scholarly journals The diversity of Smilax (Smilacaceae) in Besiq-Bermai and Bontang Forests, East Kalimantan, Indonesia

2018 ◽  
Vol 20 (1) ◽  
pp. 279-287
Author(s):  
SITI SOFIAH ◽  
LULUT DWI SULISTYANINGSIH
Keyword(s):  
Principal Component Analysis ◽  
Random Sampling ◽  
Principal Component ◽  
Environmental Data ◽  
Taxonomic Study ◽  
East Kalimantan ◽  
Taxonomic Description ◽  
Lack Of Information ◽  
Growing Environment ◽  
Species Occurrences

Sofiah S, Sulistyaningsih LD. 2019. The diversity of Smilax (Smilacaceae) in Besiq-Bermai and Bontang Forests, East Kalimantan, Indonesia. Biodiversitas 20: 279-287. The genus Smilax has taxonomic complexity problems and spacious distribution. Taxonomic study to reveal the diversity of Smilax species had been done in some regions, such as America, China, Japan, Thailand, and Indonesia. However, there is lack of information of Smilax species diversity in Kalimantan especially in East Kalimantan which lies in Sundaland biogeographic. This study was carried out to explore and record the diversity of Smilax species including the ecological and environmental data in Besiq-Bermai and Bontang forests in East Kalimantan, Indonesia. This research conducted on February and August in 2012 and July-August 2015 using exploration methods. Purposive random sampling was used to do the botanical sampling. The principal component analysis (PCA) was performed to determine the relationships between environmental components and Smilax species occurrences. There were five species of Smilax which were housed in those forests in East Kalimantan, namely, Smilax leucophylla Blume, Smilax gigantea Merr., Smilax odoratissima Blume, Smilax zeylanica L., and Smilax modesta A.DC. Smilax leucophylla and Smilax zeylanica are the most widely used by the local people for medicine. The taxonomic description, distribution, use, and vernacular name were given. The environmental factors that contribute significantly to Smilax's growing environment are temperature and light intensity.

scholarly journals HUBUNGAN KELIMPAHAN SPESIES LARVA IKAN DENGAN PARAMETER KUALITAS PERAIRAN DI DANAU RANAU, SUMATERA SELATAN

2019 ◽  
Vol 20 (1) ◽  
pp. 68-82
Author(s):  
Tuah N. M. Wulandari ◽  
Etty Riani ◽  
Agnes P Sudarmo ◽  
Budhi H Iskandar ◽  
Nurhasanah Nurhasanah
Keyword(s):  
Principal Component Analysis ◽  
Oreochromis Niloticus ◽  
Fish Larvae ◽  
Principal Component ◽  
Component Analysis ◽  
Scientific Basis ◽  
Policy Formation ◽  
Total Alkalinity ◽  
Chemical Parameters ◽  
Lack Of Information

