Freshwater ecosystems host high levels of biodiversity but are also highly vulnerable to biological invasions. Aquatic Invasive Alien Plant Species (aIAPS) can cause detrimental effects on freshwater ecosystems and their services to society, raising challenges to decision-makers regarding their correct management. Spatially and temporally explicit information on the occurrence of aIAPS in dynamic freshwater systems is essential to implement efficient regional and local action plans. The use of unmanned aerial vehicle imagery synchronized with free Sentinel-2 multispectral data allied with classifier fusion techniques may support more efficient monitoring actions for non-stationary aIAPS. Here, we explore the advantages of such a novel approach for mapping the invasive water-hyacinth (Eichhornia crassipes) in the Cávado River (northern Portugal). Invaded and non-invaded areas were used to explore the evolution of spectral attributes of Eichhornia crassipes through a time series (processed by a super-resolution algorithm) that covers March 2021 to February 2022 and to build an occurrence dataset (presence or absence). Analysis of the spectral behavior throughout the year allowed the detection of spectral regions with greater capacity to distinguish the target plant from the surrounding environment. Classifier fusion techniques were implemented in the biomod2 predictive modelling package and fed with selected spectral regions to firstly extract a spectral signature from the synchronized day and secondly to identify pixels with similar reflectance values over time. Predictions from statistical and machine-learning algorithms were ensembled to map invaded spaces across the whole study area during all seasons with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.932; Area Under the Receiver Operating Curve, ROC: 0.992; Kappa: 0.826). Our results provide evidence of the potential of our approach to mapping plant invaders in dynamic freshwater systems over time, applicable in the assessment of the success of control actions as well as in the implementation of long-term strategic monitoring.

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.

The high volumes of animal manure and sewage sludge, as a consequence of the development of intensive and specialized cattle dairy farms in peri-urban areas, pose challenges to local environmental quality and demands for systems innovation. Besides these negative impacts, energy recovery from biogas produced in anaerobic co-digestion processes should contribute to local sustainable development. This research considers technical data obtained from the optimization of biomethanization processes using sewage sludge and cattle manure liquid fraction, aiming to develop a spatially explicit model including multicriteria evaluation and an analytical hierarchy process to locate biogas production facilities, allocate energy resources and consider biogas unit pre-dimensioning analysis. According to the biophysical conditions and socioeconomic dynamics of the study area (Vila do Conde, Northwest Portugal), a spatially explicit model using multicriteria and multiobjective techniques allowed the definition of suitable locations, as well as the allocation of resources and support pre-dimensioning of biogas facilities. A p-median model allowed us to allocate resources and pre-dimensioning biogas facilities according to distance and accessibility elements. The results indicate: (i) the location of areas with adequate environmental conditions and socioeconomic suitability advantages to install biogas production facilities, and (ii) the ability to compare the options of centralized or distributed location alternatives and associated pre-dimensioning.

Pterospartum tridendatum is an important source of active compounds with anti-inflammatory properties. The ability of P. tridentatum leaves methanolic extract in preventing/reversing inflammation was studied in adult rats using a model of experimental osteoarthritis (OA) and ear edema. Control animals (SHAM) were administered phosphate buffer solution (PBS), while OA animals received either P. tridentatum 100 mg/kg, 300 mg/kg, or a commercial anti-inflammatory (15 mg/Kg, Ibuprofen) via gavage, daily, for three weeks. Ear edema was induced, and the animals were divided into five groups treated with: (i) ethanol, (ii) P. tridentatum, (iii) croton oil, (iv) croton oil + P. tridentatum, and (v) croton oil + medrol. The inflammatory effect was evaluated by the measurement of the knee and ear edema. The chromatographic profile, evaluated by HPLC-DAD, showed numerous phenolic compounds are present. In the docking analysis of these compounds, isoquercetin demonstrated strong molecular interactions for peroxisome proliferator-activated receptor alpha and gamma (PPARα and PPARƴ, respectively), protein kinase 2 subunit α (CK2 α), and 5-lipoxygenase-activating proteins. Genistein had strong docking binding energies for CK2α and prostaglandin H (2) synthase-1. Our analysis showed the treatment with P. tridentatum extract reversed OA-induced edema in the rat knee, as well as ear edema, highlights this plant as a potential source of compounds that can be used as adjuvants in the management of inflammation.

Spatial epidemiology tools play a critical role in effectively allocating resources to curb the spread of animal diseases. This study focuses on classical scrapie (CS), an animal prion disease identified in Portugal, which infects small ruminant flocks and has been shown to be experimentally transmissible to wild cervids. Utilising remote sensing technologies and semi-automatic classification models, we aimed to evaluate the risk of interspecies prion transmission from domestic small ruminants to wild cervids (hosts). To achieve this, we gathered data related to hosts and infected small ruminant flocks. Furthermore, we collected and processed freely available, medium-resolution satellite imagery to derive vegetative and biophysical spectral indices capable of representing the primary habitat features. By employing a pixel-based species distribution model, we integrated the compiled geographical distribution data and spectral data with five supervised classification algorithms (random forest, classification tree analysis, artificial neural network, generalised linear model, and generalised additive model). The consensus map allowed accurate predictions of spatialised regions exhibiting spectral characteristics similar to where CS and its hosts were initially identified. By overlapping suitable territories for disease and host occurrence, we created a spatially explicit tool that assesses the risk of prion spill-over from domestic small ruminants to wild cervids. The described methodology is highly replicable and freely accessible, thus emphasising its practical utility. This study underscores the substantial contribution of model-based spatial analysis to disease monitoring and lays the groundwork for defining populations at risk and implementing targeted control and prevention strategies, thus safeguarding both animal and public health.