Community structure and spatial distribution of understory birds in three bamboo-dominated forests in southwestern Amazonia

2021 ◽  
Author(s):  
Diego Pedroza ◽  
Edson Guilherme
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Understory Birds ◽  
Southwestern Amazonia

scholarly journals ZOOPLANKTON SPATIAL DISTRIBUTION AND COMMUNITY STRUCTURE IN BANGGAI SEA

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Arief Rachman ◽  
Elly Asniariati
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Hot Spot ◽  
Zooplankton Community ◽  
Anthropogenic Activity ◽  
Zooplankton Abundance ◽  
Total Density ◽  
Importance Value ◽  
Zooplankton Community Structure ◽  
Specific Distribution

<p>Banggai Sea is an interesting ecosystem due to mixing influences from Banda Sea in the west and Maluccas Sea in the east. Therefore, a unique zooplankton community structure and specific distribution pattern should be found in this area. This research was carried on using Baruna Jaya VIII research vessel and samples were collected in 14 sampling stations. Vertical towing using NORPAC plankton net (300 μm) was conducted to collect zooplankton samples. Result showed that inner Mesamat Bay had the lowest abundance of zooplankton, probably due to low water quality resulted from anthropogenic activity. Meanwhile the strait between Liang and Labobo Island had the highest zooplankton abundance in Banggai Sea. Calanoids was the dominant zooplankton taxa in the ecosystem and contributing 55.7% of total density of zooplankton community. The highest importance value made this taxa to be very important factor that regulates the lower trophic level organisms. Results also showed that zooplankton was distributed nearly uniform in eastern but aggregated to several stations in western Banggai Sea. Zooplankton abundance was higher in the central of Banggai Sea, compared to western and eastern area. According to Bray-Curtis clustering analysis the strait between Liang and Labobo Island has unique zooplankton community structure. This might happened due to mixing of water from two highly productive seas that influenced the Banggai Sea ecosystem. From this research we conclude that this strait probably was the zooplankton hot spot area which might also indicate that this area also a hot spot of fishes in the Banggai Sea.</p><p>Keywords: spatial distribution, zooplankton, community structure, hot spot, Banggai</p>

scholarly journals Spatial Distribution and Community Structure of Heterotrophic Protists in the Central Barents Sea of Arctic Ocean During Summer

2004 ◽  
Vol 26 (4) ◽  
pp. 567-579 ◽  
Author(s):  
Eun-Jin Yang ◽  
Joong-Ki Choi ◽  
Sun-Young Kim ◽  
Kyung-Ho Chung ◽  
Hyoung-Chul Shin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Arctic Ocean ◽  
Barents Sea

Structure of microbial communities in phosphorus-limited estuaries along the eastern Adriatic coast

2015 ◽  
Vol 95 (8) ◽  
pp. 1565-1578 ◽  
Author(s):  
Mladen Šolić ◽  
Nada Krstulović ◽  
Danijela Šantić ◽  
Stefanija Šestanović ◽  
Marin Ordulj ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Bacterial Abundance ◽  
Heterotrophic Bacteria ◽  
Potential Importance ◽  
High Contribution ◽  
Total Bacterial Abundance ◽  
Autotrophic Biomass ◽  
Adriatic Coast ◽  
Heterotrophic Biomass

The structure of the microbial food web was studied in six estuary areas along the eastern Adriatic coast during March, July and October 2012. Limitation by phosphorus, not nitrogen, was a common feature for all studied estuaries. Heterotrophic bacteria and autotrophic picoplankton (APP) (particularly picoeukaryotes andSynechococcus) can reach notable abundances and biomasses, suggesting potential importance of the picoplankton community in P-limited estuarine environments. The main features of the microbial community structure in these environments included: (1) higher heterotrophic biomass in comparison to autotrophic biomass within the picoplankton community; (2) general domination of picoeukaryotes within the APP community, and increase of absolute and relative biomass of prokaryotic autotrophs (ProchlorococcusandSynechococcus) in the total APP in P-limited conditions; (3) domination ofSynechococcusoverProchlorococcusbiomass in all studied conditions, and common spatial distribution of these two groups of cyanobacteria, which was mostly determined by concentration of phosphorus; (4) relatively high contribution (about 50%) of LNA bacteria in the total bacterial abundance; and (5) relatively high contribution (about 33%) of heterotrophic pico-flagellates in the total flagellate abundance.

