Cyril Gorlla

Targeted Advertising in Snapchat Political Ads May 13, 2020 1 Targeted Advertising in Snapchat Political Ads

come

The main dataset used in this project was created by combining two datasets for each year. The start and end dates were converted to standard Pandas datetime format, and any spend amounts not in USD were converted to USD with forex_python to allow for more meaningful statistical analyses. To narrow focus on those advertisements which were targeted, the dataset was filtered to consist of ads that • Were targeting the United States • Specifically targeted a state or states.

The final dataset consisted of 1,175 rows, compared with the original 5,432. The rows with ads in the United States not targeted to a specific state are irrelevant to these analyses as they would simply have their spend amount distributed across all 50 states, making that data effectively worthless. To be able to work with data on a state-by-state level, the dataset had to be transformed as it had multiple states on one row if more than one state was targeted. To achieve this, rows for each state were created from the same ad if it targeted multiple states, and the spend amount was evenly distributed between the states.

Looking at raw amounts of ad money spent on each state is moot as there would be an obvious bias to states with higher population. Thus, we use a normalization equation from the U.S. government and data from the census bureau to normalize money spent in each state with its respective population.

To ascertain the nature of missingness in the original dataset, the Segments column was chosen as there is no explanation from Snapchat about why this particular column would be missing. It would not seem plausible at first glance that the data is This column is "advertiser-specific data used such as Snap Audience Match or Lookalike audiences." Permutation tests were run with the first 15 columns of the dataset to see if there was any relation between the missingness seen in 1 Segments and the total variation distance of that particular column. It was determined with = 0.05 that Segments is missing at random dependent on the CandidateBallotInformation column (p = 0.02). This makes sense as ads specifically supporting a candidate for political office would likely have data on individuals already, which would be in Segments. In contrast, the missingness of segments is likely not influenced by Impressions (p = 0.87) so the amount of impressions likely has no effect on if Segments is missing. Note: There is no missing data relevant to ad targeting, as if there is no data in a column related to targeting it simply means that the ad was not targeted at that level. Finally, it was observed through geospatial plotting that Vermont seemed to have an abnormally high amount of ad dollars spent relative to its population. To further look into this, a question was posed of whether or not Vermont is disproportionately advertised to, relative to its population and other states. Specifically:

• H0: The null hypothesis is that Vermont does not have an unusually high amount of money spent in ads targeted to it; any percieved abnormality is due to random chance.

• Ha: The alternative hypothesis says that there is indeed a disproportionately high amount of money spent in advertising to Vermont.

The test statistic used was the normalized amount of money spent on Snapchat political ads in Vermont. For the hypothesis test, the column containing normalized amounts spent was repeatedly shuffled randomly, and the amount that was spent on Vermont in that particular random simulation was recorded. After all simulations were done, the likelihood of seeing the observed values in the set of simulated values was calculated. With = 0.05 the null hypothesis was rejected (p = 0.02). While this does not mean the alternative hypothesis can be accepted, it does mean that the distribution of ad dollars to Vermont is not wholly random. What percentage of each column is missing? It turns out some fields appear to be mandatory with 0% missing, while others are missing quite frequently. This is inline with the readme file.

Cleaning and EDA

[7]: sc.isnull().mean() * 100 We now look at ads with respect to the people posting them. The raw spending has a predictable linear relationship while the normalized spending is relatively more constant.

[39]: df.sort_values(by='SpendState_norm',ascending=False).head()

[

Assessment of Missingness

Here we perform a permutation test to determine if the missingess of Segments is dependent on the first 15 columns of the data. = 0.05 With = 0.05, we can say that Segments is missing dependent on the CandidateBallotInformation column (p = 0.02). This makes sense as ads specifically supporting a candidate for political office would likely have data on individuals already, which would be in Segments. In contrast, the missingness of segments is likely not influenced by Impressions (p = 0.87) so the amount of impressions likely has no effect on if Segments is missing.

[

Hypothesis Test / Permutation Test

Is Vermont disproportionately targeted by Snapchat political ads, in terms of money spent advertising?

• H0: The null hypothesis is that Vermont does not have an unusually high amount of money spent in ads targeted to it; any percieved abnormality is due to random chance.

• Ha: The alternative hypothesis says that there is indeed a disproportionately high amount of money spent in advertising to Vermont.

Our test statistic will be the total amount of money (normalized) spent on Snapchat political ads targeted to Vermont from 2019-2020. = 0.05

We will use the normalized data, because shuffling the raw amounts of money spent in each state is biased by the state's population. Using the normalized data eliminates this issue. We can see from the graph above that the observed result is seen very few times in the data we generated.

With = 0.05 the null hypothesis is rejected (p = 0.02). While this does not mean the alternative hypothesis can be accepted, it does mean that the distribution of ad dollars to Vermont is not wholly random.

  [13]: (sns.countplot(region['OrganizationName']).set_title('Number of Ads by␣ →Organization')) [13]: Text(0.5, 1.0, 'Number of Ads by Organization') [14]: region['OrganizationName'].describe() , dtype: object [37]: region.groupby('OrganizationName')['Spend'].mean().sort_values(ascending=False).

  , dtype: float64 [16]: plt.figure(figsize=(10,10)) sns.boxplot(region['Spend'],region['OrganizationName']).set_title('Spending by␣ →Organization') [16]: Text(0.5, 1.0, 'Spending by Organization') [17]: region.pivot_table( index='Regions (Included)', columns='OrganizationName', values='Spend', aggfunc='sum', fill_value=0 ).plot(figsize=(20,10),title='Company Ad Spending by Region') plt.axis('off') [17]: (0.0, 77.0, -17492.350000000002, 367339.35) [18]: region.pivot_table( index='StartDate', columns='Regions (Included)', values='Spend', aggfunc='sum', fill_value=0 ).plot(title='Spending in regions over time',legend=False,figsize=(15,10)) [18]: <matplotlib.axes._subplots.AxesSubplot at 0x22fda30b348>

[

  582]: stats = [] for x in range(5000): shuffled_col = ( #shuffle the amounts df['SpendState_norm'] .sample(replace=False, frac=1) .reset_index(drop=True) ) stats.append(shuffled_col[list(df.index).index('Vermont')]) [583]: np.count_nonzero(stats >= obs)/len(stats) #p-value is less than significance␣ →level [583]: 0.0204 [584]: pd.Series(stats).hist(bins = 20) plt.scatter(obs, 0, color='red', s=30);

pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns import folium import plotly.graph_objects as go from forex_python.converter import

  #convert non-USD currencies into USD sc.loc[(sc['Currency Code'] != 'USD'),'Spend'] = converted sc = sc.drop('Currency Code', axis=1)

	3			NaN		NaN
	4			NaN		NaN
					Interests OsType \
	0 Automotive Enthusiasts,Film & TV Fans,Movie Th…	NaN
	1	Political News Watchers,ZZ_Deprecated_1	NaN
	2 Adventure Seekers,Hipsters & Trendsetters,Conc…	NaN
	3				NaN	NaN
	4				NaN	NaN
		Segments Language AdvancedDemographics \
	0 Provided by Advertiser	ar	NaN
	1		NaN	en	NaN
	2		NaN	NaN	NaN
	3 Provided by Advertiser	NaN	NaN
	4		NaN	en	NaN
		Targeting Connection Type Targeting Carrier (ISP) \
	0		NaN		NaN
	1		NaN		NaN
	2		NaN		NaN
	3		NaN		NaN
	4	Impressions	NaN	StartDate	NaN	EndDate \
		1183287 2019-09-27 12:29:18+00:00 2019-10-05 14:00:00+00:00
		190847 2019-03-20 13:00:00+00:00 2019-04-04 03:59:59+00:00 CreativeProperties
	0	84687140 2019-10-23 13:00:00+00:00 2019-11-16 07:59:59+00:00 web_view_url:https://www.uaenec.ae/
	2555940 2019-09-30 14:00:00+00:00 2020-06-29 03:59:00+00:00 1 web_view_url:https://www.benjerry.com/values/i…
	2	323890 2019-06-03 07:00:00+00:00 2019-09-04 06:59:59+00:00 NaN
	3 web_view_url:https://www.thetruth.com/o/articl…
	OrganizationName 4 web_view_url:https://plancanada.ca/ChangeTheBi…	BillingAddress \
		Around The Clock Business Central Tower A, Office 2304A, Dubai …
	Unilever US -360i [5 rows x 33 columns]		32 Avenue of the Americas,New York,10013,US
		Mediavest Spark 375 Hudson Street	Attention: Mailroom,New …
		Assembly	711 3rd Ave, New York, NY 10017,new york city…
		The Aber Group	CurrencyRates 202-120 Eglinton Avenue East,Toronto,M4P1E2,CA
	c = CurrencyRates() CandidateBallotInformation %matplotlib inline NaN %config InlineBackend.figure_format = 'retina' # Higher resolution figures PayingAdvertiserName … \ Federal National Council … NaN Ben & Jerry's …
	NaN Recreational Equipment, Inc. … [5]: df19 = pd.read_csv('2019.csv') NaN truth … df20 = pd.read_csv('2020.csv') #concatenate both years together NaN Plan International Canada …
	sc = pd.concat([df19,df20],ignore_index=True) Location Categories (Included) Location Categories (Excluded) \
				NaN		NaN
				NaN		NaN
				NaN		NaN

[4]: import matplotlib.#convert dates to datetime sc['StartDate'] = pd.to_datetime(sc['StartDate']) sc['EndDate'] = pd.to_datetime(sc['EndDate']) converted = sc[sc['Currency Code'] != 'USD'].apply(lambda row: c. →convert(row['Currency Code'], 'USD', int(row['Spend'])), axis=1)

[36]: sc.head() [36]: ADID \ 299815fc312c9ac3558d5d03b22909f02b3583727333fb… db157a823c6190b460ebdcda8c0346814592b7c107ba58… 7033c3858de78f6e1f7dc5f47d1e5288df1a631e342200… b04be410e42e36b34db7f2bf6eb1447ba2a6757b4cc513… 9e6e16e5a7bbebe495bd8725017725e1c6be52329a84a8… CreativeUrl Spend \ https://www.snap.com/political-ads/asset/9e88f… 4187.000000 https://www.snap.com/political-ads/asset/4a68a… 1576.000000 https://www.snap.com/political-ads/asset/6d820… 99361.000000 https://www.snap.com/political-ads/asset/212b0… 10360.000000 https://www.snap.com/political-ads/asset/71e1a… 246.137571


Cyril Gorlla

Predicting Political Ad Spending on Snapchat June 8, 2020 1 Predicting Political Ad Spending on Snapchat

Introduction

The Snapchat ads dataset contains political ad data for ads on Snapchat, one of the largest social media networks in the world. A key feature of the dataset is how much money an organization spends on a particular ad, found in the Spend column. It is reasonable to assume that this amount varies based on certain factors, but can we use those factors to figure out how much is spent on an ad? We can explore this by predicting ad spending through machine learning. Specifically, we can utilize a regression model based on other features of the dataset to predict how much money will be spent on a particular ad. That is, we can use other columns of the dataset to predict the Spend column, our target variable, and we can evaluate our model's performance with R 2 , or goodness of fit, so we can ascertain how well our model is replicating the outcomes observed in the d ata. R 2 is useful in telling us how effectively o ur m odel u nderstands t he p atterns i n t he o riginal data.

