The effect of seed ingestion by a native, generalist bird on the germination of worldwide potentially invasive trees species Pittosporum undulatum and Schinus terebinthifolia

2020 ◽  
Vol 108 ◽  
pp. 103639
Author(s):  
Thales Castilhos de Freitas ◽  
Ernestino de Souza Gomes Guarino ◽  
Gustavo Crizel Gomes ◽  
Artur Ramos Molina ◽  
Isadora Moreira da Luz Real ◽  
...  
Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2436
Author(s):  
Abubakar Sadiq Mohammed ◽  
Martina Meincken

Low-cost wood–plastic composites (WPCs) were developed from invasive trees and recycled low-density polyethylene. The aim was to produce affordable building materials for low-cost social housing in South Africa. Both raw materials are regarded as waste materials, and the subsequent product development adds value to the resources, while simultaneously reducing the waste stream. The production costs were minimised by utilising the entire biomass of Acacia saligna salvaged from clearing operations without any prior processing, and low-grade recycled low-density polyethylene to make WPCs without any additives. Different biomass/plastic ratios, particle sizes, and press settings were evaluated to determine the optimum processing parameters to obtain WPCs with adequate properties. The water absorption, dimensional stability, modulus of rupture, modulus of elasticity, tensile strength, and tensile moduli were improved at longer press times and higher temperatures for all blending ratios. This has been attributed to the crystallisation of the lignocellulose and thermally induced cross-linking in the polyethylene. An increased biomass ratio and particle size were positively correlated with water absorption and thickness swelling and inversely related with MOR, tensile strength, and density due to an incomplete encapsulation of the biomass by the plastic matrix. This study demonstrates the feasibility of utilising low-grade recycled polyethylene and the whole-tree biomass of A. saligna, without the need for pre-processing and the addition of expensive modifiers, to produce WPCs with properties that satisfy the minimum requirements for interior cladding or ceiling material.


2013 ◽  
pp. 145-165 ◽  
Author(s):  
Philip E. Hulme ◽  
David F. R. P. Burslem ◽  
Wayne Dawson ◽  
Ezekiel Edward ◽  
John Richard ◽  
...  

2020 ◽  
Vol 189 ◽  
pp. 109928 ◽  
Author(s):  
Elena Fernández-Vizcaíno ◽  
Isabel G. Fernández de Mera ◽  
François Mougeot ◽  
Rafael Mateo ◽  
Manuel E. Ortiz-Santaliestra

2019 ◽  
Vol 11 (15) ◽  
pp. 1812 ◽  
Author(s):  
Jonathan P. Dash ◽  
Michael S. Watt ◽  
Thomas S. H. Paul ◽  
Justin Morgenroth ◽  
Grant D. Pearse

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.


2004 ◽  
Vol 11 (2) ◽  
pp. 119-120 ◽  
Author(s):  
Aulo Di Grande ◽  
Rosario Paradiso ◽  
Salvatore Amico ◽  
Giovanni Fulco ◽  
Bruno Fantauzza ◽  
...  

2016 ◽  
Vol 137 (2) ◽  
pp. AB239 ◽  
Author(s):  
Kristen Bortolin ◽  
Moshe Ben-Shoshan ◽  
Chrystyna Kalicinsky ◽  
Elana Lavine ◽  
Christine Lejtenyi ◽  
...  
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