Incorporating natural variability in the bioassessment of stream condition in the Atlantic Forest biome, Brazil

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• 作者：Priscilla S. Pereira^a ; Natá ; lia F. Souza^a ; Darcilio F. Baptista^a ; Jaime L.M. Oliveira^b ; Daniel F. Buss^a ; ^{dbuss@ioc.fiocruz.br" class="auth_mail" title="E-mail the corresponding author} ; ^{dbuss.rio@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Multimetric index ; Biological monitoring ; Bioassessment protocol ; Aquatic insects ; Neotropical streams
• 刊名：Ecological Indicators
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：69
• 期：Complete
• 页码：606-616
• 全文大小：1362 K

文摘

Most bioassessment programs in Brazil face difficulties when scaling up from small spatial scales because larger scales usually encompass great environmental variability. Covariance of anthropogenic pressures with natural environmental gradients can be a confounding factor in the evaluation of biologic responses to anthropogenic pressures. The objective of this study was to develop a multimetric index (MMI) with macroinvertebrates for two stream types and two ecoregions in the Atlantic Forest biome in Rio de Janeiro state, Brazil. We hypothesized that by using two approaches – (1) testing and adjusting metrics to landscape parameters, and (2) selecting metrics using a cluster analysis to avoid metrics redundancy – the final MMI would perform better than the traditional approach (unadjusted metrics, one metric representing each category). Four MMIs were thus developed: MMI-1 – adjusted MMI with metrics selected after cluster analysis); MMI-2 – adjusted MMI with one metric from each category; MMI-3 – unadjusted MMI with metrics selected after cluster analysis; MMI-4 – unadjusted MMI with one metric from each category. We used three decision criteria to assess MMI’s performance: precision, responsiveness and sensitivity. In addition, we tested the MMI’s by using an independent set of sites to validate the results. Although all MMIs performed well in the three criteria, adjusting metrics to natural variation increased MMI response and sensitivity to impairment. In addition, the selected MMI-2 was able to classify sites of two stream types and two ecoregions. The use of cluster analysis, however, did not avoid high redundancy between metrics of different branches. The MMI-4 had the poorest performance among all tested MMIs and it was not able to distinguish adequately reference and impaired sites from both ecoregions. We present some considerations on the use of metrics and on the development of MMI’s in Brazil and elsewhere.