MACROINVERTEBRADOS AQUÁTICOS COMO BIOINDICADORES NO PROCESSO DE LICENCIAMENTO AMBIENTAL NO BRASIL

Licenciamento ambiental no Brasil é um procedimento com vários níveis e etapas, concebido como ferramenta preventiva aos potenciais danos ambientais causados pela implantação de empreendimentos. Embora este procedimento seja obrigatório desde meados da década de 1980, ainda é limitado no que diz respeito ao uso de informações biológicas para a avaliação e o monitoramento de ambientes aquáticos. Neste processo, o órgão licenciador (federal, estadual ou municipal) define as variáveis a serem medidas, tendo como referência o tipo e a magnitude do empreendimento e as características específicas do local proposto para sua instalação. Respostas biológicas devem ser usadas para medir os impactos sobre ecossistemas aquáticos e os macroinvertebrados constituem um grupo que apresenta vantagens como bioindicadores, sendo os mais utilizados para este fim. Em 2011, o Grupo de Trabalho Intersetorial em Biomonitoramento foi criado para discutir o uso de macroinvertebrados em programas de monitoramento. Este trabalho apresenta as reflexões e propostas deste grupo e fornece subsídios para a inclusão destes organismos nos termos de referência a serem aplicados nos processos de licenciamento ambiental no Brasil

Frequency of mentum deformity in Chironomus sancticaroli (Diptera: Chironomidae) in a laboratory culture

The midge Chironomus sancticaroli (Diptera: Chironomidae) has been used in ecotoxicological tests because it is sensitive to a variety of inorganic pollutants. Among the parameters used to evaluate the toxicity of a substance is the frequency of mentum deformity, which is part of the oral system of this organism. However, there is still no consensus on the baseline level (percentage) of acceptable deformities in laboratory cultures not exposed to pollutants. The determination of this variable is important to ensure the validity of bioassays and to compare cultures from different research and teaching institutions. Once this value is established, it will also be used to monitor the quality of organisms cultured, since factors such as inbreeding could increase the frequency of mentum deformity. Thus, the objective of this study was to quantify the percentage of mentum deformity in the fourth instar of C. sancticaroli larvae from the culture of the Laboratory of Aquatic Ecosystems, at Embrapa Meio Ambiente. The average frequency of mentum deformity obtained was 6,63%. It is believed that factors such as the renewal of the culture with the inclusion of spawns from the laboratories of other institutions, as well as the control of the quality of the dilution water and the sediment of the breeding may have contributed to a low frequency of mentum deformity of the culture observed in this study

Large-scale prediction of tropical stream water quality using Rough Sets Theory

Assessing water quality in streams is usually measured at the local scale and often it is spatially restricted. To scale-up water-condition assessment, there is a need to use new tools that enable prediction of large-scale changes in water quality by expanding the analysis to landscape-levels and bioclimatic predictors. In addition, the traditional use of linear models in biomonitoring can be inappropriate in detecting complex relationships, such as changing patterns of aquatic community structure and complex environmental gradients. In this context, Artificial Intelligence (AI) techniques such as Rough Sets Theory (RST) can be particularly useful for dealing with vague, imprecise, inconsistent and uncertain knowledge involving biotic and abiotic data to enable the classification and prediction of changes in stream water. Here, we applied RST to estimate the water quality in streams of the Brazilian Atlantic Forest by analyzing connections between landscape and climate data with the inclusion of up to 15 families of aquatic insect groups from the orders Ephemeroptera, Plecoptera and Trichoptera (usually known as EPT taxa). First, we developed different decision sets which were the arrangements of the response variable (EPT index) and the predictor classifications. Then, we applied the best decision sets to monitor the condition of stream water in the Atlantic Forest on a large-scale. Our results showed the best decision rules were 61% accurate. Depending on the initial stream condition, this approach on a large-scale led to variable accuracy. By combining the development of different decision sets, the application of the best one on a large-scale, and the use of open-access data (landscape and climate predictors), our study approach demonstrated the potential applicability to evaluate streams in an objective with low-cost manner. This method can complement the environmental assessment of streams based only on local variables. Our framework also creates new perspectives in the analysis of water quality to generate scenarios of changes in streams based on landscape measurements to optimize monitoring networks