characterising biocomplexity and soil microbial dynamics along a smelter-damaged landscape gradient

The Science of the Total Environment 311(2003) 247–259

0048-9697/03/$ - see front matter� 2003 Elsevier Science B.V. All rights reserved.doi:10.1016/S0048-9697(03)00058-5

Characterising biocomplexity and soil microbial dynamics along asmelter-damaged landscape gradient

Madhur Anand *, Ke-Ming Ma , Alexander Okonski , Sergei Levin , Dougal McCreatha, a b b b

Biology Department of Laurentian University, Sudbury, Ontario, Canada P3E 2C6a

Elliot Lake Field Research Station of Laurentian University, Elliot Lake, Ontario, Canada, P5A 2R8b

Received 15 October 2002; accepted 25 January 2003

Abstract

Soil micro-organisms are an integral but often underestimated part of plant and soil ecosystems. Long-termindustrial air pollution in the Sudbury, Ontario region has altered vegetation and soil, and therefore, possibly, soilmicrobial function. This study focuses on the historical pollution gradient resulting from a decommissioned smelternear Sudbury, and aims to determine the effect of contaminant concentrations(such as soil heavy metals) andenvironmental variables(such as soil moisture and vegetation cover) on soil microbial populations and diversity.Results suggest that increasing distance from the pollution source did not correlate well with increasing micro-organism population or diversity. Metal concentrations also did not correlate with microbial dynamics. Only soilnutrient abundance showed a significant relationship, and revealed that phosphorous may be the rate-limiting influence.Secondary affects of pollution such as soil erosion and removal of plant litter are suggested to be important causes.The study reinforces the complex nature of landscape scale recovery and shows that recovery pathways are not linearor dependent upon single variables.� 2003 Elsevier Science B.V. All rights reserved.

Keywords: Diversity; Ecological systems; Microbial; Pollution; Soil; Vegetation

1. Introduction

Biocomplexity refers to the complex interplaybetween living systems and their environment. Theterm ‘biocomplexity’ replaces its predecessor ‘bio-diversity’ to place emphasis on the emergingnotion that complexity is much more than diversityor the study of numerous but often isolated parts(Dybas, 2001). Characterisation of biocomplexity

*Corresponding author. Tel.:q1-705-675-1151x2213; fax:q1-705-675-4859.

E-mail address: [email protected](M. Anand).

should include consideration of multiple interac-tions, multiple organisational levels and spatio-temporal scales, and the detection emergentproperties(properties that cannot be observed byexamining the system ‘piece by piece’). This canbe seen as naturally related to, or to even overlap-ping with, what system ecologists(or ecosystembiologists) basically seek to do(Orloci, 1993).´The ecosystem biologist’s goal is to study therelationships between the biological, physical andchemical components of a system, but not inisolation. An important part of attaining this goal

248 M. Anand et al. / The Science of the Total Environment 311 (2003) 247–259

entails the detecting of relevant scales and theright number of components to consider in a study,as well as to filter out meaningful dynamics(orsignals) from data that is typically noisy andyorcontradictory. In reality(in case studies), this goalbecomes difficult to achieve. The ecologist oftenis reduced to gathering fragmented pieces of evi-dence and then left to piece together the systemfrom these fragments. Interactions between thebiological, physical and chemical components areoften extremely difficult to tease apart. In thispaper, we explore the biocomplexity of an ecosys-tem that is well known for the degradation it hassuffered, namely, the terrestrial ecosystem sur-rounding Sudbury, Ontario. We place our focus onsoil microbial variation, but study several biologi-cal, chemical and physical ecosystem componentswhich, we feel, should affect this variation. Weattempt to study these components in isolation, butalso through interdisciplinary and integratedanalyses.It has become quite well documented that min-

