vietnam renowned for its rich biodiversity The park is home to numerous rare and endemic species of plants and animals, including valuable medicinal plants that serve as important resources for traditional medicine Additionally, tourism in TDNP has emerged as a significant source of economic income, highlighting the park's dual role in conservation and local development.
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
The Tam Dao National Park (TDNP) is home to a remarkable diversity of bird species, with a total of 239 identified across 140 genera and 50 families The most diverse group within this park is the Passeriformes, which includes 147 species from 73 genera and 26 families Notably, TDNP hosts 9 bird species that are endemic to Northern Vietnam and 5 species that are endemic to the entire country.
In Vietnam, study on bird species is a major field from past to current time Before
From 1945 to 1954, bird research in Vietnam was primarily conducted by foreign scientists, notably the French duo Delacour and Jabouille However, the ongoing war interrupted all research activities during this period Bird research resumed in 1957, with significant contributions from Vietnamese authors such as Vo Quy between 1962 and 1966 and Tran Gia Huan in the early 1960s.
In Vietnam, significant research on bird classification began in the early 1960s, with contributions from scientists like Do Ngoc Quang and Vo Quy In 1971, Professor Vo Quy published "Biology of Common Birds in Vietnam," summarizing seven years of research and marking a pivotal moment in avian studies Following Vietnam's independence, foundational texts such as "Bird Vietnam" and "Morphology and Classification" emerged, establishing the country's first comprehensive resources on bird morphology and classification However, rapid population growth and economic development led to significant deforestation, resulting in a decline in both plant and animal species, including birds In response, the Vietnamese government has implemented a conservation strategy that includes the establishment of 87 protected forest areas to address biodiversity loss.
Despite covering an area of 1690 km², many regions in Vietnam have not been effectively promoted for biodiversity The publication of "The List of Vietnam Birds" by Vo Quy and Nguyen Cu in 1995 highlighted this issue, cataloging 19 orders, 81 families, and 828 bird species, while emphasizing their status and distribution In recent years, numerous projects aimed at preserving biodiversity have emerged, reflecting a growing awareness of the importance of protecting Vietnam's avian diversity.
Foreign governments, including the Netherlands, Germany, and Australia, along with NGOs like BirdLife International, IUCN, and WWF, have invested in biodiversity preservation efforts in Vietnam This support has led to significant developments, exemplified by the publication of "Bird Vietnam" by Nguyen Cu, Le Trong Trai, and Karen Phillips in 2000 The book features over 500 species from a total of 850 recorded species, providing detailed descriptions, distribution information, conservation status, and accompanying color photographs.
Tam Dao's diverse fauna, particularly its bird population, has been the subject of extensive research since the early 20th century, with notable studies conducted by French professors such as J Delacouri in 1931 and Osgood in 1932 Research efforts intensified after 1954, involving university student projects Between 1990 and 1992, the Forest Inventory and Planning Institute documented a total of 281 wildlife species in the region, including 58 mammals, 46 reptiles, 19 amphibians, and 158 bird species Furthermore, a 1995 study by Vo Quy and Nguyen Cu confirmed the presence of 239 bird species in Tam Dao.
Despite numerous studies on birds in TDNP, most have been general in nature, lacking a focus on the density and diversity of specific orders This research aims to evaluate the density and diversity of various species within the Passeriformes order, recognized for its high diversity, utilizing the distance sampling method.
GOALS AND OBJECTIVES
Goals
This research aims to deliver essential insights into the diversity and density of Passerine birds, which are crucial for effective management and conservation of biodiversity in TDNP.
