A second life for old data: Global patterns in pollution ecology revealed from published observational studies

Environmental Pollution 159 (2011) 1067e1075

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Review

A second life for old data: Global patterns in pollution ecology revealed from published observational studies Mikhail V. Kozlov*, Elena L. Zvereva Section of Ecology, University of Turku, 20014 Turku, Finland

Research synthesis demonstrated that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 September 2010 Accepted 18 October 2010

A synthesis of research on the responses of terrestrial biota (1095 effect sizes) to industrial pollution (206 point emission sources) was conducted to reveal regional and global patterns from small-scale observational studies. A meta-analysis, in combination with other statistical methods, showed that the effects of pollution depend on characteristics of the specific polluter (type, amount of emission, duration of impact on biota), the affected organism (trophic group, life history), the level at which the response was measured (organism, population, community), and the environment (biome, climate). In spite of high heterogeneity in responses, we have detected several general patterns. We suggest that the development of evolutionary adaptations to pollution is a common phenomenon and that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms. We argue that community- and ecosystem-level responses to pollution should be explored directly, rather than deduced from organism-level studies. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Climate Trophic groups Meta-analysis Ecosystem structure and functions Evolutionary adaptations

1. Introduction Adverse consequences of the impact of pollution on terrestrial ecosystems have been under thorough investigation since the very beginning of the twentieth century, and several thousand case studies have documented various biotic effects occurring in contaminated areas. During the past decade, pollution ecology studies have shifted from the exploration of the acute damage imposed by large industrial enterprises (emitting sulphur dioxide, fluorine, and metals) towards investigation of regional effects that are caused primarily by ozone and, to a lesser extent, nitrogen deposition (Paoletti et al., 2010). However, this shift has in no way compromised the legacy of earlier studies, reporting comparisons of different characteristics of organisms, populations, and communities between polluted and unpolluted habitats. Disturbance-induced changes in ecosystems are of central concern in ecology, and a challenge for ecologists is to understand the factors that affect the resilience of community structures and ecosystem functions (Moretti et al., 2006). These factors are generally explored by testing clearly articulated hypotheses in experimental studies. However, from a scientific perspective, the effects caused by industrial pollutants are the result of unintentional

* Corresponding author. E-mail address: mikoz@utu.fi (M.V. Kozlov). 0269-7491/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2010.10.028

experiments on ecosystems, and the results of these accidental experiments can be utilised by ecologists (Lee, 1998). Habitats transformed by pollution, including unusual landscapes of denuded barren land with lifeless lakes, can be considered opportunistic macrocosms (unique laboratories) for integrated research on the effects of harsh environmental conditions on ecosystem structure and function (Nriagu et al., 1998; Kozlov and Zvereva, 2007). It has been repeatedly claimed that there are commonly observed patterns of ecosystem responses to stress (e.g., Woodwell, 1970; Odum, 1985; Rapport et al., 1985). These generalisations were based on a few impressive case studies that were available at that time (Gorham and Gordon, 1960a,b; Hutchinson and Whitby, 1974; Jordan, 1975; Wood and Nash, 1976; Freedman and Hutchinson, 1980a,b). However, further exploration of the effects imposed by the wide range of polluters across the globe has resulted in the accumulation of a vast amount of diverse data that are sometimes contradictory or inconclusive. This accumulation of knowledge has led to an urgent need to integrate the research findings of individual studies and to establish consistency across the results obtained for various groups of biota in different environments. This goal can be accomplished by meta-analysis, which has become widely used in ecological and environmental research during the past decade (Lei et al., 2007; Stewart, 2010). Meta-analysis is a technique that was primarily developed to statistically integrate the results from individual studies to find common trends and differences (Gurevitch and Hedges, 2001).

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However, it can also be used to generate or test new hypotheses. In particular, meta-analysis can detect large-scale patterns that extend beyond the level of resolution or the capability of conventional descriptive studies (Hughes et al., 2002). More than a decade ago, our team started a quantitative research synthesis of published data on the responses of terrestrial biota to industrial pollution (Kozlov and Zvereva, 2003). This task has been extremely time-consuming: to date we have been able to process information on the diversity, abundance, and performance of soil microfungi (Ruotsalainen and Kozlov, 2006), bryophytes (Zvereva and Kozlov, 2010b), vascular plants (Zvereva et al., 2008, 2010b), and terrestrial arthropods (Zvereva and Kozlov, 2010a). Although some patterns have been discovered by each particular meta-analysis, their generality remained unclear. We hypothesised that the exploration of the combined database would be likely to generate new knowledge of the impact of pollution on ecosystem structure and functions through comparisons between the responses of trophic groups measured at different levels of the hierarchy of biological organisation (from individuals to communities) in different environments. In this paper, we attempt to improve our understanding of the changes in ecosystem structure and functions that have been caused by aerial pollution. Our objective was to identify general regularities and sources of variation in the responses of terrestrial ecosystems to industrial pollution by exploring relationships between the magnitude of these effects and the various characteristics of the biota, polluters, and environments.