This research conducted due to lack of information about fish larvae in Ranau Lake, South Sumatera. This information is quite essential to explore because this can be used as a scientific basis for policy formation in this area. The objectives of this research are to analyze the correlation between fish larvae abundance to physicochemical parameters in Ranau Lake waters. Sampling was carried out at six stations (Muara Silabung, Dermaga, Way Maisin, Pemandian Air Panas, Lumbok, and Talang Teluk). Physico-chemical parameters measured directly in the field are temperature, pH, depth, brightness, CO2, O2, hardness, electrical conductivity, total alkalinity, and turbidity; while the chemical parameters measured in the laboratory are COD, NO2, NO3, NH3, and PO4. Larvae species identified through DNA sequence. Principal Component Analysis (PCA) was used to measure the relationship between fish larvae abundance to the water parameters. Results show that generally there were forty-two fish larvae from nine species. The dominant species was Oreochromis niloticus. The results of the Principal Component Analysis show that the highest abundance of fish larvae was in water with the highest level of turbidity and dissolved oxygen, whereas the lowest abundance was in water with the highest level nitrate and depth.   Belum ada informasi tentang kelimpahan larva ikan diperairan Danau Ranau Sumatera Selatan melatarbelakangi penelitian ini. Informasi ini sangat penting untuk diketahui karena dapat dijadikan acuan dalam pengelolaan perikanan di wilayah ini. Penelitian bertujuan untuk menganalisis hubungan kelimpahan larva ikan dengan parameter fisika-kimia di perairan Danau Ranau. Pengambilan sampel dilakukan di enam stasiun (Muara Silabung, Dermaga, Way Maissin, Pemandian Air Panas, Lumbok dan Talang Teluk). Parameter fisika-kimia perairan yang diukur langsung di lapangan adalah suhu, pH, kedalaman, kecerahan, CO2, O2, kesadahan, daya hantar listrik, total alkalinitas, dan turbiditas; sedangkan parameter kimia yang diukur di laboratorim adalah COD, NO2, NO3, NH3, dan PO4. Spesies larva ikan diidentifikasi dengan sekuen DNA. Analisis Komponen Utama dilakukan untuk mengetahui hubungan antara kelimpahan larva ikan dengan parameter fisika-kimia perairan. Hasil penelitian menunjukkan secara keseluruhan ada 42 larva ikan yang berasal dari 9 spesies. Spesies yang paling dominan adalah Oreochromis niloticus. Hasil Analisis Komponen Utama menunjukkan bahwa kelimpahan larva ikan tertinggi (102,9 individu/100m3) berada pada stasiun pengamatan yang memiliki turbiditas dan oksigen terlarut tertinggi, sedangkan kelimpahan larva ikan terendah (10,83 individu/100m3) berada pada stasiun pengamatan yang memiliki kadar nitrat dan kedalaman tertinggi.

scholarly journals DISTRIBUSI SPASIAL DAN KARAKTERISTIK HABITAT UDANG KELONG (Penaeus indicus) PADA PERAIRAN EKOSISTEM MANGROVE SICANANG-BELAWAN, SUMATERA UTARA

2018 ◽  
Vol 4 (1) ◽  
pp. 19-25
Author(s):  
Rivo Hasper Dimenta ◽  
Rusdi Machrizal ◽  
Khairul Khairul
Keyword(s):  
Principal Component Analysis ◽  
Random Sampling ◽  
Principal Component ◽  
Component Analysis ◽  
Penaeus Indicus

Perairan ekosistem mangrove Sicanang-Belawan merupakan salah satu wilayah pasang-surut yang dipengaruhi oleh arus dari sungai Belawan dan arus laut pantai timur Sumatera yang mempengaruhi adanya perbedaan karakteristik habitat yang berdampak pada sebaran kelimpahan udang kelong (P. indicus). Penelitian bertujuan untuk mengetahui distribusi spasial dan karakteristik habitat udang kelong menerapkan metode deskriptif. Pengambilan sampel dilakukan pada bulan September - November 2017 di sekitar perairan ekosistem mangrove Sicanang-Belawan. Alat tangkap udang menggunakan jaring ambai berbahan nilon polyfilament. Stasiun pengamatan ditentukan menggunakan metode purposive random sampling. Analisa data menggunakan metode statitik multivariabel yang didasarkan pada Analisis Komponen Utama (Principal Component Analysis, PCA) dan Analisis Korelasi (Corresponden Analysis, CA). Hasil analisis PCA menunjukkan bahwa parameter lingkungan membentuk pengelompokkan yang mampu menggambarkan karakteristik habitat udang kelong (P. indicus). Habitat dikelompokan menjadi tiga kelompok karakter, yaitu kelompok habitat dekat daratan (asosiasi stasiun 1, 4 dan 5), kelompok habitat dekat estuaria (asosiasi stasiun 2), dan kelompok habitat dekat aliran sungai besar Belawan (asosiasi stasiun 3). Hasil analisis CA menunjukkan bahwa letak lokasi sampling terbukti mempengaruhi pengelompokkan dari distribusi populasi udang kelong (P.indicus) berdasarkan ukuran, jenis kelamin dan tingkat kematangan gonadnya

