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Day, J.A. and De Moor, I.J. (2002) Guides to the Freshwater Invertebrates of Southern Africa: Volume 5 Non-Arthropods—The Protozoans, Porifera, Cnidaria, Platyhelminthes, Nemertea, Rotifera, Nematoda, Nematomorpha, Gastrotrichia, Bryozoa, Tardigrada, Polychaeta, Oligochaeta and Hirudinea. WRC Report No. TT 167/02. Water Research Commission, Pretoria, 293 p.

has been cited by the following article:

• TITLE: Simple and Cost-Effective Biomonitoring Method for Assessing Pollution in Tropical African Rivers

  AUTHORS: Julius D. Elias

  KEYWORDS: Assessment, Biomonitoring Method, Pollution, Ecosystem, Macroinvertebrate

  JOURNAL NAME: Open Journal of Ecology, Vol.11 No.4, April 27, 2021

  ABSTRACT: Adoption rule whereby tropical African regions rely on non-tropical biomonitoring methods to assess water pollution in their rivers has been hindered by geographical incompatibility due to environmental variations between the regions that affect the capability and reliability of adopted method. Likewise, inclusion of all identified macroinvertebrate assemblages in developing the existing biomonitoring methods (i.e., South African Scoring System (SASS5) and Tanzania Riverine Scoring System (TARISS)) has made them complex taxonomically as their use requires users of greater expertise and much time during taxa identification. Such taxonomic complications and conflicting aspects regarding the adoption or modification of non-tropical biomonitoring methods in developing tropical biomonitoring methods have therefore necessitated the design of this study in order to develop simple and cost-effective tropical African biomonitoring methods, for initial application in Tanzanian rivers. Six pairwise screening criteria were employed to select orders with distinctive potential for inclusion in developing simple and cost-effective biomonitoring method. Only Ephemeroptera, Diptera, Odonata and Trichoptera (EDOT) orders met all six inclusion criteria after showing their ability to discern reference sites from monitoring sites and correlating strongly with environmental variables. Being developed using only four diverse orders with the wider range of occurrences and sensitivity to pollution, the EDOT method will minimize data variability, the need for greater expertise, cost, and time during taxa identification. The novelty of the present approach lies on the simplification of the taxonomic complication that is inherent in existing indices for four decades and modelling application to simulate sensitivity weightings for taxa with unknown sensitivity score ratings.