The value of trophic interactions for ecosystem function:Dung beetle communities influence seed burial and seedling recruitment in tropical forests

Anthropogenic activities are causing species extinctions, raising concerns about the consequences of changing biological communities for ecosystem functioning. To address this, we investigated how dung beetle communities influence seed burial and seedling recruitment in the Brazilian Amazon. First, we conducted a burial and retrieval experiment using seed mimics. We found that dung beetle biomass had a stronger positive effect on the burial of large than small beads, suggesting that anthropogenic reductions in large-bodied beetles will have the greatest effect on the secondary dispersal of large-seeded plant species. Second, we established mesocosm experiments in which dung beetle communities buried Myrciaria dubia seeds to examine plant emergence and survival. Contrary to expectations, we found that beetle diversity and biomass negatively influenced seedling emergence, but positively affected the survival of seedlings that emerged. Finally, we conducted germination trials to establish the optimum burial depth of experimental seeds, revealing a negative relationship between burial depth and seedling emergence success. Our results provide novel evidence that seed burial by dung beetles may be detrimental for the emergence of some seed species. However, we also detected positive impacts of beetle activity on seedling recruitment, which are probably because of their influence on soil properties. Overall, this study provides new evidence that anthropogenic impacts on dung beetle communities could influence the structure of tropical forests; in particular, their capacity to regenerate and continue to provide valuable functions and services. </jats:p

Dung Beetle community and functions along a habitat-disturbance gradient in the Amazon:a rapid assessment of ecological functions associated to biodiversity

Although there is increasing interest in the effects of habitat disturbance on community attributes and the potential consequences for ecosystem functioning, objective approaches linking biodiversity loss to functional loss are uncommon. The objectives of this study were to implement simultaneous assessment of community attributes (richness, abundance and biomass, each calculated for total-beetle assemblages as well as small- and large-beetle assemblages) and three ecological functions of dung beetles (dung removal, soil perturbation and secondary seed dispersal), to compare the effects of habitat disturbance on both sets of response variables, and their relations. We studied dung beetle community attributes and functions in five land-use systems representing a disturbance gradient in the Brazilian Amazon: primary forest, secondary forest, agroforestry, agriculture and pasture. All response variables were affected negatively by the intensification of habitat disturbance regimes, but community attributes and ecological functions did not follow the same pattern of decline. A hierarchical partitioning analysis showed that, although all community attributes had a significant effect on the three ecological functions (except the abundance of small beetles on all three ecological functions and the biomass of small beetles on secondary dispersal of large seed mimics), species richness and abundance of large beetles were the community attributes with the highest explanatory value. Our results show the importance of measuring ecological function empirically instead of deducing it from community metrics

Dissémination de l’information et dynamique des opinions dans les réseaux sociaux

Our aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topicsLa dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds influents, la détection des communautés, la détection des tendances, etc. Cette thèse est donc basée sur ces deux facettes de la dissémination d'information: nous développons d'abord des cadres théoriques mathématiquement solides pour étudier les relations entre les personnes et l'information, et dans un deuxième moment nous créons des outils responsables pour une exploration plus cohérente des liens cachés dans ces relations. Les outils théoriques développés ici sont les modèles de dynamique d'opinions et de dissémination d'information, où nous étudions le flot d'informations des utilisateurs dans les réseaux sociaux, et les outils pratiques développés ici sont un nouveau algorithme de détection de communautés et un nouveau algorithme de détection de tendances dans les réseaux sociau

Assessing the importance of intraspecific variability in dung beetle functional traits

Functional diversity indices are used to facilitate a mechanistic understanding of many theoretical and applied questions in current ecological research. The use of mean trait values in functional indices assumes that traits are robust, in that greater variability exists between than within species. While the assertion of robust traits has been explored in plants, there exists little information on the source and extent of variability in the functional traits of higher trophic level organisms. Here we investigated variability in two functionally relevant dung beetle traits, measured from individuals collected from three primary forest sites containing distinct beetle communities: body mass and back leg length. In doing so we to addressed the following questions: (i) what is the contribution of intra vs. interspecific differences in trait values; (ii) what sample size is needed to provide representative species mean trait values; and (iii) what impact does omission of intraspecific trait information have on the calculation of functional diversity (FD) indices from naturally assembled communities? At the population level, interspecific differences explained the majority of variability in measured traits (between 94% and 96%). In accordance with this, the error associated with calculating FD without inclusion of intraspecific variability was low, less than 20% in all cases. This suggests that complete sampling to capture intraspecific variance in traits is not necessary even when investigating the FD of small and/or naturally formed communities. To gain an accurate estimation of species mean trait values we encourage the measurement of 30-60 individuals and, where possible, these should be taken from specimens collected from the site of study

Quantifying responses of dung beetles to fire disturbance in tropical forests:the importance of trapping method and seasonality