Objectives
- To assess the diversity of Passeriformes in TDNP
- To estimate the detection probability of four Passeriformes in TDNP by using
- To estimate the density of four Passeriformes in TDNP
METHODS
Study area
Data collected from August to September 2015 in Tam Dao National Park (TDNP), located at coordinates 21°21’-21°42’ N latitude and 105°23’-105°44’ E longitude, reveals that the park spans an area of 34.995 hectares across three provinces: Vinh Phuc, Thai Nguyen, and Tuyen Quang The park is situated along the Tam Dao mountain range, which extends over 80 kilometers from Son Duong District in Tuyen Quang to Phuc Yen Town in Vinh Phuc This range features several peaks exceeding 1,300 meters, with Tam Dao Bac being the highest at 1,592 meters Due to human activities such as harvesting, cultivation, and forest fires, the remaining forest areas are primarily found at altitudes around 700 meters, while lower elevations have been cleared for secondary shrubs, grass-shrubs, and pine plantations.
TDNP spans 34,995 hectares, featuring 26,163 hectares of predominantly natural moist evergreen forest, which covers 70% of the area Additionally, the park is home to various other forest types, including evergreen subtropical moist low mountain forests, pygmy forests, bamboo forests, restoration areas post-mining, plantations, scrubland, and grasslands.
TDNP is recognized for its high biodiversity value in Vietnam, hosting numerous species of significant conservation importance, particularly rare and endemic species at various risk levels of extinction An initial survey conducted in 2000 identified 1,282 plant species across 660 genera.
The Tam Dao National Park (TDNP) is home to 179 families of vascular plants, including 42 endemic and 64 rare species that require protection at both national and global levels Additionally, the park boasts a rich diversity of fauna, highlighted by the notable presence of Paramesotriton deloustali TDNP serves as a vital habitat for numerous reptiles and amphibians, contributing significantly to Vietnam's natural reserves and national parks.
TDNP has been recognized as one of the 63 important bird areas of Vietnam (Tordoff et al
The TDNP is recognized for its diverse bird species, particularly those with limited distribution in a specific biogeographic unit Notably, it is home to two globally threatened species: the Eastern Imperial Eagle (Aquila heliacal) and the Red-bellied Pitta (Pitta nympha), both classified as Vulnerable (VU) by Birdlife International.
According to Nguyen Xuan Dang et al (2005), TDNP is home to 77 mammal species, with 29 identified as priority for conservation Research indicates that TDNP boasts the highest insect diversity among national parks in Vietnam Vu Van Lien (2005) documented 360 butterfly species, including 9 that are of conservation concern, while Dang Thi Dap (2000) identified 122 leaf-eating insect species, many of which are highly valued and frequently targeted for trade.
In addition to its biodiversity significance, Tam Dao National Park (TDNP) plays a crucial role in water regulation and soil protection, supporting the conservation and sustainable development of the Red River watershed Furthermore, TDNP significantly contributes to the local economy of Tam Dao by offering substantial tourism potential.
Species descriptions
This project aims to estimate the population density of four abundant Passerine bird species: the Buff-breasted babbler (Pellorneum tickelli), Grey-throated babbler (Stachyris nigriceps), White-bellied erpornis (Erpornis zantholeuca), and Grey-cheeked fulvetta (Alcippe morrisonia), which provide a sufficient sample size for analysis.
The Pellorneum tickelli species has been divided into two distinct species, tickelli and buettikoferi, which are now classified under the genus Trichastoma according to Wells et al (2001) The buff-breasted babbler measures between 13-15 cm in body size, featuring a squarish-tipped long tail and forehead feathers that appear slightly scaled with pale shaft-streaks, displaying a warm brown coloration on its upper body and a warm duff hue.
The species, characterized by a whitish belly and a lack of grey on its face, is found across Northeast India, Bangladesh, Burma, and extends to southern China, Thailand, Laos, Myanmar, and Vietnam It inhabits tropical and subtropical forests Currently, the population is believed to be stable, with no significant evidence indicating a decline or major threats.