Baseline Model

For our intial model, we choose to include six features: Impressions (quantitative), StartMonth (Ordinal), StartDay (Ordinal), EndMonth (Ordinal), EndDay (Ordinal), and PayingAdvertiserName (Nominal). The date-related features were chosen as there may be a correlation between the amount spent and what day or month the ad started or ended. The number of impressions is important as an ad with more impressions likely had a higher budget behind it. Lastly, the advertiser name (which was converted into one-hot encoding) may also be useful as certain advertisers may tend to spend more. We have a total of one quantitative, four ordinal, and one nominal feature(s). With this linear regression model, we achieve a R 2 of .66. This essentially means our model explains 66% of the variation in the original data. There is still a large portion of the data that the model is not understanding, so to speak.

Final Model

We can improve our model by engineering two new features. Specifically, w e c an s tandardize the number of impressions by z-scoring, z = (x-mean)/stddev. This yields data with a mean of 0 and a standard deviation of 1, better allowing us to see deviations from the mean number of impressions. Second, we can utilize Pandas' datetime functionality to calculate the difference in time from when 1 the ad ended to when it started, giving us the total duration of the ad. It's reasonable to assume that the longer the duration of the ad, the more was spent on it, so this should be a useful feature in our model. We remove the other date features in favor of this feature. After trying various other regression models, it was determined that linear regression was still the best in terms of R 2 , so this was chosen to be the final model. GridSearchCV was used to determine the optimal parameters for the linear regression model, these were fit_intercept = True and normalize = False. The other parameters of the model are unrelated to the actual model output. These were the default values for the model, so they were left unchanged. Our final model yielded a R 2 of .84, a large improvement from our previous model. This indicates that the new model replicates the outcomes of the observed data much better.

Fairness Evaluation

We now wish to ascertain how well our model performs on certain portions of the data. Specifically, on those ads with low amounts of impressions. We define "low" as slightly lower than the 25th percentile, 18,000 impressions. To determine how fair our model is to ads with lower impressions vs. regular and higher impressions, we separate the dataset into two, one with ads with low impressions as defined above and one with the rest of the ads. We then see how well the model performs on various permutations of each subset to get a clearer picture of its fairness to ads with low vs other amounts of impressions. We use α = 0.1 with the null hypothesis that the model treats both the same in terms of R 2 and the alternative hypothesis being that it treats ads with low impressions more poorly with a lower R 2 . The model has an average R 2 of .45 on the low dataset but .72 on the rest of the data. Using the Kolmogorov-Smirnov test, we can compare the distributions of the scores of the model for both subsets of the data. We have an extremely small p-value of 3.65 * 10 -153 , so we reject the null hypothesis and determine that the model is likely unfair in that it performs worse on ads with lower impressions.

Baseline Model

  EndDate']).dt.hour -pd.to_datetime(x['StartDate']).

	])
	[ ]: #currency was converted and months/days were added in previous project sc = pd.read_csv('sc.csv').drop('Unnamed: 0',axis=1) #we drop null values, the only null values are in EndMonth/Day def get_hours(x): [461]: improved[['Impressions']].describe() #what if we looked at ads with low impressions, below the 25%? df = pd.to_datetime(x['→dt.hour base =␣ →sc[['Impressions','StartMonth','StartDay','EndMonth','EndDay','PayingAdvertiserName','Spend' →dropna() #select the columns we want to use [ ]: # Numeric columns and associated transformers return pd.DataFrame(df) [461]: Impressions dates = ['StartDate','EndDate'] count 4.184000e+03 date_transformer = Pipeline(steps=[ mean 9.269965e+05 ('duration', FunctionTransformer(get_hours)) std 5.490533e+06 #time duration min 1.000000e+00 ]) 25% 1.851525e+04 num_feat = ['Impressions'] num_transformer = Pipeline(steps=[ ('passthrough', FunctionTransformer(lambda x:x)) # passthrough ]) 50% 1.017510e+05 # Categorical columns and associated transformers 75% 4.533338e+05 cat_feat = ['PayingAdvertiserName'] cat_transformer = Pipeline(steps=[ max 2.349018e+08
	# Categorical columns and associated transformers cat_feat = ['StartMonth','StartDay','EndMonth','EndDay','PayingAdvertiserName'] ('intenc', OrdinalEncoder()), # converts to int ('onehot', OneHotEncoder()) [536]: improved['Binarized'] = Binarizer(threshold=18000). # output from Ordinal becomes input to␣ →fit_transform(improved[['Impressions']])
	cat_transformer = Pipeline(steps=[ ('intenc', OrdinalEncoder()), [555]: improved.head(10)	# converts to int
	('onehot', OneHotEncoder()) →OneHot Impressions StartDate	# output from Ordinal becomes input to␣ EndDate \
	0 ]) 1	1183287 2019/09/27 12:29:18Z 2019/10/05 14:00:00Z 190847 2019/03/20 13:00:00Z 2019/04/04 03:59:59Z
	2 # preprocessing pipeline (put them together) 84687140 2019/10/23 13:00:00Z 2019/11/16 07:59:59Z pl2 = Pipeline(steps=[('regressor', LinearRegression())]) 3 2555940 2019/09/30 14:00:00Z 2020/06/29 03:59:00Z preproc = ColumnTransformer(transformers=[('num', num_transformer, num_feat),␣ 4 323890 2019/06/03 07:00:00Z 2019/09/04 06:59:59Z →('cat', cat_transformer, cat_feat)]) [ ]: #select features for new model 5 3231 2019/11/26 00:05:10Z 2019/11/26 23:00:00Z
	improved =␣ 6 2762599 2019/11/12 13:11:17Z 2019/11/18 23:59:59Z pl = Pipeline(steps=[('regressor',LinearRegression())]) →sc[['Impressions','StartDate','EndDate','PayingAdvertiserName','Spend']]. 7 8779 2019/09/13 09:32:01Z 2019/09/14 09:32:01Z
	→dropna() 2585 2019/12/13 00:29:58Z 2020/01/01 04:59:59Z [ ]: X = preproc.fit_transform(base.drop('Spend', axis=1)) #process the dataset 8 [420]: #get parameters to optimize y = base.Spend 9 50946 2019/10/17 20:09:20Z 2019/11/06 00:00:00Z
	pl2.get_params().keys() PayingAdvertiserName [437]: score = [] 0 Federal National Council for x in range(100): [420]: dict_keys(['memory', 'steps', 'verbose', 'regressor', 'regressor__copy_X', Spend Binarized 4187.000000 1 1 Ben & Jerry's 1576.000000 1 X_train, X_test, y_train, y_test = train_test_split(X, y) 'regressor__fit_intercept', 'regressor__n_jobs', 'regressor__normalize']) 2 Recreational Equipment, Inc. 99361.000000 1 pl.fit(X_train, y_train) 3 truth 10360.000000 1 score.append(pl.score(X_test, y_test)) [423]: params = {'regressor__fit_intercept': [True, False], 'regressor__normalize':␣ 4 Plan International Canada 260.219623 1 print(np.mean(score)) #average R^2 →[True, False]} 5 HOPE not hate Charitable Trust 7.760268 0
	grids = GridSearchCV(pl2, param_grid=params, cv=5) 6 The Labour Party 6466.890193 0.6611158867619407 7 NCDHD 23.000000	1 0
	[424]: X_tr, X_ts, y_tr, y_ts = train_test_split(X, y) 8 Warren for President 48.000000 3.0.2 Final Model grids.fit(X_tr, y_tr) 9 ACRONYM 365.000000	0 1
	[ ]: # Numeric columns and associated transformers
	num_feat = ['Impressions'] [424]: GridSearchCV(cv=5,
	num_transformer = Pipeline(steps=[ estimator=Pipeline(steps=[('regressor', LinearRegression())]),
		('scaler', StandardScaler()) param_grid={'regressor__fit_intercept': [True, False], # z-scale impressions

→OneHot ]) # preprocessing pipeline (put them together) preproc2 = ColumnTransformer(transformers=[('num', num_transformer, num_feat),␣ →('dates',date_transformer,dates), ('cat', cat_transformer, cat_feat)]) [538]: #separate data low = df[improved['Binarized'] == 0] rest = df[improved['Binarized'] == 1]

Code [ ]: import matplotlib.pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns from sklearn import * from sklearn.preprocessing import * from sklearn.linear_model import * from sklearn.metrics import * from sklearn.model_selection import * from sklearn.pipeline import * from sklearn.compose import * import scipy.stats %matplotlib inline %config InlineBackend.figure_format = 'retina' # Higher resolution figures

[544]: scipy.stats.ks_2samp(score_low,score_rest) #very low P-val, dist. are not␣ →similar [544]: Ks_2sampResult(statistic=0.832, pvalue=3.6552424432544507e-153) 4 References ing, vol. 9, no. 3, pp. 90-95, 2007• Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011


