A new multimetric macroinvertebrate index for the ecological assessment of Mediterranean lakes

Developing and applying a macroinvertebrate‐based multimetric index for urban rivers in the Niger Delta, Nigeria

“…Abundances of very large‐bodied macroinvertebrates have been hypothesized to decrease in response to environmental stress because they are often associated with long reproductive cycle and fewer offspring per reductive event compared to small bodied individuals, which often reproduce rapidly (Castro, Dolédec, & Callisto, ; Serra et al, ; Townsend & Hildrew, ). Studies testing metrics for integration into multimetric indices have often ended up with one or two trait‐based metrics in the final indices, indicating that the present study, which found only a single trait to be highly sensitive and non‐redundant was in accordance with most other studies (e.g., Baptista et al, ; Fierro et al, ; Gieswein et al, ; Ntislidou et al, ). The inclusion of the trait‐based metric into the final MINDU is particularly useful because while taxonomic metrics relate to structural measure, traits relate to the functional aspects of the biota (Desrosiers et al, ; Ding et al, ; Monaghan & Soares, ).…”

“…The diversity measures, Margalef's index, Shannon‐weiner, Simpson diversity, and Evenness index were all discriminatory of the MIS and HIS from the LIS but only Evenness index was confirmed sensitive. Similar studies elsewhere have reported most diversity measures to have high discriminatory potentials (Edegbene et al, ; Ntislidou et al, ). Edegbene et al () integrated two diversity measures namely Margalef index and Shannon diversity index into the Chanchaga multimetric index (MMIchanchaga) developed for a river in northern Nigeria.…”

“…The inadequacies of physicochemical monitoring alone have necessitated the complementary use of biological monitoring (i.e., biomonitoring) tools and approaches (Arimoro, Ikomi, Nwadukwe, Eruotor, & Edegbene, ; Bonada et al, ; Serra, Graca, Doledec, & Feio, ). Biomonitoring tools/approaches widely used include single biotic indices (e.g., South African Scoring System version 5, Dickens & Graham, ) functional feeding group (FFG; e.g., Akamagwuna, Mensah, Nnadozie, & Oghenekaro, ; Baptista et al, ; Lakew & Moog, ; Ntislidou, Lazaridou, Tsiaoussi, & Bobori, ), multivariate approaches (e.g., Chowdhury, Gallardo, & Aldridge, ; Gieswein et al, ; Oliveira, Mugnai, Pereira, Souza, & Baptista, ), and multimetric indices (e.g., Bonada et al, ; Edegbene et al, ; Mereta et al, ; Monaghan & Soares, ). Of these approaches, the multimetric indices have been shown to perform extremely well particularly because they integrate information and data from multiple dimension of aquatic biota and the ecosystem as a whole (Bonada et al, ).…”

“…Of these approaches, the multimetric indices have been shown to perform extremely well particularly because they integrate information and data from multiple dimension of aquatic biota and the ecosystem as a whole (Bonada et al, ). Multimetric indices have been developed based on aquatic macrophytes (Aguiar, Feio, & Ferreira, ; Zervas, Tsiaoussi, & Tsiripidis, ); diatoms (Stevenson et al, ); phytoplankton (Katsiapi, Moustaka‐Gouni, & Sommer, ; Lugoli et al, ; Tsiaoussi, Mavromatic, & Kemitzoglou, ; Wu, Schmaz, & Fohrer, ); macroinvertebrates (Edegbene et al, ; Gieswein et al, ; Lu, Wu, Xue, Lu, & Batzer, ; Ntislidou et al, ); and fish (Petriki, Lazaridou, & Bobori, ). Macroinvertebrates are particularly useful for index development because they occupy an important position as consumers, can easily be collected, have high diversity, and are differentially sensitive to a gradient of pollution (Bonada et al, ; Odume et al, ).…”

“…African Scoring System version 5, Dickens & Graham, 2002) functional feeding group (FFG; e.g., Akamagwuna, Mensah, Nnadozie, & Oghenekaro, 2019;Baptista et al, 2013;Lakew & Moog, 2015;Ntislidou, Lazaridou, Tsiaoussi, & Bobori, 2018), multivariate approaches (e.g., Chowdhury, Gallardo, & Aldridge, 2016;Gieswein et al, 2019;Oliveira, Mugnai, Pereira, Souza, & Baptista, 2019), and multimetric indices (e.g., Bonada et al, 2006;Edegbene et al, 2019;Mereta et al, 2013;Monaghan & Soares, 2012). Of these approaches, the multimetric indices have been shown to perform extremely well particularly because they integrate information and data from multiple dimension of aquatic biota and the ecosystem as a whole (Bonada et al, 2006).…”