Figure 3.2 Buff-breasted babbler (Pellorneum tickelli)
The Grey-throated Babbler (Stachyris nigriceps) is a bird species belonging to the Timallidae family, measuring 12-15 cm in length This bird features a distinctive plumage with alternating black and white on the forehead, top of the head, and nape, complemented by a white eye ring, a yellow or red face, and a brown or light black upper beak with a gray under beak It inhabits subtropical and tropical lowland forests, as well as montane forests across several countries, including Bangladesh, Bhutan, China, India, Indonesia, Laos, Malaysia, Myanmar, Nepal, Thailand, and Vietnam.
Figure 3.3 Grey-throated babbler (Stachyris nigriceps)
The white-bellied yuhina (Erpornis zantholeuca) is a unique species within its genus, characterized by its yellowish-green upper body and grayish-white underparts with a yellow vent This small bird, measuring between 11-13 cm, features a short crest and a squared-off tail It inhabits a wide geographical range, including countries such as Bangladesh, Bhutan, Brunei, Cambodia, China, India, Indonesia, Laos, Malaysia, Myanmar, Nepal, Singapore, Taiwan, Thailand, and Vietnam, primarily residing in subtropical and tropical moist montane forests.
Figure 3.4 White-bellied yuhina (Erpornis zantholeuca)
The Grey-cheeked Fulvetta (Alcippe morrisonia), a member of the Alcippe genus within the Pellorneidae family, measures 12.5-14 cm in length This bird features a distinctive grey head with a white eye ring and a long black eye stripe extending from its bill to the side of its neck, with olive coloration on its back and yellow underneath The species is primarily found in subtropical and tropical moist montane forests across Taiwan, Laos, China, Myanmar, Thailand, and Vietnam.
Figure 3.5 Grey-cheeked fulvetta (Alcippe morrisonia)
Because of the large distribution range and great population, the populations of all four species do not approach the thresholds for Vulnerable at current time.
Data collection
Based on the interview results, the survey was conducted in Tay Thien and Dao Tru communes, identified as the primary locations for observing Passeriformes This focused approach allowed for a more refined study area in preparation for the investigation site.
Before collecting data, a survey was conducted to verify the information on maps (topographic map, administrative map, forest status), if there is any change or difference, supplement the information collected
Twelve evenly distributed line transects, each 500 meters long, are established in a secondary forest at elevations ranging from 200m to 600m To reduce edge effects and the impact of status changes on detection probability, these transects are positioned at least 75 meters away from the status boundary.
100 m apart to make sure independence
Figure 3.6 Simulating objects detected in line transects
Essential equipment for conducting surveys includes binoculars, cameras, data sheets, and maps For bird identification and classification, reference materials such as "Birds of Southeast Asia" by Craig Robson (2005) and "Birds of Vietnam" by Nguyen Cu et al (2000) are invaluable resources for accurate data collection.
All transects are surveyed during optimal bird activity periods, specifically from early morning to mid-noon (11:00 to 12:00) and again from 15:00 to 18:00, which are ideal for observing their feeding and operational behaviors.
Observers traverse transects at a speed of approximately 0.5 km every 40 minutes, meticulously recording the presence and number of birds through both visual observation and their songs Additionally, they estimate the distance from each bird to the transect line Each transect is surveyed three times to ensure accurate data collection.
There are two primary methods for estimating distance: the first involves directly measuring the perpendicular distance (y_i) from the transect line to the bird, while the second method calculates the distance between the observer and the bird, factoring in the sighting angle (θ_i) to determine y_i using the distance (r_i) These techniques are visually represented in Figure 3.7.
Figure 3.7 Distance estimation/measurement along transects
W = strip width (1/2) r i = sighting distance (flushing distance) θi (theta) = sighting angle y i = perpendicular distance (note: y = r i sin θ i )
The collected data is recorded in data sheet as table below:
Table 3.1 Field data sheet used to collect information
Transect number: …… Time start/finish: ………
Quantity Distance (m) Heard/Seen c Data analysis
To estimate the density of objectives, surveyors rely on detection probability derived from investigative data This probability is calculated using frequency distribution at varying distances from the transect or observer A widely used software for analyzing detection probability is DISTANCE 6.0 (Thomas et al., 2010).
