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Aim Researchers measuring beta diversity have rarely concerned themselves with the problems of how complete the species lists of studied communities are, and of how the varying degrees of completeness can actually change estimates of beta diversity. No comprehensive assessment has been made regarding the behaviour of most beta diversity indices when applied to incomplete samples, a situation which is more common than usually recognized. Our objective was to assess the behaviour and robustness of a number of beta diversity measures for incidence data from undersampled communities. Location Mainland Portugal and the Azorean archipelago (North Atlantic). Methods Data from intensive sampling of spiders in mainland Portugal and arthropods in Azores were collected. We examined the properties of 15 beta diversity measures developed for incidence data. We simulated varying degrees of completeness, whereas computing beta diversity for selected pairs of samples. The robustness of these beta diversity accumulation curves was assessed for the purpose of finding the best measures for undersampled communities. Results The Harrison et al.β-2 and the Williams β-3 are particularly robust to undersampling. These measures are also insensitive to differences of alpha diversity (species richness) between communities, and therefore to nestedness. Colwell & Coddington βcc and the related Jaccard βj and Gaston et al.βg performed best of the measures sensitive to alpha diversity. They performed poorly, however, when compared communities exhibited very low values of beta diversity. In such cases, the Routledge βr performed the best. Main conclusions No index was found to perform without bias in all circumstances. Overall, β-2, β-3 and βcc (or related measures βj and βg) are recommended as they seem to be the most robust to undersampling. [ABSTRACT FROM AUTHOR]

1. Fields such as ecology, macroecology, and conservation biology rely on accurate and comparable data. This is especially important for mostly unknown and megadiverse taxa such as spiders and regions such as the Mediterranean. Short-term sampling programmes are increasingly seen as the best option for sampling spiders. Comparability of results, however, demands standard procedures both in methodology and in sampling period. Cost-efficiency dictates that this period should be the most species rich. 2. Pitfall trapping was conducted in 23 sites from north to south Portugal, comprising three large-scale environmental zones and many different habitat types, during 10 months in each site. The annual richness pattern, differences in this pattern between areas and habitats, the complementarity between sampling periods and possible environmental correlates of richness were studied. 3. May and June present the optimal time for collecting spiders in Mediterranean areas. Northern areas have a later peak in richness and dense tree-cover sites offer more flexibility for sampling, with a higher proportion of species present at each period throughout the year. 4. Day length is the environmental factor most correlated with species richness. Maximum daily temperature may reduce richness, especially in southernmost areas, where summer temperatures can be extremely harsh. 5. It is recommended that short-term sampling programmes, intended to give a reasonable picture of spider communities in Portugal and in the Iberian Peninsula (and possibly extending to all the Mediterranean), should be conducted during May or June, with variable flexibility according to area and habitat. The proposed suggestions should appeal to everyone working in the field, given the cost-efficiency and comparability of results by adopting a common standardised approach. [ABSTRACT FROM AUTHOR]

A considerable number of alternative approaches have been suggested during the last years to predict species richness of a given taxon, while retaining information on the identities of the observed individuals. Such information can be extremely useful for choosing conservation priority areas, either by using raw richness values or, preferentially, by considering the complementarity between potential sites. Among the most popular approaches is the use of indicator taxa. Both one single family and a group of several families are here tested in their ability to predict the number of spider (Araneae) species independently of sampling effort, geographical location and type of habitat. We use data from three Portuguese protected areas as a test case. A two-family indicator group – Gnaphosidae and Theridiidae – is found to be a good surrogate of species richness, even if caution is needed regarding the comparison of sites with considerably different sampling effort or vegetation cover. No single family can be seen as a good surrogate of the totality of spiders. In addition, only a group of the two mentioned families is found to be efficient and reliable either when used to rank sites according to taxa richness or for determining near-minimum sets of sites for conservation. We therefore recommend surrogacy with this indicator group as a promising approach for the prediction of spider species richness or evaluation and ranking of areas according to their conservation importance. The reached conclusions should uphold for Portugal and the entire Mediterranean region. [Copyright &y& Elsevier]

