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Quantitative methods of ecological control:

Quantitative methods of ecological control:

diagnostics, standardisation, prediction

V.N.Maximov, N.G.Bulgakov and A.P.Levich

Laboratory of General Ecology. Biological faculty of Moscow State University, Vorobyovy gory, 119899 Moscow, Russia.

Phone: +7(095)939-5560

E-mail: )

Proposing methods require two groups of techniques to be developed. The first one includes integrated methods for estimating ecosystem conditions using the norm-pathology scale. The second group contains methods of searching for and pattern recognition of the boundaries between the fields of normal and pathological ecosystem functioning in multidimensional space of environmental factors. These boundaries have been named the ecologically tolerable levels (ETL) of disturbing influences. Proposing method allows to calculate ETL values not only for chemical substances, but also for heat pollution and consequences of climate changes, for environment acidification and alkalinization, for water expenditure rates and other influences. The method is useful for prediction and extrapolation of ecosystem conditions according to anthropogenic influencing scenarios.

1. Concept of ecological tolerance and biotic

approach to realization of environmental control

The assessment of ecological state of natural objects (ecological diagnostics), introduction of admissible levels of anthropogenic influences (ecological standardisation) and detection of consequences of the various biota disturbing scenarios (ecological prediction) are the main tasks of the system of ecological control. In the whole territory of former USSR this system is based on the concept of maximum admissible concentrations (MAC) of pollutants. The MAC values are used in solution of all tasks of an ecological control mentioned above. Particularly, the exceeding of MAC forms the basis for unsuccessful assessment of ecosystem state. The MAC standards are determined in laboratory conditions during short-term (days) and chronic (weeks) experiments with isolated populations of organisms, belonging to small number of selected test species, and using limited set of physiological and behavioural responses. The evaluation of natural objects state by MAC levels is an actually unjustified extrapolation of test organisms tolerance borders in relation to isolated influences on multispecific ecosystems, where complicated complexes of tens and hundreds factors of most different nature are operating, on ecosystems being (unlike standard laboratory populations) in completely various background conditions of functioning [3].

The concept of an ecological tolerance proposing admissible levels of influences for biotic part of real ecosystems could be alternative to the methodology of MAC, which is biologically based on existence of tolerance limits for separate organisms. According to the offered concept [22], for any ecological system it is possible to find such limits of the ecological factors variation, at which indications, distinguishing this ecosystem from another, adjacent ecosystems, save their relative stability. In indicated sense it is possible to identify limits of ecological tolerance with borders, inside which the ecosystem state is considered as normal. Then in relation to xenobiotic pollutants the lower limit of tolerance is established automatically: this is their complete absence in ecosystem. The upper limit of tolerance may be considered as ecologically tolerable level of pollution.

The idea about tolerance limits is well-known in ecology, but only in the application to individual organisms and ecological populations. The mechanical transferring of this idea to communities and ecosystems is unevenly, because they differ from individual organisms first of all by principles of self-organisation. Thereof at exit of any factor out of tolerance limits it is observed modification of functional indices of organism state while indices of its composition and structure, i.e. morphological indices, remain constant, (even in those extreme cases when organism death is observed). In contrast, any modification of environmental conditions causes first of all structural modifications (fluctuation of number of species, trophic groups of size and age composition, etc.) in community (and in ecosystem) at relative constancy of such functional indices as efficiency, rate of destruction and other processes of ecological metabolism. By virtue of it at excessive deviation of environmental conditions from some “norm” rather gradual transformation of one ecosystem into another is observed and such transformation cannot be characterised as death.

It is necessary to mention other difference between ecosystem and organism. Though this difference has not so basic character as one considered above, but nevertheless it should be taken into account at development of methodological bases of ecosystem study. The difference implies that the tolerance limits of an organism or population can be established directly in experiment, in which any ecological factor can be varied in rather wide range, also testing in particular such levels of this factor, at which death at least of some percent specimens in investigated population is possible. It is obvious, that the similar experiment cannot be carried out with any natural ecosystem. Hence, under the essence of matter rather wide area and regular observation in this ecosystem, i.e. ecological monitoring can be a unique way of establishment of ecosystem tolerance limits in relation to any ecological and xenobiotic factors.

In other words, we are dealing with passive “experiment”, which is carried out for a long time by mankind in places of the residing and economic activity not on separate organisms, but on populations and communities really occupying natural ecosystems; not with isolated chemical substance, but with a complete complex of factors impacting on biota of given ecosystem; in conditions of concrete region with regard to its background and other local characteristics.