Before estimating detection probability, surveyors must define detection probability function Four basic functions which are used to estimate detection probability by distance are:
With: g(y) is the detection probability of an object with distance to line transect is y (length unit); w, , b are the estimated parameters;
In addition to the four primary functions, three expansion series—Cosine, Simple Polynomials, and Hermite Polynomials—can be utilized to modify detection probability curves and enhance the simulation of detection probability variations The integration of standard functions with these expansion series is represented by the equation: g(y) = basic function(y) + expansion series(y).
In some cases, this combination can indicate the relationships between detection probability and distance better
Figure 3.8 Graph of four standard functions used in Distance method
The Akaike Information Criterion (AIC) is a statistical tool used to identify the most effective function for estimating detection probability, as outlined by Anderson (2007) This criterion emphasizes a balance between standard deviation and variance, with the function exhibiting the lowest AIC value being selected Furthermore, the Distance method offers χ2 values to facilitate additional verification.
The suitability of a function is assessed through the distribution of empirical frequency Once the optimal function is identified, the detection probability (P a ) is calculated by dividing the area under the upper function curve by the rectangle area and then multiplying by the transect width.
The estimation of density with distance sampling cannot exactly unless five fundamental assumptions have to be satisfied:
Objects positioned directly on the detection line are identified with complete certainty, achieving a detection probability of 100% (g(0) = 1) However, as the distance from the line increases, the likelihood of accurately detecting these objects significantly diminishes (Buckland et al 2001).
Objects do not move All measurements are made from the objects’ initial location, before it was affected by the observer (Buckland et al 2001)
Measurements are accurate All angles, distance, objects, sex and other necessary measurements are measured with accuracy without any errors (Buckland et al 2001)
Accurate recording of cluster sizes is essential for animal species that exist in groups, as these sizes can be reliably estimated near the observation point but may become inaccurate at greater distances To mitigate bias from size-biased sampling in clustered data, it is recommended to utilize the regression correction feature available in the software Distance (Buckland et al 2001).
The sampled plots, whether in circular or strip form, should accurately represent the entire survey region While this assumption is typically inherent in a well-randomized design, it gains significance when non-random plots are utilized, highlighting the necessity of ensuring representativeness in such cases.
Although other assumptions are made, generally only the above five have any practical significance
In each transect measuring L meters, n objects are detected, with their perpendicular distances to the transect line recorded When all objects along the line are confidently identified, the object density (D) in the survey is estimated according to Buckland et al (2001).
With: n – Total objects is detected in the surveys ̂ - Detection probability ≤ 1 a = 2Lw (w – width of line transect, L – total line length)
Variance of density estimation is calculated by the formula: ̂ ( ̂) = ̂² { ̂( ) + ̂( ̂ )
( ) } Variance of density estimation show fluctuations the number of individuals detected in transect (E(s) is group size)
The total number of individual in each species (population size) is calculated by time density ( )̂ with area (A): ̂ ̂
The variance of population size is: ̂ ( ̂) ̂ ( ̂)
RESULT
Diversity of Passeriformes in TDNP
A total of 68 species from 41 genera and 19 families of Passerine birds were identified, with the Puff-throated Bulbul (Alophoixus pallidus) being the most abundant, comprising 227 groups in TDNP All Passerine species are classified as Least Concern on the IUCN Red List.
