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Fundam. Appl. Limnol. Vol. 187/1 (2015), 21–32Article published online 20 May 2015, published in print September 2015
Development of a multimetric benthic macroinvertebrate index for assessing the ecological condition of Basque streams (north of Spain) Begoña Gartzia de Bikuña1, Eva López1, Jose Manuel Leonardo1, Jesús Arrate1, Aingeru Martínez1, 2, * and Alberto Manzanos3 With 5 figures and 4 tables Abstract: Multimetric indices based on biological communities for the assessment of the ecological status of streams and rivers have recently been developed for many regions. However, local differences in reference conditions, human impacts and biological communities make developing suitable indices for different regions of the world necessary. Thus, our goal was to develop and validate a multimetric index based on macroinvertebrates to evaluate the ecological status of streams from the Basque Country (north Spain). For this, 22 lowland calcareous streams (7 reference and 15 disturbed) were monitored from 1993 to 2005. At each study site, a stress level was calculated based on human impacts. Fifty-three macroinvertebrate metrics were tested for their capability to discern stress level. Our multimetric index was calculated as the mean of six metrics – genus richness, IBMWP, abundance of Sel_EPTD, abundance of Sel_ETD’, family richness of Sel_ETD and family richness of EPT. The index was split into five ecological status classes, ranging from 0 (bad ecological status) to 1 (high ecological status). This index was validated in 231 study sites in 108 streams of six different typologies, demonstrating suitability for application in biomonitoring and assessment studies in Basque streams. Key words: multimetric index, aquatic macroinvertebrates, ecological status, biomonitoring, Basque Country.
Introduction Streams and rivers are among the most threatened habitats in the world due to human activities (Malmqvist & Rundle 2002). Activities such as agriculture, forestry, mining, or urban and industrial development, alter the physicochemical properties of the water (Perona et al. 1999, Ferrier et al. 2001), the biological communities (Miserendino & Masi 2010, Martínez et al. 2013a) and the functioning of the overall ecosystem (Aristi et al. 2012, Arroita et al. 2013). As a consequence of
this broad impact and as the streams and rivers provide important ecosystem services (Costanza et al. 1997, Thorp et al. 2010), it is crucial to understand the consequences of human perturbations and to preserve or restore the integrity of these systems (Meybeck 2003). Therefore, a major challenge in freshwater ecology is to provide assessment and monitoring tools for stream and river management. The first monitoring works were focused mainly on physicochemical water characteristics. However, as streams and rivers are ecosystems, physicochemical studies are of lim-
Authors’ addresses: 1 Anbiotek S.L. Axpe Industrialdea, Ribera de Axpe 11 B-201, 48950 Erandio, Spain 2 Laboratory of Stream Ecology, Department of Plant Biology and Ecology, University of the Basque Country, P.O. Box 644, 48080 Bilbao, Spain 3 Basque Water Agency (URA). C/ Orio, 1-3, 01010 Vitoria-Gasteiz, Spain * Corresponding author;
[email protected] © 2015 E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany DOI: 10.1127/fal/2015/0741 eschweizerbart_xxx
www.schweizerbart.de 1863 - 9135/15/0741 $ 3.00
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ited use in evaluating ecological status (Mendoza-Lera et al. 2012, Martínez et al. 2013b). The evaluation of the ecological status of stream and rivers based on biological elements is more accurate. Among the different biological communities inhabiting freshwater ecosystems, macroinvertebrate assemblages have frequently been used for monitoring programmes (Bonada et al. 2006). Macroinvertebrate communities have demonstrated their capability in detecting various alterations in water physicochemical properties (Hirst et al. 2002, Larrañaga et al. 2010, Pérez et al. 2013), but also in other anthropogenic impacts such as alterations in river structural attributes and land use changes by shifts in specific composition, density, richness, diversity and functional organisation (Martínez et al. 2013a, Martínez et al. 2013b). Thus, a large number of biological indices have been proposed based on macroinvertebrate assemblages (e.g. Hellawell 1978, Armitage et al. 1983, Extence et al. 1999, Armanini et al. 2011). Nevertheless, indices based on a single metric are often unable to give an integrated picture of ecosystem ecological status under multiple anthropogenic impacts (Gabriels et al. 2010). The development of