There is the possibility to replace “chemical” (based on the MAC methodology) approach to realisation of ecological control by biotic approach [16] based on the concept of ecological tolerance [22] and on ideas about priority of biological control [1]. This concept assumes the existence of “cause and effect” connection between levels of influences on biota and biota response. The task of biotic approach consists in revealing the boundary between areas of normal and pathological functioning of natural objects in space of abiotic factors. Such boundaries replace the MAC standards and are named as ecologically tolerable levels (ETL) of disturbing influences. According to the biotic approach, assessment of an ecological state on the scale “norm — pathology” should be carried out by complex of biotic indices, and not by levels of abiotic factors. In this case abiotic factors (pollution, other chemical characteristics, climatic indices, transferring intensities, etc.) should be considered as agents of influence on populations, on ecological relationships between them and as potential causes of an ecological trouble.

For realisation of biotic approach a set of methods for obtaining of communities state estimations is necessary. By these methods ecologically safe ecosystem could be distinguished from ecosystem, in which there were the essential modifications caused by external (first of all, anthropogenic) influences Then it will be possible to establish the boundaries of “norm” and “pathology” in some scale of communities states. However, in view of the above mentioned basic distinctions between ecosystem and organism, in this case it is better to speak about establishment of boundaries of ecosystem stable existence, i.e. such limits of biotic parameters modification, at which ecosystem “saves its face”. Systematic control for modification of selected state estimations just should make a basis of biological part of ecological monitoring.

Other group of methods should provide the detection of those physico-chemical characteristics of ecosystem which are responsible for a modification of community state and for its exit out of established boundaries of stable existence. It should be mathematical methods of analysis, permitting to select the area of ecological safe in multidimensional space of the ecological factors. Certainly, the case in point is only those factors, which are controlled according to the chemical component of ecological monitoring program. Those mathematical methods, which help to establish ETL for detected damaging influences, should be referred to this the group.

We shall point out, that the case in point is the analysis of the ecological monitoring data in real natural objects, i.e. the analysis of huge arrays of the ecological data. Such analysis is accessible only to modern information technologies including computer ecological databases.

 

 

 

2. Diagnostics of an ecological state of natural objects by biotic identifiers

2.1. Expert evaluations

The evaluation of an ecological systems state presents a serious problem, far not yet solved, to discussion of which a huge amount of works is devoted. The review of these works does not enter into our task, the more so as the similar review was recently made in the book “Ecological standardisation of technogenic pollution of terrestrial ecosystems” [34]. At all variety of approaches to solution of this problem any of them has not led to elaboration of any method which could unconditionally be recommended for practical use. For this reason in existing systems of ecological monitoring only expert evaluations of environmental quality are used.

Table 1

Classification of water quality in reservoirs and streams by hydrobiological indices

Class of water quality

Degree of water pollution

By phytoplankton, zooplankton, periphyton

By zoobenthos

By bacterioplankton

   

Saprobe index by Pantle and Buck (in Slá decek modification), marks

Ratio of total number of Oligochaeta to total number of bottom organisms, %

Biotic index by Woodiwiss, marks

Total number of bacteria, 106 cells/ml

Number of saprophyte bacteria, 103 cells/ml

Ratio of total bacteria number to saprophyte bacteria number

I

Very clear

< 1,0

1-2

10

< 0,5

< 0,5

> 103

II

Clear

1,00-1,50

21-35

7-9

0,5-1,0

0,5-5,0

> 103

III

Moderately polluted

1,51-2,50

36-50

5-6

1,1-3,0

5,1-10,0

103-102

IV

Polluted

2.51-3,50

51-65

4

3,1-5,0

10,1-50,0

< 102

V

Dirty

3,51-4,0

66-85

2-3

5,1-10,0

50,1-100,0

< 102

VI

Very dirty

> 4,00

86-100 or macrobenthos is absent

0-1

> 10

> 100

< 102

We shall consider for example the qualifier [30], using in evaluation of freshwater quality in the system of environmental control of Russian Hydrometeorological Committee (see Table 1). According to this qualifier at realisation of ecological monitoring an abundance and specific structure of plankton, periphyton and zoobenthos are defined, and the state of each of these biotic identifiers is estimated by account of a widely known saprobe index (for phyto- and zooplankton), biotic index of Woodiwiss and oligochaetal index of Goodnight-Whitley (for zoobenthos) and by abundance of saprophyte bacteria in bacterioplankton. At all difference of these indices they are united by common principle: in a basis of each of them the analysis of distribution of organisms or groups of organisms on a gradient of pollution lays, and a word “pollution” implies first of all an amount of organic substances in water. There is a tight feedback between this amount and oxygen content in water.

