Search Results (11)

Concerns on the expanding infestation of several Acacia species in the southern Mediterranean European countries have triggered an ever-growing requirement for costly targeted control actions. Valorizing biomass waste produced could help promote and better finance these actions. For that purpose, wood wastes from invasive control actions were tested regarding their pulp and paper potential, aiming to entice cellulose industries to partake in future conservation actions. Wood waste from the five most pervasive Acacia species was studied (Acacia dealbata Link, Acacia longifolia Willd, Acacia mearnsii De Wild, Acacia melanoxylon R.Br, and Acacia saligna Labill) regarding physical and chemical characteristics, and a central composite design was used to optimize alkali charge and reaction temperature on pulping yield and delignification. Bleached kraft pulps were produced with each species’ optimized conditions and for an equitable mixture of all species. Optimized pulp yields (52.6%–53.5%) and pulp polymerization degree (2867–3690) of Acacia species were higher than those of Eucalyptus globulus Labill (used as reference). Optimized bleached pulps were refined and fiber, pulp, and handsheet properties determined. Acacia dealbata and A. longifolia presented high specific wood consumption and lower handsheet strength properties, pointing to overall lower pulping potential, while A. melanoxylon and A. mearnsii characteristics were equal to or higher than those of E. globulus. A. saligna pulp and handsheet characteristics appear more suited for tissue paper. The Acacia mixture achieved acceptable characteristics, enabling the indiscriminate use of Acacia wood regardless of the species. As a shortcoming, the Acacia pulps showed the worst optical characteristics, with brightness dropping substantially with beating (64%–76%) when compared to E. globulus (81%). Full article

This study intended to evaluate the potential industrial applications of various Acacia species (Acacia melanoxylon, Acacia longifolia, Acacia cyclops, Acacia retinodes, Acacia pycnantha, Acacia mearnsii, and Acacia dealbata) by examining their chemical composition, antioxidant, and antimicrobial properties. Using high-resolution mass spectrometry, a comprehensive analysis successfully identified targeted compounds, including flavonoids (flavonols/flavones) and phenolic acids, such as 4-hydroxybenzoic acid, p-coumaric acid, and ellagic acid. Additionally, p-coumaric acid was specifically identified and quantified within the hydroxycinnamic aldehydes. This comprehensive characterization provides valuable insights into the chemical profiles of the studied species. Among the studied species, A. pycnantha exhibited a higher concentration of total phenolic compounds, including catechin, myricetin, quercetin, and coniferaldehyde. Furthermore, A. pycnantha displayed notable antibacterial activity against K. pneumoniae, E. coli, S. Typhimurium, and B. cereus. The identified compounds in Acacia pods and their shown antibacterial activities exhibit promising potential for future applications. Moreover, vibrational spectroscopy was a reliable method for distinguishing between species. These significant findings enhance our understanding of Acacia species and their potential for various industrial applications. Full article

Acacia longifolia is one of the most aggressive invaders worldwide whose invasion is potentiated after a fire, a common perturbation in Mediterranean climates. As a legume, this species establishes symbioses with nitrogen-fixing bacteria inside root nodules; however, the overall microbial diversity is still unclear. In this study, we addressed root nodules’ structure and biodiversity through histology and Next-Generation Sequencing, targeting 16S and 25S-28S rDNA genes for bacteria and fungi, respectively. We wanted to evaluate the effect of fire in root nodules from 1-year-old saplings, by comparing unburnt and burnt sites. We found that although having the same general structure, after a fire event, nodules had a higher number of infected cells and greater starch accumulation. Starch accumulated in uninfected cells can be a possible carbon source for the microbiota. Regarding diversity, Bradyrhizobium was dominant in both sites (ca. 77%), suggesting it is the preferential partner, followed by Tardiphaga (ca. 9%), a non-rhizobial Alphaproteobacteria, and Synechococcus, a cyanobacteria (ca. 5%). However, at the burnt site, additional N-fixing bacteria were included in the top 10 genera, highlighting the importance of this process. Major differences were found in the mycobiome, which was diverse in both sites and included genera mostly described as plant endophytes. Coniochaeta was dominant in nodules from the burnt site (69%), suggesting its role as a facilitator of symbiotic associations. We highlight the presence of a large bacterial and fungal community in nodules, suggesting nodulation is not restricted to nitrogen fixation. Thus, this microbiome can be involved in facilitating A. longifolia invasive success. Full article