multimetric indices based on macroinvertebrates offers a more integrated approach to the evaluation of freshwater ecosystem health (Karr & Chu 1997, Karr 1999). This kind of index is based on the integration of factors into a single value using multiple metrics of invertebrate assemblages previously tested for their capability to discern ecological perturbation levels (Barbour et al. 1996, Baptista et al. 2007). Therefore, multimetric indices have been developed (e.g. Weigel et al. 2002, Buffagni et al. 2004, Hering et al. 2004, Gabriels et al. 2010, Couceiro et al. 2012, Villamarín et al. 2013, Nguyen et al. 2014) and used in routine water management programmes all around the world. Nevertheless, local differences in reference conditions, human impacts and biological communities make developing suitable indices for different regions in the world necessary despite the large number of examples. In Spain, there are not a great number of these multimetric indices based on benthic macroinvertebrates (but see Pardo et al. 2010, Cuadrado et al. 2013). However, there is a great variability in climatic conditions, biological communities, distribution of human settlements and anthropogenic pressures on ecosystems among regions within Spain. In the Basque Country, a region in the north of Spain near to the Atlantic Ocean, the applicability of these two indices from other regions of Spain is not very accurate. On the one hand, the index of Cuadrado et al. (2013) was developed in the headwater of only one
river and focused exclusively on discerning the impacts of agriculture in this basin. On the other hand, the index developed by Pardo et al. (2010) does not work very well in some river typologies present in this region, making it difficult to carry out biomonitoring programmes using this tool. Thus, our goal was to develop and test a multimetric index based on stream benthic macroinvertebrates in the Basque Country (north of Spain). The Multimetric Basque index (MBi) was developed using 22 calcareous rivers (7 reference and 15 disturbed) within the Atlantic region of the Basque Country. We used the data from these rivers because they have been monitored since 1993 and their typology is the most usual and representative within this region. Our aim was to develop a direct and user-friendly tool which could be used by managers and decision-makers to assess the ecological status of streams from the Basque Country and close regions from north of Spain and south of France. For this reason, we assessed the capability of this index to discriminate disturbed streams from a reference stream and validate its performance at 231 study sites in 108 streams of six different typologies within the Basque Country and contiguous regions. The reason for the present work is the adaptation of biomonitoring programmes to fit within the rules of the European Water Framework Directive (EU 2000), to assess the ecological status of freshwater ecosystems by not only focusing on physicochemical properties, but also on biological communities. Therefore, the development of the MBi followed the methodology presented in Hering et al. (2006), a paper which condenses the experiences in developing Multimetric Indices in Europe gained in the AQEM (2002) and Star (2002) projects.
Material and methods Study area Twenty-two small calcareous lowland rivers (RC6) within the Atlantic region of the Basque Country (north of Spain) were monitored (Fig. 1) following the river classification of the Central Baltic – GIG (CB – GIG). The climate in this region is oceanic, with cool (but not cold) winters and warm (but not hot) summers, and with a mean annual rainfall of 1000 –1200 mm distributed throughout the year. Despite the lack of a drought period, the lowest precipitation occurs during summer. The most common river alteration in this region is mixed contamination due to organic and industrial waste and hydromorphological changes (Gartzia de Bikuña & Docampo 1990, Docampo & Gartzia de Bikuña 1993). The study sites were split into 7 reference sites and 15 disturbed ones on the basis of the human impact records following the methodology of REFCOND (Wallin
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Development of a multimetric benthic macroinvertebrate index
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Fig. 1. Map of the study area and sampling sites.
et al. 2003) and specified criteria of the CB – GIG classification. From 1993 to 2005, data were collected at selected sites in two periods of the year: during early spring (May), coinciding with the highest biological development and flow and during late summer (September), coinciding with the highest biological stress and lowest water flow.