2. Materials and methods 2.1. Search for and selection of published studies The combined database (Table 1) was built from databases used in earlier metaanalyses (Ruotsalainen and Kozlov, 2006; Zvereva et al., 2008, 2010b; Zvreva and Kozlov, 2010a,b). All studies included in the database fit the following criteria: 1. The study was conducted near a point polluter (i.e., an individual factory), which had an impact zone that was not overlapped by any impact zones of other polluters; 2. The polluter influenced the surrounding habitats primarily via the ambient air; 3. The data were collected in natural ecosystems that were not modified by experimental treatments; 4. The data were collected from organisms naturally inhabiting the study area; 5. The data were collected from both impacted and non-impacted habitats allowing for comparisons; 6. The study provided numerical information sufficient for the calculation of effect sizes (ESs; consult Section 2.5 for details). For the overview of the data used in meta-analysis consult Table 1. We excluded some ESs from the databases that were used in earlier meta-analyses to assure better comparability of the data. The most important of these omissions are (1) mycorrhizal fungi, whose attribution to trophic level is somewhat questionable; (2) performance of arthropods reared in the laboratory environment on pollutionaffected food; (3) leaf thickness; (4) enumerable characters reflecting plant performance (e.g., number of leaves in a shoot); (5) plant reproduction; (6) measures of diversity other than species richness; and (7) species richness calculated separately

for different groups of plants when total species richness was already included in the database. 2.2. Response variables We classified all response variables into three groups reflecting (1) individual performance of organisms, measured by their size or survival (organism-level responses), (2) abundance measured by different indices, including cover and/or biomass of vascular plants and bryophytes and population densities of arthropods (population- or community-level responses), and (3) diversity, measured as species (morphotype, recognisable taxonomic unit) richness of certain taxa within the community (community-level responses). 2.3. Classificatory variables Organisms were classified by their position in the trophic web (Table 1) as: producers (bryophytes and vascular plants), primary consumers (herbivorous arthropods), secondary consumers (predatory and parasitic arthropods), or decomposers (saprotrophic fungi and arthropods). Classification of terrestrial biomes follows Woodward (2003). Any industrial enterprise emits thousands of substances, many of which have been experimentally demonstrated to affect biota. However, only a few of these substances are commonly monitored, and only a tiny fraction of studies exploring biotic effects of pollution reports concentrations of one or more pollutants measured simultaneously with collection of the biotic information. Since observational studies do not allow unequivocal attribution of observed effects to a certain pollutant, we classified the observed effects by the type of the emission source. We used two classification schemes for polluters. First, we grouped polluters into six classes that differed in their primary components of aerial emissions: nonferrous industries emitting heavy metals and SO2; aluminium plants (combined with cryolite plants and ceramic factories) emitting fluorine; cement and magnesite plants emitting alkaline dust; fertiliser plants emitting various nitrogen-, phosphate-, sulphur-, and (often) fluorine-containing compounds; and chemical plants emitting (along with SO2) various organic substances (mostly hydrocarbons); a number of other polluters (including power stations, cocking plants, gas and timber processing plants, pulp and paper mills, iron and steel producing factories) were combined and termed ‘SO2-emitting’ because SO2 was the major phytotoxic component of their emissions. Second, we grouped polluters into acidifying, neutral and alkalifying based on their impacts on soil pH. 2.4. Covariates Climatic characteristics (mean temperature of July, mean annual precipitation, and potential evapotranspiration) were obtained using New_LocClim (FAO, 2006). We interchanged July and January temperatures for studies conducted in the southern hemisphere to make the data comparable with studies from the northern hemisphere. Water balance was calculated as the difference between annual precipitation and potential evapotranspiration. Pollution severity was quantified by annual emissions of sulphur dioxide, fluorine, and heavy metals. The duration of impact was calculated as the difference between the year of data collection and the year of polluter establishment. In studies where the year of data collection had not been reported, we arbitrarily accepted that the data were collected 3 years prior to the date of the publication (the median value obtained from the dated part of the data set). Biome-specific species richness of vascular plants was extracted from Scheiner and Reybenayas (1994). 2.5. Data analysis Hedge’s d, a measure of the ES (one of indices that measure the magnitude of a treatment effect), was calculated as the difference between the mean values of the parameter in polluted and control site(s) divided by the pooled standard deviation and weighted by the sample size. Thus, a negative ES indicated that the variable under study attained lower values in polluted site(s) than in control site(s). When

Table 1 Overview of the data used in the analyses. Trophic level

Taxon

Producers

Bryophytes Vascular plants

Primary consumers Secondary consumers Decomposers

Arthropods Arthropods Fungi Arthropods

Numbers of effect sizes/polluters

References to databases

Performance

Abundance

Diversity

9/5 388/101

33/30 159/74

33/33 45/45

24/11 7/4 0/0 0/0

161/41 95/35 48/36 29/20

12/9 28/16 18/12 6/6

Zvereva and Kozlov, 2010b Zvereva et al., 2008, 2010b, and unpublished Zvereva and Kozlov, 2010a Zvereva and Kozlov, 2010a Ruotsalainen and Kozlov, 2006 Zvereva and Kozlov, 2010a

