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Minimum data required for deriving soil quality criteria from invertebrate ecotoxicity experiments

Milagrosa M. Vega

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, Spain

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Arantzazu Urzelai,

Arantzazu Urzelai

IHOBE, c/Ibáñez de Bilbao 28-8°, E-48009 Bilbao, Spain

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Eduardo Angulo,

Corresponding Author

Eduardo Angulo

[email protected]

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, Spain

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, SpainSearch for more papers by this author

Milagrosa M. Vega,

Milagrosa M. Vega

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, Spain

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Arantzazu Urzelai,

Arantzazu Urzelai

IHOBE, c/Ibáñez de Bilbao 28-8°, E-48009 Bilbao, Spain

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Eduardo Angulo,

Corresponding Author

Eduardo Angulo

[email protected]

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, Spain

Department of Zoology and Animal Cell Dynamics, Faculty of Sciences, University of the Basque Country, E-48080 Bilbao, SpainSearch for more papers by this author

First published: 02 November 2009

https://doi.org/10.1002/etc.5620180633

Citations: 6

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Abstract

The suitability of a new method to determine the minimum number of data required to stabilize a calculation is studied. Five different calculations have been computed, changing the order of data randomly. The statistical reliability of numerical values has also been calculated depending on the successive increase in the number of data included. In our study, when few data are considered, a great variation in the estimations is observed depending on the individual data included. The calculation is stabilized for a sample size between 5 and 15 data points, and the sample can therefore be considered representative enough. Results are in agreement with those presented by other authors.

INTRODUCTION

Statistics generally deals with populations or groups of individuals, that is, with quantities of information. Thus, the measurement of a single animal or the response from a single biochemical test will not generally be of interest. Unless a sample of animals is measured or several such tests are performed, ordinary statistics can play no role. So what sample size is needed? It is obvious that the more heterogeneous a population is the larger should be the sample size required to obtain a given level of accuracy. The less variable (the more homogeneous) a population, the smaller the sample size. If variability within measurements is great, many samples will be needed to empirically determine a representative mean (or median) value, and this will also imply that it will probably be difficult to predict such a mean value by models since this will be influenced by many sources of variability [1].

However, the optimal size of predictive models is generally achieved with a surprisingly small number of dependant variables. The results presented by Hakanson [1] indicate that predictive models should not have more than two to six x variables (or compartments). Although it is evident that there may be many specific cases where more x variables would do more good than harm, it is also important to note that the potential model uncertainty would then increase, too [1].

This paper deals with the reliability of the calculation procedure followed in the Basque country to derive soil quality criteria aiming at protecting ecosystems. These values, designated with the acronym VIE (indicative values for assessment) have been drawn up as a tool for risk assessment. They have been formulated so that they delimit clearly differentiated areas with regard to risk along the pollutant concentration gradient [2, 3]. Two different values with two different meanings have been established. In this paper, we will focus on the so-called VIE-C, defined as the level above which the risk becomes unacceptable. From an ecotoxicological point of view, this level implies a potential hazard for 50% of the ecosystem species [4] and the subsequent impairment in the ecological function.

The suitability of a new probabilistic method to determine minimum data needs in order to stabilize the VIE-C values for four metals (cadmium, copper, lead, and zinc) is studied and compared with the results obtained following the method proposed by Carral et al. [5].

MATERIAL AND METHODS

Data were collected by an extensive literature review and were selected based on sublethal effects of long-duration exposure (NOECs) tests on invertebrates. Priority was given to parameters affecting reproduction and growth as well as other attributes important to population dynamics (see Appendix 1). The VIE-C values for Cd, Cu, Pb, and Zn were calculated following the method of Van Straalen and Denneman [6] and Denneman and Van Gestel [7] adapted to the soil characteristics of the Basque country [3]. These values are of the same order of magnitude, although 12 to 33% higher, than the equivalent ones established in The Netherlands [8]. The reliability index of the calculation was established according to Denneman and Van Gestel [7] and Van den Berg and Roels [8], who take into account the number of both species and taxonomic groups for which data are available. The four metals were selected because they showed a good reliability from this point of view as well as a great number of available ecotoxicity data.

