Physico-Chemical Influence of Surface Water Contaminated by Acid Mine Drainage on the Populations of Diatoms in Dams (Iberian Pyrite Belt, SW Spain)

Abstract

Twenty-three water dams located in the Iberian Pyrite Belt were studied during March 2012 (early spring) in order to carry out an environmental assessment based on diatom communities and to define the relationships between these biological communities and the physico-chemical characteristics of the dam surface water. This is the first time that a diatom inventory has been done for dams affected by acid mine drainage (AMD) in the Spanish part of the Iberian Pyrite Belt (IPB). It was found that the pH was the main factor influencing the behaviour of the diatom communities. Then, using a dbRDA approach it was possible to organize the aggrupation of diatoms into four groups in response to the physico-chemical conditions of the ecosystem, especially pH: (1) Maris, Aac, Gos, Cmora (pH 2-3); (2) Andc, San, And, Dpin (pH 3-4.5); (3) Gran, Pleon, Oliv, Lagu, Chan, SilI, SilII, Joya, Gar, Agrio, Camp, Corum (pH 4.5-6); (4) Herr, Diq I, Diq II (pH 6-7). The obtained results confirmed the response of benthic diatom communities to changes in the physico-chemical characteristics of surface water, and helped to understand the role of diatoms as indicators of the degree of AMD contamination in those 23 dams. Special attention was given to those that have an acidophilic or acid-tolerant profile (pH 2-3 and pH 3-4.5) such as Pinnularia aljustrelica, Pinnularia acidophila, Pinnularia acoricola and Eunotia exigua, which are the two groups found in the most AMD contaminated dams.

Keywords: Iberian Pyrite Belt (IPB); Odiel River; Pyritic mines; Tinto River; acid mine drainage (AMD); acidophiles; benthic diatoms; dams.

Figure 1

Sampling sites: dams 1–23 located in the Spanish sector of the IPB. (adapted from [37]).

Figure 2

Dendogram resulting from the cluster of physico-chemical and biological variables using the Ward, Euclidean Square Method.

Figure 3

Dendrogram resulting from the cluster of the sampling sites using the Ward, Euclidean.

Figure 4

Distance based redundancy analysis (dbRDA) for the diatom resemblance matrix showing the samples’ arrangement and the environmental variables that explain 32.7% + 9.4% of total fitted variation. pH was the main variable that divided treatments into 4 groups: Maris, Aac, Gos, Cmora (pH 2–3); Andc, San, And, Dpin (pH 3–4.5); Gran, Lagu, Pleon, Oliv, Chan, SilI, SilII, Joya, Gar, Agrio, Camp, Corum (pH 4.5–6); Herr, Diq I, Diq II pH 6–7. For the meaning of the dam codes, please see Table 2 where the full name is given.

Figure 5

Graphical treatment showing the % of contribution of each species to the observed similarity between samples in each group through SIMPER analysis. Species codes: ADMI- Achnanthidium minutissimum, ADEG- Achnanthidium exiguum, BNEO- Brachysira neoexilis, CMEN- Cyclotella meneghiniana, ENMI- Encyonema minutum, EEXI- Eunotia exigua, PALJ- Pinnularia aljustrelica, GDEC- Geissleria decussis, NVEN- Navicula veneta, NTHM- Nitzschia thermalis, NITZ- Nitzschia sp., NVDA- Navicula vandamii, NFBU- Nitzschia frustulum var. bulnheimiana, NAMP- Nitzschia amphibia, NROS- Navicula rostellata, NPAL- Nitzschia palea, PACO- Pinnularia acoricola, NNAN- Nitzschia nana, NCRY- Navicula cryptocephala, PACI- Pinnularia acidophila, PSCA- Pinnularia subcapitata.