Jérôme Cortet
email: jerome.cortet@univ-montp3.fr

Annette Gomot-De Vauflerl

Nicole Poinsot-Balaguer\

Lucien Gomot

Christine Texier

Daniel Cluzeau

The use of invertebrate soil f auna in monitoring poilu tant effects

A critical review of biological parameters used to indicate pollutant impact on soi! quality was conducted. These param eters mention some soi! invertebrates. The value of an indicative organism depends on its life expectancy, life style and specific importance. Nematodes, mites, collembolans, enchytraeids, earthworms, isopods and molluscs are good potential biological indica tors. Biological indicators of bioaccumulation and biological indicators of effects (toxicological and ecological) can be distin guished. Bioaccumulation studies are difficult to interpret, as wide variations could be found, depending on taxonomie group, habitat, organ studied, soi) type or even pollutant type. Sorne groups, such as Collembola, require in depth bioaccumulation studies. It is suggested to use a pool of macro-concentrators, including at least some earthworm, isopod and gastropod species. Toxicological indicators have been well studied and their lethal and sublethal pollutant effects are well known. However, studies have focused on only a few species, such as the earthworm Eiseniafoetida or the collembolan Folsomia candida. These studies should be extended to other zoological groups, as well as to several species from the same group, to generate a representative test battery. Exposure biom arkers and physiological change studies should be emphasised, as they act as very early warning systems of contamination. Data are currently lacking on how soil biological processes malfunction due to pollution. We need to explore the links between pollutant effects on soi! fauna and pollutant effects on soi! functioning. Concerning ecological indicators, studies should develop sampling techniques and parameters, which are specific to ecotoxicological goals. Before-after impact contrai procedures should be carried out, to eliminate the background noise of the study site and only evaluate the influence of pollutants. On the other hand, ecological indices, such as taxonomie diversity or richness, should be used carefully especially concerning chronic pollution. Effects of pollut ants on biological cycle studies seem very promising, but need further information on the life history strategies of many species. Furthermore, the pollutant tolerance of rare species should be considered. The different types of biological indicators yield comple mentary information on pollutant effects. They ail need standard procedures. In this context, studies should be extended and diversi fied, and associate bioaccumulation, toxicological and ecological indicators to provide better information on soi! quality.

Ecotoxicology / bioindicators of effects / bioaccumulation / soit ecology / soit quality / soit inv ertebrates / soit fauna / earthworms / microarthropods / mites / collembolans / isopods / enchytraeids / snails / soit pollution Résumé -Utilisation de la faune invertébrée du sol comme bio-indicateur des effets des polluants. Une liste critique des para mètres biologiques utilisés dans certains travaux pour indiquer l'impact des polluants sur la qualité des sols a été établie. Ces para mètres font référence à un ou plusieurs invertébrés du sol. Le rôle d'organisme indicateur dépend de leurs caractéristiques biodémo graphiques, de leur mode de vie, de leur taille spécifique. Les nématodes, acariens, collemboles, enchytréides, vers de terre, isopodes. et gastéropodes sont potentiellement des indicateurs biologiques. Indicateurs biologiques de bioaccumulation et indicateurs biolo giques d'effets (toxicologiques et écologiques) peuvent être distingués. Les études de bioaccumulation sont difficiles à interpréter, car de fortes variations sont observées. Ces variations dépendent du groupe taxonomique étudié, de l'habitat, de l'organe étudié, du type de sol ou bien encore du type de pollution. Certains groupes, comme les collemboles demandent des études plus poussées à ce

INTRODUCTION

Fauna is an important part of soil environments. It is involved in man y aspects of organic matter decomposi tion. partial regulation of microbial activities, nutricnt cycles and crumbly structure. Depending on thcir diet (saprophagous. phytophagous or prcdation). soi! fauna are closely linked both to cach other and to micro organisms, plants and soi! [START_REF] Steen | Soils animais in relation to agricultural prac tices and soi! productivity[END_REF]. They are a vital key to understanding the "oil ccosystem.

Disturbances caused by pollutants in the soi! results in bath qualitative and quantitative changes in fauna. which affect soi! functioning. Some of soil fauna groups can act as hioindicators 1 136 ].

A bioindicator must fulfil the followîng rcquire ments [START_REF] Hopkin | ln situ biological monitoring of pollution in terrestrial and aquatic ecosystems[END_REF]:

play an important role in the functioning of the soil ecosystcrn; be widely distributed. common and easy to sample: be robust: should not be killed by vcry low levels of pollutants;

have measurable responses (pollutant concentra tion in tissues. characteristk biological disturbances in growth and fertil ity or resi�tancc J: � have reproducible responses: should produce simi lar responses to the same lcvels of pollutant exposure in different site�.

ln addition, the selected zoological group!>, should be practical for use in both laboratorv and in situ studies. Laboratory tests provide a simplified. analytical appro ach to obtain inf o rmation on how an individual rcacts and how ils organs or cellular and molecular fonctions are affected. ln situ ecotox icological studies arc more complicated than lahoratory tests, but are hettcr adap-ted to environmental management as they examine the effects and consequences of pollutants in the ecosystem.

Several zoological groups can be used: Soi! microfauna (smaller than 0.02 mm). These are primarily Protozoa and Nematoda living in the water free soi! compartments. They can be used to study soil ecotoxicology. Tests using these organisms are similar to th ose used in aquatic tox icology [START_REF] Van Straalcn | inverte brate, and micrn-organisms[END_REF]. The�e organ isms have a high hioaccumulation potential 111 J.

Microcosm tests are currently hcing designed f or several Proto;:oa (Cercomonas, Acant/l(lmoeha and Col p oda ge11era) [START_REF] Ekelund | Etl ects of pesti cides on PrntoLoa[END_REF]. Toxicity te�ts in artificial soils using the nematodc Plecrus acw1ti11mus have provided addi tional perspectives for studics of soi] contaminants [START_REF] Kammenga | A toxicity test in artificial soi! based on the life history strategy of the nematode Plectus acuminatus[END_REF]. In situ studîe� using nematode� have yielded several maturity indices which providc data on the soit functinn. These indices could be uscd to monitor the soi! ecology as thcy ret1ect changes in soi! condition 1130. 1311.

-Soit mesofauna (from 0.02 to 4 mm). Enehytraeids (oligochaete annelids) are eommon in most soils (pri marily in the first 5 cm or soil) and play an important role in decomposîtîon and humification processes. They are general!y micmphagous and capable of rcus ing othcr animais (earthworms or microarthropodsJ excrcments [41. Few ecotoxicological studic� have been conducted on them. but initi7il reports. particu larly ex situ studics have been promising [ 121, l50J. Springtaib (aptcrygotc insects) and acarids (Arach nida) are the rnost common soi! arthropods [START_REF] Coineau | Introduction à l'étude des microarthro podes du sol et de ses annexes[END_REF] and play a rolc in soi! functioning. They are vital indi cators for soi] ecotoxicology.

Soil macrofauna (larger than 4 mm). Snails and slugs (terre striai gastropod molluscs) mnve along the soi! surface eating plants and dirL hut part of their bio-logical cycle (rest, shelter, egg-laying, embryo devel opment, hibernation) takes place in the soil, from which they absorb nutrients and contaminants. They have a wide range, are easy to sample, have a high bio accumulation capacity and a phytophagous or saproph agous diet which make them very good bioindicators [8]. Earthworms (oligochaete annelids) are also good indicators, as almost every soil contains at least one earthworm species [START_REF] Bouché | Lombriciens de France. Écologie et systématique[END_REF]. Their range varies depending on depth: epigeous, endogeous and anecic worms can be found in temperate zones [116]. In addition, they migrate over short distances [START_REF] Hopkin | Ecophysiology of Metals in Terrestrial Invertebrates[END_REF]. They are both resis tant and sensitive. Isopods (crustacean arthropods) are particularly interesting due to their capacity to accu mulate metals and their abundance in some soils [START_REF] Hopkin | Ecophysiology of Metals in Terrestrial Invertebrates[END_REF]. They are detritivore or microphytophagous but strongly linked to humid environments, which could limit their in situ utilisation. Holometabolous insects are often only present in the soil during a particular phase in their development and are generally c:onsid ered to be poor accumulators. However, some studies have been undertaken on the beetle Oxythyrea funesta (Cetonidae) for laboratory use [128] and xylophagous beetles could be used as bioindicators of forest quality [START_REF] Andersen | Lead and cadmium content in earth worms (Lumbricidae) from sewage sludge amended arable soil[END_REF]72,[START_REF] Brustel | Les coléoptères saproxyliques, bioindica teurs de la qualité des milieux forestiers. Cas des forêts feuillues métropolitaines en plaines et collines[END_REF]. In addition, a laboratory test has been standardised using staphylinid adults (Aleochara bilin eata) in agriculture [START_REF] Sams0e-Petersen | Laboratory method for testing side-effects of pesticides on the rove beetle Aleochara bilineata adults[END_REF][START_REF] Sams0e-Petersen | Effects of 45 insecticides, acari cides and molluscicides on the rove beetle Aleochara bilineata (Col.: Staphylinidae) in the laboratory[END_REF].

Several works on the use of soil fauna in soi! eco toxicology have already been published [START_REF] Greig-Smith | Ecotoxicology of Earthv,orms[END_REF][START_REF] Kula | Development, improvement and standardization of test systems for assessing sublethal effects of chemicals on fauna in the soi] ecosystem[END_REF]193,195,200] and are published still.

The goal of the present work is to identify the most significant results obtained for each faunal group in terms of soil monitoring, and evaluate how suitable a group might be used as bioindicator, with a particular focus on expected measurable responses.

We also suggest new requirements and new study directions, that should be carried out.

For this purpose, we will follow Echaubard [50], who makes a distinction between:

-biological indicators of bioaccumulation; -biological indicators of effects, including toxicological and ecological bioindicators.

Biomarkers reflect either an individual biological reaction or lower level reactions (cellular, molecular, genetic) and are thus toxicological indicators of effects. However, they cannot always accurately indi cate a genuinely toxic effect, at the individual or the upper levels [START_REF] Krogh | Microarthropods as bio-indicators, a study of disturbed populations[END_REF]202].

BIOINDICATORS OF BIOACCUMULATION

Bioaccumulation occurs when a substance present in the biotope penetrates into an organism where it is stored in various forms. Bioaccumulation of heavy metals by terrestrial organisms has been well studied. It is a very important phenomenon which provides information on the bioavailability of trace elements which often can lead to biomagnification of pollutants during transfers along the food chain. The same pro cess occurs for some organic compounds, particularly pesticides. However, no standard procedure exists to measure bioaccumulation [ 137].

