Canopy gaps in rain forest on Christmas Island, Indian Ocean: size distribution and methods of measurement

1996 ◽  
Vol 12 (3) ◽  
pp. 427-434 ◽  
Author(s):  
Peter T. Green
Keyword(s):  
Indian Ocean ◽  
Size Distribution ◽  
Rain Forest ◽  
Canopy Gaps ◽  
Christmas Island ◽  
Methods Of Measurement

scholarly journals Erratum

1997 ◽  
Vol 13 (4) ◽  
pp. 578-578
Author(s):  
P. Green
Keyword(s):  
Indian Ocean ◽  
Total Variation ◽  
Rain Forest ◽  
Activity Patterns ◽  
Christmas Island ◽  
Density And Biomass

Green, P. T. (1997)Red crabs in rain forest on Christmas Island, Indian Ocean: activity patterns, density and biomass. Vol. 13 Part 1: 17–38. On page 29 the phrase: “but a significant correlation existed across sites (r=0.81, n=44, P<0.001).” should read: “but a significant correlation existed across sites (r=-0.69, n=44, P<0.001).”On page 33 the phrase “but the same correlation accounted for 81% of total variation when all sites were analysed together.” should read: “but the same correlation accounted for 48% of total variation when all sites were analysed together.”

Red crabs in rain forest on Christmas Island, Indian Ocean: activity patterns, density and biomass

1997 ◽  
Vol 13 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Peter T. Green
Keyword(s):  
Indian Ocean ◽  
Surface Activity ◽  
Rain Forest ◽  
Activity Patterns ◽  
Carapace Width ◽  
Main Study ◽  
Wet Season ◽  
Christmas Island ◽  
Study Sites ◽  
Density And Biomass

ABSTRACTThe red crab Gecarcoidea natalis is the most abundant land crab on Christmas Island, Indian Ocean, and is found wherever native rain forest is still intact. The largest crabs grow to > 120 mm carapace width and reach > 500 g in mass. Red crabs are diurnally active, and moisture appears to be the most important factor governing surface activity – activity was nil below 77% RH, relatively low at 87% RH, and high above 95% RH. The crabs retreat to the interior of their burrows during dry periods, but emerge rapidly in response to rain. Because of their dependence on moisture for surface activity, red crabs show greater levels of activity during the wet season. Annual breeding migrations and moulting activity decreased the density of surface-active crabs at the main study sites for several weeks at the start of the wet season, and fruit and leaf fall caused dramatic local increases in the density of active red crabs. Density and biomass at the main study sites averaged 1.3 crabs m-2 and 1454 kg ha-1, respectively, and 1.2 crabs m-2 and 1137 kg ha-1 at a total of five sites across the island. Rough calculations suggest that the island-wide population is at least 100 million individuals. Red crabs are the dominant consumer on the forest floor, and consume seeds, seedlings and leaf litter. Because they are both abundant and widespread, these animals have the potential to be the most important determinant of pattern and process in rain forest on Christmas Island.

Red Crabs in Rain Forest, Christmas Island: Differential Herbivory of Seedlings

Oikos ◽  
1990 ◽  
Vol 58 (3) ◽  
pp. 289 ◽  
Author(s):  
Dennis J. O'Dowd ◽  
P. S. Lake
Keyword(s):  
Rain Forest ◽  
Christmas Island

Continuous flow analysis of the Mount Brown South ice core

2021 ◽  
Author(s):  
Margaret Harlan ◽  
Helle Astrid Kjær ◽  
Tessa Vance ◽  
Paul Vallelonga ◽  
Vasileios Gkinis ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Size Distribution ◽  
Continuous Flow ◽  
Ice Core ◽  
Flow Analysis ◽  
East Antarctica ◽  
Southern Indian Ocean ◽  
Indian Ocean Region ◽  
Continuous Flow Analysis ◽  
Ocean Region

&lt;p&gt;The Mount Brown South (MBS) ice core is an approximately 300-meter-long ice core, drilled in 2016-2017 to the south of Mount Brown, Wilhelm II Land, East Antarctica. This location in East Antarctica was chosen as it produces an ice core with well-preserved sub-annual records of both chemistry and isotope concentrations, spanning back over 1000 years. MBS is particularly well suited to represent climate variations of the Indian Ocean sector of Antarctica, and to provide information about regional volcanism in the Southern Indian Ocean region.&lt;/p&gt;&lt;p&gt;A section of ice spanning the length of the MBS core was melted as part of the autumn 2019 continuous flow analysis (CFA) campaign at the Physics of Ice, Climate, and Earth (PICE) group at the University of Copenhagen. During this campaign, measurements were conducted for chemistry and impurities contained in the ice, in addition to water isotopes. The data measured in Copenhagen include measurements of H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2,&lt;/sub&gt; pH, electrolytic conductivity, and NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, Ca&lt;sup&gt;2+&lt;/sup&gt;, and Na&lt;sup&gt;+&lt;/sup&gt;&amp;#160;ions, in addition to insoluble particulate concentrations and size distribution measured using an Abakus laser particle counter.&lt;/p&gt;&lt;p&gt;Here, we present an overview of the CFA chemistry and impurity data, as well as preliminary investigations into the size distribution of insoluble particles and the presence of volcanic material within the ice. These initial chemistry and particulate size distribution data sets are useful in order to identify sections of the MBS core to subject to further analysis to increase our understanding of volcanic activity in the Southern Indian Ocean region.&lt;/p&gt;

scholarly journals Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Indian Ocean ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Deep Neural Network ◽  
Inverse Model ◽  
Indian Ocean Tsunami ◽  
Flow Conditions ◽  
2004 Indian Ocean Tsunami

Abstract. The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

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