Understanding how biodiversity responds to environmental changes is essential to provide the evidence-base that underpins conservation initiatives. The present study provides a standardized comparison between unbaited flight intercept traps (FIT) and baited pitfall traps (BPT) for sampling dung beetles. We examine the effectiveness of the two to assess fire disturbance effects and how trap performance is affected by seasonality. The study was carried out in a transitional forest between Cerrado (Brazilian Savanna) and Amazon Forest. Dung beetles were collected during one wet and one dry sampling season. The two methods sampled different portions of the local beetle assemblage. Both FIT and BPT were sensitive to fire disturbance during the wet season, but only BPT detected community differences during the dry season. Both traps showed similar correlation with environmental factors. Our results indicate that seasonality had a stronger effect than trap type, with BPT more effective and robust under low population numbers, and FIT more sensitive to fine scale heterogeneity patterns. This study shows the strengths and weaknesses of two commonly used methodologies for sampling dung beetles in tropical forests, as well as highlighting the importance of seasonality in shaping the results obtained by both sampling strategies

Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles

Summary Human alteration of the global environment is leading to a pervasive loss of biodiversity. Most studies evaluating human impacts on biodiversity occur after the disturbance has taken place using spatially distinct sites to determine the undisturbed reference condition. This approach is known as a space‐for‐time (SFT) substitution. However, SFT substitution could be underestimating biodiversity loss if spatial controls fail to provide adequate inferences about pre‐disturbance conditions. We compare the SFT substitution with a before–after control–impact (BACI) approach by assessing dung beetles before and after a logging exploration in the Brazilian Amazon. We sampled 34 logging management units, of which 29 were selectively logged with different intensities after our first collection. We used dung beetle species richness, species composition and biomass as our biodiversity response metrics and the gradient of selective logging intensity as our explanatory metric. Only the BACI approach consistently demonstrated the negative impacts of logging intensification on all dung beetle community metrics. Moreover, the BACI approach explained significantly more of the variance in all the relationships and it doubled the estimates of species loss along the gradient of logging intensity when compared to SFT. Synthesis and applications. Our results suggest that space‐for‐time (SFT) substitution may greatly underestimate the consequences on local species diversity and community turnover. These results have important implications for researchers investigating human impacts on biodiversity. Incentivizing before–after control–impact (BACI) approaches will require longer‐term funding to gather the data and stronger links between researchers and landowners. However, BACI approaches are accompanied by many logistical constraints, making the continued use of SFT studies inevitable in many cases. We highlight that non‐significant results and weak effects should be viewed with caution. </jats:p

Análise contrastiva de produções em português de estudantes hispanofalantes: questões de acentuação

Anais e artigos do 28º Fórum Acadêmico de Letras, realizado nos dias 23 a 25 de agosto de 2017 na Universidade Federal da Integração Latino-Americana (Unila) e Universidade Estadual do Oeste do Paraná (Unioeste) com tema: A pesquisa nos cursos de letras em contexto de línguas e culturas em contato.A insuficiência de estudos contrastivos que contemplem as dificuldades de uso da acentuação em produções de universitários hispanofalantes aprendizes de português, impulsionou a realização deste trabalho. Com base nas regras de acentuação gráfica presentes na “Moderna Gramática Portuguesa” (Bechara, 2009) e na “Gramática Contrastiva del Español para Brasileños” (Fernández e Moreno, 2007) foi realizada a análise e descrição de erros de acentuação nas produções em português de estudantes hispanofalantes do nível básico. Em tal contexto, as dificuldades no emprego da acentuação do português brasileiro ocorrem, principalmente, devido a uma interferência das regras da língua materna bem como da distinção entre vogais orais e nasais, ocasionando uma omissão ou acréscimo de acento equivocado. Visando minimizar a recorrência de tais erros por parte dos estudantes hispanofalantes em suas produções em português, são sugeridas propostas de abordagens para que o estudante por meio do contraste das regras de acentuação do português e do espanhol consiga ter um melhor desempenh

Trend detection in social networks using Hawkes processes

International audienceWe develop in this paper a trend detection algorithm , designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between users and topics. We formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the time between the detection and the message broadcasts, the distance between the real broadcast intensity and the maximum expected broadcast intensity, and the social network topology. The proposed trend detection algorithm is simple and uses stochastic control techniques in order to calculate the trend indices. It is also fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of data necessary to the detection

A framework for information dissemination in social networks using Hawkes processes

International audienceWe define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion, which provides a complete analysis of the hidden influences in social networks. This framework can be combined with topic modeling, for which modified collapsed Gibbs sampling and variational Bayes techniques are derived. We provide an estimation algorithm based on nonnegative tensor factorization techniques, which together with a dimensionality reduction argument are able to discover , in addition, the latent community structure of the social network. At last, we provide numerical examples from real-life networks: a Game of Thrones and a MemeTracker datasets