Thus, we obtain in an implicit aspect the same “chemical” approach, distinguished only by that instead of direct detection of concrete pollutants (for example, dissolved organic matter or sediments) or other hydrochemical indices conjugated with them (for example, BOD or dissolved oxygen) we estimate an abundance of groups of organisms, distinguishing by sensitivities to these hydrochemical indices. As this takes place, any admissible limits of measuring variables are not established, but instead of it expert evaluations in marks, saprobe index values, classes of water quality are introduced. It is natural, that at practical use of such approach there are the diverse misunderstanding, repeatedly discussed in the literature. In particular, cases are frequent, when the evaluations obtained by different biotic identifiers do not coincide and it is necessary to invent methods of account of so-called integrated estimations. This circumstance derives “indices of pollution”, number of which grows from year by year. It is possible, however, to agree upon some rules, following which it is necessary to make a decision at presence of inconsistencies in expert evaluations.

The mentioned above indices — saprobe index, biotic index of Woodiwiss, etc. — also unite their common fault: at their construction biotic relations between populations in real communities, such as a competition, mutualism, etc. are purely ignored. The attempt to correct this shortage was undertaken by V.A.Abakumov [1, 2]. On the basis of developing by him ideas about ecological modifications it is offered to enter gradations of ecosystem state: background state, state of anthropogenic ecological tension, state of anthropogenic ecological regress and state of anthropogenic metabolic regress. Undoubtedly, this approach is more “ecological” and consequently looks more justified, than, for example, saprobe system or diverse biotic indices, marks, etc. However, in this case we also obtain a set of expert estimations of community state for which construction of scales of type “norm — pathology” is too necessary

We shall remark that in modern ecology the tendency to introduce quantitative indices for diagnostics of systems state remains. An integrated index of sea ecosystem anomaly, index of total anthropogenic loading, criterion of potential ecological danger [9] may serve the examples for sea areas. We shall mark that, except designing and substantiation of indexes themselves for the diagnostics purposes, method of reflection of a set of occurring indices values on any variety of scale “norm — pathology” for ecosystem states, for example, method of the desirability function [29], is necessary.

2.2. Parameters of rank distributions as the functions of community response to abiotic influences

It’s tempting to discover ecological regularities giving more convincing basis for ecological diagnostics than expert ones. The analysis of rank distributions of number or biomass of organisms groups can be perspective in this concern. biological taxons, size classes, specimens aggregates joined by any physiological or other indicators can considered as such groups.

For phenomenological description of rank distributions [15] in ecology various approximations are applied: the exponential model , hyperbolic model , zeta-distribution ~ joining them, model of “broken stick” ~ (in the formulas designates number of specimens of rank i; z, b and B are parameters of models). Instead of functions sometimes their mathematical equivalents is analysed: distributions of accumulated numbers (total number of groups of all ranks from 1 up to i), incident distributions (number of groups with number from n up to n+ D n) or incident distributions of number logs, function of an ecological nonadditivity (dependence of groups number in sampling on sampling size).

It was detected [15] that in normal (not disturbing, background, etc.) community state the parameter of rank distribution is put in a quite defined range of values, for example, for planktonic organisms number in case of an exponential model z » 0.7-0.9 or in case of a hyperbolic model b » 1.5-2.5. The parameter of rank distribution is specific for a type of community (for example, for phytoplankton, zooplankton or periphyton communities), for concrete ecosystem, for usual complex of environmental conditions, to which community is adapted. In that degree, in which indicated law is fair, the deviations from it can be a measure of pathology of community state. In other words, “thermometer” for ecosystems is offered, where the parameter of rank distribution plays the role of temperature. We shall notice that the given analogy is not a superficial and is connected to models of an origin of rank distributions [17].

The practice of ecosystem state diagnostics by indices of its diversity taking roots in ecology thus has the acquitting that all indices of diversity are univalently connected to parameters of rank distributions [15] and the parameters are interpreted more often as indices of ecological diversity.

It is necessary to specify that the deviations of rank distributions from a norm register stress influences on communities. At long duration of disturbing influence there can be the essential reorganisation of community structure, the replacement of species included in it, but as a result of adaptation the parameters of rank distribution of new numbers of new organism groups will appear in normal limits.