Table 4.1 Passeriformes diversity in TDNP
1 Campephagidae Pericrocotus Scarlet minivet Pericrocotus speciosus
2 Cettiidae Abroscopus Yellow-bellied warbler
4 Corvidae Cissa Indochinese green magpie/ yellow- breasted magpie
6 Eurylaimidae Pasarisomus Long-tailed broadbill
7 Leiothrichidae Garrulax Black-throated laughingthrush
8 Monarchidae Terpsiphone Asian paradise flycatcher
Hypothymis Black-naped blue flycatcher
9 Muscicapidae Monticola White-throated rock thrush
Niltava Large niltava Niltava grandis 10 LC
Fujian niltava Niltava davidi 1 LC
10 Nectariniidae Aethopyga Fork-tailed sunbird
11 Oriolidae Oriolus Golden oriole Oriolus oriolus 1 LC
12 Paridae Melanochlora Sultan tit Melanochlora sultanea
13 Phylloscopidae Phylloscopus Yellow- browed warbler
14 Pellorneidae Alcippe Rufous- Alcippe 1 LC
Puff-throated babbler/Spotte d babbler
15 Pycnonotidae Alophoixus Puff-throated bulbul
16 Rhipiduridae Rhipidura White-throated fantail
17 Stenostiridae Culicicapa Grey-headed canary- flycatcher
18 Timaliidae Mixornis Pin-striped tit- babbler
Pomatorhinus Coral-billed scimitar babbler
19 Turdidae Geokichla Orange-headed thrush
Density of species
The survey identified a total of 71 Buff-breasted babblers, 53 Grey-throated babblers, 49 White-bellied yuhinas, and 125 Grey-cheeked fulvetta groups, with each group's distance data categorized as a "distance data set." Notably, most groups were observed at distances of less than 24 meters.
22 distance from transect lines In order to model the detection as distance increase, each species’s distance data was divided into 5 groups as the following table:
Table 4.3 refers the number and percentage of Observation and Hearing in detection species:
Table 4.3 Number and percentage of Observation and Hearing in detection species
In the study of four species, the majority of groups were identified through their songs or calls, with 190 groups (63.8%) detected by sound compared to 108 groups (36.2%) identified by observation Notably, as the distance from the transect increased, there was a consistent decline in the proportion of observations and a corresponding rise in the proportion of detections by hearing, as illustrated in the accompanying graphs.
Figure 4.1 Percentage of observation and hearing in Buff-breasted babbler group detection Hearing percentage is double the percentage of observation when the distance up to
12 meters And from 24 meters, all the Buff-breasted babbler groups were detected by hearing without any observation
Percentage of observation and hearing in Buff-breasted babblergroup detection
Figure 4.2 Percentage of observation and hearing in Grey-throated babbler group detection
As distance increases, the ability to hear surpasses the ability to observe, with all groups of Grey-throated babblers identified by their calls from 48 meters away.
Figure 4.3 Percentage of observation and hearing in White-bellied yuhina (erpornis) group detection
Percentage of observation and hearing in Grey-throated babbler group detection
Percentage of observation and hearing in White-bellied yuhina group detection
Figure 4.4 Percentage of observation and hearing in Grey-cheeked fulvetta group detection
In case of White-bellied yuhina and Grey-cheeked fulvetta, from 12 meters there were no groups detected by observation, and 100% of them were discovered by hearing
4.2.2 Modeling detection probability by distance method
The analysis of detection probability across four data sets reveals a consistent trend: as the distance from transect lines increases, the detection probability decreases This pattern is visually represented in the detection probability histograms generated by the distance method The most appropriate functions for each species were identified using this method, and the χ2 values were calculated to evaluate the fit of these functions against the empirical frequency distribution A χ2 value exceeding 0.05 indicates that the selected function effectively models the variation in detection probability with distance Below, we present the distance data analysis, including histograms and relevant parameter tables generated by the distance software.
Percentage of observation and hearing in Grey-cheeked fulvetta group detection
Figure 4.5 Detection probability functions of Buff-breasted babbler
Table 4.4 Buff-breasted babbler_Functions’ parameters
(AIC- Akaike’s Information Criteria; CV- Coefficient of variation)
The Half-normal function, with the lowest AIC value of 186.48, is identified as the optimal model for representing the detection probability of Buff-breasted babbler density Following this, the Uniform, Negative Exponential, and Hazard-rate models present higher AIC values Additionally, the χ2 value is 0.22, with 2 degrees of freedom and a p-value of 0.89, indicating a strong fit for modeling the variation in detection probability based on distance.
