Indices Based on Indicator Species

Measures Based on the Occurrence of Indicator Species

Species usually associated with environmental deterioration may be favored by it, or simply be more tolerant to a given type of pollution. Many authors do not advise the use of indicator species because they may occur naturally in relative high densities, which may lead to a significant exercise of subjectivity.

Annelida pollution index (API). This index was introduced by G. Bellan in 1980 and it has been applied to marine environment:

Dominance of pollution indicators Dominance of clean water indicators

Species considered as pollution indicators are Platenereis dumerilli, Theosthema oerstedi, Cirratulus cirratus, and Dodecaria concharum; whereas species considered as indicators of clear waters are Syllis gracillis, Typosyllis prolifera, Typosyllis spp., and Amphiglena mediterranea. Index values above 1 show that the community is pollution disturbed. As organic pollution increases, the index values become higher allowing in theory to establish different pollution grades, although these have not been defined.

This index was first designed to be applied on rocky superficial substrates and later modified to be applied to soft bottoms. In this case, the pollution indicator species are Capitella capitata, Malococerus fuliginosus, and Prionospio malmgremi, and the clear water indicator species is Chone duneri.

Pollution index (PI). This index was introduced by D. Bellan-Santini in 1980 and it follows the same concept of the previous one, but takes into account the amphipods group:

Species considered as pollution indicators are in this case Caprella acutrifans and Podocerus variegatus, and species considered as indicators of clean conditions are Hyale sp., Elasmopus pocillamanus, and Caprella liparotensis. The pollution index is extensively treated in Pollution Indices.

A^BZIntroduced by A. Borja in 2000, this index accounts not only for the presence of species indicating a given type of pollution, but also of species indicating a nonpolluted situation. In addition, it has been shown useful to assess other anthropogenic impacts, such as habitat physical disturbance, heavy metals inputs, etc. To apply it, the soft bottom macrofauna is divided into five groups, according to their sensitivity as a function of an increasing stress gradient:

1. Species very sensitive to organic enrichment and present under unpolluted conditions.

2. Species indifferent to enrichment, always in low densities with nonsignificant variations through time.

3. Species tolerant to excess of organic matter enrichment. These species may occur under normal conditions, but their populations are stimulated by organic enrichment.

4. Second-order opportunist species, mainly small-sized polychaetes.

5. First-order opportunist species, essentially deposit feeders.

The index is estimated following the given algorithm:

{(G x %GI) + (1.5 x %GII) + (3 x %GIII) + (4.5 x %GIV) + (6 x %GV)}

pi-E

Dominance of pollution indicators Dominance of clean water indicators

Five categories are considered as a function of AMBI values (Table 1).

To implement this index, more than 3000 taxa have been classified, representing the most important soft bottom communities present in European estuarine and coastal systems. The Marine Biotic Index can be applied using the AMBI© software, available at www.azti.es.

Table 1 Categories considered as a function of AMBI index values

Classification

AMBI value

Normal

0-1.2

Slightly polluted

1.2-3.2

Moderately polluted

3.2-5

Highly polluted

5-6

Very highly polluted

6-7

The marine biotic index can be applied using the AMBI© software freely available at www.azti.es.

The marine biotic index can be applied using the AMBI© software freely available at www.azti.es.

BENTIX. This index, elaborated by N. Simboura and A. Zenetos in 2002, is based upon AMBI and was designed to fit the Mediterranean benthic ecosystem and to render a five-step numerical scheme for the classification of benthic communities. It is a biotic index based on the concept of indicator groups and uses the relative contribution of tolerant and sensitive texa weighting the percentages in an ecologically relevant way. This index differentiates from AMBI in the following aspects: (1) it is more simple involving less ecological groups, thus reducing the risk of assigning a species to a wrong group; (2) it uses an ecological rational in the selection of the factors weighting the groups in the formula; and (3) the boundary limits among classes are different compared to the AMBI scheme. The algorithm is:

BENTIX =

Group I: This group includes species sensitive to disturbance in general. Group II: Species tolerant to disturbance or stress whose populations may respond to organic enrichment or other sources of pollution. Group III: This group includes the first-order opportunistic species (pronounced unbalanced situation), pioneer, colonizers, or species tolerant to hypoxia.

A list of indicator species from the Mediterranean Sea was compiled, assigning a score ranging from 1 to 3, corresponding to each one of the three ecological groups. Five categories are considered as a function of the index values (Table 2). The boundary limits among classes were set after multiple tests with real data rendering a five-step scale with equal distances among the three central boundary limits.

Macrofauna monitoring index. This index, elaborated by R. D. Roberts in 1998, addresses the biological monitoring of dredged spoil disposal. Each of the 12 indicator species is assigned a score, taking primarily into account the ratio of its abundance in control versus impacted sites' samples. The index value is the average score of those indicator species present in the sample.

Index values of <2.2 to 6 and >6 indicate, respectively, severe impact, patchy impact, and no impact. This index is site and impact specific, but the process of developing efficient monitoring tools from an initial impact study could be widely applicable. Besides, since environmental impact assessments are often followed by ongoing monitoring, this index may be very labor and cost effective. In fact, the application of this index capitalizes on the redundancy in data sets by using a small, informative subset of the fauna, which can be readily sorted from samples and are simple to identify.

Benthic response index (BRI). This index, introduced by R. Smith in 2001, corresponds to the abundance weighted average pollution tolerance ofspecies occurring in a sample, which is in fact similar to the weighted average approach used in gradient analysis. The algorithm is

where Is is the index value for sample s, n is the number of species for sample s, pi is the position for species i on the pollution gradient (pollution tolerance score), and asi is the abundance of species i in sample s.