To check the mathematical robustness of the derived VIE-C values, successive calculations were carried out in which the number of ecotoxicity data included was progressively increased randomly to minimize factors of variability [9]. For each of the four heavy metals (Cd, Cu, Pb, and Zn), the computation was repeated five times, changing randomly the order in which data were added in each of the runs in order to avoid bias in the stabilization interpretation.

Table Table 1.. Toxicity data considered for cadmium calculations^a

Species	T (d)	Pter	[C]	% OM	% Clay	Reference
Acrobeloides nanus	10	NOEC	0.05	—	—	[16]
	14	NOEC-d	2	—	—	[17]
	35	NOEC-c	10	—	—	[18]
Aphelenchus avenae	21	NOEC-cpo	0.094	—	—	[19]
	21	NOEC-po	0.01	—	—	[19]
Dendrobaena rubida	14	NOEC	100	5.7	—	[20]
	90	NOEC-h	101	9.7	5	[20]
Eisenia andrei	21	NOEC-c	100	10	20	[21]
	21	NOEC-r	100	10	20	[21]
	21	NOEC-c	10	10	20	[21]
	21	NOEC-h	10	10	20	[21]
	84	NOEC-c	10	8	10	[22]
	84	NOEC-c	18	8	10	[23]
	21	NOEC-j	108	38	5	[24]
	21	NOEC-j	100	10	20	[21]
Eisenia fetida	56	NOEC-l	300	10	20	[25]
	14	NOEC	25	50	—	[26]
	56	NOEC-h	39.2	10	20	[25]
	56	NOEC-h	25	50	5	[26]
	21	NOEC-h	152	20	10	[27]
	21	NOEC-c	207	20	10	[27]
	21	NOEC-l	300	20	10	[27]
	56	NOEC-c	50	50	5	[26]
Folsomia candida	63	NOEC-h	323	—	—	[28]
	50	NOEC-c	148	95	—	[29]
	50	NOEC-po	326	95	—	[29]
	63	NOEC-c	324	—	—	[28]
Helix aspersa	30	NOEC-c	10	86	—	[30]
	30	NOEC-con	50	86	—	[30]
	14	NOEC	10	86	—	[30]
	21	NOEC	50	—	—	[31]
Lumbricus rubellus	12	NOEC-h	10	3.4	17	[32]
	14	NOEC	10	3.4	—	[32]
Mesorhabditis monhystera	22	NOEC-cpo	0.23	—	—	[19]
Orchesella cincta	61	NOEC-l	56	95	0	[33]
	42	NOEC-l	56	95	—	[34]
	61	NOEC-c	4	95	0	[33]
	84	NOEC-r	56	95	—	[34]
	63	NOEC-r	56	95	—	[35]
	63	NOEC-c	4.7	95	—	[35]
	14	NOEC	56	95	—	[35]
	84	NOEC-c	4	95	—	[34]
	63	NOEC-po	56	95	—	[35]
	63	NOEC-con	56	95	—	[35]
	61	NOEC-pd	56	95	—	[33]
Platynothrus peltifer	63	NOEC-h	1	51	—	[28]
	14	NOEC	2.9	95	—	[35]
	84	NOEC-con	27.3	95	—	[35]
	84	NOEC-po	2.9	95	—	[35]
	84	NOEC-l	117	95	—	[35]
	84	NOEC-r	2.9	95	—	[35]
Plectus acuminatus	21	NOEC-po	32	10	20	[36]
Porcellio scaber	67	NOEC-r	10	95	0	[37]
	67	NOEC-con	2.25	95	0	[37]
	67	NOEC-c	2.25	95	0	[37]
	67	NOEC-t	10.1	95	0	[37]
	28	NOEC-con	10	86	0	[38]
	28	NOEC-aa	10	86	0	[38]
	14	NOEC	10	95	—	[37]
	28	NOEC-c	4	86	0	[38]
	308	NOEC-l	95	—	—	[28]

^aT (d) = test duration (in days); Pter = parameter considered (see Appendix); [C] = concentration in mg/kg; % OM = organic matter in percent; — = no data specified.