scholarly journals Bryophyte flora in an Amazon island: structure of communities in areas of different levels of anthropization

Rodriguésia ◽  
2021 ◽  
Vol 72 ◽  
Author(s):  
João Paulo Silva Souza ◽  
Paulo Weslem Portal Gomes ◽  
Rita de Cássia Pereira dos Santos ◽  
Ana Cláudia Caldeira Tavares-Martins
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
The State ◽  
Amazon Forest ◽  
High Similarity ◽  
Moss Species ◽  
Generalist Species ◽  
Structure Of Communities ◽  
First Time ◽  
Different Levels

Abstract The present study is aimed to evaluate the richness, composition and spatial distribution of bryophytes occurring in Mosqueiro Island, in the Amazon forest. Forty-one 100-m² plots in 37 flooded and 4 non-flooded environments were selected for data collection, all substrate found were considered. The results were compared with surveys in other islands from the state of Pará and were analyzed according to frequency of populations, colonized substrates, light tolerance guilds, and distribution in Brazilian biomes. Ninety-seven species were recorded, distributed in 36 genera and 17 families, being 57 (58.7%) liverwort and 40 (41.2%) moss species. The rare species stood out with 53 species (54.08%). High similarity was observed between corticolous and epixylic communities, and between the terricolous community and the bryophytes found growing on charcoal, which is an artificial substrate. In relation to light tolerance guilds, generalist species prevailed (52 species, 53.6%). As for phytogeographic distribution, there was a predominance of taxa with occurrence in the Amazon and Atlantic rainforest (35 species, 37.11%). Ceratolejeunea ceratantha is reported for the first time for the state of Pará. The level of anthropization in the island was showed mainly by high richness and occurrence of generalist species underscores and well-adapted species to stressed conditions, evidencing changes in the bryophyte community structure.

scholarly journals Spatial Distribution and Community Structure of Macrobenthos on the Sandy Shore of Anmyeondo, Korea

2013 ◽  
Vol 35 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Yun-Hwan Jung ◽  
Kon-Tak Yoon ◽  
Heung-Sik Park ◽  
Chae-Woo Ma
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Sandy Shore

scholarly journals The spatio-temporal evolution of the genus Nitzschia Longissima at the level of the lagoon in Nador, Morocco

2021 ◽  
Vol 234 ◽  
pp. 00081
Author(s):  
Ouassila Riouchi ◽  
Faid El Madani ◽  
Eric Abadie ◽  
Ali Skalli ◽  
Mourad Baghour
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Light Intensity ◽  
Temporal Evolution ◽  
Warm Season ◽  
Zooplankton Community ◽  
Zooplankton Community Structure ◽  
Planktonic Diatoms ◽  
A Value ◽  
Spatio Temporal

This work aims to study the spatio-temporal evolution of the genus Nitzschia longissima, one of the most important genera of marine plankton diatoms, from 3 sampling stations in the Nador lagoon and during 2 seasons (spring and summer 2018), Using Nitzschia longissima, as a study system, one of the most diverse and abundant genera among marine planktonic diatoms. This species counts, in addition to the form Nitzschia longissima forma parva Grunow, three varieties namely Nitzschia longissima var. closterium (W. Smith) Van Heurck, Nitzschia longissima var. longissima (Breb.) Ralfs and Nitzschia longissima var. reversa Grunow. Nitzschia Longissima genus density was high during the warm season (Summer 2018) with a value of 8000 cells/liter, and low during the cold seasons (Spring 2018), which may be caused by water temperature and zooplankton community structure; and underwater light intensity was an important factor influencing the spatial distribution of Nitzschia density.

The spatial distribution of the community structure of picophytoplankton in Lake Fuxian

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
吴凡，任名栋，陈非洲，吴庆龙，史小丽 WU Fan
Keyword(s):  
Spatial Distribution ◽  
Community Structure ◽  
Lake Fuxian

scholarly journals Uncovering the Relationship between Human Connectivity Dynamics and Land Use

2020 ◽  
Vol 9 (3) ◽  
pp. 140 ◽  
Author(s):  
Olivera Novović ◽  
Sanja Brdar ◽  
Minučer Mesaroš ◽  
Vladimir Crnojević ◽  
Apostolos N. Papadopoulos
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Community Structure ◽  
Dynamic Change ◽  
Base Station ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Data Set ◽  
Human Dynamics ◽  
The City

CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

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