It should be noted that there is no direct relationship between bioconcentration of a pollutant and intoxica tion [START_REF] Van Straalcn | inverte brate, and micrn-organisms[END_REF]. It is thus necessary to study not only the internai concentration but also the effects of pollutants on organisms.

Concentrations of pollutants are measured using var ious chemical extraction techniques followed by gas chromatography [ 122] or atomic absorption spectro photometry analysis [START_REF] Terhivuo | Pb uptake by ecologically dissimilar earthworm (Lum bricidae) species near a Iead smelter in south Finland[END_REF]196 ]. Pollutant fluxes can also be calculated by kinetics studies (ingestion and excretion) of pollutants [START_REF] Janssen | Comparison of cadmium kinetics in four soi! arthropod species[END_REF].

Although often associated with toxicological tests [START_REF] Smit | Influence of tempera ture on the regulation and toxicity of zinc in Folsomia candida (collembola)[END_REF]182], few bioaccumulation studies have been made on Collembola [53]. However, Collembola feed partly on fungal mycelia, which are known to concen trate both metals and organic pollutants and radio nucleotides [START_REF] Hopkin | Effects of metal pollutants on decomposi tion processes in terrestrial ecosystems with special reference to fungivorous soi! arthropods[END_REF]. In addition, they are often preyed upon by other arthropods [START_REF] Ernsting | Predation on two specics of surface dwelling collembola. A study with radio isotope lahelled prey[END_REF].

For copper accumulation, Collembola can be ranked second among detritivorous invertebrate right after iso pods [START_REF] Hopkin | Biology of the Springtails (lnsecta: Col lembola)[END_REF]. Hunter et al. [START_REF] Hunter | Ecotoxi cology of copper and cadmium in a contaminated grassland ecosystem. II. Invertebrates[END_REF] found that copper concen tration was 45 times higher in the same species pool at a polluted industrial site than that at an unpolluted site.

However, due to the microarthropods small size, a large number of organisms must be sampled for test ing, which increases the margin of error. ln addition, although interclonal variation in some species (particu larly Folsomia candida) does not influence their use fulness in ecotoxicological tests [START_REF] Crommentuijn | Comparative eco toxicity of cadmium, chlorpyrifos and triphenyltin hydroxide for four clones of the parthogenetic collem bolan Folsomia candida in an artificial soi![END_REF], a wide range of reactions exists between Collembola species. Lupetti et al. [ 122] found considerable differences between DDT and PCB concentrations in seven species sam pled at the same site. Orchesella cincta was found to be highly vulnerable to DDT concentrations in its tis sues. These differences can be explained by the excre tion/assimilation ratio and the capacity to eliminate substances from some tissues, which varies between species [START_REF] Hopkin | ln situ biological monitoring of pollution in terrestrial and aquatic ecosystems[END_REF].

In vivo metal contents in earthworms and isopods vary depending on species or metal type. Differences in concentrations have been observed:

-between species from the same taxa (earthworms: [START_REF] Ireland | Heavy metal binding properties of earth worm chloragosomes[END_REF][START_REF] Ash | Lead. cadmium, copper and iron in earthworms from roa<lsicl e sites[END_REF][START_REF] Terhivuo | Pb uptake by ecologically dissimilar earthworm (Lum bricidae) species near a Iead smelter in south Finland[END_REF], isopods: [205, [START_REF] Joosse | Iron, manganese and zinc inputs in soi! and litter near a blast-furnace plant and the effects on the respiration of woodlice[END_REF][START_REF] Hopkin | Heavy metals in isopods from the supra-littoral zone on the southem shore of the Severn Estuary, U.K[END_REF]); -between different aged individuals from the same species ( earthworms: [21], isopods: [START_REF] Hopkin | The distribution of zinc, cadmium, lead and copper within the woodlouse Onis cus asellus (Crustacea, Isopoda)[END_REF] ); -between seasons (earthworms: [92, 2], isopods: [204]).

Bioaccumulation of metals depends on:

-the element (isopods: [START_REF] Hopkin | The wood louse Porcellio scaber as a biological indicator of zinc, cadmium, lead and copper pollution[END_REF]); -its concentration in the environment [6 7, 73]; -the physical and chemical soi! conditions (earthworm: [ 123 ]). -------------------------------------------------------�--- In addition, gastropods can be used to study metal transfers into the ecosystem through the food chain [START_REF] Beeby | The role of Helix aspersa as a major herbi vore in the transfer of lead through a polluted ecosys tem[END_REF]. As they are eaten by many different mammals, they are probably involved in the biomagnification of metals [START_REF] Dallinger | Strategies of metals detoxification in ter restrial invertebrates[END_REF]206].

Bioaccumulation of pesticides by terrestrial gastro pods has not yet been well studied, but atrazine and azinphosmethyl do not appear to be accumulated through the food chain [START_REF] Schuytema | Effects of dietary exposure to forest pesticides on the brown garden snail Helix aspersa Müller[END_REF].

Current studies on bioc:oncentration and bioaccumu lation have provided information about the bioavail ability of contaminants found in the surrounding environment and contained in food. To compare results from different analyses performed on animais sampled in nature or on animais which have been used for con tamination experiments, bioconcentration (BCF) or bioaccumulation (BAF) factors can be calculated using the following ratio: concentration of pollutant in animal (µg•g-1 dry weight) co i:i centration of p ollutan � in the environment or 111 food (µg-g-dry we1ght) Concentration factors are often used for aquatic organisms which are bioindicators of accumulation, but they are slightly more difficult to use with terres trial animais where the environment is more heteroge neous. Furthermore, such ratios are only useful for non-regulated metals and persistent pesticides. Com paring metal concentrations (Cd, Pb, Zn) in substrates and food to study metal accumulation in isopods, collembolans, diplopods (]eaf Iitter) and slugs (soil) yielded highly different results (from 14 to 750 mg-kg-1 of Cd and from 490 to 22 000 mg-kg-1 of Zn) when they were experimentally soaked in the same solutions [73]. This experiment illustrates the problems inherent in between species and between habitat comparisons. Nevertheless, the overall data indicate that inverte brates behave as either macro-concentrators, micro concentrators or de-concentrators [ 13,[START_REF] Dallinger | Strategies of metals detoxification in ter restrial invertebrates[END_REF]. Macro concentrator species are the ones that provide the most direct information about environmental contamination. It thus seems wise to use a pool of macro-concentra tors: for example, isopods (e.g. Oniscus asellus) and pulmonate gastropods (e.g. Helix aspersa) have pro vided information about the degree of metal contami nation (Cu, Cd, Pb, Zn) in sites located near English foundries (table Ill, [START_REF] Hopkin | Ecophysiology of Metals in Terrestrial Invertebrates[END_REF]). This author also noted that bioaccumulation varies depending on the organ and is particularly high in the digestive gland for Cd, Zn and Pb. Earthworms can have very high and significant BCFs for organic pollutants, such as chlorophenols (particularly, pentachlorophenol), which vary depend ing on soil type (476 and 956 in two agricultural soils for Lumbricus rube!lus [181]).

Until recently, the indications provided by contami nant concentration analysis in animais sampled in the field have not yielded any information about exposure time. To compensate for this, sentine) animals (from standardised cultures) are used either in microcosms, mesocosms or on site to evaluate specific exposure times. Copper accumulation was thus studied in Lum bricus rubellus [126] and in Eisenia andrei [START_REF] Svendsen | Relevance and applicability of a simple earthworm biomarker of copper exposure. I. Links of ecological effects in a laboratory study with Eisenia andrei[END_REF][START_REF] Svendsen | Relevance and applicability of a simple earthworm biomarker of copper exposure. Il. Validation and applicability under field conditions in a mesocosm experiment with Lumbricus rubellus[END_REF]. Exposing cultured snails (Helix aspersa) for 30 d at distances from 1 to 200 m away from a busy motorway (Nancy-Metz: 40 000 vehicles per day) demonstrated that lead pollution remains high at distances up to 40 m away and causes lead accumulation in the snail viscera ( 16-22 mg-kg-1 dry weight; Pihan and Gomot, unpubl.).

Controlling breeding of some main macro-concen trator soil fauna species should make it possible to study pollution over time and define its fluctuations.

BIOINDICATORS OF EFFECTS

Data based on the biological responses of organisms to pollutant is used to link pollutant toxicity with eco logical consequences. Several biological levels exist, from the molecule to the community level, which affect the degree of toxicological or ecological reac tions.

Table III. Heavy metal concentrations in the bodies of Helix aspersa and Oniscus asel/us from an uncontaminated site and a contaminated (smelting work) in England (from [START_REF] Hopkin | Ecophysiology of Metals in Terrestrial Invertebrates[END_REF]).

Species

Helix aspersa

(garden s nai 1 Among the biological indicators used (which pro vide data on reaction to pollutant exposure). two main categories can be found:

-toxicological indicators (molecules. cells. organ isms. groups of organisms). A close rclationship exists between intoxication and an observed reaction. This data helps reveal phenomcna occurring in the upper levcls but has little ccological value. These indicators can act as early warning systems of contamination whose effects are possibly revcrsible 1501:

-ecological indicators. Possible changes in popula tion. community or population group structure as shown by ccological analysis indicate changes occur ring but provide no information on causes of distur bances.

Concerning methodologies. except for mathematical models. Iwo approaches can be used [ 14 71:

-experimental models. with single spccies and mul tispecies tests and bioassays:

-in situ indicators. based on field studies and biota which live or are introduced in these sites.

However. the difference bctween these two approaches is often arbitrary. For cxample. tïeld obser vations need an cxperimental part: and although bio assays are linked to laboratory tests because they are often standardised. they could be considered to be a particular form of in situ indicator as they use polluted substrates rather than pure substances. These consider ations concur with Forbes and Forbes [63 J. who distin guish between field indicators and experimental tests, but prcfer the more gencral concept of ecotoxicity tests. where observations or cxperimcntal manipula tions are used to evaluatc the fate or cf f ects of chemical pollutants. without any refercnce to the biological organisation levcl.