RESULTS

Tables 1 through 4 show the toxicity data for cadmium, copper, lead, and zinc, respectively, and the different parameters considered in the calculations. Figure 1 displays the regression curve slopes for the VIE-C values according to the succesive addition of data included in the calculations. According to this figure, it can be concluded that the stabilization of the values for the four metals arises within the range of 5 to 15 data points, at around 10 data points Curve shapes are unpredictable for calculations based on few data points, which is reasonable if we take into account the high standard deviations observed in these initial sectors.

Table Table 2.. Toxicity data considered for copper calculations^a

Species	T (d)	Pter	[C]	% OM	% Clay	Reference
Aporrectodea caliginosa	14	NOEC-c	100	2	5	[39]
	15	NOEC-h	50	5	2	[39]
	14	NOEC-l	500	2	5	[39]
	28	NOEC-h	28	6	4	[32]
	28	NOEC-h	27	6	4	[32]
Arion ater	27	NOEC-con	25	86	0	[40]
Dendrobaena rubida	90	NOEC-h	22	9.7	5	[20]
Eisenia andrei	21	NOEC-po	306	8	10	[41]
	84	NOEC-c	62	8	10	[22]
	84	NOEC	106	8	10	[22]
	21	NOEC-h	120	8	10	[41]
	—	NOEC-c	56	8	10	[23]
	7	NOEC-h	60	8	10	[41]
Eisenia fetida	21	NOEC-l	293	20	10	[27]
	56	NOEC-l	210	10	20	[25]
	42	NOEC-c	1,000	38	5	[42]
	42	NOEC-h	1,000	38	5	[42]
	56	NOEC-c	500	50	5	[26]
	56	NOEC-c	1,700	—	—	[43]
	21	NOEC-h	29	20	10	[27]
	56	NOEC-h	32	10	20	[25]
	56	NOEC-h	500	50	5	[26]
Lumbricus rubellus	84	NOEC-hj	63	3.4	17	[44]
	84	NOEC-h	30	3.4	17	[32]
	84	NOEC-h	13	3.4	17	[44]
	84	NOEC-c	373	3.4	17	[44]
	28	NOEC-h	80	6	4	[32]
	84	NOEC-h	130	5.7	2	[44]
Octolasium cyaneum	14	NOEC-l	153	24.1	—	[45]
	14	NOEC-l	1,214	—	—	[45]
Onychiurus armatus	119	NOEC-c	2,608	95	0	[20]
Platynothrus peltifer	84	NOEC-c	598	95	0	[46]
	84	NOEC-r	168	95	0	[46]
	84	NOEC-r	200	20	10	[47]
	84	NOEC-c	200	20	10	[47]
Plectus acuminatus	21	NOEC-po	32	10	20	[36]

^aT (d) = test duration (in days); Pter = parameter considered (see Appendix); [C] = concentration in mg/kg; % OM = organic matter in percent; — = no data specified.

Table Table 3.. Toxicity data considered for lead calculations^a

Species	T (d)	Pter	[C]	% OM	% Clay	Reference
Aiolopus thalassinus	35	NOEC-c	50	—	—	[18]
	14	NOEC-nf	50	—	—	[17]
	14	NOEC	100	—	—	[17]
Aporrectodea caliginosa	—	NOEC	1,000	1	0	[39]
Aphelenchus avenae	21	NOEC-po	0.01	—	—	[19]
Arion ater	27	NOEC-con	1,000	86	0	[40]
Dendrobaena rubida	90	NOEC-r	564	9.7	5	[20]
	90	NOEC-r	130	9.7	5	[20]
	90	NOEC-r	560	9.7	5	[20]
Eisenia fetida	140	NOEC-h	1,000	50	5	[26]
	140	NOEC-c	1,000	50	5	[26]
	56	NOEC-c	36,000	—	—	[43]
	42	NOEC-c	5,000	38	5	[42]
	56	NOEC-l	2,190	10	20	[25]
	56	NOEC-h	1,810	10	20	[25]
	21	NOEC-c	1,966	20	10	[27]
	21	NOEC-l	4,793	20	10	[27]
	21	NOEC-h	608	20	10	[27]
Lumbricus rubellus	12–84	NOEC-c	1,000	8	17	[32]
	42	NOEC-l	3,000	8	17	[32]
	84	NOEC-h	200	3.4	17	[32]
Mesorhabditis monhystera	22	NOEC-po	7.6	—	—	[19]
Onychiurus armatus	119	NOEC-r	1,096	95	0	[20]
	119	NOEC-c	1,096	95	0	[20]
Platynothrus peltifer	84	NOEC-c	430	95	0	[46]
	84	NOEC-r	430	95	0	[46]
Porcellio scaber	—	NOEC	40	95	0	[48]
	448	NOEC-po	6,400	95	0	[48]

