Kernel Methods in Line and Point Transect Sampling

Abstract Point-transect sampling is widely used for monitoring trends in abundance of songbirds. It is conceptualized as a “snapshot” method in which birds are “frozen” at a single location. With conventional methods, an observer records birds detected from a point for several minutes, during which birds may move around. This generates upward bias in the density estimate. I compared this conventional approach with two other approaches: in one, the observer records locations of detected birds at a snapshot moment; in the other, distances to detected cues (songbursts), rather than birds, are recorded. I implemented all three approaches, together with line-transect sampling and territory mapping in a survey of four bird species. The conventional method gave a biased estimate of density for one species. The snapshot method was found to be the most efficient of the point-sampling methods. Line-transect sampling proved more efficient than the point-sampling methods for all four species. This is likely to be generally true, provided that terrain and habitat allow easy use of a design with random transect lines. I concluded that the snapshot method is more appropriate than the conventional timed-count method for surveying songbirds. Although precision was rather poor with the cue-based method (partly because too few resources were devoted to cue rate estimation), it may be particularly useful for some single-species surveys. In addition, it is the only valid method for estimating abundance from surveys in which acoustic equipment is used to detect birds. Muestreos en Transectos Puntuales para Aves Canoras: Metodologías Robustas

Download Full-text

SEAGRESS COVERAGE AND ECOSYSTEM CONDITION AT THE COASTAL AREA OF MADASANGER, JELENGA AND MALUK, WEST SUMBAWA

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.29244/jitkt.v5i1.7744 ◽

2013 ◽

Vol 5 (1) ◽

Author(s):

Erny Poedjirahajoe ◽

Ni Putu Diana Mahayani ◽

Boy Rahardjo Sidharta ◽

Muhamad Salamuddin

Keyword(s):

Coastal Area ◽

Seagrass Species ◽

Seagrass Bed ◽

Water Turbidity ◽

Transect Sampling ◽

Sampling Points ◽

Ecosystem Condition ◽

High Turbidity ◽

Line Transects ◽

Seagrass Coverage

The increase of temperature might affect the distribution and reproduction of seagrass. This research aims to determine the seagrass bed coverage and the ecosystem condition. Three line transects were established perpendicular to the coastal line with the distance of 50-100 m, or up to the border of the intertidal area. In each transect, sampling points were determined with a distance of 10-20 m. At the sampling points, a plot of 50 cm x 50 cm was established to measure the coverage percentage of seagrass vegetation. The seagrass species were also observed and recorded along the line transects. The percentage of seagrass coverage was measured using a method from Saito and Atobe (1994). The results showed that the coastal area of Jelenga has the highest percentage of seagrass coverage (>60%, healthy) among other coastal areas. This may be caused by the characteristic of Jelenga coast which was relatively calm, few visitors, low water turbidity, and high light penetration. While, other transects have percentage coverage of less than 60% (less healthy). There was one transect on Maluk coast which has coverage percentage of less than 29% (lack of seagrass species). The small coverage percentage on Maluk coast can be caused by the high number of visitors and high activity of fishing boats around the coast which results in high turbidity. Keywords: coverage, ecosystem condition, seagrass bed, west Sumbawa.

Download Full-text

Imbalanced Data Detection Kernel Method in Closed Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3652 ◽

2013 ◽

Vol 756-759 ◽

pp. 3652-3658

Author(s):

You Li Lu ◽

Jun Luo

Keyword(s):

Kernel Methods ◽

Kernel Method ◽

Imbalanced Data ◽

Data Detection ◽

Data Sets ◽

System Call ◽

Data Set ◽

Imbalanced Data Sets ◽

Lower Complexity ◽

Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

Download Full-text