Toxicological indicators

Testing lethal and sublethal effects

The first descriptor used to evaluate the impact of a pollutant is acute toxicity. Mortality in a population exposed to xenobiotics is a severe reaction. Extreme toxicity is expressed as a proportion or a concentration which leads to the death of 50 •Ir, of the tested indivi duals. Lethal dose or LD 50 , cxprcssed in mg•kg -1 of body weight, depends on the animal. LC 50 indicatcs a lethal concentration exprcssed in mg-kg-1 of substrate. It is probably more reasonable to speak of LC 'io for soi! fauna.

Studies on sublethal effects generally calculate EC 50 or the effective concentration for a biological process which leads to a 50 '7r inhibition or decrease [ 143]. LOEC (lowest observed effect concentration) is the lowest concentration yielding a significant differencc for the studied parametcr compared to the control. Occasionally the tenn NOEC (no observed cffect concentration) is used in toxicity tcsting. NOEC and LOEC are not always considered to be good criteria as they requirc using a large range of concentrations to obtain a value close to reality. LC 50 and EC 50 are more accurate and have a better ecolo gical value as they dircctly reflcct fondamental changes in stages of an organisms biological processes (growth, reproduction). ln addition. some specics appear to be more sensi tive when lethal concentration is tested. while others are more sensitive whcn sublethal concentration is testcd. Crommcntuijn et al. f 321 have suggcsted using a new index to harmonise results. which they called the sublcthal scnsiti,ity index (SSI). It is calculated using the ratio LC 50 /EC III or LC, 1 /NOEC when sublethal concentration cannot hc measurecl.

Rccent studies uscd the ILL (incipient lethal lcvel), which is the concentration of toxicant below which 50 rx-of the exposed organisms can be expected to survive for an indefinitc period f 164 ]. lt normalises the toxic rcsponse so that the assumption can be made that all comparisons of toxicity within and bctween chcmi cals and spccies of organisms arc macle at a steady state with respect to the dose response curve [ 114].

lJ ntil now. toxicity tests for tcrrestrial organisms have been bascd on single species to measurc the impact of toxic products on survival. growth and repro duction. Although three tests have been standardised (two for eaithworms. one for Collembola). experimen tal data arc available ror most soi! falma groups. VI). These organisrns can survive high levels of heavy rnetals in their food. However. growth and reproduction are inhibited differently depending on the metal and spe c1es.

Schuyterna et al. [ l 57

] studied toxicity of pesticides on snails and identified four substance groups accord ing to their toxicity through ingestion mixed with food. In group 1. acephate. atrazine, glyphosate. hexazinone and piclorarn were not lethal for snails at 5 000 mg-kg 1 • The death rate was 13 7c for group 2 ( carbaryl) and 75 % for group 3 (fenitrothion and rnethyl parathion). These high relative differences in pesticide toxicity indicate the need for additional studies to evaluate the risks involved in pesticide treatments.

Collernbola

The Collembola Folsomia candida is the most fre quently used microarthropod both for sublethal and lethal testing. Testing can be performed for organic or metallic pollutants. Sorne exarnples are shown in table VII. F cczndida is currently used as a mode] for comparative studies of new molecules behaviour. Data obtained on this species can also be used for improving testing techniques 1106].

Although one test using F candida has been standar dised. most publications on this species indicate widc differences between test results depcnding on strain. individual age [START_REF] Crommentuijn | Comparative eco toxicity of cadmium, chlorpyrifos and triphenyltin hydroxide for four clones of the parthogenetic collem bolan Folsomia candida in an artificial soi![END_REF] and soit moisturc content [182]. ln addition. this parthenogenic species is easily raised. but does not provide an accurate picture of natural environments. This is why other spccies, such as lso toma viridis are also studied [START_REF] Jepson | Sublethal toxicity test with the springtail lsotoma viridis (Bourlet, 1839) and Folsomia candida (Willem, 1902) (Collembola: Isotomidae)[END_REF]. Tests have also been performed on Orchesella cincw [5. 142. 197]. Onv chiurus annatus [7. 177] and/. notabilis [START_REF] Tranvik | Relative abun dance and resistance traits of two Collembola species under metal stress[END_REF]. Oth. er species should also be �tudied to generate a comple mentary test pool. . reproduc tion is a better pararneter than mortality and provides more accuratc information. This parameter integrates the possible long-terrn effects of a pollutant by demon strating the possible changes which may occur in the species fuwre reproduction capacities.

Isopods

The most frequently used species are Porce!!io scaher [31. 32. 35. 59. 194, 198]. Oniscus ase!lus [31, 1941[31, , Tricl1011i.1cï1s pusill11s [ 1941[31, and P laevis [ 1341. . According to Crommentuijn et al. [START_REF] Crommentuijn | Lethal body concentrations and accumulation pat terns determine time dependent toxicity of cadmium in soil arthropods[END_REF], care must be taken in studies involving isopods. particularly P scaher and O. asell11s. Thcse specics can accumulate high concentrations of pollutants and lethal cffects occur only whcn their accumulation capacities are fully saturated. Exposurc timc to a pollutant plays a kcy role. lsopods generally show low sensitivity in sublethal tesh which increases test duration. Drobne [START_REF] Drobne | Terrestrial lsopods-a good choice for tox icity testing of pollutants in the terrestrial envirnn ment[END_REF] esti mates that growth. reproduction and life cycle studies arc not usually the best for isopods. ln fact, growth rates can vary over a period of several weeks. even for one individual. and females may retain sperm for a long lime before reproducing. Furthermore. lite cycles are relatively long, often more than 6 to 8 months. Howcver, sublethal tests performed with copper on P srnher revealcd neOEative effects on food intakc and individual growth at high concentrations ( 1 000 mg-kg-1 ) while low amounts stimulated juvenile growth rates [59]. ln addition. thesc animais arc easy to raise.

Arachnids

Lethal and sublethal tests were performed on Platynothrus peltifer for copper and dimethoate [START_REF] Van Gestel | Sublethal toxic ity test with the oribatid mite Pl111 _ rnorhnrs pelri j ' er (Koch, 1839) (Oribatida: Acari)[END_REF]. The rcsults have been encouraging and it may be possi ble to use this acarid. although cmTent study methods Work has also been performed on the effect of dimethoate on the predator gamaside Hypoaspis acu leifer [62,78,[START_REF] Krogh | Microarthropods as bio-indicators, a study of disturbed populations[END_REF][START_REF] Krogh | Does a heterogeneous distribution of food or pesticide affect the outcome of toxicity tests with collembola?[END_REF]. These tests have demon strated the importance of examining between species relationships, particularly predator-prey interactions, as test results indicatecl that dimethoate essentially affects the gamaside indirectly due to a clecrease in number of available prey (i.e. the Collembola Folsomia fimetaria).

In summary

Irrespective of the chosen organism, the tests must be replicable and standardised according to:

-species (genetic stability and growth stage); -breeding techniques and contamination method; -experiment duration; -measurement of effects (NOEC, LOEC, EC 50 , etc.). Substrate is a vital element, as an organism's reac tion is influenced by substrate composition, which can interact with pollutants. Every test made with natural, artificial (ISO defined soil) or inert (silica, glass beads) substrate must be double checked by tests using the substrate alone as a standard. To overcome the disadvantages inherent in each type of test, several assays should be run using different zoological groups as well as several species from the same group to create a test battery and an evaluation grid for toxic product hazards. In addition, the most sensitive variables for a given species should not be the only ones examined [START_REF] Kammenga | Reaction norms for life-history traits as the basis for the evaluation of critical effect levels of toxicants[END_REF]. Severa) studies have already been performed using the cadmium model (table VII[): these test results should be numerically evaluated.

Dutch scientists have attempted to design an index which integrates toxicological data for several organ isms: Van Straalen and Denneman [START_REF] Van Straalcn | Ecotoxicologi cal evaluation of soil quality criteria[END_REF] suggested the HC 5 index (hazardous concentration for 5 % of the species) to harmonise and correlate toxicological data based on NOEC for different organisms for a given pollutant. However, as Hopkin [START_REF] Hopkin | Biology of the Springtails (lnsecta: Col lembola)[END_REF] has pointed out, this index was designed using a very small number of organisms and suffers from a Jack of toxicological data for different organism types. In addition, it fluctuates widely depending on the species analysed [5).

Exposure biomarkers and physiological changes

The search for exposure biomarkers which react to pollutants is currently under way, for both plants and animals. The goal is to define the measurable molecu lar and biochemical changes which occur after expo sure to toxic substances [ 41, 50. 111-1131. -specific target molecules (acetylchlolinesterase. DNA. etc.) . Tranvik et al. 1178] focused their study on allozyme polymorphisms in the springtail Orchcse//a bifasciata and found no significant differences between several populations subjected to dif f erent degrees of metallic stress. However. Frati et al. 166] observed differcnt reactions to heavv metals in several loci in tolerant and sensitive O. cincta populations.

Due to their induction bv metals. metallothioneins can be simultaneously respo'nsible for detoxification of trace elements. such as cadmium. and regulation of essential trace elements. They could be used as bio markers for environmental pt;llution. Berger et al. 19] isolated metallothioneins in land snaib (Ariwzta arbus torum) which had been fcd Cd contaminated food (lettuce) and sequenced two isofonns (Mta and Mth) which they compared to the structure of other inver tebrates. Other metallothioneins have been identified for the Collembola O. cincta 181] and the eanhworm Lwnbricus rubellus [START_REF] Stürzenbaum | The identifi cation, cloning and characterization of earthworm met allothionein[END_REF].

Walsh et al.

[203] studied early, sensitive markers for pollution in carthworms. They verified the feasibil- A good exarnple of a physiological effect is the neu rotoxic effect (disturhances in nerve impulse speed) of pollutants on earthworms 143--471. Nevertheless. this in vivo method is rather costly 11()11.

Behavioural studies are essentially ways of quantita tively evaluating the effect of pollutants: e.g. measur ing the effect on hurrowing hehaviour in earthworms exposed to carhamate in microcosms [START_REF] Texier | Étude expérimentale de divers modes d'action de l'aldicarbe sur Aporrectodea caliginosa (Oligochaeta, Lumbricidae)[END_REF]. On the other hand. Collemhola dispersal can be af f ected hy metallic pollution in soils 11601 and some isopods, such as P. laevis, can identify and avoid leaves contam inated with high cadmium sulphate levels [134].