^aT (d) = test duration (in days); Pter = parameter considered (see Appendix); [C] = concentration in mg/kg; % OM = organic matter in percent; — = no data specified.

Table Table 4.. Toxicity data considered for zinc calculations^a

Species	T (d)	Pter	[C]	% OM	% Clay	Reference
Arion ater	27	NOEC-con	10	86	5	[40]
Arion lusitanicus	—	NOEC-r	98	—	—	[34]
Eisenia andrei	21	NOEC-h	320	38	5	[24]
Eisenia fetida	56	NOEC-c	2,400	—	—	[43]
	42	NOEC-h	1,000	38	5	[50]
	42	NOEC-c	2,500	38	5	[50]
	56	NOEC-1	289	10	20	[25]
	56	NOEC-h	199	10	20	[25]
	21	NOEC-1	702	10	—	[51]
	140	NOEC-c	5,000	50	5	[26]
	21	NOEC-1	256	10	—	[51]
	21	NOEC-1	274	5	—	[51]
	21	NOEC-c	400	20	10	[27]
	21	NOEC-1	1,048	15	—	[51]
	21	NOEC-1	366	5	—	[51]
	140	NOEC-h	1,000	50	5	[26]
	21	NOEC-h	85	5	—	[51]
	21	NOEC-h	183	10	—	[51]
	21	NOEC-h	115	5	—	[51]
	21	NOEC-h	161	10	—	[51]
	21	NOEC-1	368	15	—	[51]
	21	NOEC-h	414	15	—	[51]
	21	NOEC-1	442	20	10	[27]
	21	NOEC-h	484	15	—	[51]
	21	NOEC-h	237	20	10	[27]
	21	NOEC-h	553	10	—	[51]
	21	NOEC-h	97	5	—	[51]
	21	NOEC-1	184	15	—	[51]
	21	NOEC-1	168	10	—	[51]
	21	NOEC-1	197	5	—	[51]
	21	NOEC-h	223	15	—	[51]
Oniscus asellus	1	NOEC-a	2,000	95	0	[52]
Porcellio scaber	67	NOEC	2,000	95	0	[37]
	448	NOEC-po	800	95	0	[48]
	67	NOEC-con	1,000	95	0	[37]
	67	NOEC	2,000	95	0	[37]
	42	NOEC-con	1,700	95	0	[53]
	7	NOEC-a	2,000	95	0	[52]
	67	NOEC-c	398	95	0	[37]

^aT (d) = test duration (in days); Pter = parameter considered (see Appendix 1); [C] = concentration in mg/kg; % OM = organic matter (%); — = no data specified.

Identical results are obtained following the Carral et al. [5] test based on the standard error calculation. Stabilization of the values is also reached for about 10 data points, as Figures 2 a, b, c, and d show. These graphics correspond to the standard error representation for only one of the computation sequence of cadmium, copper, lead, and zinc, respectively.

DISCUSSION

Different criteria are usually followed to determine the appropriate sample size, and these have to consider both mathematical and scientific–technical issues. Among others, Miaoulis and Michener [10] stated three of these criteria that would need to be specified in any study, including (1) the degree of variability within the variables to be measured, (2) the level of accuracy in the estimation, and (3) the level of reliability. The first two can be related to mathematical issues, while the third refers to the scientific reliability of the approach adopted (relevance of parameters, sampling strategies, etc.) for the problem under investigation.