Figure 4.6 Detection probability functions of Grey-throated babbler Table 4.5 Grey-throated babbler_Functions’ parameters
The Half-normal function, with the lowest AIC value of 117.03, is identified as the best fitting model for estimating the detection probability of Grey-throated babbler density Following this, the Negative exponential, Hazard-rate, and Uniform models exhibit higher AIC values Additionally, the χ2 value is 1.74, with 1 degree of freedom and a p-value of 0.19 (greater than 0.05), indicating effective parameters for modeling the variation in detection probability based on distance.
Figure 4.7 Detection probability functions of White-ballied yuhina Table 4.6 White-ballied yuhina_Functions’ parameters
The Negative Exponential function, with the lowest AIC value of 107.84, is identified as the most effective model for representing the detection probability of White-bellied Yuhina density Following this, the Half-Normal, Hazard-rate, and Uniform models exhibit higher AIC values Additionally, the χ2 value is 1.25, with 2 degrees of freedom and a p-value of 0.54, indicating that these parameters are also suitable for modeling the variations in detection probability based on distance.
Figure 4.8 Grey-cheeked fulvetta_Uniform function Table 4.7 Grey-cheeked fulvetta_Functions’ parameters
The Negative Exponential function, with the lowest AIC value of 260.77, is identified as the optimal model for representing the detection probability of Grey-cheeked fulvetta density Following this model, the Hazard-rate, Half-normal, and Uniform functions exhibit higher AIC values Additionally, the χ2 value is 1.51 with 2 degrees of freedom and a p-value of 0.47 (greater than 0.05), indicating that these parameters are also effective in modeling the variability of detection probability across different distances.
The below tables show transects’ density of each data set of each species:
Table 4.8 Transect density of Buff-breasted babbler
Transect Density (Birds/ha) LB UB
Table 4.9 Transect density of Grey-throated babbler
Transect Density (Birds/ha) LB UB
12 No detection No detection No detection
Table 4.10 Transect density of White-bellied yuhina
Transect Density (Birds/ha) LB UB
Table 4.11 Transect density of Grey-cheeked fulvetta
Transect Density (Birds/ha) LB UB
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
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
DISCUSSION
Changes in distance can significantly impact detection probability and the proportion of observations or hearings Generally, as distance increases, the number of detected groups declines, leading to a gradual decrease in detection probability However, this trend does not hold true for all species For instance, the Buff-breasted Babbler shows no change in the number of detected groups when the distance increases from 36-48 meters to 48-60 meters Similarly, the Grey-cheeked Fulvetta exhibits a consistent detection rate within this range Interestingly, the Grey-throated Babbler shows an increase in detected groups when the distance rises from 24-36 meters to 36-48 meters Such discrepancies may arise from surveyor inexperience or obstacles in the survey transects, such as trees, which can lead to inaccurate distance estimations.
The small size (11-15cm) of the four bird species and their habitat in dense primary forests make them difficult to spot from a distance As the distance increases, the likelihood of observing these bird groups decreases significantly, leading to a reliance on their sounds for detection However, due to the quiet nature of their songs, only a limited number of groups can be identified by acoustic signals from afar.
The densities of the Buff-breasted babbler, Grey-throated babbler, White-bellied yuhina, and Grey-cheeked fulvetta are 1.0, 2.25, 5.23, and 11.68 birds per hectare, respectively, indicating that the Grey-cheeked fulvetta is the most abundant species among them All four species exhibit uneven distribution, as shown by significant differences in density across various transects For instance, the density of White-bellied yuhina and Grey-cheeked fulvetta varies by 16 and 17 times between minimum and maximum transects Factors influencing these variations include the location and natural conditions of the transect lines; those near human settlements and impacted by human activities tend to have fewer detected groups compared to transects in more restricted areas Additionally, transect lines with lower tree density facilitate easier surveying for researchers.