Stressor gradient At each study site, the existing human pressure was characterized at three spatial scales: site, reach and catchment. At site level, water physicochemical properties were measured in situ. The considered physicochemical variables were the organic demand of oxygen, percentage of dissolved oxygen, pH, chemical oxygen demand, total nitrogen, ammonium, nitrate, total phosphorous and orthophosphate. All these physicochemical variables are included within the IFQ-R index (Uragentzia 2008) used to quantify the organic contamination pressure level in the Basque rivers. At reach and catchment scale, data of human pressures were obtained from geographical information databases (CORINE Land Cover) and computed via ArcGIS. At reach scale (500 m long and 30 m width in each river bank), the following measurements were determined: percentage of water
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abstraction, channelization, defence structures, occupation of public property, the number of dams and bridges, the quality of riparian forest (QBR, Munné et al. 2003) and whether the flow was regulated. At catchment level, the punctual contamination (percentage of artificial land use) and diffused contamination (percentage of intensive agriculture) were calculated. To determine the stressor gradient, a discrete score ranging from 1 (lowest human pressure) to 5 (highest human pressure) was assigned to each variable following the REFCOND criteria (Wallin et al. 2003). Given the same weight to each variable, the stress level was calculated at each study site as a mean value (Baptista et al. 2013). Thus, the stress level ranged from 1 (lowest stress level) to 5 (highest stress level).
Macroinvertebrate data For the macroinvertebrate collection, a multihabitat, stratified and semiquantitative sampling (AQEM 2002) adapted to the Basque streams (Gartzia de Bikuña et al. 2008) was carried out. The samples were taken with a standard Kicker hand net (25 × 20.5 cm; 500 µm). In rivers with a width less than 10 m, four samples were taken in habitats that represent more than 25 % of surface along a reach of 20 m. Meanwhile, in larger riv-
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Table 1. Measured metrics and test values for temporal stability, sensitivity and correlation with stress level.
Metric Density Family richness Taxa richness Shannon diversity Shannon diversity (log2) Genus eveness Family eveness 100-B% IBMWP IASPT Family richness EPT Family richness Sel_ETD Density Sel_EPTD (log) Density Sel_ETD (log) Density Sel_ETD’ (log) % EPT %ETD 1-GOLD Density Sel_Ephemeroptera Density Sel_Plecoptera Density Sel_Trichoptera Density Sel_Trichoptera_GS Density Diptera_good Density Diptera_bad Density EPTOH/A D Density Trophic_Sel_Grazers # Ephemeoptera # Plecoptera # Trichoptera # Coleoptera # Diptera # Odonata # Heteroptera # other Insects # Annelida # Crustacea # Mollusca # Platelmintha # Others % Ephemeroptera % Plecoptera % Trichoptera % Coleoptera % Diptera % Odonata % Heteroptera % Other Insects % Annelida % Crustacea % Mollusca % Platelmintha % Others
Stability p value 0.458 0.524 0.603 0.349 0.881 0.720 0.259 0.217 0.634 1.000 0.546 0.843 0.198 0.077 0.320 0.045 0.824 0.033 0.001 0.810 0.355 0.338 0.003 0.000 0.825 0.370 0.000 0.410 0.013 0.970 0.853 0.697 0.301 0.001 0.154 0.947 0.001 0.527 0.124 0.000 0.412 0.000 0.700 0.001 0.914 0.340 0.001 0.260 0.045 0.000 0.044 0.026
Valid
Sensitivity Box plot
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– – – – – – – – + + + + + – + – + – – – – – – – – + – – – + – – – – – – – – – – – – – – – – – – – – – –
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Correlation with stress level
U-test
r2
F1,21
p value
