Package betaper

Documentation for the R-package "betaper

The Tree Biodiversity Network (BIOTREE-NET): prospects for biodiversity research and conservation in the Neotropics

Long Database Report.-- et al.Biodiversity research and conservation efforts in the tropics are hindered by the lack of knowledge of the assemblages found there, with many species undescribed or poorly known. Our initiative, the Tree Biodiversity Network (BIOTREE-NET), aims to ad-dress this problem by assembling georeferenced data from a wide range of sources, making these data easily accessible and easily que-ried, and promoting data sharing. The database (GIVD ID NA-00-002) currently comprises ca. 50,000 tree records of ca. 5,000 species (230 in the IUCN Red List) from >2,000 forest plots in 11 countries. The focus is on trees because of their pivotal role in tropical for-est ecosystems (which contain most of the world's biodiversity) in terms of ecosystem function, carbon storage and effects on other species. BIOTREE-NET currently focuses on southern Mexico and Central America, but we aim to expand coverage to other parts of tropical America. The database is relational, comprising 12 linked data tables. We summarise its structure and contents. Key tables contain data on forest plots (including size, location and date(s) sampled), individual trees (including diameter, when available, and both recorded and standardised species name), species (including biological traits of each species) and the researchers who collected the data. Many types of queries are facilitated and species distribution modelling is enabled. Examining the data in BIOTREE-NET to date, we found an uneven distribution of data in space and across biomes, reflecting the general state of knowledge of the tropics. More than 90% of the data were collected since 1990 and plot size varies widely, but with most less than one hectare in size. A wide range of minimum sizes is used to define a 'tree'. The database helps to identify gaps that need filling by further data collection and collation. The data can be publicly accessed through a web application at http://portal.biotreenet.com. Researchers are invited and encouraged to contribute data to BIOTREE-NET.BIOTREE-NET development has been supported primarily by Fundación BBVA (project BIOCON08_044).Peer Reviewe

Soil and climate drive floristic composition in tropical forests: a literature review

A vast literature indicates that environment plays a paramount role in determining floristic composition in tropical forests. However, it remains unclear which are the most important environmental factors and their relative effect across different spatial scales, plant life forms or forest types. This study reviews the state of knowledge on the effect of soil and climate on floristic composition in tropical forests. From 137 publications, we collated information regarding: (1) spatial scale, continent, country, life form, and forest type; (2) proportion of variance in floristic composition explained by soil and climatic variables and how it varies across spatial scales; and (3) which soil and climate variables had a significant relationship on community composition for each life form and forest type. Most studies were conducted at landscape spatial scales (67%) and mainly in South America (74%), particularly in Brazil (40%). Studies majorly focused on trees (82%) and on lowland evergreen tropical forests (74%). Both soil and climate variables explained in average the same amount (14% each) of the variation observed in plant species composition, although soils appear to exert a stronger influence at smaller spatial scales while climate effect increases toward larger ones. Temperature, precipitation, seasonality, soil moisture, soil texture, aluminum, and base cations—calcium and magnesium–and their related variables (e.g., cation exchange capacity, or base saturation) were frequently reported as important variables in structuring plant communities. Yet there was variability when comparing different life forms or forest types, which renders clues about certain ecological peculiarities. We recommend the use of standardized protocols for collecting environmental and floristic information in as much as possible, and to fill knowledge gaps in certain geographic regions. These actions will be especially beneficial to share uniform data between researchers, conduct analysis at large spatial scales and get a better understanding of the link between soils and climate gradients and plant strategies, which is key to propose better conservation policies under the light of global chang

