scholarly journals Aluminium Accumulation and Intra-Tree Distribution Patterns in Three Arbor aluminosa (Symplocos) Species from Central Sulawesi

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149078 ◽  
Author(s):  
Marco Schmitt ◽  
Sven Boras ◽  
Aiyen Tjoa ◽  
Toshihiro Watanabe ◽  
Steven Jansen
Keyword(s):  
Distribution Patterns ◽  
Tree Distribution ◽  
Aluminium Accumulation ◽  
Central Sulawesi

scholarly journals Retention of tree-related microhabitats is more dependent on selection of habitat trees than their spatial distribution

2020 ◽  
Vol 139 (6) ◽  
pp. 1015-1028 ◽  
Author(s):  
Thomas Asbeck ◽  
Christian Messier ◽  
Jürgen Bauhus
Keyword(s):  
Spatial Distribution ◽  
Convex Hull ◽  
Distribution Patterns ◽  
Managed Forests ◽  
Tree Distribution ◽  
Reference Selection ◽  
Tree Retention ◽  
Evans Index ◽  
The Difference ◽  
Selection Of

Abstract Habitat trees, which provide roosting, foraging and nesting for multiple taxa, are retained in managed forests to support biodiversity conservation. To what extent their spatial distribution influences provisioning of habitats has rarely been addressed. In this study, we investigated whether abundance and richness of tree-related microhabitats (TreMs) differ between habitat trees in clumped and dispersed distributions and whether the abundance of fifteen groups of TreMs is related to tree distribution patterns. To identify habitat trees, we quantified TreMs in temperate mountain forests of Germany. We determined clumping (the Clark–Evans index), size of the convex hull, diameter at breast height, as well as altitude, slope and aspect of sites for their possible influence on TreMs. We additionally determined the difference in TreM abundance and richness among four options of selecting five habitat trees per ha from 15 candidates: (a) the most clumped trees, (b) five randomly selected and dispersed trees, (c) the single tree with highest abundance or richness of TreMs and its four closest neighbors and (d) a “reference selection” of five trees with known highest abundance or richness of TreMs irrespective of their distribution. The degree of clumping and the size of the convex hull influenced neither the abundance nor richness of TreMs. The reference selection, option (d), contained more than twice the number of TreMs compared to the most clumped, (a), or random distributions, (b), of five habitat trees, while option (c) assumed an intermediate position. If the goal of habitat tree retention is to maximize stand-level abundance and richness of TreMs, then it is clearly more important to select habitat trees irrespective of their spatial pattern.

scholarly journals Comparison ofT-Square, Point Centered Quarter, andN-Tree Sampling Methods inPittosporum undulatumInvaded Woodlands

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Lurdes Borges Silva ◽  
Mário Alves ◽  
Rui Bento Elias ◽  
Luís Silva
Keyword(s):  
Sampling Design ◽  
Sampling Methods ◽  
Distribution Patterns ◽  
Tree Density ◽  
Parameter Estimates ◽  
Sampling Point ◽  
Density Range ◽  
Biomass Valorization ◽  
Precise Method ◽  
Tree Distribution

Tree density is an important parameter affecting ecosystems functions and management decisions, while tree distribution patterns affect sampling design.Pittosporum undulatumstands in the Azores are being targeted with a biomass valorization program, for which efficient tree density estimators are required. We comparedT-Square sampling, Point Centered Quarter Method (PCQM), andN-tree sampling with benchmark quadrat (QD) sampling in six 900 m2plots established atP. undulatumstands in São Miguel Island. A total of 15 estimators were tested using a data resampling approach. The estimated density range (344–5056 trees/ha) was found to agree with previous studies using PCQM only. Although with a tendency to underestimate tree density (in comparison with QD), overall,T-Square sampling appeared to be the most accurate and precise method, followed by PCQM. Tree distribution pattern was found to be slightly aggregated in 4 of the 6 stands. Considering (1) the low level of bias and high precision, (2) the consistency among three estimators, (3) the possibility of use with aggregated patterns, and (4) the possibility of obtaining a larger number of independent tree parameter estimates, we recommend the use ofT-Square sampling inP. undulatumstands within the framework of a biomass valorization program.

