Antonio Mauro Saraiva

Crop pollinators in Brazil: a review of reported interactions

Pollinators are important to maintain ecosystem services, being part of the reproduction and seed formation process of plant species. In this study, we reviewed the literature and developed a database of interactions between pollinators and agricultural crops for Brazil. We classified the pollinators as effective, occasional, or potential, and also identified those species quoted simply as “visitors” (without reference to pollination). We found 250 crop pollinators pertaining to the three categories quoted, with 168 effective ones. Besides, we identified the effective pollinators of 75 agricultural crops. Bees pertaining to the family Apidae, mainly those from the genera Melipona, Xylocopa, Centris, and Bombus, were reportedly the most effective pollinators of agricultural crops. We also found that the exotic managed species Apis mellifera and the stingless bee Trigona spinipes are effective pollinators of some crops. In spite of some data having been originated from gray literature and the taxonomic impediment, this effort is a crucial step to clarify the gaps and bias on data. This study is the first to attempt to build, analyze, and make available a comprehensive data set about pollinators of agricultural crops in a country level, aiming to contribute to protective measures and to enhance the sustainable use of native pollinators in agriculture.

A reference business process for ecological niche modelling

Ecological niche modelling combines species occurrence points with environmental raster layers in order to obtain models for describing the probabilistic distribution of species. The process to generate an ecological niche model is complex. It requires dealing with a large amount of data, use of different software packages for data conversion, for model generation and for different types of processing and analyses, among other functionalities. A software platform that integrates all requirements under a single and seamless interface would be very helpful for users. Furthermore, since biodiversity modelling is constantly evolving, new requirements are constantly being added in terms of functions, algorithms and data formats. This evolution must be accompanied by any software intended to be used in this area. In this scenario, a Service-Oriented Architecture (SOA) is an appropriate choice for designing such systems. According to SOA best practices and methodologies, the design of a reference business process must be performed prior to the architecture definition. The purpose is to understand the complexities of the process (business process in this context refers to the ecological niche modelling problem) and to design an architecture able to offer a comprehensive solution, called a reference architecture, that can be further detailed when implementing specific systems. This paper presents a reference business process for ecological niche modelling, as part of a major work focused on the definition of a reference architecture based on SOA concepts that will be used to evolve the openModeller software package for species modelling. The basic steps that are performed while developing a model are described, highlighting important aspects, based on the knowledge of modelling experts. In order to illustrate the steps defined for the process, an experiment was developed, modelling the distribution of Ouratea spectabilis (Mart.) Engl. (Ochnaceae) using openModeller. As a consequence of the knowledge gained with this work, many desirable improvements on the modelling software packages have been identified and are presented. Also, a discussion on the potential for large-scale experimentation in ecological niche modelling is provided, highlighting opportunities for research. The results obtained are very important for those involved in the development of modelling tools and systems, for requirement analysis and to provide insight on new features and trends for this category of systems. They can also be very helpful for beginners in modelling research, who can use the process and the experiment example as a guide to this complex activity.

Desafios atuais da modelagem preditiva de distribuição de espécies

Predictive modelling has been used to analyze species geographic distribution through extrapolation of environmental characteristics of known occurrence areas. Interest in this kind of modelling is derived from the need for rapid and well-grounded answers to the threats faced by species due to habitat loss, exotic species invasion, climate changes, and others. This article provides an overall view of recent advances in modelling and aims to encourage the discussion and application of this method that can help with basic biological knowledge acquisition as well as with public policies aimed at their conservation.

Safeguarding Ecosystem Services: A Methodological Framework to Buffer the Joint Effect of Habitat Configuration and Climate Change.