The Acacia genus is considered one of the most invasive taxa in some habitats, namely coastal dunes, maritime calcareous soils, fresh lands in the valleys, mountainous areas, and the banks of watercourses and roadsides. In Portugal, the severity risk is very high, so this study aimed to evaluate the nutritional and mineral contents of the green pods as a potential source for livestock feeds and soil fertilizer because, as far as we know, there is no use for this species. The seven different species of Acacia (Acacia mearnsii Link, Acacia longifolia (Andrews) Willd, Acacia melanoxylon R. Br., Acacia pycnantha Bentham, Acacia dealbata Link., Acacia retinodes Schlecht, and Acacia cyclops A. Cunn. ex G. Don fil) were evaluated. The results showed that Acacia green pods have a high protein, fibre and minerals content, especially in potassium (K), calcium (Ca) and magnesium (Mg). All species present a different profile of the studied parameters, suggesting different potentials for their future use. Near-infrared spectroscopy was a potential tool to predict the earlier quality of the Acacia green pods to better select the raw material for the different applications. Full article

Woody invasive species pose a big threat to ecosystems worldwide. Among them, Acacia longifolia is especially aggressive, fundamentally changing ecosystem structure through massive biomass input. This biomass is rarely harvested for usage; thus, these plants constitute a nuisance for stakeholders who invest time and money for control without monetary return. Simultaneously, there is an increased effort to valorise its biomass, e.g., for compost, growth substrate or as biofuel. However, to incentivise A. longifolia harvest and usage, stakeholders need to be able to estimate what can be obtained from management actions. Thus, the total biomass and its quality (C/N ratio) need to be predicted to perform cost–benefit analyses for usage and determine the level of invasion that has already occurred. Here, we report allometric biomass models for major biomass pools, as well as give an overview of biomass quality. Subsequently, we derive a simplified volume-based model (BM ~ 6.297 + 0.982 × Vol; BM = total dry biomass and Vol = plant volume), which can be applied to remote sensing data or with in situ manual measurements. This toolkit will help local stakeholders, forest managers or municipalities to predict the impact and valorisation potential of this invasive species and could ultimately encourage its management. Full article

Acacia spp. are invasive in Southern Europe, and their high propagation rates produce excessive biomass, exacerbating wildfire risk. However, lignocellulosic biomass from Acacia spp. may be utilised for diverse biorefinery applications. In this study, attenuated total reflectance Fourier transform infrared spectroscopy (FTIR-ATR), high-performance anion-exchange chromatography pulsed amperometric detection (HPAEC-PAD) and lignin content determinations were used for a comparative compositional characterisation of A. dealbata, A. longifolia and A. melanoxylon. Additionally, biomass was treated with three white-rot fungi species (Ganoderma lucidum, Pleurotus ostreatus and Trametes versicolor), which preferentially degrade lignin. Our results showed that the pre-treatments do not significantly alter neutral sugar composition while reducing lignin content. Sugar release from enzymatic saccharification was enhanced, in some cases possibly due to a synergy between white-rot fungi and mild alkali pretreatments. For example, in A. dealbata stems treated with alkali and P. ostreatus, saccharification yield was 702.3 nmol mg⁻¹, which is higher than the samples treated only with alkali (608.1 nmol mg⁻¹), and 2.9-fold higher than the non-pretreated controls (243.9 nmol mg⁻¹). By characterising biomass and pretreatments, generated data creates value for unused biomass resources, contributing to the implementation of sustainable biorefining systems. In due course, the generated value will lead to economic incentives for landowners to cut back invasive Acacia spp. more frequently, thus reducing excess biomass, which exacerbates wildfire risk. Full article