scholarly journals Estimasi Periode Ulang Gempa Bumi Di Wilayah Sulawesi Dengan Menggunakan Distribusi Gumbel

Jurnal MIPA ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
Yustiani Frastika ◽  
Guntur Pasau ◽  
Jantje D. Prang
Keyword(s):  
Data Processing ◽  
Return Period ◽  
Data Distribution ◽  
Gumbel Distribution ◽  
Distribution Patterns ◽  
North Sulawesi ◽  
South Sulawesi ◽  
Using Data ◽  
Two Stages ◽  
Central Sulawesi

Estimasi periode ulang gempa bumi yang bersifat ekstrim dengan menggunakan Distribusi Gumbel dilakukan untuk menganalisis kejadian gempa bumi yang telah terjadi sebelumnya menggunakan data sejak Januari 1905-Juni 2013. Pengolahan dan analisis data dilakukan dengan dua tahap. Pertama,Pengujian dan pemeriksaan pola sebaran data. Kedua, menentukan periode ulang gempa bumi untuk mengetahui keberulangan gempa ekstrim yang akan terjadi selanjutnya. Hasil analisis yang diperoleh adalah Wilayah Sulawesi sangat rawan terhadap kejadian gempa bumi yang bersifat ekstrim. Tingkat pengulangan kejadian gempa bumi untuk Wilayah  Provinsi Sulawesi Utara dalam kurun waktu 62-100  tahun adalah 7 Mw, Wilayah Provinsi Gorontalodalam kurun waktu 75-100 tahun adalah 6,8 Mw, Wilayah Provinsi Sulawesi Tengah dalam kurun waktu 82-100 tahun adalah 6,9 Mw, Wilayah Provinsi Sulawesi Selatan dalam kurun waktu 319-686 bulan adalah 6,2 Mw, Wilayah Provinsi Sulawesi Barat dalam kurun waktu 113-217 bulan adalah  6,2 Mw dan Wilayah Provinsi Sulawesi Tenggara dalam kurun waktu 45-97 tahun adalah 6,0 Mw.The estimation of extreme earthquake return period by using the Gumbel distribution is made to analyze the occurrence of earthquakes that have occurred previously using data from January 1905 to June 2013. Data processing and analysis has been in two stages. First, testing and examination of data distribution patterns to see whether the data follow the theoretical distribution, in this case the Gumbel distribution. Second, determining the return period of the earthquake to see a recurrence of extreme earthquake is going to happen in the future. Results of the analysis showed that Sulawesi Region is highly vulnerable to earthquakes which are extreme. Return period of earthquake on the region of North Sulawesi province in the period of 62-100 years is 7.0 Mw, the region of Gorontalo Province in the period of 75-100 years is 6.8 Mw, the region of Central Sulawesi Province in the period of 82-100 years is 6.9 Mw, the region of South Sulawesi Province in the period of 319-686 months is 6,.2 Mw, the region of West Sulawesi province in the period of 113-217 months is 6.2 Mw and the region of Southeast Sulawesi province in the period of 45-97 years is 6.0 Mw.

