Historically, the creation of protected areas has occupied a forefront role among conservation strategies to protect wildlife. However, the effectiveness of such areas in maintaining viable populations has been a matter of debate. The present study aims to evaluate the efficiency of the protected areas network in the state of Minas Gerais, southeastern Brazil, in maintaining viable populations of Tapirus terrestris. We used the software VORTEX to model the viability of tapir populations in 65 protected areas found in the state. Our results indicate that 14 protected areas are not able to maintain lowland tapir populations in the long-term. It was also observed that 16 protected areas would suffer from genetic erosion and demographic stochasticity. Four protected areas would hold populations under the negative effects of genetic stochasticity. A total of 31 protected areas are predicted to hold viable populations. The results stress the necessity of more efficient and careful planning for resource allocation in the management of protected areas in the state of Minas Gerais, or population declines and local extinctions are expected to affect the lowland tapir in the near future.

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Chagas disease represents one of the major health issue in Latin America. Epidemiological control is focused on disease vectors, so studies on the ecology of triatomine vectors constitute a central strategy. Recently, research at large spatial scale has been produced, and authors commonly rely on the assumption that geographical regions presenting good environmental conditions for most vector species are also those with high risk of infection. In the present work, we provide an explicit evaluation for this assumption. Employing species distribution models and epidemiological data for Chagas disease in Brazilian territory, our results show that species richness is a poor predictor for the observed pattern of Chagas disease occurrence. Species composition proved to be a better predictor. We stress that research on macroecology of infectious diseases should go beyond the analysis of biodiversity patterns and consider human infections as a central part of the focal ecological systems.

Historically, studies aimed at prospecting and analyzing paleontological and neontological data to investigate species distribution have developed separately. Research at the interface between paleontology and biogeography has shown a unidirectional bias, mostly focusing on how paleontological information can aid biogeography to understand species distribution through time. However, the modern suit of techniques of ecological biogeography, particularly species distribution models (SDM), can be instrumental for paleontologists as well, improving the biogeography-paleontology interchange. In this study, we explore how to use paleoclimatic data and SDMs to support paleontological investigation regarding reduction of taxonomic uncertainty. Employing current data from two neotropical species (Lagostomus maximus and Myocastor coipus), we implemented SDMs and performed model validation comparing hindcasts with dated fossil occurrences (~14k and ~20k years back present, respectively). Finally, we employed the hindcasting process for two South American fossil records of a misidentified species of caiman (Caiman sp.) to show that C. latirostris is the most likely species identity of these fossils (among four candidate species: C. latirostris, C. yacare, C. crocodilus, and Melanosuchus niger). Possible limitations of the approach are discussed. With this strategy, we have shown that current developments in biogeography research can favour paleontology, extending the (biased) current interchange between these two scientific disciplines.

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Clinical decision support systems (CDSS) figures out as one of the most promising technologies for data-centered and AI-prompted healthcare. Its current developments are mainly guided by two disparate mindsets, namely a machine learning-centered framework and a classical rule-based framework. These respective approaches presents contrastive pros and cons. In the present study we provide an analysis showing that these two mindsets are actually related to each other, and straightforward algorithms are feasible by combining current standards for machine learning and classic decision tables algorithms. A theoretical analysis are provided, as well a computational implementation (in python). A real case scenario on radiological immaging exam prescription is used to ilustrate the successfully application of our results. Future work on benchmarking the proposed algorithms embodied in a fully operational clinical decision support system could extend our findings towards daily used systems.

Aim Despite longstanding investigation, the gradients of species richness remain unknown for most taxa because of shortfalls in knowledge regarding the quantity and distribution of species. Here, we explore the ability of a geostatistical interpolation model, regression‐kriging, to recover geographical gradients of species richness. We examined the technique with an in silico gradient of species richness and evaluated the effect of different configurations of knowledge shortfalls. We also took the same approach for empirical data with large knowledge gaps, the infraorder Furnariides of suboscine birds. Innovation Regression‐kriging builds upon two cornerstones of geographical gradients of biodiversity, the spatial autocorrelation of species richness and the conspicuous association of species with environmental factors. With this technique, we recovered a simulated gradient of richness using < 0.01% of sampling sites across the region. The accuracy of the regression‐kriging is higher when input samples are more evenly distributed throughout the geographical space rather than the environmental space of the target region. Moreover, the accuracy of this method is more sensitive to the sufficiency of sampling effort within cells than to the quantity of sampled localities. For Furnariides birds, regression‐kriging provided a geographical gradient of species richness that resembles purported patterns of other groups and illustrated ubiquitous shortfalls of knowledge about bird diversity. Main conclusions Geostatistical interpolation, such as regression‐kriging, might be a useful tool to overcome shortfalls in knowledge that plague our understanding of geographical gradients of biodiversity, with many applications in ecology, palaeoecology and conservation.

The diversity of Brazilian vertebrates is regarded among the highest in the world. However, the biological diversity is still mostly unknown and a good part of it is seriously threatened by human activities. This study aimed to inventory the medium and large size mammals present in the Reserva Biológica de Santa Rita do Sapucaí, an Atlantic forest reserve located in Santa Rita do Sapucaí, southeastern Brazil. Sand-plots, photographic traps and searches for animal tracks on pre-existent trails in the area, were carried out once every two months between May 2006 and February 2007. The sand-plots and tracks were inspected during five consecutive days per sampling. We obtained 108 records of 15 species, mostly of carnivorans. Two confirmed species are threatened with extinction in Brazil (Callithrix aurita and Leopardus pardalis). The results suggest that the sampled reserve has high species richness and plays an important role in conservation of mammals in this landscape, including species threatened with extinction.