Malfunction of soil biological processes

Few studies have compared the interruption of bio logical process in contaminated soils and soil fauna biological reactions. These changes are difficult to evaluate in the field as soil biological processes are quite complex and involve many different factors, including soil fauna. However, their activity is essen tial for the whole ecosystem and must be maintained for ecosystem survival.

Laboratory studies have attempted to analyse the different biological processes, particularly changes caused by a pollutant on soil fauna.

Salminen et al. [START_REF] Salminen | Regulation of decom poser community structure and decomposition pro cesses in herbicide stressed humus soil[END_REF] have demonstrated that the herbicide terbuthylazine can provoke changes in microarthropods introduced into forest soils by effect ing trophic interactions and thus indirectly disturbing soil functioning. However, the same experiments were performed with PCP and did not yield any significant results [START_REF] Salminen | Decomposer communities in contaminated soi!: is altered community regulation a proper tool in ecological risk assessment of toxi cants?[END_REF]. Komulainen and Mikola [START_REF] Komulainen | Soi! processes as influ enced by heavy metals and the composition of soi! fauna[END_REF] observed a clear reduction in respiration and nitrification in soils polluted by heavy metals, although the role of soi! mesofauna could not be defined. The effects of differ ent pesticides on litter-bag microarthropod coloni sation and maize decomposition in a maize field yield complementary information, as some pesticides strongly effect microarthropods but had no visible effect on litter decomposition [START_REF] Cortet | Impact de produits phytopharmaceutiques sur les microarthropodes du sol en culture de maïs irrigué : approche fonctionnelle par la méthode des sacs de litière[END_REF].

Kula and Rômbke [START_REF] Kula | Evaluation of soil ecotoxicity tests with fonctional endpoints for the risk assessment of plant protection products[END_REF] have compared methods for testing organic matter breakdown in the field (bait lam ina tests, litterbags and litterboxes, minicontainers, cotton strip ). Different criteria have been used: ecolog ical and ecotoxicological (in terms of functional importance for ecosystems, realistic exposure or sensi tivity), practicability (in terms of possibilities of stan dardisation or cost effective) and evaluation (in terms of reproducibility or variation coefficient). Bait lamina test was recommended to study biological activity parameters. The authors pointed out the urgent need for further studies on the litter decomposition parame ter, especially to improve test reproductiveness.

Earthworm activity, which is usually responsible for fragmentation and incorporation of organic malter into the soi!, is modified by pollutants [START_REF] Cook | Effects of some fongicide treatments on earthworm populations and leaf removal in apple orchards[END_REF]. These changes have been measured for Lumbricus terrestris by evalu ating the disappearance of litter or other bait in a Daniel funnel [ 10].

Conclusion

The lethal effects of severe changes ( disappearance of certain species) and particularly the sublethal effects on the most sensitive and important phases (growth and reproduction) in the animal life cycle should be closely studied as they affect species survival. Biomar ker studies will help explain how pollutants internet in organisms, both at cellular and subcellular levels, to understand how toxic substances affect genomes. Stud ies on how soi! processes malfunction should be extended and diversified to involve multi-disciplinary research teams to examine a wide number of para meters and thus better analyse the different factors involved.

Ecological indicators

Severa! studies have attempted to extrapolate labora tory results to the field. Diekkrüger and Rôske [START_REF] Diekkrüger | Modelling the population dynamics of lsotoma notabilis (collembola) on sites of different agricultural usage[END_REF] tried to simulate collembolan population dynamics using Isotoma notabilis in agricultural fields based on data obtained in the laboratory on this species ecophys iology. L0kke [120] also tried to link laboratory exper iments on the dimethoate mode! with experiments using mesocosms with different collembolan species (Folsomia candida, F.fimetaria, /. notabilis).

Although the first results have been encouraging, results obtained in laboratory experiments cannot yet be extrapolated to the field. Spurgeon [START_REF] Spurgeon | Extrapolation of laboratory toxicity results to the field: a case study using the OECD artifi cial soi] earthworm toxicity test[END_REF] noted that a particular earthworm, Eisenia fetida, could live in soils containing much higher concentrations of metals than the LC 50 and EC 50 values found in the laboratory.

Since it is impossible to control ail the different fac tors affecting soi! fauna, bioindicators of ecological effects are used.

Studies to evaluate the in situ impact of different types of pollution on animal groups have been per formed for soil microarthropods and earthworms.

Both synchronie (two similar polluted or unpolluted sites) and diachronie (measurements performed before and after pollution) comparisons must be made. In general, these two approaches are combined: this pro cedure is well known as before-after impact control (BACI) design [START_REF] Jagers Op Akkerhuis | A dose-effect relationship for the effect of deltamethrin on a Linyphiid spider population in winter wheat[END_REF]. In fact, different populations often reveal natural fluctuations which can be due either to standard (climate, photoperiod) or exceptional (severe cold, drought) abiotic factors or to biotic factors (between and within species competition, individual species life cycle). These different factors are like background noise for a study site and can hide the effects of a pollutant. They must be quantified as accu rately as possible.

Comparisons are generally based on two types of criteria:

those involving population density, i.e. species, population or community abundance or biomass;

-those involving population structure, i.e. specific richness or diversity.

Sampling techniques for microarthropods can vary: boring cores, litter-bags [START_REF] Crossley | A litter-bag method for the study of microarthropods inhabiting leaf litter[END_REF] or pitfall traps [74,96]. Results obtained using these techniques are often quite different.

In fact, different studied species often react differ ently to a given pollutant. Frampton et al. [65] demon strated the different reactions of Collembola specie;. subjected to different culture pressures: while Fol.1• 0mia quadrioculata was shown to benefit from inte grated culture systems, the genus LPpidocvrtus reacted better using standard systems.

In addition, apart from the pollutant itself, the pol lutant and the site's history can affect the reactions of a microarthropod cornrnunity. Sahatini et al. [START_REF] Sabatini | Microarthropod communities in soi! treated with atra zine[END_REF] found no significant differences hctwcen microarthropod populations in sites treated with atrazine and untreatecl sites when the study site had been exposcd to the pol lutant for scveral years, as pollution-sensitive specics hacl probably vanishecl prior to the study.

Diversity indices shoulcl be used carcfully: as Ramade l 143] pointed out for the Shannon index, at sublethal doses, the dominant pollution-sensitive spe cies can decreasc, without disappearing. which para cloxically may increasc the index. These indices should only be used in extrerncly polluted sites.

Taxonomie diversity alone is not enough to evaluatc a site•s biodiversity. Coenotic diversity, which expres ses the distribution of taxa within a species. should also be consiclered [ 19].

Cortct [ 271 has demonstrated significant differences between two eu I turc system;. ( standard and integrated) for microarthropod biodiversity in Normandy (France) using an index which evaluated taxonomie diversity, specitïc divcr;.ity, abundance, taxonomie richness and spatial distribution of taxa [START_REF] Cancela Da Fonseca | On the evaluation of spatial diversity of soi] microarthropod communi ties[END_REF].

Normality, in the mathematical scnse of the word, is not a rcliable criteria cither: highly disturbed commu nities can have a log normal distribution, whosc domi nant species or groups are not the samc as those founcl for stressed communities. Hagvar [ 77] i nsists that rare species should also be considered, as they cvolve towards the lower dominance classes if thcv are sensi tive and towards hiOEhcr clominanoe classes ' if thev are tolerant.

� '

Frampton 164] also suggests using an indicative spc cies pool with a knovd1 sensitivity to certain pollutants for field studies. U sing a single species cou Id obscure inter-specics relationships. whilc considering ail the species in a system will not rcveal ef f ects on rare species, which can be more affected than abundant spccics. These conclusions shoulcl be treatecl with cau tion, however. as different statistical analvsis tech niques. such as correspondence analysis. no�v make it possible to evaluate the influence of hiQhlv abundant species compared to rarer specics. � ,

The functional aspect must also be considercd, par ticularly in tcrms of the influence of pollutants on spe cies hiological cycle;.. Howevcr. these biological cycles must fïrst be characterised and the different taxa must be ranked. Siepel 1159] designed an evaluation grid for rnicroarthropod life cycles which differcnti atcd betwecn twelvc dif f erent lite cycle stratcgies on either a trophic or a sexual basis. This is particularly interesting in the field of ecotoxicology. Howevcr. it is difficult to rank specics whose biological cycle is not totally understood. This is wherc lahoratory diYersity studies are vital, particularly for studies of a large numhcr of taxa.

Results analysing the role of mesofauna activity on changes in organic matter arc both highly varied and irregular, retlecting the cnmplexity of decomposition processes which an: only partly dependent on soi! fatma [ 158. 175]. In addition. no studics using the litter bag method havé been able to signitïcantly C(melatc decompOEition of organic malter and microarthropod density.

Currently. there is no standardised method for in situ studies of rnicroarthropods.

Differcnt extraction techniques have been used for earthworms: hand sorting [START_REF] Cluzeau | Relations between earthworms and agricultural practices in the vineyards of Cham pagne. Preliminary results[END_REF]. formaldchycle [START_REF] Raw | Estimating earthworm populations by using formalin[END_REF] and the cthophysical method.

Most earthwonn studics have only examined lethal toxicity of pollutants l:'i-1 -. 55. 117). This is an illustra tion of the difficulty of seriously cstimating the rcal toxicity of differcnt pollutants. as a lot of diffcrences occur hctween study protocols.

During the Sheffield ecotoxicology workshop, scv eral proposais were made to standardise and co-ordi natc certain rcsearch topics 1 :'i 1. 109, 1 181 (tc1h/c IX).

lt should be nnted that field studies primarily use mcthods clcrived f r om ccolog:y. Howevcr. it is vital to devclop spccific methods for ecotoxicology, whose goals arc differenL Stuclics must be made on dif f crent types of organisms to de�cribc species or groups which could be used as bioindicators in the field. Thcsc stud ies should lead to the standardisation of rcsearch pro cedures. In the lonOE run, an evaluation OErid should be designed which i;,cludes many diffe;ent types of organisms and would help creatc a biotic index of soi! quality tu objectively quantify pollution.

CONCLUSION

This paper has reviewecl the main data available about hov. pollution effects soi! fauna invertebrates used as indicator organisms. Therc is no ideal inclicator l 83): howcvcr, enchytracids. earthworms. Col lem bol a, acaricls, isopod-, and pulmonate gastropods have pro vided additional data on the three bioindicator cate gorics (bioacrnmulation. toxicological and ccological consequcnL•cs L Taxa which could be used as bioindicators, in tcrms of rneasurahlc responses. are shown in tah/c X.