Measuring diversity of tree distribution patterns in natural forests

2016 ◽  
Vol 24 (3) ◽  
pp. 280-286 ◽  
Author(s):  
Gangying Hui ◽  
◽  
Gongqiao Zhang ◽  
Zhonghua Zhao ◽  
Yanbo Hu ◽  
...  
Keyword(s):  
Distribution Patterns ◽  
Natural Forests ◽  
Tree Distribution ◽  
Measuring Diversity

Factor Analysis of Tree Distribution Patterns in Oklahoma

Ecology ◽  
1977 ◽  
Vol 58 (6) ◽  
pp. 1345-1355 ◽  
Author(s):  
Gary D. Schnell ◽  
Paul G. Risser ◽  
John F. Helsel
Keyword(s):  
Factor Analysis ◽  
Distribution Patterns ◽  
Tree Distribution

scholarly journals Seasonal Variation and Characterization of the Micrometeorology in Linpan Settlements in the Chengdu Plain, China: Microclimatic Effects of Linpan Size and Tree Distribution

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Hua Zong ◽  
De-hua Pu ◽  
Mei-lin Liu
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Air Temperature ◽  
Radiation Flux ◽  
Negative Relationship ◽  
Distribution Patterns ◽  
Tree Distribution ◽  
Chengdu Plain ◽  
Solar Radiation Flux

Linpan settlements (abbreviated as Linpan) are the most important traditional type of rural settlement in the Chengdu Plain, and they are an important part of the agroforestry ecological system in southwest China. In this study, we measured the micrometeorological parameters (air temperature, solar radiation, relative humidity, and wind speed) in 12 Linpans for two years to determine the seasonal micrometeorology variations; then, we explored the impacts of Linpan size and tree distribution on the Linpan micrometeorology. The results show that the Linpans undergo seasonal cooling (from 0.6 to 1.3°C), humidification (from 0.9% to 4.1%), reduction in solar radiation flux (from 92.1 to 496.0 W/m2), and changes in wind speed (by 0.4 to 0.5 m/s) compared to the surrounding environment. Both solar radiation flux and wind speed showed the following decreasing trend with respect to sampling positions in the Linpan: outside > edge > center. The Linpan size did not affect the solar radiation flux or wind speed over the four seasons. The main factor affecting solar radiation flux and wind speed was the horizontal tree distribution not the Linpan size. However, the Linpan size was significantly correlated with the air temperature in summer and winter. Large Linpans (>5 × 103 m2) showed better ability to control the temperature to within a comfortable range in extremely hot and cold seasons. The Linpan size also showed a negative relationship with the relative humidity, but only in winter. Among the tree distribution patterns, a scattered distribution was optimal to achieve a comfortable micrometeorology over the course of the year. In addition, we suggest some ways to adapt the Linpan micrometeorology, which could be used to protect traditional Linpans, as well as for ecological restoration.

HUBUNGAN KEPADATAN BIVALVIA DENGAN PARAMETER LINGKUNGAN DI PESISIR TANJUNG PURA KABUPATEN BANGKA TENGAH

2020 ◽  
Vol 13 (2) ◽  
pp. 112-121
Author(s):  
Sudiyar . ◽  
Okto Supratman ◽  
Indra Ambalika Syari
Keyword(s):  
Negative Impact ◽  
Principal Component ◽  
Environmental Parameters ◽  
Data Retrieval ◽  
Distribution Patterns ◽  
Random System ◽  
System Method ◽  
Relationship Of ◽  
The Relationship ◽  
Anadara Granosa

The destructive fishing feared will give a negative impact on the survival of this organism. This study aims to analyze the density of bivalves, distribution patterns, and to analyze the relationship of bivalves with environmental parameters in Tanjung Pura village. This research was conducted in March 2019. The systematic random system method was used for collecting data of bivalves. The collecting Data retrieval divided into five research stasions. The results obtained 6 types of bivalves from 3 families and the total is 115 individuals. The highest bivalve density is 4.56 ind / m², and the lowest bivalves are located at station 2,1.56 ind / m²,  The pattern of bivalve distribution in the Coastal of Tanjung Pura Village is grouping. The results of principal component analysis (PCA) showed that Anadara granosa species was positively correlated with TSS r = 0.890, Dosinia contusa, Anomalocardia squamosa, Mererix meretrix, Placamen isabellina, and Tellinella spengleri were positively correlated with currents r = 0.933.

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