M.V. Kozlov, E.L. Zvereva / Environmental Pollution 159 (2011) 1067e1075 the primary meta-analysis employed the Pearson’s correlation coefficients between the response variable and the logarithmic distance to the polluter (which is roughly proportional to the deposition of pollutants) as a measure of the effect (Ruotsalainen and Kozlov, 2006; Zvereva et al., 2008), we converted it into Hedge’s d using the equation: r2 ¼ d2/(d2 þ 4) (Rosenberg et al., 2000). After appropriate transformations, three methods of ESs calculations (correlation with the distance from the polluter, correlation with the pollution load, and the contrast between the most and the least polluted sites) produced similar effect sizes and yielded similar results in all particular analyses (Kozlov et al., 2009). All analyses are based on the entire data set (consisting of 1095 ES and including all classes of response variables) unless otherwise stated. The mean ESs for each group were calculated and compared using the MetaWin 2.0 program (Rosenberg et al., 2000). The effect was considered statistically significant if the 95% confidence interval (CI) of the mean ES did not include zero. All analyses were performed using random effects categorical models, assuming that the studies differed not only by sampling error, but also by a random component of ES. Variation in effect sizes within and among classes of categorical variables was explored by calculating the heterogeneity indices (QT and QB, respectively) and testing these against the c2 distribution (Gurevitch and Hedges, 2001). The most important continuous variables explaining the magnitude of responses to pollution were identified by stepwise regression analysis of ESs (SAS REG procedure; SAS Institute, 2009). The effects of climate and the characteristics of the polluters (amount of emissions and duration of impact) on ESs were further explored using analysis of variance (ANOVA) and analysis of covariance (ANCOVA), followed by linear regression analysis (SAS Institute, 2009). From all these analyses we excluded 3% of the extreme ESs (statistical outliers). The use of linear models (with log-transformed values of pollutant emission, duration of pollution impact, annual precipitation, and the water balance) is justified by the rarity (5e10%) of nonlinear effects in all of the data sets we have examined so far (Zvereva et al., 2008; Kozlov et al., 2009; Zvereva and Kozlov, 2010a). The significance of all regression models was checked by continuous metaanalysis using the MetaWin 2.0 program. Although the probability levels differed between continuous meta-analysis and linear regression analysis, the conclusions on the significance of the effects (at the probability level P ¼ 0.05) based on both of these methods were identical.

3. Results 3.1. Database A total of 1095 ESs were calculated from about 500 data sources published between 1953 and 2009. The primary data were collected near 206 point polluters in 36 countries, including 21 fluorine-emitting industries (18 of which were aluminium smelters), 20 cement and magnesite plants, 34 chemical enterprises, 14 fertiliser factories, 46 non-ferrous smelters, and 73 SO2emitting enterprises. The largest amount of data originated from Russia (61 polluter), Poland (23 polluters), Canada (17 polluters) and USA (17 polluters). For distribution of ESs by trophic levels, taxa, and response variables consult Table 1.

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Fig. 1. Mean effect sizes, 95% confidence intervals, and sample sizes for the effects of industrial polluters on individual performance, abundance, and diversity of organisms from four trophic levels. The effect is significant if the 95% confidence interval does not overlap zero.

performance, while the effects of pollution on abundance and diversity were diverse (Fig. 1). Performance, abundance and diversity characteristics responded differently to pollution within producers (QB ¼ 33.1, df ¼ 2, P < 0.00001), primary consumers (QB ¼ 24.7, df ¼ 2, P < 0.00001) and decomposers (QB ¼ 20.9, df ¼ 1, P < 0.00001), but uniformly within secondary consumers (QB ¼ 1.56, df ¼ 2, P ¼ 0.46). In the covariation analyses with two class variables (trophic level and measured characteristic), most of the continuous variables (amount of emission, duration of pollution impact, and climate near the polluter) demonstrated significant double and triple interactions with both class variables (data not shown). Because the interpretation of triple interactions is far from being simple, we restricted further analyses to ANCOVAs with one class variable. The models reported below (Tables 2, 4, and 5) were selected to explain the larger part of overall variation in ESs.

3.4. Polluter types, amount of emission, and duration of pollution impact

3.2. Overall effect The overall biotic response to the impacts of 206 polluters (quantified by 1095 ESs) was negative (d ¼ 0.83, CI ¼ 0.92. 0.69) but highly heterogeneous (QT ¼ 1998.3, df ¼ 1094, P < 0.00001). Differences between trophic levels, classes of polluters, and biomes explained 4.3% (ANOVA: F3, 1046 ¼ 16.3, P < 0.0001), 1.6% (F5, 1046 ¼ 3.54, P ¼ 0.0035), and 1.8% of the total variation in the ESs (F6, 1046 ¼ 3.42, P ¼ 0.0024), respectively. Stepwise regression analysis demonstrated that the adverse effects of pollution on biota increased with increases in SO2 emission (R2 ¼ 0.0102; F1, 929 ¼ 9.53, P ¼ 0.0021) and in the mean temperature of July (R2 ¼ 0.0053; F1, 929 ¼ 5.00, P ¼ 0.026). 3.3. Trophic levels and measured characteristics The overall effects of pollution on individual performance, abundance, and diversity were negative (Fig. 1). Organisms from different trophic groups demonstrated similar changes in