According to the present author, determining the pollution tolerance score (pi) for the different species involves four steps: (1) assembling a calibration infaunal data set; (2) carrying out an ordination analysis to place each sample in the calibration set on a pollution gradient; (3) computing the average position of each species along the gradient; and (4) standardizing and scaling the position to achieve comparability across depth zones.

The average position of species i (p) on the pollution gradient defined in the ordination is computed as

Table 2 Categories considered as a function of BENTIX index values

Classification

BENTIX value

Normal

4.5-6.0

Slightly polluted

3.5-4.5

Moderately polluted

2.5-3.5

Highly polluted

2.0-2.5

Very highly polluted

where t is the number of samples to be used in the sum, with only the highest t species abundance values included in the sum. The gjis the position on the pollution gradient in the ordination space for sample j. The BRI is extensively treated in Benthic Response Index.

Benthic quality index (BQI). This index has been utilized by R. Rosenberg in 2004 in Baltic Sea, using

tot A

Tolerant species are by definition predominantly found in disturbed environments. That means that they mainly occur at stations with low ES50, where ES is the diversity value measured by the Hulbert index and s the mean number of species. In contrast, sensitive species usually occur in areas with no or minor disturbance, being then associated with high ES50 values. Taking into account the abundance frequency distribution of a particular species in relation to the ES50 values at the stations where it has been recorded, the most tolerant individuals of a species are likely to be associated with the lowest ES50 values. The authors estimated that 5% of the population will be associated to this category, and defined this value as the species tolerance value: ES500 05.

The tolerance value of each species found at a given station is then multiplied by the average relative abundance (A) of that species (i ), in order to weight the common species in relation to the rare ones. Next, the sum is multiplied by the log10 of the mean number of species (s) at that station, since higher species diversity is assumed to be related to better environmental quality. All information related to the number of species and their abundance at a given station is therefore used for this quality assessment. This index has only been applied in the Baltic Sea.

Conservation index (CI). This index, utilized by Moreno in 2001, is based on the health of one marine seagrass, Posidonia oceanica:

Considering a given area under assessment, L is the proportion of living P. oceanica meadow and D the proportion of dead meadow coverage.

Different authors applied this index in the neighborhood of chemical industries, with results leading to establish four grades of Posidonia meadow conservation. These grades correspond to increasing impacted areas, allowing the detection of changes in the industry activity as a function of the conservation status in a given location:

<0.33: advanced regression;

> 0.79: high conservation status.

Ecological evaluation index (EEI). Shifts in marine ecosystem structure and function are evaluated by Orfanidis in 2001 classifying marine benthic macrophytes in two ecological groups (ESG I and ESG II). ESG I includes seaweed species with a thick or calcareous thalus, low growth rates, and long life cycles, whereas the ESG II includes sheet-like and filamentous seaweed species with high growth rates and short life cycles.

The absolute abundance (%) of each ESG is estimated by coverage (%) in each sample. It is recommended to obtain at least three samples per season. The estimation of the EEI values and the equivalent ecological status is shown in Table 3.

Bioaccumulator Indicator Species

Bioaccumulator indicator species are those capable of resisting and accumulating various pollutant substances in their tissues, which facilitate their detection whenever they are in the environment in very low levels. The fact that a number of biotic and abiotic variables may affect the rate at which the pollutant is accumulated represents the main disadvantage, implying the need of both laboratory and field tests to identify the effects of extraneous parameters.

Mollusks, particularly the bivalves, have been the most used group to determine the existence and quantity oftoxic substances. Individuals of the genera Mytilus, for instance, have been considered ideal in many works to detect the n

Table 3 Ecological evaluation index values and equivalent ecological status

Mean coverage of ESG I (%)

Mean coverage of ESG II (%)

ESC

EEI

Spatial scale weighted EEI and equivalent ESCs

0-30

0-30

Moderate

6

<6 to >4 = Moderate

>30-60

Low

4

<4 to >2 = Low

>60

Bad

2

2 = Bad

>30-60

0-30

Moderate

8

<8 to >6 = Good

>30-60

Low

6

<6 to >4 = Moderate

>60

Bad

4

<4 to >2 = Low

>60

0-30

Moderate

10

<10 to >8 = High

>30-60

Low

8

<8 to >6 = Good

>60

Bad

6

<6 to >4 = Moderate

concentration oftoxic substances in the environment, due to their sessile nature, wide geographical distribution, and capability to accumulate those substances in their tissues and to detoxify when pollution ceases.

Likewise, certain amphipod species are considered capable of accumulating toxic substances, as well as poly-chaete species such as Nereis diversicolor, Neanthes arenaceodentata, Glycera alba, Tharix marioni, or Nephtys hombergi. Some fish species have also been used in various works focused on the effects of toxic pollution of the marine environment, due to their bioaccumulative capability and to the existing relationship between pathologies suffered by any benthic fish and the presence of polluting substances.

Algae have also been looked upon as most favorable for heavy metals, pesticides, and radionuclides detection, Fucus, Ascophyllum, and Enteromorpha being the most utilized taxa.

Ecological reference index (ERI). For reasons of comparison, the concentrations of substances in organisms must be translated to uniform and comparable units. This may be done using the ERI, which has been applied only using mussels:

Measured concentration

where BCR is the value of the background/reference concentration. The upper limit of BCR for hazardous substances in blue mussels according to the 1998 OSPAR Summary Report on Monitoring (OSPAR/ MON) is provided in Table 4.

Only few indices based on the use of bioaccumulative species have been proposed, and simple measurements of the effects (e.g., % incidence and mortality percentage) of a certain pollutant on those species, or the use of biomar-kers, are more common, although these are rather difficult for environmental managers to interpret.

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