Effects of human trampling on the sub-Antarctic vegetation of Macquarie Island

Polar Record ◽  
1994 ◽  
Vol 30 (174) ◽  
pp. 207-220 ◽  
Author(s):  
J. J. Scott ◽  
J. B. Kirkpatrick

AbstractThe effects of trampling on six types of vegetation and their underlying soils were investigated on sub-Antarctic Macquarie Island. One hundred and fifty foot-passes per year for at least the past 10 years have occurred on a typical 6-km stretch of walking track on the island's upland plateau. Trampling favours vascular plants including exotics, especially Poa annua, while bryophytes and lichens are more common in undisturbed vegetation. The abundance of 19 of the 39 most common species appears to be affected by trampling. Track width is positively correlated with exposure and wet soils, and trampling increases the soil bulk density of the track. The contrast between the soil bulk density of the trampled and untrampled soils increases with increasing exposure. While present environmental damage is within an acceptable range over the majority of the island, the extreme environments are likely to suffer unacceptable levels of damage if increased usage occurs with more tourism or expansion of scientific and related activities. This is demonstrated by the diversion of a short section of plateau track in an atypically heavily used area; the diversion sustained substantial damage after 890 foot-passes during a 15-month period.

2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Randall W. Myster

Igapó forests are a key part of the Amazon.  And so, it is important to know their floristics and physical structure, and how they may be influenced by their soil.  The floristics and physical structure of 16 primary [1o] and secondary [2o] igapó forest plots in Loreto Province, Peru was described and linear regressions were computed to explore whether soil bulk density could predict structural parameters. In the 1o forest, Fabaceae, Malvaceae and Rubiaceae were the most common families and Calycophyllum spruceanum, Ceiba samauma, Inga spp., Cedrela odorata, Copaifera reticulata, Phytelephas macrocarpa, Guazuma rosea, and Piptadenia pteroclada were the most common species. And as flooding increased, bulk density, stem density, stem size, species richness, Fishers α, basal area and above-ground biomass all decreased. In the 2o forest, Urticaceae, Rubiaceae and Euphorbiaceae were the most common families and Cecropia membranacea, Sapium glandulosum, Pourouma guianensis and Byrsonima arthropoda were the most common species. The number of stems was greatest in the island 2o forest and lowest in the 1o forest under water for more than four months, and mean stem size, species richness, Fishers α, basal area and above-ground biomass was lowest in the sandy beach 2o forest and highest in the 1o forest under water one to two months. Soil bulk density predicted mean stem size, species richness and Fishers α well, where all three decreased as soils became more sandy. I conclude that as soil becomes less sandy with more clay content there is an increase in forest structural complexity, unpredictable flooding in 2o forests reduces structure more than the predictable flood pulse 1o forests receive, and soil bulk density may have a causal role for diversity in igapó forests.


2010 ◽  
Vol 30 (2) ◽  
pp. 127-132
Author(s):  
Jinbo ZAN ◽  
Shengli YANG ◽  
Xiaomin FANG ◽  
Xiangyu LI ◽  
Yibo YANG ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.


2021 ◽  
pp. 126389
Author(s):  
Marco Bittelli ◽  
Fausto Tomei ◽  
Anbazhagan P. ◽  
Raghuveer Rao Pallapati ◽  
Puskar Mahajan ◽  
...  

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