Responses of terrestrial biota to industrial pollution varied between industries (Fig. 2). This variation was not explained by changes in the soil pH (Fig. 2), and industry-specific ESs were independent of the mean amounts of pollutants that were emitted by the respective industries (Spearman’s rank correlation coefficients, n ¼ 6: SO2, r ¼ 0.54, P ¼ 0.27; heavy metals, r ¼ 0.14, Table 2 Results of ANCOVAs (F-values and significance levels P) investigating dependence of the effect sizes (magnitudes of the responses to pollution) from the polluter type and the amounts of aerial emissions. Source of variation Sulphur dioxide df Polluter type (T) Annual emission (E) T *E Error

F 5

P 2.72 0.02

1 10.78 0.001 5 2.87 0.01 941 e e

Heavy metals

Fluorine

df

df

F

P

F

P

5 3.10 0.009

5 5.61 <0.0001

1 0.12 0.73

1 0.18

3 0.14 0.94 1018 e e

0.67

5 6.12 <0.0001 993 e e

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M.V. Kozlov, E.L. Zvereva / Environmental Pollution 159 (2011) 1067e1075 Table 4 Results of ANCOVAs (F-values and significance levels P) investigating dependence of the effect sizes (magnitudes of the responses to pollution) from the character under study (individual performance, abundance, and diversity) and different characteristics of polluters. Source of variation

Annual F emissionb

Annual SO2 emissiona df

F

P

df

Character (C) 2 6.33 0.0019 2 Impact 1 14.91 0.0001 1 measure (I) I *C 2 6.22 0.0021 2 Error 865 e e 158 a b

Fig. 2. Mean effect sizes, 95% confidence intervals, and sample sizes for the effects of different types of industrial polluters on terrestrial biota. Al ¼ aluminium smelters, Cem ¼ cement factories, Chem ¼ chemical factories, Fert ¼ fertiliser factories, NF ¼ non-ferrous metal smelters, SO2 ¼ sulphur dioxide emitters. Impacts on soil pH: A ¼ acidifying, B ¼ alkalifying, N ¼ no impact.

P ¼ 0.79; fluorine, r ¼ 0.43, P ¼ 0.40) and of the average duration of the pollution impact (r ¼ 0.14, P ¼ 0.79). Types of polluters differed in the composition and amount of emissions; accordingly, biotic consequences of the increase in emissions differed between the classes of polluters (interaction term in Table 2). Correlation analysis demonstrated that a substantial part of this variation was due to cement factories: their impacts became less detrimental with increases in both SO2 and fluorine emissions (Table 3). When cement plants were excluded, the variation in SO2 emission showed similar effects on biota across the remaining industries (interaction between polluter type and SO2 emission: F4, 861 ¼ 1.80, P ¼ 0.13). Responses to fluorine emissions were similar for aluminium plants and fertiliser factories (interaction between polluter type and fluorine emission: F1, 177 ¼ 2.46, P ¼ 0.12). We refrained from performing a separate analysis of the effects of heavy metals because, for non-ferrous smelters, metal emissions were strongly correlated with SO2 emissions (Pearson correlation coefficient: r ¼ 0.68, n ¼ 37 polluters, P < 0.0001), and, therefore, responses to these two classes of pollutants could not be separated using our data. The severity and duration of pollution impacts affected individual performance, abundance, and the diversity of organisms differently (Table 4). Increases in SO2 emission resulted in enhancement of adverse effects on diversity, while the effects on Table 3 Results of correlation analyses (Pearson correlation coefficients r, sample sizes n, and significance levels P) investigating dependence of the effect sizes (magnitudes of the responses to pollution) from aerial emissions of principal pollutants for six classes of polluters. Source of variation

Aluminium smelters Cement factories Chemical factories Fertiliser factories Non-ferrous smelters SO2-emitting plants

Sulphur dioxide

Heavy metals

r

r

n

P

r

e e 0.06 0.00 0.11 0.04

e e 124 73 352 262

e e 0.48 0.99 0.04 0.52

0.34 119 0.0002 0.27 92 0.01 0.15 94 0.15 0.25 62 0.05 0.06 380 0.21 0.06 258 0.36

n

P

0.26 103 0.009 0.18 82 0.11 0.05 93 0.65 0.21 61 0.11 0.10 356 0.05 0.17 258 0.006

Fluorine n

P

F

Duration of the pollution impact P

3.49 0.03 5.46 0.02

df

F

P

2 12.67 <0.0001 1 0.30 0.58

3.70 0.03 2 7.91 0.0004 e e 1052 e e

Cement factories were excluded from this analysis. Only aluminium smelters and fertiliser factories were included in this analysis.