Ecosystem services provided by mobile agents are increasingly threatened by the loss and modification of natural habitats and by climate change, risking the maintenance of biodiversity, ecosystem functions, and human welfare. Research oriented towards a better understanding of the joint effects of land use and climate change over the provision of specific ecosystem services is therefore essential to safeguard such services. Here we propose a methodological framework, which integrates species distribution forecasts and graph theory to identify key conservation areas, which if protected or restored could improve habitat connectivity and safeguard ecosystem services. We applied the proposed framework to the provision of pollination services by a tropical stingless bee (Melipona quadrifasciata), a key pollinator of native flora from the Brazilian Atlantic Forest and important agricultural crops. Based on the current distribution of this bee and that of the plant species used to feed and nest, we projected the joint distribution of bees and plants in the future, considering a moderate climate change scenario (following IPPC). We then used this information, the bee’s flight range, and the current mapping of Atlantic Forest remnants to infer habitat suitability and quantify local and regional habitat connectivity for 2030, 2050 and 2080. Our results revealed north to south and coastal to inland shifts in the pollinator distribution during the next 70 years. Current and future connectivity maps unraveled the most important corridors, which if protected or restored, could facilitate the dispersal and establishment of bees during distribution shifts. Our results also suggest that coffee plantations from eastern São Paulo and southern Minas Gerais States could suffer a pollinator deficit in the future, whereas pollination services seem to be secured in southern Brazil. Landowners and governmental agencies could use this information to implement new land use schemes. Overall, our proposed methodological framework could help design novel conservational and agricultural practices that can be crucial to conserve ecosystem services by buffering the joint effect of habitat configuration and climate change.

Native and Non-Native Supergeneralist Bee Species Have Different Effects on Plant-Bee Networks

Supergeneralists, defined as species that interact with multiple groups of species in ecological networks, can act as important connectors of otherwise disconnected species subsets. In Brazil, there are two supergeneralist bees: the honeybee Apis mellifera, a non-native species, and Trigona spinipes, a native stingless bee. We compared the role of both species and the effect of geographic and local factors on networks by addressing three questions: 1) Do both species have similar abundance and interaction patterns (degree and strength) in plant-bee networks? 2) Are both species equally influential to the network structure (nestedness, connectance, and plant and bee niche overlap)? 3) How are these species affected by geographic (altitude, temperature, precipitation) and local (natural vs. disturbed habitat) factors? We analyzed 21 plant-bee weighted interaction networks, encompassing most of the main biomes in Brazil. We found no significant difference between both species in abundance, in the number of plant species with which each bee species interacts (degree), and in the sum of their dependencies (strength). Structural equation models revealed the effect of A. mellifera and T. spinipes, respectively, on the interaction network pattern (nestedness) and in the similarity in bee’s interactive partners (bee niche overlap). It is most likely that the recent invasion of A. mellifera resulted in its rapid settlement inside the core of species that retain the largest number of interactions, resulting in a strong influence on nestedness. However, the long-term interaction between native T. spinipes and other bees most likely has a more direct effect on their interactive behavior. Moreover, temperature negatively affected A. mellifera bees, whereas disturbed habitats positively affected T. spinipes. Conversely, precipitation showed no effect. Being positively (T. spinipes) or indifferently (A. mellifera) affected by disturbed habitats makes these species prone to pollinate plant species in these areas, which are potentially poor in pollinators.

Ad Hoc Wireless Sensor Networks Applied to Animal Welfare Research

Ad hoc Wireless Sensor Networks (AWSN) are arrays of miniaturized electronic devices that embed sensors and have communication capabilities that allow them to interconnect using a radio network. They are available with different technologies, configurations, and sensing capabilities, and can be used on different network topologies. As an AWSN is composed of a large number of compact low-cost and energy-constrained sensor nodes, it offers unprecedented flexibility and mobility, which makes them suitable for environmental data acquisition in the field, and allows fine-granular observations. The sensors are easy to deploy and can be used to detect previously unrecognized relationships and behaviors in natural systems, which cannot be detected by conventional approaches. In many cases, power consumption is still an issue. Research related to biological and environmental systems can be enhanced with AWSNs. Monitoring and control of the quality of indoor environment is very important for animal health and welfare and directly impacts productivity and quality. The goal of this work was to evaluate the capability and usefulness of an AWSN in studies related to animal housing environment. An AWSN has been set up and applied to monitor environmental variables in an animal housing facility. The network is composed of several sensing nodes and a gateway that links the network to a microcomputer. They were distributed over strategic points inside an animal housing facility to allow us to collect data about those environmental variables in real time. The inherent flexibility and mobility of the nodes allowed us to move them through the facility to determine different profiles of temperature, humidity and luminosity across the facility. Model based on this data can be derived to enhance animal welfare.

Projected climate change threatens pollinators and crop production in Brazil.

