Restauração passiva em pastagens abandonadas a partir de núcleos de vegetação na Mata Atlântica, Brasil

The nuclei of vegetation are structures spontaneously formed by small clusters of individual shrubs and trees that, upon evolving in disturbed pastures can contribute to the ecological succession. In order to understand the evolution of passive restoration processes in tropical ecosystems, after extensive cattle ranching was stopped, environments with low environmental attributes were studied, located in the northern slope of the isolated mounds between floodplain. After 40 years of abandonment, the nuclei of vegetation colonized 20% of the graminoid ecosystems sampled, presenting at a moderate stage of disturbance. The floristic composition of the upper strata and regenerating showed that nuclei with two or more pioneer forest species and anemocoric dispersion syndrome, such as Moquiniastrum polymorphum, present a high probability of having their restoration processes catalyzed in time. Developed nuclei present higher richness and abundance in their regenerating stratum, besides zoological and non-pioneering species, suggesting positive interactions between the ecological processes, a condition that gives sustainability to the advances of the passive forest restoration. The improvement of this information can contribute to the development of models of induced restoration based on the effects of nucleation.Os núcleos de vegetação são estruturas formadas espontaneamente por pequenos agrupamentos de indivíduos arbustivos e arbóreos, que, ao evoluírem em pastagens perturbadas, podem contribuir na sucessão ecológica. Para compreender a evolução dos processos de restauração florestal passiva em ecossistemas tropicais, depois de paralisada a pecuária extensiva, foram estudados ambientes com baixa oferta de atributos ambientais localizados na vertente norte dos morrotes isolados entre planície de inundação. Após 40 anos de abandono, os núcleos de vegetação colonizaram 20% dos ecossistemas graminoides amostrados, apresentando-se em estágio moderado de perturbação. A composição florística dos estratos superior e regenerante evidenciou que núcleos com duas ou mais espécies florestais pioneiras e com síndrome de dispersão anemocórica, como Moquiniastrum polymorphum, apresentam alta probabilidade de terem seus processos de restauração catalisados no tempo. Núcleos desenvolvidos apresentam riqueza e abundância maiores em seu estrato regenerante, além de espécies zoocóricas e não pioneiras, sugerindo interações positivas entre processos ecológicos, condição que confere sustentabilidade aos avanços da restauração florestal passiva. O aprimoramento destas informações pode contribuir para o desenvolvimento de modelos de restauração induzidos com base nos efeitos da nucleação

Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data