individual performance did not change. In contrast, increases in fluorine emission were associated with substantial decreases of individual performance, while the effects on diversity remained nearly constant (Table 5). Adverse effects on individual performance decreased, while adverse effects on the abundance of organisms became stronger with time from the beginning of the pollution impact (Fig. 3, Table 5). 3.5. Climate and biodiversity Responses to pollution varied between terrestrial biomes (Fig. 4). The biome-specific ESs were independent of the mean amounts of pollutants emitted by the polluters that were situated in these biomes (Spearman’s rank correlation coefficients, n ¼ 7: SO2, r ¼ 0.14, P ¼ 0.76; heavy metals, r ¼ 0.36, P ¼ 0.43; fluorine, r ¼ 0.43, P ¼ 0.34) and of the average duration of the pollution impact (r ¼ 0.18, P ¼ 0.70). Differences in ESs between biomes were explained by variations in the mean temperature of July (r ¼ 0.93, P ¼ 0.0025) and in water balance (r ¼ 0.93, P ¼ 0.0025), but they did not change with precipitation (r ¼ 0.29, P ¼ 0.53). Consistent with these results, ANCOVA demonstrated a significant effect of the mean temperature of July and a marginally significant effect of the water balance on the magnitude of the responses to pollution (Table 6). Increases in temperature enhanced the adverse effects of pollution on producers and decomposers but mitigated the adverse effects on primary and secondary consumers (Fig. 5, Table 5). We found no correlation between biome-specific biodiversity (quantified by species richness of vascular plants) and the mean response to pollution (Spearman’s rank correlation coefficient: r ¼ 0.13, n ¼ 7, P ¼ 0.79). 4. Discussion 4.1. General patterns emerged from diverse responses The first explorations of the effects of industrial pollution on terrestrial biota were driven by economic losses in agriculture and forestry, rather than by scientific curiosity (e.g., National Research Council of Canada, 1939). As a result, the most polluted areas, where plants have been severely damaged or even killed by industrial emissions, have attracted the most attention from scientists, resulting in an obvious research bias. For instance, publications describing lifeless industrial barrens around several non-ferrous smelters (Gorham and Gordon, 1960a,b; Hutchinson and Whitby, 1974; Jordan, 1975; Wood and Nash, 1976; Freedman and Hutchinson, 1980a,b) created an impression that pollution caused uniform changes in different ecosystems. An external similarity between the processes that are observed in forests affected by severe pollution, ionising radiation, and

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Table 5 Parameters explaining variation in the effect sizes (results of regression analyses). Model R2

F (df)

P

0.02  0.05 0.18  0.06 0.38  0.13

0.001 0.025 0.071

0.19 (1, 325) 11.10 (1, 492) 8.42 (1, 110)

0.66 0.0009 0.0045

1.43  1.00 0.50  0.56 0.63  0.91

0.45  0.15 0.14  0.10 0.07  0.19

0.123 0.025 0.005

9.40 (1, 67) 2.19 (1, 84) 0.12 (1, 24)

0.0031 0.14 0.73

Producers Primary consumers Secondary consumers Decomposers

0.40 3.11 2.16 0.05

0.04 0.19 0.09 0.06

0.02 0.08 0.06 0.08

0.008 0.027 0.016 0.006

5.51 5.30 2.01 0.50

0.02 0.02 0.16 0.48

Performance Abundance Diversity

2.68  0.51 0.67  0.41 1.63  0.91

0.42  0.14 0.32  0.11 0.04  0.24

0.022 0.016 0.000

9.38 (1, 422) 8.08 (1, 498) 0.03 (1, 131)

Class variable

Intercept (mean  SE)

SO2 emission

Performance Abundance Diversity

1.30  0.56 1.27  0.55 2.08  1.29

Fluorine emissionb

Performance Abundance Diversity

Mean temperature of July

Duration of the impact

Explanatory variable a

a b

   

0.35 1.43 1.05 1.44

Slope (mean  SE)

   

(1, (1, (1, (1,

650) 188) 126) 90)

0.0023 0.0047 0.87

Cement factories were excluded from this analysis. Only aluminium smelters and fertiliser factories were included in this analysis.

herbicides served as the basis for the suggestion that “changes in natural ecosystems caused by many different types of disturbances are similar and predictable” (Woodwell, 1970, p. 429). Although Woodwell (1970) appreciated the shortage of information on which his conclusion was based, several scientists nonetheless tried to identify a set of simple rules governing ecosystem responses to abiotic stress (Odum, 1985; Rapport et al., 1985). Further accumulation of data, which was especially intensive in 1980s (Kozlov et al., 2009), demonstrated that the responses of biota to pollution are far from being uniform. It soon became clear that, because each impacted area developed in its own way due to a unique history of events, the experiences from one system are rarely directly applicable to predict the fate of another system (Cairns and Niederlehner, 1996; Matthews et al., 1996). The need to reduce uncertainties in predicting the consequences of the impact of pollution called for the quantitative exploration of factors influencing the responses of organisms and ecosystems to pollution. However, accomplishing this task became possible only with the development of meta-analysis, which allowed statistical exploration of an unlimited amount of diverse information. Our database allowed the testing of some predictions on general ecosystem responses to stress that were made by Odum (1985) and Rapport et al. (1985). Our analysis unequivocally supported only two of these predictions, namely that there is an overall decrease in

the diversity (Fig. 1) and in the size of organisms with increasing pollution. The size of plants and their individual metamers (leaves and shoots), as well as body size in arthropods, were smaller near point polluters (Zvereva and Kozlov, 2010a; Zvereva et al., 2010b). At the community level, larger plants (trees) suffered more from pollution than smaller (herbaceous) plants (Zvereva et al., 2010b); tree cover consistently decreased more strongly than did the cover of field layer vegetation (Zvereva and Kozlov, unpublished). Predictions of the greater stress sensitivity of predators were not supported by our performance and diversity data (Fig. 1). In addition, we revealed some other general patterns, uncovered sources of variation, and provided numerical estimates for some relationships linking the responses of terrestrial biota to industrial pollution with the characteristics of polluters and the climate of the impacted area.