Bioaccurnulation measurements provide data on the hioavailability of pollutants in the environment and make it possible to evaluate species sensitivity to xeno hiotics [31 J. The currcnt Jack of standardiscd measurc menl proc,:dures for hioaccumulation will hopcfully Indicators of toxicological or ecological effects pro vide information which should help develop a series of tests which can be used not only with earthworms [START_REF] Bouché | Mise au point et évaluation de la signi tïcation d • une batterie de tests cl• écotoxicologie utilisant les lombriciens[END_REF], but also with other invertebrate species to localise pollutant effects at different levels in the lite cycle (figure 1).

In the near future, the various existing tests and those soon-to-be standardised should provide a means of evaluating the environmental quality of different soils. Microcosm and mesocosm experiments should Ali groups can be used be continued and expanded to species living in differ ent soil layers to design field study methods. Toxicological data from laboratory and field studies should be used to evaluate environmental hazards linked to pollutants. Organism species of ail sizes (microfauna, mesofauna and macrofauna) and diver sity living in the soi] should be used to design micro cosm and mesocosm models reflecting the diversity and the vital trophic relationships which characterise a living soi!. Single species tests are a step towards envi ronmental risk evaluation, but progressively more complicated systems should be designed to analyse interactions between the environment and the creatures which live there.

  3. 1.1.1. NematodesThese are the most common soi] invertebrates. Tests have been performed with Caenorhabditis clegans[42. 141. 187]. but toxic exposure conditions (mainly to Cd) differed too widely to yield correct comparisons of results.Rccently. experiments have been performed with the ncmatode Plcctus acu111i11atus, a bacterivorous species which lives freely in the soi] hut is not associated with plant roots or fungal hyphae. Kammenga et al. [98 J used the juveniles to adults ratio as the test parameter in an artificial soi! (OECD) under optimal conditions for P. arn111inatus population growth in the soi!. Te sts were performed using Cd. Cu. and PCP [98 [.3.1.1.2. Enchytraeid� Few studies have currently been performed (table IV).Howcver. thcy have illustrated the complexity involved in ecotoxicological studies in soils and the need for mcthod standardisation: Rundgren and Augustsson [ 1501 notcd a dccrease in f r agmentation rate for Co gnettio sphagnetoru111 individuals exposed to increas ing concentrations of copper in an artificial soi] (LUFA). and Sjügrcn et al. 1161] noted an increase in fragmentation rate in the samc species in a forest soi!..

28 d

 28 Dier for snails 1Hclixal R'•) + CdCI, _________ ,. __ _ According to Krogh and Petersen 1105]

  Table VIII. EC, 0 values and NOEC for the cffect of cadmium. one of the most toxic among the heavy mctals rolluting the soi!, on the growth. the rerroduction and the survi,al of soil in,ertcbrates. �Jo\vth and cgg.., Cru:-.. .tacca hopoda 1321 Pon-el/io scaba growth Arachnidea Orihatida 1:121 l'iul_ rnorhrus 1wl1i/à cg.i;� Molluscs Gastropoda 1691 lleli.\ osper.-,a a.\/JCr\"a grnwth fi. O\'fh'rSCl ll/U.\ill1Cl growth Current works are examining: -induction of proteins in biotransformation and detoxification systems ( cytochrome P450 . conjugation enzymes. etc.) or more or Jess specific stress proteins (metallothioneins. catalases. etc.):

  of measuring DNA adducts by i:'p post-labelling in L. terrestris exposed to more or Jess strongly polluted soils. Determining DNA adducts was considered to be a good way of detecting genotoxic substances in soils.CytologicaL physiological and behavioural modifi cations can also be considered to be valuable hiomark ers. In a studv of snails whose food was contarninated by Cd for 30 �I. Russel et al. 1151] ohserved hyperplasy and necrosis of the epithelium and a disorganisation of the sperrnatozoid flagella in the ovotestis. although the ovocytes die! not appear to be affected. ln a study of Arion a/er slugs. Marigomez et al. 11251 ohserved sig nificant quantitative modifications in the digestive tube structure and hlood vesse! walls depending on Hg concentration in food and exposure time. Studies on isopods have examined the effects or metals on the digestive gland epithelium and hepatopancreatic reac tions of Porce!lio srnher 149, IOOJ.

  hand extraction (0.25-1 rn 2 ) *30-60 cm; non-destructive extraction; formol extraction (0.25-1 m 2 ); mixed * * * formaldehyde extraction; electric extraction (> 0.25 m 2 ) extraction (0.25-1 m 2 ) *5-10 cm Number/duration of sampling Experiment and > 2 years for remnants 1, 4, 6, 12 months after treatment of pesticides ( 1, 2, 4, 8, 12. 24 months after treatment) Table X. The use of different taxonomie groups as bioindicators. (-), No available studies; -. use unlikely; +, possible use under certain conditions; ++, interesting for use. development of an appropriate methodo logy [137].

•

  Figure l. The differcnt ]evcls of polluwnt acti,ity.

Table I .

 I Lead concentration in carthworms (mg-kg-1 dry wcight) and concentration factor (ratio of Ph c•<lnccntration in worms and in soi!) for thrce common earthwonns species at Tikkurila (soi! heavily polluted hy Ph) and Pornainen (control site) 117-1 1. •a and . significant dif

	signitïcant differences.		f ercncc. s: ns. non
	Spccics	Tikkurila
		Mean± SD	Il
	Ph concentrations in worms Aporrectodea caligino,• " Lumhric11. 1 ruhe/111.1 L castaneu, Kruskall-Wallis hetween species Concentration factor /\. caliginosa [ .. mhel/u, L CCL\f{ll1t'llS Kruskall-Wallis hctween spccies	1 058.0 ± 480.4 IJJ.o ± l lb.5 75.6 ± 109.4 0.98 ± 0.38 0.07 ± 0.05 005 ± 0.06	10 9 (, 10 l) 6

  Terhivuo et al. l 173 j noted different bioaccumula tion rates among different earthworm species at a site damaged by lead pollution (tahlc /). Most studies of terrestrial gastropods (tah/e If) have focused on analysing bioaccumulation among some experimentally contaminated animais or animais sam pled at a polluted site. Gastropods survive in sites polluted by metals. This resistance stems from their ability to retai n and inactivate toxic metals. either through intraccllular compartmentalisation ( confine ment within granules. vesicles. etc. l and excretion. or Tahle Il. Pulmonated land gastropods as contamination indicators. By evaluating the metal contents in different gastro pod specics f r om dif f erent cnvironments. Berger and Dallinger 18 j were able to reveal three pollution levels. In a similar study on cdible snail quality (in fanns and natural environments ). Pi han et al. [ 138] suggcsted a classification according to metal contents (Cu. Zn and Pb) and studied the relationship bctween metal con centrations in snail tissue� and their food supply [71 ].

					P()rnaincn	Mann-Whitney
				Median	Mean± SD	Il	Mcdian
				904.:'i 129.1 11.9	hl .7 ± 42.7 56.2 ± _"\(1_7 88 .. s ± 'il)_/\	7 5	'i()_() 61 1 97.2	ns ns
				0.92 0.08 0.01	3.8 ± _l_ 1 J.2 ± 2. l :'i -1 ± 5.0	5 -'	3.1 �.� .. . i J	Il
				through protein binding, including metallothioneins.
				which can scavenge metals (primarily cadmium) and
				store them for long periods [38].
	Rcfcrence Pollutant	Cuntamination source	Spccics	Test duratinn	Paramckr..,
	[ 1291	Fe. Ph. Zn		ffe!ix /N1mr1lio	..i v,ceks 1 Aug.-Sq,t. 1l/711	Tcmperaturc effect on Fe. Ph. Zn in diffcrelll organ,
	[29)	Cd. Cu. Ph. Zn	Cuntaminatcd and uncontaminatcd �ite-; Il. os1wrso	Natural ,naib 1Sep1. 107�1	\lctal c,mcentration
	[206]	Cd. Ph.Zn	Roadsidcs	Cepoeo hortenv;. \	tJuL-Scpl. i'l7:'i)
	[371	Cd. Cu. Ph. Zn	Contaminatcd fond (lcttuce)	H. 11011w1io	3il--J5 d i\1arch !li811
	[61	Ph	Agar gelose +Ph or +Ph and Ca	1-f. (/lf>l'/"\r/
	[761	Ca. Cd. Cu. Ph. Ahandoncd mine Pb/Zn Zn	Six .., 1ug -;pccic-;	I\atural ... tug, 1 Sept. i 'l861
	136]	diverse	Di,crse	Castrnpods and land i nwrtchrate,	Variahlc
	[38]	Cel. Zn	Agar plates with Cd et Zn	H. /J0!1/(lfÙI
	[8]	Cd. Cu. Ph. Zn Urhan areas	A rianto urhustorwn hct\vecn , compari�un -,pcciè"	Scasonal ..,amplinf!
	I IJ8J	Cu. Zn	N atural and through food	H. u.,per.�o a. \pcrsu and li ospcr.,,1 mfv. :ima adult :,,naih i\:atural and raiscd
				JJ, pomatio
	[ 139]	Ph	Contaminatcd sites and hrceding	H. 01pa. \"{/. H. 1101110/iu Natural ami raiscd adult snails
	[731	Cd. Ph.Zn	Food and suhstratc	/)e,ncero. \ rcth•ula!ll111 3 \\ cck:-,
	[711	Cu. Zn	Food

H. uspersa cHper, \u 10 \Vl'L'k'-. and fi. n.,pcna mnrinw

  -

	in nrga11--
	Bioac•c,11nulation. ,•cllular and molecular dis1rihution ui" 1nctals
	Bioaccumulation: Ph a..,si,ni lation and cxcrdion
	l\ktal ,•onccnlration in 1i .... .._uc-;
	Contamination. cellular n)mpartmcnt. dctnxification
	Subcl'ilular distrihutinn in digestive gland-;
	Meta! concentration in li\:... UL':... (+ influence of indi\ îdua! "i1.c l
	Meta! c•onccntration in ti-. . :-.uc:....
	l:lioaccurnulation and pPlluliPn r�1L•tnr:...
	Ph ,•onccntration in foot and, Î'-iccra
	Meta[ concentration, in the hoc!\
	13 ioaccu mu lati<111

.