Animal pollination can impact food security since many crops depend on pollinators to produce fruits and seeds. However, the effects of projected climate change on crop pollinators and therefore on crop production are still unclear, especially for wild pollinators and aggregate community responses. Using species distributional modeling, we assessed the effects of climate change on the geographic distribution of 95 pollinator species of 13 Brazilian crops, and we estimated their relative impacts on crop production. We described these effects at the municipality level, and we assessed the crops that were grown, the gross production volume of these crops, the total crop production value, and the number of inhabitants. Overall, considering all crop species, we found that the projected climate change will reduce the probability of pollinator occurrence by almost 0.13 by 2050. Our models predict that almost 90% of the municipalities analyzed will face species loss. Decreases in the pollinator occurrence probability varied from 0.08 (persimmon) to 0.25 (tomato) and will potentially affect 9% (mandarin) to 100% (sunflower) of the municipalities that produce each crop. Municipalities in central and southern Brazil will potentially face relatively large impacts on crop production due to pollinator loss. In contrast, some municipalities in northern Brazil, particularly in the northwestern Amazon, could potentially benefit from climate change because pollinators of some crops may increase. The decline in the probability of pollinator occurrence is found in a large number of municipalities with the lowest GDP and will also likely affect some places where crop production is high (20% to 90% of the GDP) and where the number of inhabitants is also high (more than 6 million people). Our study highlights key municipalities where crops are economically important and where pollinators will potentially face the worst conditions due to climate change. However, pollinators may be able to find new suitable areas that have the potential to improve crop production. The results shown here could guide policy decisions for adapting to climate change and for preventing the loss of pollinator species and crop production.

Applying Neural Networks to Automated Visual Fruit Sorting

A common problem in fruit production systems is sorting and classification. A usual procedure to carry out this task is based on human visual inspection considering general fruit attributes like color and size. Color contains important information about fruit status and in some cases it is decisive for fruit quality differentiation. An adequate color classification can improve system accuracy and productivity. Large-scale utilization of automatic classification system for this purpose demands a robust color classification even under different color saturation, variations of environment lighting and light reflections. This paper provides an investigation on the applicability of color classification using an artificial neural network in the fruit-sorting domain. Using the well-known network generalization property we investigate the applicability of this approach to the segmentation of colored images represented by the RGB color system. Jointly with color analysis, we also use some shape analysis to generate a robust and real time system that was tested for orange classification according to a Brazilian standard and which was able to provide fruit classification under less restricted visual conditions.

Clustering of water bodies in unpolluted and polluted environments based on Escherichia coli phylogroup abundance using a simple interaction database

Different types of water bodies, including lakes, streams, and coastal marine waters, are often susceptible to fecal contamination from a range of point and nonpoint sources, and have been evaluated using fecal indicator microorganisms. The most commonly used fecal indicator is Escherichia coli, but traditional cultivation methods do not allow discrimination of the source of pollution. The use of triplex PCR offers an approach that is fast and inexpensive, and here enabled the identification of phylogroups. The phylogenetic distribution of E. coli subgroups isolated from water samples revealed higher frequencies of subgroups A1 and B23 in rivers impacted by human pollution sources, while subgroups D1 and D2 were associated with pristine sites, and subgroup B1 with domesticated animal sources, suggesting their use as a first screening for pollution source identification. A simple classification is also proposed based on phylogenetic subgroup distribution using the w-clique metric, enabling differentiation of polluted and unpolluted sites.

Climate Warming May Threaten Reproductive Diapause of a Highly Eusocial Bee

ABSTRACT Climate changes are predicted to affect the diapause of many insect species around the world adversely. In this context, bees are of interest due to their pollination services. In southern Brazil, the highly eusocial bee species Plebeia droryana (Friese) (Hymenoptera: Apidae: Meliponini) exhibits reproductive diapause in response to the regions rigorous winters. That diapause is characterized by a temporary interruption in brood cell construction by nurse bees and egg-laying by the queen, regardless of other internal tasks underway in the nests. In this study, we evaluated whether P. droryana enter diapause under experimental conditions. P. droryana colonies were kept in a germination chamber, and the temperature was progressively reduced from 20°C over a period of a few weeks until diapause was detected. Additionally, we also estimated the environmental conditions in the actual geographic range occupied by P. droryana and modeled it for predicted changes in climate up to the year 2080. Our findings indicate that P. droryana enter diapause between 10 and 8°C. We also found that the current minimum winter temperature (10.1°C, median) in the distributional range of P. droryana will probably rise (13.4°C, median). Thus, if our experimental data are somewhat accurate, ∼36% of the southern Brazilian P. droryana population may be active during the expected milder winter months in 2080. In this scenario, there may be a larger demand for pollen and nectar for that bee species. Greater conservation efforts will be required to preserve P. droryana populations and keep them viable in the coming decades.