Fig. 3. Relationships between the duration of pollution impact and the magnitudes of the pollution effects on individual performance, abundance, and diversity of organisms. Dotted line indicates non-significant regression model. For statistics, consult Table 5.

Fig. 4. Mean effect sizes, 95% confidence intervals, and sample sizes for effects of industrial polluters observed in different biomes. BF ¼ boreal forest or taiga, DS ¼ desert scrub, MS ¼ Mediterranean scrub, TF ¼ temperate broadleaf deciduous forest, TG ¼ temperate grassland, TS ¼ tropical savannah, TU ¼ tundra.

4.2. Plurality of variation sources Researchers exploring the effects of industrial pollution on terrestrial ecosystems usually choose a single polluter as ‘a model’,

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Table 6 Results of ANCOVAs (F-values and significance levels P) investigating dependence of the effect sizes (magnitudes of the responses to pollution) from the trophic level (producers, primary consumers, secondary consumers, and decomposers) and climatic characteristics of the area adjacent to the polluter. Source of variation

Trophic level (L) Climate (C) L *C Error

Mean temperature of July

Annual precipitation

df

df

F

P

3

1.66

0.17

1 3 1056

1.73 3.36 e

0.19 0.02 e

Water balance

F

P

3

1.78

0.15

1 3 1056

1.68 1.68 e

0.20 0.17 e

df

F

P

3

1.95

0.12

1 3 1056

3.42 0.62 e

0.07 0.60 e

and examples of this are endless, while comparisons between at least two polluted areas are very rare. In a random sample of 1000 publications describing the effects of point polluters on terrestrial biota, only 9% of the publications reported the effects of two or more polluters (Kozlov et al., 2009). Even among these studies, only rarely was it asked whether some effects were shared among the studied impact zones, and what were the reasons behind the observed differences. Prediction of long-term consequences of pollution’s impact on biota at the local, regional and (to a certain extent) global scales requires numerical estimates linking magnitudes of the effects with some characteristics of both the pollution and the affected community (Zvereva et al., 2008; Kozlov et al., 2009). In spite of the long history of the research, we are still far from reaching this goal, which was determined to be a major end purpose of any stressresponse analysis decades ago (Barrett et al., 1976). One of the reasons for this slow progress was the lack of knowledge of the factors that should be accounted for to achieve sufficient predictive power of a model. Our analysis, in combination with earlier meta-analyses by our team (Ruotsalainen and Kozlov, 2006; Zvereva et al., 2008, 2010b; Zvereva and Kozlov, 2010a,b), demonstrates that the effects of pollution depend on characteristics of the polluter (type, amount of emissions, duration of the impact), the affected organisms (trophic level, life history), the measured character (individual performance, abundance, diversity), and the environment (biome, climate). Furthermore, the effect of one factor is often modified by other factors, as indicated by the numerous interactions that were discovered in our analysis. These findings stress, in particular, the need to record (in observational studies) or to control (in manipulative studies) not only the concentration(s) of principal pollutant (s), but also other environmental factors that may substantially

Fig. 5. Relationships between mean temperatures in July and the magnitudes of the pollution effects on organisms from four trophic levels. Dotted lines indicate nonsignificant regression models. For statistics, consult Table 5.