A taxonomic study of Melanelixia fuliginosa in Europe

2011 ◽  
Vol 43 (2) ◽  
pp. 89-97 ◽  
Author(s):  
Ulf ARUP ◽  
Emma SANDLER BERLIN
Keyword(s):  
Principal Component Analysis ◽  
Molecular Analysis ◽  
Morphometric Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Bootstrap Support ◽  
Taxonomic Study ◽  
Separate Species ◽  
Long Time

AbstractFor a long time it has been discussed as to whether Melanelixia fuliginosa comprises one or two species: one darker, mainly saxicolous, and one lighter, mainly corticolous. To settle the question, a morphometric and a molecular analysis were carried out and analyzed using a principal component analysis (PCA). The morphometric analysis indicates a differentiation in several characters between material previously recognized as subspecies fuliginosa and glabratula, but also a considerable overlap in some of them. The molecular analysis of the nrITS DNA gene reveals a clear division of the taxa. Specimens belonging to Melanelia fuliginosa fall out in two different clades, which have good bootstrap support, corresponding to the subspecies fuliginosa and glabratula. Accordingly, we propose that the subspecies should be acknowledged as separate species, Melanelixia fuliginosa (Fr. ex Duby) O. Blanco et al. and Melanelixia glabratula (Lamy) Sandler & Arup.

Habitat suitability for Proboscis Monkey (Nasalis larvatus Wurmb, 1781) in the mangrove forest of Kutai National Park, East Kalimantan

2016 ◽  
Vol 6 (1) ◽  
pp. 12-25
Author(s):  
SUWARTO SUWARTO ◽  
LILIK BUDI PRASETYO ◽  
AGUS PRIYONO KARTONO
Keyword(s):  
Principal Component Analysis ◽  
Spatial Modeling ◽  
Habitat Suitability ◽  
Mangrove Forest ◽  
National Park ◽  
Principal Component ◽  
Component Analysis ◽  
Proboscis Monkey ◽  
Nasalis Larvatus ◽  
East Kalimantan

Suwarto, Prasetyo LB, Kartono AP. 2016. Habitat suitability for Proboscis Monkey (Nasalis larvatus Wurmb, 1781) in the mangrove forest of Kutai National Park, East Kalimantan. Bonorowo Wetlands 6: 12-25. This study aims to identify the factors determining that influence the suitability proboscis monkey (Nasalis larvatus Wurmb, 1781) in the mangrove habitat Kutai National Park through spatial modeling. Habitat suitability was analyzed using Principal Component Analysis (PCA) and linear regression were integrated with geographical information systems. Principal Component Analysis is a technique to construct new variables that are linear combinations of the original variables by reducing the variables used. The presence of groups of proboscis monkey marked with GPS. Satellite images from Landsat 8 path 116 row 60 processed digitally to generate proboscis vegetation distribution and Normalization Difference Vegetation Index, Variable distance from roads, distance from settlements, the distance from the fishpond, and the distance from the source of water is obtained from the analysis euclidean distance of Indonesia Earth Appearance map. Spatial modeling using the coordinates of the encounter group proboscis as the dependent variable and the predictor variables used in the regression model is the distance from the road, the distance from the settlement, the distance from the pond, the distance from the source of water, the distance of Avicennia, distance from Bruguiera, distance from Rhizophora, distance from Sonneratia, and LAI (Leaf Area Index). The overall area of the study area was used to build the model is 7 343.88 hectares. The results habitat suitability modeling proboscis monkey in the mangroves of TNK showed that only 99.50 hectares or 1.35% have high compatibility, the suitability being has a total area of 384.58 hectares or 18.85%, whereas an area of 5 859.81 hectares or 79.79% low suitability. The results of models have explained that the distribution of the proboscis monkey habitat suitability is influenced by factors of disturbance.

scholarly journals Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

2016 ◽  
Vol 35 (2) ◽  
pp. 173-190 ◽  
Author(s):  
S. Shahid Shaukat ◽  
Toqeer Ahmed Rao ◽  
Moazzam A. Khan
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Environmental Data ◽  
Data Matrix ◽  
Data Sets ◽  
Data Set ◽  
The Impact

AbstractIn this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50) on the eigenvalues and eigenvectors resulting from principal component analysis (PCA). For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22) of a small data set comprising of 55 samples (stations from where water samples were collected). Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

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