In our study, the reliability of the calculation from the scientific viewpoint was established according to Van den Berg and Roels [8] and Denneman and Van Gestel [7]. The quantitative reliability scale formulated by these authors is based on the availability and representativity of toxicity data, both in terms of number and kind of species and taxonomic goups. The species and groups of interest are previously identified in relation to their biological significance to the soil ecosystem function. So our study has been focused on the statistical aspects and, more specifically, on the number of data required to stabilize the mathemathical calculation.

The two factors considered are the variability of the input data and the variability of the output value as a measure of the degree of accuracy in the calculation. Due to the natural interspecies variations, the range of species sensitivity to the pollutants under test is the most important factor in assessing ecosystem risk on the basis of a few single test data [11]. Nevertheless, variations between laboratories and even variations between tests within a laboratory are generally thought to become a more determining factor. Toxicity values are not constant values but depend on different environmental factors and thus on experimental conditions. In order to minimize as much as possible this source of variability, the selection of input data involved a careful appraisal of test conditions. Only NOEC-type data from nonlethal effects on long-duration exposure tests on soil invertebrates were selected. Moreover, it was considered necessary for toxicity data to have been obtained in standardized or generally well-accepted laboratory test methods. In this sense, these types of data have also been used to study the relationships among VIE-C values derived independently for soil, marine, and freshwater ecosystems, showing a strong correlation between media [12].

Details are in the caption following the image — **Figure Fig. 1.**
Open in figure viewer PowerPoint

Variability on all metal slopes obtained through each of the five different runs on increasing the number of data for all the metals used in the calculations.

With respect to the stabilization topic, Carral et al. [5] consider that minimum sample size is reached when the standard error is small (curve shape becomes flat, i.e., the slope tends to zero). Our results indicate a similar behavior. The addition of new data diminishes the variability of the VIE-C values derived while the curve shape tends to the horizontal. This happens from 10 data points onward.

The use of a similar approach has also been suggested by Solomon [13] for the evaluation of the species sensitivity to many substances. In this study, the decrease and stabilization of the distribution slope was reached with nine data points (one less than our results), which is in very good agreement with our results. The author advocates for this minimum number of data to apply a probabilistic ecological risk assessment.

CONCLUSIONS

The reliability of values estimated from sample data should take into account both scientific and statistical criteria. In our example, the unique use of biological criteria (based on the number of species and taxonomic groups) to establish the reliability of the derived value could have led to incorrect conclusions if mathemathical stability were not considered. As Fobes and Forbes [14] suggested, the rational use of these models as ecotoxicological tools for environmetal regulation requires basic knowledge concerning the relationships between the structure and function of ecosystems and the nature of the statistical distribution. The need to encourage the checking of statistical aspects is not only necessary for scientific purposes but also for management and economic ones. If both biological and statistical criteria are not taken into account, extrapolations can lead to mistaken management decisions. In this sense, to reduce uncertainty, it is necessary to make the tests more realistic by using multispecies tests, by including other toxicants that may occur with the substance of interest, and by using indigenous species [15].

Acknowledgements

This study has been promoted and funded by IHOBE, SA, a public society for environmental management that depends on the Department of Spatial Planning, Housing, and the Environment of the Basque Country.

APPENDIX

Table . Biological parameters considered in this study

NOEC	no observed effect concentration
NOEC-f	feeding
NOEC-fa	food assimilation
NOEC-g	growth
NOEC-con	consumption
NOEC-po	population
NOEC-ep	egg production
NOEC-dj	eggs developed to juveniles
NOEC-j	number of juveniles
NOEC-l	lethality
NOEC-m	mobility
NOEC-n	number of nymphs developed
NOEC-r	reproduction
NOEC-t	trembling

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Volume18, Issue6

June 1999

Pages 1304-1310

Minimum data required for deriving soil quality criteria from invertebrate ecotoxicity experiments

Abstract

INTRODUCTION

MATERIAL AND METHODS

RESULTS

DISCUSSION

CONCLUSIONS

Acknowledgements

APPENDIX

References

Citing Literature

Figures

References

Information

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Minimum data required for deriving soil quality criteria from invertebrate ecotoxicity experiments

Abstract

INTRODUCTION

MATERIAL AND METHODS

RESULTS

DISCUSSION

CONCLUSIONS

Acknowledgements

APPENDIX

References

Citing Literature

Figures

References

Related

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