modify the responses of organisms and ecosystems to pollution. Significant differences between the effects imposed by different industries (Fig. 2) suggest that the specificity of the impact should not be underestimated if the goal is to accurately predict pollution effects on biota. 4.3. Low explanatory power of individual characteristics Although we have detected a number of significant relationships between the studied continuous variables and the magnitudes of the responses to pollution (Figs. 3 and 5, Tables 3 and 5), these relationships are generally weak. The three most important groups of classificatory variables (trophic level, type of polluter, and biome) jointly explain only 7.7% of the total variation in ESs. The median value of the variation in ESs that was explained by the significant regression models (Table 5) was only 2.5%. This result, in particular, demonstrates that the discovery of these patterns is possible only after the accumulation of a sufficient amount of data: a sample of at least 500 ESs is needed to detect these weak correlations with a statistical power 95%. The plurality of variation sources and the low explanatory power of individual regression models stress the need to properly account for variation in the characteristics under study to be able to separate the pollution effects (signal) from variation caused by other factors (noise). The low signal to noise ratio increases the probability of erroneous interpretation when natural spatial or temporal variation is considered to be the consequence of the pollution impacts. 4.4. Evolutionary adaptation to pollution as general phenomenon Pollution may affect the genetic structure of populations, often leading to the development of pollution tolerance. These processes, referred as micro-evolution due to pollution (Medina et al., 2007), are best documented for microbiota, and they have been reported more frequently for plants than for animals. The heavy metal tolerance of plants, especially grasses, has been studied extensively and provides a well-documented example of rapid evolutionary adaptation (Bradshaw and McNeilly, 1981; Shaw, 1990; Macnair, 1997). Our analysis has demonstrated that the abundance of organisms generally decreases with time from the beginning of pollution, while the adverse effects of pollution on the individual performance of organisms living in polluted environments become weaker (Fig. 3). The latter result can be seen as evidence of the development of pollution resistance in the affected populations, while the decline in abundance can either be the mechanism behind this process (survival selection, which is best documented for long-lived trees: Kozlov, 2005; Eränen, 2008; Zverev, 2009) or the consequence of adaptation, which may involve some costs (Meyer and Di Giuliuo, 2003; Lukasik and Laskowski, 2007; Eränen et al., 2009). Because the detected effects were uniform across the investigated groups of organisms, we suggest that evolutionary adaptation to the impact of pollution is a general phenomenon. Therefore, the recently identified (Medina et al., 2007) gap between the studies addressing changes in the genetic structure of populations and those assessing effects at higher levels of biological organisation should be overcome to improve our understanding of ecosystem responses to pollution. The adaptation potential of individual organisms and entire ecosystems should be accounted for in models predicting pollution effects at regional and global scales, and our results provide the first numerical estimate of changes in biotic responses to pollution over time from the beginning of the impact.

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4.5. Links between individual and community-level responses are not evident Although the limitations of organism-level studies for understanding community behaviour under environmental stress have repeatedly been appreciated (Attrill and Depledge, 1997; Clements and Newman, 2002), changes in individual performance still comprise 39% of the ESs that are included in the pooled database (Table 1). Not surprisingly, we found little concordance in the responses to pollution between different levels of the hierarchy of biological organisation. First, the average magnitudes of responses, and sometimes even their directions, differ between individuals, populations, and communities for all trophic levels except for secondary consumers (Fig. 1), for which the non-significant level of variation most likely reflects the low amount of data for this trophic group (Table 1), rather than the uniformity of responses. Second, the effects of pollution on the performance and abundance of organisms demonstrated different signs of correlation with the severity of the impact (Table 5) and its duration (Fig. 3). The absence of relationships between the magnitude of the effect of pollution on diversity and the duration of the impact of pollution may suggest that a rapid decline in species richness occurs during the first years that the polluter is functioning, and diversity then remains relatively stable over time (as discussed by Zvereva et al., 2008). Unfortunately, a shortage of data on the changes in ecosystems during the first years of pollution impact prevents testing of this hypothesis. The dissimilarity between the responses observed at different levels of biological organisation suggests that ecosystems can, at least sometimes, alleviate or amplify the effects of pollution exhibited at lower levels of organisation through community- and ecosystem-level mechanisms. For example, the abundance of herbivorous arthropods increased in polluted areas in spite of a significant decrease in their individual performance (Fig. 1). At the same time, the effects of pollution on the diversity of plants are two to three times larger than the effects of pollution on the performance or abundance of these organisms (Fig. 1). The latter difference may indicate that pollution first eliminates more sensitive species from communities, and, therefore, studies addressing individual performance are generally conducted with less sensitive species. We suggest that the future efforts of pollution ecologists should be primarily directed towards exploration of the effects of pollution on populations, communities, and ecosystems. In particular, the routine comparisons of plant growth in polluted and unpolluted habitats that are still conducted by many researchers (reviewed by Zvereva et al., 2010b) can be seen as a poor use of research money, unless these comparisons form part of an integrated study exploring the effects of pollution on biotic interactions. The impact of pollution on ecosystem-level processes should be explored directly, rather than deduced from organism-level studies. 4.6. Differential responses of trophic levels may affect ecosystem functions The magnitude of the effects of pollution on individual performance did not differ between the trophic levels. This result suggests that the accumulation of some pollutants (such as heavy metals) in trophic chains (biomagnification) either is not translated into an increase in adverse effects on individual performance, or that this accumulation itself (as demonstrated by Laskowski, 1991) is an exception rather than the general rule. In contrast to the organism-level effects, the pollution-induced changes in the abundance and diversity of organisms differ between trophic levels. The most striking of these differences