Ecological and biogeographic null hypotheses for comparing rarefaction curves

The statistical framework of rarefaction curves and asymptotic estimators allows for an effective standardization of biodiversity measures. However, most statistical analyses still consist of point comparisons of diversity estimators for a particular sampling level. We introduce new randomization methods that incorporate sampling variability encompassing the entire length of the rarefaction curve and allow for statistical comparison of i ≥ 2 individual-based, sample-based, or coverage-based rarefaction curves. These methods distinguish between two distinct null hypotheses: the ecological null hypothesis (H0eco) and the biogeographical null hypothesis (H0biog). H0eco states that the i samples were drawn from a single assemblage, and any differences among them in species richness, composition, or relative abundance reflect only sampling effects. H0biog states that the i samples were drawn from assemblages that differ in their species composition but share similar species richness and species abundance distributions. To test H0eco, we created a composite rarefaction curve by summing the abundances of all species from the i samples. We then calculated a test statistic Zeco, the (cumulative) summed areas of difference between each of the i individual curves and the composite curve. For H0biog, the test statistic Zbiog was calculated by summing the area of difference between all possible pairs of the i individual curves. Bootstrap sampling from the composite curve (H0eco) or random sampling from different simulated assemblages using alternative abundance distributions (H0biog) was used to create the null distribution of Z, and to provide a frequentist test of ZjH0. Rejection of H0eco does not pinpoint whether the samples differ in species richness, species composition, and/or relative abundance. In benchmark comparisons, both tests performed satisfactorily against artificial data sets randomly drawn from a single assemblage (low Type I error). In benchmark comparisons with different species abundance distributions and richness, the tests had adequate power to detect differences among curves (low Type II error), although power diminished at small sample sizes and for small differences among underlying species rank abundances

Identification of critical areas for mammal conservation in the Brazilian Atlantic Forest Biosphere Reserve

Herein we identified the geographic location of protected areas (PAs) critical for strengthening mammalian conservation in the Brazilian Atlantic Forest Biosphere Reserve (RMBA) by assessing sites of particular importance for mammal diversity using different biodiversity criteria (richness, rarity, vulnerability) and a connectivity index. Although 95% of mammal species were represented by PAs, most of them had less than 10% of their distribution range protected by these areas. A total of 94 critical areas for mammal conservation-representing 49.60% of the total PAs were identified. Most of these areas were located at endangered ecoregions. We recommend that conservationists and policy makers should identify critical areas in order to guarantee biodiversity fluxes among landscapes, and enhance the connectivity between PAs to increase biodiversity protection and conservation. Knowledge about the location of critical areas may encourage managers and policy makers to develop specific programs to strengthen mammal biodiversity protection, especially for threatened species.This study was supported by BIOTREE-net - project funded by BBVA Foundation and M.J.T. Assunção-Albuquerque was supported by the Brazilian Ministry of Education, through CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) Doctorate scholarship.Peer Reviewe

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty

Cartografía Corológica Ibérica. Aportaciones 99 - 101.

Sin resume

Travel time impacts analysis of system-wide signal timing optimization methodology

This study analyzes the economic impact that users would experience with the travel time variation due to system-wide signal timing optimization. To do this, a comprehensive analysis of travel time user benefits is conducted using traffic volume, speed and other attributes of road network, before and after signal timing optimization

A feedback simulation procedure for real-time control of urban drainage systems

This paper presents a feedback simulation procedure for the real-time control (RTC) of urban drainage systems (UDS) with the aim of providing accurate state evolutions to the RTC optimizer as well as illustrating the optimization performance in a virtual reality. Model predictive control (MPC) has been implemented to generate optimal solutions for the multiple objectives of UDS using a simplified conceptual model. A high-fidelity simulator InfoWorks ICM is used to carry on the simulation based on a high level detailed model of a UDS. Communication between optimizer and simulator is realized in a feedback manner, from which both the state dynamics and the optimal solutions have been implemented through realistic demonstrations. In order to validate the proposed procedure, a real pilot based on Badalona UDS has been applied as the case study.Peer ReviewedPostprint (author's final draft

Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, we append to the actual measurements a subset of relevant estimated states to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, and several promising performance results are attained, even deriving different mechanisms to increase the resilience to classical issues (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed in the BattLeDIM2020 competition.Peer ReviewedPostprint (author's final draft