The rank distributions were applied to the analysis of processes of water eutrophication, to the evaluation of influence of pollutants and thermal pollution on biota, to the study of successions, seasonal modifications and many other features [5, 12, 14, 15, 22, 23, 31, 32].

Method of diagnostics based on comparison of rank distributions of number and biomass of the same organism groups [35] is known. This ABC-method (abundance-biomass comparison) is based on the supposition that in stable communities large species with slow dynamics prevail, and in disturbing communities small and more dynamic species prevail. As a rule, method users offer their ways of account of difference indices in indicated distributions [4, 5, 27, 28].

In the paper of Maximov with co-authors [25] for description of dependence of phytoperiphyton and zooperiphyton species abundance in three reservoirs of Kalmykia (Elista river, Ulan-Erginsky pond and Yarmarotchny pond) on their ranks in ranked by decrease number set zeta-model [15] n(i) = = is used, the parameters of which z and b determine the form of rank distribution curve, and C is a constant, sense of which easily to understand by putting i=1, n (i) = = C, therefore C is a number of a species dominating in community. At b = 1 zeta - model describes hyperbolic relationship (n (i) = C/ib), and at b = 0 relationship becomes exponential (n (i) = Czi-1).

For definition of model parameters under the experimental data the model equation was linearised:

ln n (i) = C + (i-1) lnz - b ln i.

Then the parameters z and b was calculated by the program of linear regression analysis.

Normal (the most frequently encountering) value of the rank distributions parameter z for phytoperiphyton was 0.94, and for zooperiphyton 0.90 Optimal value b for phytoperiphyton was accepted equal 0.09, and for zooperiphyton 0.03.

For both parameters the desirability function was constructed which establish correspondence between concrete values of these parameters and conditional numbers in a range from 0 (the worst value) up to 1 (the best value) [29]. Values of parameters z and b varied in rather wide limits. It signifies that the type of rank distributions differs both in one reservoir during a season and in different reservoirs in the same time. The frequency of appearance of some average (modal) parameters values is explicitly higher, than frequency of appearance of extreme (the lowest or the highest)values.

Therefore for parameters z and b so-called statistical norm was established and by desirability functions “quality” of each concrete rank distribution curve for phytoperiphyton and zooperiphyton in all investigated samples was appreciated. According to such approach the most frequently encountering values of each parameter were accepted for a norm and maximum desirability, equal 1, was assigned to modal or close to them values,. For the rest of parameters values which are higher or lower than statistical norm, desirability values decreased proportionally to deviation from a settled norm so that desirability for the most deviating values was equal 0 or close to it.

The greatest deviations from a statistical norm (desirabilities close or equal zero) both for phytoperiphyton, and zooperiphyton were observed more often in Ulan-Erginsky pond which was the most unsuccessful reservoir by a character of pollution (flows from drainage collectors and from stock-breeding complexes). Other situation is observed in Elista river, i.e. in the least polluted site. The distribution of desirability values for Yarmarotchny pond had an intermediate character, but by the frequency of appearance of values, more than 0.8, is closer to Elista river than to Yarmarotchny pond. For periphyton community conditions in Yarmarotchny pond, polluted basically by flows from Elista river, are less disastrous than conditions in Ulan-Erginsky pond.

In other work [26] a possibility of application for the same purpose of distributions by abundance of so-called size-morphological phytoperiphyton groups was investigated. The study of such groups allows to reveal seasonal modifications of microalgae and their dependence on a level of anthropogenic influence not less successfully than by traditional approach based on definition of specific structure of community. At the same time the reference of algal cells to some group is methodically much easier than definition of their specific status.

In a basis of classification the data on average maximum length and width of algal species cells investigated in Kalmykia reservoirs were fixed. Totally 16 groups of phytoplankton was obtained, and zeta-model was applied to them. The analysis of generalised desirability of rank distributions parameters of size-morphological groups has shown that the greatest deviation from a statistical norm was fixed in Yarmarotchny pond. On the contrary, in pond desirability more often was close to 1. Elista river Elista occupied an intermediate position by this index. As we see, the results don’t coincide with ones, which were obtained for rank distributions of phytoperiphyton species. Probably, it is associated with differences in a character of pollution in investigated reservoirs: in Yarmarotchny pond pollution of inorganic nature prevails, for example, concentration of nutrients, chlorides, sulphates, and in Ulan-Erginsky pond high values of BOD and regained for