We provide a framework to upscale biodiversity in tropical forests from local samples of species richness and abundances.

Abstract Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. Determining microbiomes diversity implies the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases. In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and shotgun sequencing to three mock communities of bacteria, of which the compositions are known. We show that none of these methods can infer both the true number of taxa and their abundances. We thus propose a novel approach, named Core-Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method similar to 16S, but based on emergent statistics of core protein domain families. We thus test the proposed method on various mock communities and we show that Core-Kaiju reliably predicts both number of taxa and abundances. Finally, we apply our method on human gut samples, showing how Core-Kaiju may give more accurate ecological characterization and a fresh view on real microbiomes.

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.

In this paper we are concerned with the analytical description of the change in floristic composition (species turnover) with the distance between two plots of a tropical rainforest due to the clustering of the individuals of the different species. We describe the plant arrangement by a superposition of spatial point processes and in this framework we introduce an analytical function which represents the average spatial density of the Sørensen similarity between two infinitesimal plots at distance r. We see that the decay in similarity with the distance is essentially described by the pair correlation function of the superposed process and that it is governed by the most abundant species. We test our analytical model with empirical data obtained for the Barro Colorado Island and Pasoh rainforests. To this end we adopt the statistical estimator for the pair correlation function in Shimatani (2001) and we design a novel one for the Sørensen similarity. Furthermore, we test our analytical formula by modeling the forest study area with Neyman-Scott point processes. We conclude comparing the advantages of our approach with other ones existing in literature.

The population of adults with congenital heart disease is increasing due to advancements in cardiology and cardiac surgery. Many patients face medical complications and psychosocial difficulties; however, it is not yet clear whether there is a direct relationship between medical status and the psychological functioning of these patients. This systematic review of the relevant literature is an attempt to: provide a comparison between the population of adults with congenital heart disease, the healthy reference population and similar cardiac populations when it comes to psychological functioning; explore the relationship between medical status/cardiac condition and psychological functioning; and identify the predictors of psychological distress in this population.

Many adults with congenital heart disease (ACHD) have to face considerable psychosocial difficulties. The aim of this study was to explore the life experiences of ACHD patients, from when they become aware of having a condition, till after the open heart surgery they underwent. The study was conducted with the use of unstructured, in-depth interviews, performed on 11 patients (age ranging: 20 -56 y) after they recovered from open heart surgery and a focus group, which included 16 participants (age ranging: 22 -46 y). Both the interviews and the focus group were recorded, transcribed and analyzed according to Grounded Theory procedures. Our findings show that the condition of diversity is the core of the emotional experiences connected to ACHD. Feeling different and being perceived as being different are clearly interlinked and coping strategies adopted resulted as being influenced by this perception. This study also clearly outlines the importance of having an adequate perception of one's condition and the link between maladaptive coping strategies and an incorrect perception of one's heart condition. Results are discussed in order to promote psychosocial interventions within and outside of the hospital setting in order to improve the patients' emotional wellbeing.

4. Our estimates are robust and accurate, as confirmed by tests performed on both in silico-generated and real forests. We demonstrate the accuracy of our predictions using data from two well-studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence-absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.

In this paper we investigate a method proposed recently by K.H. Knuth to find the optimal bin size of an histogram as a tool for statistical analysis of spatial point processes. We test it through numerical simulations on various spatial processes which are of interest in ecology. We show that Knuth optimal bin size rule reducing noisy fluctuations performs better than standard kernel methods to infer the intensity of the underlying process