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread. Full article

Acacia longifolia is a worldwide invader that cause damage in ecosystems, expanding largely after wildfires, which promote germination of a massive seed bank. As a legume, symbiosis is determinant for adaptation. Our study aims to isolate a wider consortium of bacteria harboured in nodules, including both nitrogen and non-nitrogen fixers. Furthermore, we aim to evaluate the effects of fire in nodulation and bacterial diversity on young acacias growing in unburnt and burnt zones, one year after the fire. For this, we used molecular approaches, M13 fingerprinting and 16S rRNA partial sequencing, to identify species/genera involved and δ¹⁵N isotopic composition in leaves and plant nodules. Nitrogen isotopic analyses in leaves suggest that in unburnt zones, nitrogen fixation contributes more to plant nitrogen content. Overall, A. longifolia seems to be promiscuous and despite Bradyrhizobium spp. dominance, Paraburkholderia spp. followed by Pseudomonas spp. was also found. Several species not previously reported as nitrogen-fixers were identified, proposing other functions besides ammonia acquisition. Our study shows that bacterial communities are different in nodules after fire. Fire seems to potentiate nodulation and drives symbiosis towards nitrogen-fixers. Taken together, a multifunctional community inside nodules is pointed out which potentiate A. longifolia invasiveness and adaptation. Full article

The prevalence of obesity is increasing globally. Despite the availability of a variety of anti-obesogenic drugs, including therapies under clinical development, these treatments are often indicated for patients with severe obesity, making them unsuitable for patients with mild obesity or for preventative use. In Thailand, traditional remedies employing medicinal plants are widely used to maintain health and treat disease. These treatments are generally inexpensive and readily available at markets, making them good treatment options for preventing obesity. To evaluate the anti-obesogenic potential of Thai medicinal plants, we employed three in vitro methods: pancreatic lipase inhibition, lipolysis enhancement, and lipid accumulation reduction assays. Among 70 Thai medicinal plants, Eurycoma longifolia Jack, Tiliacora triandra Diels, and Acacia concinna (Willd.) DC. were selected as the most favorable candidates because they exhibited anti-obesogenic activity in all three assays. These medicinal plants are expected to have efficient anti-obesogenic effects, making them promising candidates for further study. Full article

Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms and spectral regions for successfully classifying species remains an open field of research. Here, we tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected Mediterranean dune ecosystem. We used continuous visible, near-infrared and short wave infrared (VNIR-SWIR) data as well as vegetation indices at the leaf and canopy level for classification, comparing five different classification algorithms. We were able to successfully distinguish A. longifolia from surrounding vegetation based on vegetation indices. At the leaf level, radial-basis function kernel Support Vector Machine (SVM) and Random Forest (RF) achieved both a high Sensitivity (SVM: 0.83, RF: 0.78) and a high Positive Predicted Value (PPV) (0.86, 0.83). At the canopy level, RF was the classifier with an optimal balance of Sensitivity (0.75) and PPV (0.75). The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia. Our results highlight the potential to use remote sensing as a tool for an early detection of A. longifolia in Mediterranean coastal ecosystems. Full article

The invasive shrub, Acacia longifolia, native to southeastern Australia, has a negative impact on vegetation and ecosystem functioning in Portuguese dune ecosystems. In order to spectrally discriminate A. longifolia from other non-native and native species, we developed a classification model based on leaf reflectance spectra (350–2500 nm) and condensed leaf tannin content. High variation of leaf tannin content is common for Mediterranean shrub and tree species, in particular between N-fixing and non-N-fixing species, as well as within the genus, Acacia. However, variation in leaf tannin content has not been studied in coastal dune ecosystems in southwest Portugal. We hypothesized that condensed tannin concentration varies significantly across species, further allowing for distinguishing invasive, nitrogen-fixing A. longifolia from other vegetation based on leaf spectral reflectance data. Spectral field measurements were carried out using an ASD FieldSpec FR spectroradiometer attached to an ASD leaf clip in order to collect 750 in situ leaf reflectance spectra of seven frequent plant species at three study sites in southwest Portugal. We applied partial least squares (PLS) regression to predict the obtained leaf reflectance spectra of A. longifolia individuals to their corresponding tannin concentration. A. longifolia had the lowest tannin concentration of all investigated species. Four wavelength regions (675–710 nm, 1060–1170 nm, 1360–1450 nm and 1630–1740 nm) were identified as being highly correlated with tannin concentration. A spectra-based classification model of the different plant species was calculated using a principal component analysis-linear discriminant analysis (PCA-LDA). The best prediction of A. longifolia was achieved by using wavelength regions between 1360–1450 nm and 1630–1740 nm, resulting in a user’s accuracy of 98.9%. In comparison, selecting the entire wavelength range, the best user accuracy only reached 86.5% for A. longifolia individuals. Full article