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concerns primary consumers, the densities of which increase with pollution, while the abundance of all other trophic groups decreases (Fig. 1). Although this effect may well be overestimated due to a number of research biases that we discovered in published data (Kozlov et al., 2009; Zvereva and Kozlov, 2010a), the detected effect still differs from the adverse effects of pollution on the abundance of both producers and secondary consumers (Fig. 1). Differential, and sometimes opposite, pollution effects on organisms that belong to different levels of trophic chains may seriously disrupt ecosystem structure and functions. 4.7. Climate modifies responses of terrestrial biota to pollution The toxicity of pollutants, as well as the susceptibility of organisms to pollutants, may change with temperature (Cairns et al., 1975; Treshow, 1984), partly explaining the interactive effects of pollution and climate on biota that have been discovered in several case studies (Alekseev, 1991; Shevtsova, 1998). Climate influences the mobility of nutrients and toxic compounds (Klein, 1989; Tipping et al., 1999), thus changing the soil quality. Pollution may also alter the susceptibility of species and ecosystems to extreme climatic events (Sutinen et al., 1996; Grigoryev and Pakharkova, 2001). However, extensive studies on both the distribution of pollutants and their impacts on biota generally do not take into account the importance of climate. None of primary studies in our database addresses the impacts of climate on the effects caused by pollution. However, comparisons between these studies revealed geographical variation in the responses of plant communities to aerial emissions and explained this by variation in both the diversity of the original (undisturbed) communities and the mean summer temperatures (Zvereva et al., 2008). The variation in ESs among terrestrial biomes that was detected in our study confirms the pattern we observed in the effects of pollution on the diversity of vascular plants: the magnitudes of the responses increased from North to South (Fig. 4). This result casts doubt on the generality of the widespread opinion (e.g., Kryuchkov, 1993; Mallory et al., 2006; Ólafsdóttir and Runnström, 2009) that northern ecosystems are most sensitive to different kinds of human-induced disturbances. Although variation in the magnitude of pollution effects between biomes (Fig. 4) may be explained by several factors, our results indirectly suggest that the observed pattern is primarily driven by climate. The mean temperature of the growing season was one of two explanatory variables identified by stepwise regression analysis of the combined data set, and further analyses demonstrated that climate modifies pollution responses at all levels of biological hierarchy. In particular, the difference between the mean effects of pollution on plants and herbivores will increase with temperature faster than the difference between the effects of pollution on herbivores and their natural enemies (Fig. 5). Thus, we have revealed an almost overlooked (but see Settele et al., 2005) potential consequence of global warming: an increase of harmful impacts of pollution on terrestrial ecosystems. This increase may result not only from enhancement of pollution effects in a warmer climate, but also from differential responses of trophic groups to temperature increases in polluted environments, and these differential responses may lead to ‘ecological surprises’ (sensu Paine et al., 1998). 4.8. When old data might hope for a second life? We have demonstrated that information extracted from published observational studies can be used to generate new knowledge in the domain of pollution ecology. Comparisons across studies, polluters, biomes and groups of biota allowed for scaling up

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from small-scale investigations to unveil regional or global phenomena. In particular, the discovery of significant, but weak, effects of climate on biotic responses to pollution and the detection of differences between the reactions of trophic groups at various levels of biological organisation was possible only by the analysis of data from several hundred publications. These results demonstrate the importance of observational data for the development of ecological theories and call for preserving and analysing the legacy of studies that have been conducted around industrial polluters by multiple generations of scientists. However, only a fraction of the publications describing the effect of pollution on biota can be used in quantitative research synthesis. The most common reason for excluding a potentially suitable study from our meta-analysis was a lack of the numerical information (usually on variation, and often also on sample size) necessary for calculation of the effect sizes. The relatively scant reporting of summary statistics in recent publications is especially disappointing. Because this problem has been independently discovered in several meta-analyses in different fields of ecology, we strongly support the suggestion by Paillet et al. (2010) that scientific journals should ask for mean values to always be presented along with their corresponding variance and sample size. Furthermore, we would like to make it crystal clear that only properly reported data can hope for a second life in subsequent analyses; other results will be lost in time. We conclude that further observations on the changes in ecosystems, communities, and populations occurring in polluted habitats are badly needed for the development of the field of pollution ecology. Neglecting field-collected data in favour of simplified short-term experiments that tend to overestimate adverse effects (Kozlov and Zvereva, 2007; Kozlov et al., 2009; Zvereva et al., 2010a) will obviously have detrimental consequences for understanding, predicting, and mitigating the consequences of the impact of pollution on biota.

4.9. Implications for pollution control and ecosystem management Although environmental pollution is an integral part of global change (Taylor et al., 1994), current projections of ecosystem responses to climate change only rarely consider the effects of pollutants on biota that may amplify climatic stress. Our results suggest that the consequences of climate warming will differ for unpolluted and polluted areas, and polluted areas will, therefore, require specific management. We found solid support for the suggestion of Holmstrup et al. (1998) that, under a warmer climate, the existing pollution loads may become more harmful, leading to the appearance of adverse effects in areas where contamination is below the recently adopted critical limits. This is of particular concern in metal-contaminated sites because natural leaching of metals from polluted soils may last for decades, or even centuries (Tyler, 1978; Barcan, 2002). Our focus on the data collected around point polluters does not mean that we have overestimated the importance of local effects. There is no doubt that relatively minor regional increases in pollutant deposition may result in much larger ecological and economic consequences than the acute impacts of the largest individual emitters. However, our analysis demonstrated that data on the effects caused by industrial polluters are well-suited to reveal the direction and magnitude of the biotic effects of pollution in general, as well as to explore the sources of variation in the responses of organisms, communities and ecosystems. Comparison of the data on environmental contamination of the areas adjacent to point polluters with data on global deposition of pollutants (Fowler et al., 1999) suggests that our estimates of average local

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