A number of alternative approaches have been suggested during recent years to predict species richness of a given taxa, while retaining information on the identities of the observed individuals. Such information can be extremely useful for choosing conservation priority areas, either by using simple richness values or, preferentially, by considering the complementarity of potential sites. Among the most popular approaches is the use of higher taxa surrogates. Both family and genus richness are here tested in their ability to predict the number of spider (Araneae) species independently of sampling effort, geographical location and type of habitat. We use data from three Portuguese protected areas as a test case. Genus richness is considered a good surrogate of species richness, despite some caution being needed regarding the comparison of sites with considerably different sampling effort, the same not happening with families. Only genera are also found to be reliable either for ranking sites according to taxa richness or for determining near-minimum sets of sites for conservation. We therefore recommend surrogacy at this taxonomic level as a promising approach for the prediction of spider species richness or evaluation and ranking of areas according to their conservation importance. The reached conclusions should uphold for Portugal and the entire Mediterranean region. [Copyright &y& Elsevier]

Simple Summary: Device-assisted enteroscopy is the only diagnostic and therapeutic exam capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this procedure is not sufficient enough to assure a cost-effective panendoscopy, and there is significant interobserver variability during the exam. Artificial intelligence tools have been proved to be beneficial in several areas of medicine, namely in Gastroenterology, with a strong image component. However, the development of deep learning models for application in device-assisted enteroscopy is still in an embryonic phase. The authors herein aimed to develop a multidevice convolutional neural network based on 338 exams performed in two renowned centers. The present model was able to accurately identify multiple clinically relevant lesions across the entire gastrointestinal tract, with an image processing time that favors its clinical applicability. The first worldwide panendoscopic model showed the potential of artificial intelligence in augmenting the accuracy and cost-effectiveness of device-assisted enteroscopy. Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosis and characterization of biliary strictures is challenging, even after the introduction of digital single-operator cholangioscopy (D-SOC). The endoscopist's visual impression has a suboptimal accuracy and there is a significant interobserver variability. Artificial intelligence tools for image analysis have presented important contributions in several fields of gastroenterology. Convolutional neural networks are highly efficient multi-layered deep neural networks for image analysis, with great results in several fields of medicine. Nevertheless, the role of these deep learning models in digital cholangioscopy is still in a premature phase. With this bicentric international study, the authors aimed to create a deep learning-based algorithm for digital cholangioscopy capable of distinguishing benign from malignant biliary lesions. The present model accurately detected malignant biliary lesions with an image processing rate that favors its clinical applicability. The authors believe that the use of an AI-based model may change the landscape in the digital cholangioscopy diagnostic yield. Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures. [ABSTRACT FROM AUTHOR]

There is a growing interest in academia to provide biodiversity data to both the scientific community and the public. We present an internet database of the terrestrial lichens, bryophytes, vascular plants, molluscs, arthropods, vertebrates and coastal invertebrates of the Azores archipelago (Portugal, North Atlantic): the Azorean Biodiversity Portal (ABP, http://www.azoresbioportal.angra.uac.pt/). This is a unique resource for fundamental research in systematics, biodiversity, education and conservation management. The ABP was based on a regional species database (ATLANTIS), comprised of grid-based spatial incidence information for c. 5000 species. Most of the data rely on a comprehensive literature survey (dating back to the 19th century) as well as unpublished records from recent field surveys in the Azores. The ABP disseminates the ATLANTIS database to the public, allowing universal, unrestricted access to much of its data. Complementarily, the ABP includes additional information of interest to the general public (e.g. literature on Macaronesian biodiversity) together with images from collections and/or live specimens for many species. In this contribution we explain the implementation of a regional biodiversity database, its architecture, achievements and outcomes, strengths and limitations; we further include a number of suggestions in order to implement similar initiatives. [ABSTRACT FROM AUTHOR]