1. Droughts may be responsible for important ecological impacts in freshwater ecosystems, including the death of rare species.2. This study assessed the mortality of the endangered pearl mussel Margaritifera margaritifera (Linnaeus, 1758) in the Mente, Rabaçal and Tuela rivers (north west of the Iberian Peninsula) during the extreme summer drought of 2017.3. Mortality occurred as a result of the low river flow, which led to mussel stranding near the banks and consumption (by predation or scavenging) by wild boar Sus scrofa (Linnaeus, 1758). 4. Mortality differed across sites. The shell lengths of live mussels (measured before the drought) and dead mussels significantly differed in the Rabaçal River, but not in the Mente and Tuela rivers.5. Extreme droughts are predicted to increase in number and intensity in the future, and possible impacts on rare species such as M. margaritifera should be carefully monitored. Several M. margaritifera populations in Iberia (and elsewhere) may now be at increased risk, and measures should be implemented in order to mitigate the impacts of future extreme droughts.

We assessed the predation of crayfishes on freshwater pearl mussels. • In the laboratory, predation of freshwater pearl mussels was size dependent. • In the field, predation of freshwater pearl mussels was density dependent. • Invasive crayfishes may impair the conservation of freshwater pearl mussels.

Freshwater mussels are undergoing rapid global declines due to habitat loss and fragmentation, among other factors, but little is known about the effects of small hydropower plants. Here we assessed the impact of small hydropower plants on the abundance and size structure of the imperilled pearl mussel Margaritifera margaritifera. For this, we sampled 66 sites in three Portuguese rivers (Mente, Rabaçal and Tuela) located upstream and downstream of dams and within the reservoirs. Pearl mussels were significantly more abundant upstream than downstream of dams (97.4% more) or within reservoirs (98.5% more). In addition, juveniles were mostly found upstream of dams. The most significant environmental alterations that explained the observed patterns were related to changes in sediment characteristics (accumulation of fine sediments and organic matter in reservoirs) and water chemistry, most notably suspended solids (highest values in reservoirs) and dissolved oxygen (lowest values in reservoirs). Overall, results show that small hydropower plants can deeply affect pearl mussel populations: specimens almost disappeared from the areas within the reservoirs and sites located downstream only retained adults without signs of recent recruitment. Future management measures devoted to the conservation of pearl mussels should take into account the results reported here to avoid the construction of new dams in pearl mussel rivers; improve management of the river flow in downstream areas; and consider the decommissioning

Summary Bycatch is one of the main threats to marine biodiversity, affecting ocean ecosystems at a worldwide scale. The main focus of bycatch studies has been on the impact of larger vessels, with few studies assessing the impact of artisanal fisheries. Moreover, bycatch studies are often limited to a small number of marine regions, and significant gaps still exist in our knowledge of the spatial and temporal patterns of seabird bycatch. Here we present a multi-approach method to accurately quantify seabird bycatch driven by small- and medium-sized fishing fleets operating in a high priority area for seabird conservation on the Portuguese mainland. Results of three mitigation measures to reduce seabird bycatch on fishing gear where seabird bycatch is most likely to occur were also tested: high contrast panels in bottom gillnets, black hooks in demersal longlines and a bird scaring device in purse seines. The efficacy, acceptance, and economic viability were tested for each mitigation measure. Sixty-seven individuals of seven seabird species were bycaught during 295 monitored fishing trips between 2015 and 2018. Bycatch occurred mainly in demersal longlines (0.07 birds fishing event-1), followed by purse seines (0.02 birds fishing event-1) and bottom gillnets (0.01 birds fishing event-1). Nevertheless, the bird scaring device caused birds to interact less with the vessel (the presence of gulls was reduced by 11%), thus decreasing the likelihood of bycatch. This device has proved to be low-cost (representing less than 5% income of a single day’s landings) and easy to implement, being also well accepted by purse seine fishermen. It was not possible to evaluate the efficacy of high contrast panels and black hooks, as no bycatch events were recorded during trials.

Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients’ clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.