34 detection And there will be more detection in a transect has good condition for bird living (food, shelter,…)
The volume of bird sound significantly impacts detection probability, as illustrated in Table 5.1 The Buff-breasted babbler is detected primarily through its songs and calls (69%), demonstrating a higher detection probability compared to three other species The Half-normal function model (Figure 4.5) indicates a gradual decrease in detection probability with increasing distance, suggesting that their calls are loud and easily recognizable from afar In contrast, the Grey-throated babbler, White-bellied yuhina, and Grey-cheeked fulvetta have softer calls, making them difficult to hear and resulting in lower detection probabilities for surveyors at greater distances.
Table 5.1 Detection probability comparison of four Passeriformes
Detection Probability Buff-breasted babbler
Distance sampling surveys offer the flexibility to be conducted at any time throughout the year, as they are not influenced by visibility variations The comparison of estimation results between traditional and distance sampling methods is illustrated in Table 5.2 below.
Table 5.2 Comparing density estimation results using traditional and distance sampling method
The distance sampling method offers significant advantages over traditional methods, as evidenced by the cases presented, excluding the Buff-breasted babbler Typically, density estimates derived from distance sampling are consistently higher than those obtained through traditional approaches Moreover, the density results from traditional methods tend to increase as the transect width decreases, suggesting that surveying smaller areas yields more accurate results However, this approach can be time-consuming and costly Therefore, adopting distance sampling as a standard practice is recommended to enhance efficiency and accuracy in density estimation.
The distance sampling method is an effective technique for estimating bird detection probability and density; however, it can still introduce biases To minimize these biases and achieve highly accurate data during field investigations, surveyors must exercise greater care in both counting and estimating distances.
CONCLUSION
A total of 298 groups were detected, consisting of 71 Buff-breasted babblers, 53 Grey-throated babblers, 49 White-bellied yuhinas, and 125 Grey-cheeked fulvetta groups Detection probability declines with increasing distance, leading to a decrease in observed groups At greater distances, groups are primarily identified through acoustic signals rather than visual observation, resulting in a higher proportion of detections by sound as distance increases.
The detection probabilities for various bird species are as follows: Buff-breasted babbler at 0.45, Grey-throated babbler at 0.28, White-bellied yuhina at 0.22, and Grey-cheeked fulvetta at 0.21 These probabilities are influenced by factors such as distance and the volume of the birds' calls.
Using the distance sampling method, the best fit models for each bird species were determined The Half-normal model estimated the densities of the Buff-breasted Babbler and Grey-throated Babbler at 1.00 (CI: 0.66-1.50) and 2.25 (CI: 1.37-3.69), respectively For the White-bellied Yuhina and Grey-cheeked Fulvetta, the Negative Exponential model was the best fit, with densities of 5.23 (CI: 2.66-10.30) and 11.68 (CI: 7.89-17.31), respectively Among these species, the Grey-cheeked Fulvetta is the most abundant due to its highest density.
The four studied species exhibit uneven distribution due to varying densities across transect lines, influenced by the distinct positions and natural conditions of each line.
The distance sampling method offers greater accuracy in estimating higher density results compared to traditional methods, making it a more effective choice for bird and fauna surveys Therefore, it is essential to increase the application of distance sampling in ecological research.
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
The impact of habitat loss, pollution, and harvesting on bird populations, particularly in TDNP, remains under-researched To ensure the viability of isolated populations, it is essential to conduct more studies focused on the diversity and density of Passeriformes and other avian species, especially those that are threatened Increased research efforts in TDNP are crucial for understanding and preserving bird species in the area.
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