Table IV .

 IV Examples of evaluation tests with enchytraeids.

	Reference	Pollutant
	Weistheide et al.	benomyl
	(1991), in [194]
	[148]	parathion amitrole diuron
	[22]	dimethoate pirimicarb
		fenpropirimorph
	[150]	dimethoate
	[161]	Cu
	[102]	Cu+ Ni
	[127]	dimethoate
	3.1

.1.3. Oligochaetes (earthworms)

	Contamination source
	Contaminated gelose
	Substrate
	Food (oatmeal)
	Substrate, food (spores)
	Substrate
	Forest soi!
	Artificial soi! (OECD,
	humic. sandy and clayey)

This group has undergone considerable lethal and sublethal testing [52, 135]. The species Eisenia fetida andrei from the E. fetida complex is generally used for lethal testing, as it is genetically relatively uniform [ 15]. Sorne results are shown in table V. However, other studies have shown that E. fetida worms with a long-term history of expo sure to cadmium developed resistance to the metal [146]. The species Lumbricus terrestris and Allolobo phora caliginosa, although more sensitive, are less fre quently used as they are harder to raise [39, 56, 79, 80, 127, 168]. Sublethal testing is generally performed on cocoon production [24, 119, 132, 145, 185, 188-191. 199]. Juvenile growth studies are more difficult to perform

	Species	Test duration	Parameters
	Enchytraeus crypticus. 30 d	Number of hatched
	E. minutus		juveniles cocoons
	E. albidus	56 d	LC 50 on cocoons
	E. bigeminus	7 d	LC 50 ; growth
	Cognettia	70 d	NOEC; LOEC; ECx
	sphagnetorum		(fragmentation rate and
			number of formed
			segments)
	C. sphagnetorum	15-50 d	Fragmentation rate,
			mode] of dispersal. mini
			mum population size
	C. sphagnetorum	138 d	Litter mass loss, soi!
	(add to other organisms)	respiration, change
			in nitrates
	E. crypticus / mriatus	32 d	NOEC on juveniles
	as they involve several parameters: test duration, fate
	of pollutant, interaction between pollutant and sub
	strate [ l O 1]. Maturation of secondary sexual character
	istics is often combined with earthworm growth rate
	[119, 145, 165, 199].
	On the other hand, several studies propose to use
	toxicant kinetics and critical body residues (CBR) in
	relation to toxicity endpoints, which may provide a
	better estimate of toxic dose than soil concentration of
	contaminants, especially for hydrophobie organic
	chemicals [60, 61, 114].
	3.1.1.4. Gastropods
	Experimental studies on metal tox1c1ty have been
	performed on snails and slugs from the stylommato-

Table V .

 V Using the Eiseniafetida group as a toxicological bioindicator of effect.

	Reference Pollutant	Contamination source	Test duration	Parameters
	[80]	Nineteen pollutants fongicides con tact ( fil ter paper ). and insecticides artificial soi!, artisol [ 1 J	2 and 14 d	LC so
	[133]	Cd, Cu, Ni, Pb, Zn; ten organic pollutants	contact (filter paper), artificial soi!	2 and 14 d	LC so
	[145, 199] Dieldrin	organic culture medium	90 d	Growth, sexual maturity
					and reproduction
	[140]	Five herbicides	contact (filter paper), brown soi!	2 and 7 d	LC 50 ; growth (biomass)
	[184]	Cu, pentachlorophenol,	artificial soi!	21 and 35 d	Growth, reproduction
		2,4-dichloroaniline
	[132]	Ten pollutants	horse manure + sand	28 and 42 d	Growth, reproduction
	[185]	Cd, Cu, PCP	artificial soi! + cow dung	6, 8. 10 and 12 weeks	EC 50 , NOEC. growth,
					reproduction
	[109]	Parathion, propuxur	artificial soil	14, 42 and 56 d	Growth, reproduction
	[186]	Cd, Cr, insecticides, fongicides artificial soi!	14, 21 and 35 d	LC 50 ; growth, reproduction
		and herbicides
	[16]	Soi 1s and waste	artisol	14 d	Non-toxic threshold
	[60]	Pentachlorophenol	artificial soi!	28 d	LC 50 ; ILL
	[201]	Terbuthylazine, carbofuran	food	14, 42 and 56 d	Reproduction, respiration
					(CO 2 emissions), excretion (nitrogen)

Table VI .

 VI Examples of cxperirncntal studics for dctermination of lcthal and sublethal cffiècts of chemicah in land gastropods pulmonatcs.

	Rcference Species	Test parametcr	Pol lutant		Exposure
					Duration	Fecding te:,,, t,
	11511	He/i_r t1sperso	Growth; fceding activity; rcpn>Lluction	Cd	. "\Il d	Rat food+ CaCO, +CdCL
	[ 12.+j	Arion arer	Growth; f"od consurnption	Cu. Hg. Ph. Zn	27 d
	f 1571		NOAEL. LOAEL; sublethal EC,,,; klha\ity: L C',11	Twclvc pcstici,ks	12. 1 + d	rat fond + agar + pc:--t icidc..,
	11251	.-1. li/l'i"	()uantitali\ c structure nf Liigc\tivc tuhuk:--	Hg	:\(J d	Natural dict with 1 � <.; agar+ ditlcrcnt Hg cnnccntrations
	1115[	fi. ll.\/Jt'rsa	Growth rate; food consumption; CJ. Cu. Ph. Zn i"ccumlity	90 d (_jU\cnilcsJ; ,\nificial dict + dilfrrcnt 120 d (adults) mctal c•Dnccntrations
	[69[	Il. mpersu ffl/>er.rn	(irowth	Cd. Cr. Ph. Zn	21. 28 d	hrnd lleli.xcd R• � Cd or contaminatcd soi! !Cd. Cr. Ph. Zn 1
	[70[

Natural dièt \\ ith 1.5 ',' -( agar+ di!Terent do:,,,age:,, of rnc-tal.. .., ff. (/.\/)('/"\(/ H. mpe1 '" mper. rn; Growth. \:OEC. EC,,,. EC:,--Cd H. tnpena mtP. :imu feeding activity phorous pulrnonate gastropod group (table

Table VII .

 VII Using Folsomia candida as a toxicological bioindicator of effects.

	Reference Pollutant	Contamination	Duration	Variable	Parameter
	[18]	DDT	Food (yeast)		Temperature;	Life span. mortality, fecundity,
					concentration	egg viability
	[176]	Seventeen insecticides Artificial soil (sand)	1 d	Concentration	LD so
	[170]	Paraquat, atrazine	Food (yeast)	154 cl	Concentration	Mortality. moulting frequency,
						fecunclity. egg viability.
						incubation
	[30]	Cel	Artificial soi! (OECD)	63 cl	Concentration	LC 5 ,i: EC 50 on growth
						and egg-laying
	[103]	Dimethoate	Methylated soi!	28 cl	Spatial distribution of food and pollutant	LC 50 ; EC 50 , NOAEC, LOAEC on reproduction
	[33]	Cel, chlorpyrifos,	Artificial soi! (OECD)	35 cl	Genetie variability	LC 50 ; EC 50 on growth, juveniles
		triphenyltin hyclroxicle				and population
	[32]	Cd	Food (yeast)	63 d (egg-laying); Specific variability 392 cl (growth)	LC 50 : EC 10 , EC 50 on egg-laying and growth; SS!
	[127]	Dimethoate	Artificial soi! (OECD,	32 cl
			humic, sandy and clayey)
	[162]	Zn	Artificial soi! (LUFA);	42 cl
			natural soi!
	(149]	Dimethoate, parathion Artificial soi!, water	14 d
	[891	Parathion, dimethoate. Water	14 d
		carbofuran, oxamyl
	[180]	Cd, Zn	Artificial soi!	42 d
	[ 182]	Cd	Artificial soi! (OECD)	42 d
	[163]	Zn	Artificial soi!	42 d
	are time-consuming and difficult (highjuvenile mortal
	ity, lack of biological synchronisation).

  EC 50 , EC 111 on biomass.

	Soi! type	NOEC on juveniles
	Soi! type	LC 50 ; EC 50 on growth
		and reproduction
	Comparison of measure-EC 50 on mobility in water
	ment methocls
	Di lferences between	EC 50 on mobility in water
	pesticides
	Interaction between Cd LC 50 ; EC 50 on growth and Zn and juveniles
	Soi] moisture	LC 50 ; EC 50 , EC 10 on biomass . juveniles
	Tcmperature;	LC 50 ;

concentration juveniles

Table IX .

 IX Summary of suggestions for field study standard protocols[START_REF] Texier | Étude expérimentale de divers modes d'action de l'aldicarbe sur Aporrectodea caliginosa (Oligochaeta, Lumbricidae)[END_REF]. *** Undefined characteristics.

		Edwards [51]	Lofs fi 18]	Kula [108]
	Site	grass, arable land	***	grass. arable land, artificial site
	Soi! description	texture, OM, homogeneity	texture	at least texture, OM, pH, pF
	History	> 5 years without pesticides	***	***
	Additional parameters			temperature, rainfall
	Density	> 100 ind-m 2	> 100 ind•m 2	> 100 ind-m 2
	Representative species	at least the six most common species	one endogeous + one anecic	Lumhricus terrestris;
				Aporrectodea caliginosa
	Initial plot size	5 to 10 m 2	***	100 m 2
	Number of replicates	> 4 per treatment	> 4 per trcatment	> 4 pcr treatment
	Pesticide	standard pesticide + physical	physical and chemical toxicity	physical and chemical toxicity
		and chcmical toxicity parametcrs	parameters	parameters
	Dosages used	the most important recommended	***	***
	Treatment dates	spring	earthworms activity period	spring
	Estimated criteria	abundance and biomass	abundance and biomass;	abundance; biomass with reserves
			growth stages
	Sampling methods
	(sample size)

Acknowledgments

The authors thank the Ademe ( 'Agence de !"environnement et de la maîtrise de l'énergie'), and particularly D. Savanne. for their financial support. We wish also to thank Sylvie Dupouyet for her technical assistance. Special thanks to Julia Fady-Wertelen for English corrections.