ing activities in the Sudbury region over the lastcentury have resulted in serious damage to thesurrounding terrestrial ecosystems(Hutchinsonand Symington, 1997; Dudka et al., 1995; Gunn,1995; Freedman and Hutchinson, 1980). Human-induced disturbances such as logging and oresmelting led to large-scale sulfur-dioxide emis-sions, heavy metal contamination(copper, Cu;iron, Fe; nickel, Ni, zinc, Zn), and declined flo-ristic richness. By the 1970s, smelting operationsin Coniston, Ontario(;10 km east of Sudbury)had created a barren landscape that was little morethan bare rock, mud and desiccated stumps, with-out even enough bacteria to decompose them(Lees, 2000). Several studies have attempted todocument a pollution gradient(decreasing environ-mental impact of the smelters with increasingdistance away from the point source) that seemsto dominate the landscape(Amiro and Courtin,1981; Anand et al., 2002). Nevertheless, the envi-ronmental state and the future of this ecosystemare still largely unknown. The landscape presentsitself as an important case study not just toecologists interested in the complex effects ofmanmade disturbance on ecosystems, but also to

those involved with the determination of publicpolicy related to environmental pollution.Why the focus on the soil microbial variation

of this landscape? It has been shown that revege-tation of barren lands is usually slow, due to lackof soil microorganisms(Greipsson and El-Mayas,1999), and similar situations are observed in manyother mining areas all over the world(Sadovni-kova, 1994; Pennanen et al., 1996). This is notsurprising, since microorganisms play a uniquerole in ecosystem material cycling. They areresponsible for nitrogen fixation, nutrient cycling,immobilisation of essential nutrients and produc-tion of phytohormones(Perry and Amaranthus,1990; Turkington et al., 1988; Curl and Truelove,1986). The composition of the soil microbialassemblage can: affect plant growth and the com-petitive outcome between plants; alter speciescomposition; and directly affect a species’ abilityto colonise an area(Bever, 1994; Turkington etal., 1988). Perturbations on soil microbial assem-blage can be very consequential for ecosystems.In degraded soils, micro-organism populationshave been shown to lose their resilience to distur-bances and become no longer able to perform theirnormal processes of cycling nutrients, assimilatingorganic residues and maintaining soil structure(Brady and Weil, 1999). Without essential micro-bial processes, plants can become nutrient-deprived, which in extreme cases can lead to plantdeath. The absence of plant growth can lead tosoil erosion and severely damaged ecosystems(Landmark, 1999). Therefore, soil microbialassemblage structure is considered a crucial factorin ecosystem recovery, but few studies exist onmicrobial assemblage structure response to distur-bance(Zak et al., 1992).Environmental factors that affect and potentially

alter soil microbial assemblage structure and func-tion are complex. It is well known that environ-mental variables, such as temperature, moisture,nutrient availability and soil pH, can influence thecomposition and activity of microbial assemblages(Insam, 1990; Scheu and Parkinson, 1994; Raub-uch and Beese, 1995; Couteaux et al., 1998).ˆSome authors have found that soil acidificationmay decrease microbial biomass and activity, andmay change the structure of microbial assemblages

249M. Anand et al. / The Science of the Total Environment 311 (2003) 247–259

Table 1Site characteristics along the spatial gradient

Site pHa ECa DOC TOCb Nb CyNb Vegetation(mS cm )y1 (mg kg )y1 (%) (g kg )y1

1 4.27 68 75 2.00 0.99 20.5 Mosses, green algae, dead tufted hair grass roots(Deschampsia caespitosa), Agrostis scabra, Betulapapyrifera, Vaccinium angustifolium; ss4

2 4.56 46 160 4.74 2.41 19.7 Mosses, green algae, dead tufted hair grass(D.caespitosa), B. papyrifera, Deschampsia flexuosa,V. angustifolium; ss7

3 4.53 53 243 5.87 2.68 21.7 Mosses, green algae,B. papyrifera, D. flexuosa,V. angustifolium, Quercus rubra, Acer rubrum,Pteridium aquilinum; ss15

4 4.04 54 316 5.50 2.35 23.3 Mosses, green algae,B. papyrifera, D. flexuosa,V. nangustifolium, A. rubrum, P. aquilinum,Pinus strobus, Maianthemum canadensis; ss15

5 4.19 58 481 7.59 2.85 26.2 Mosses, green algae,B. papyrifera, D. flexuosa,V. angustifolium, Q. rubra, A. rubrum, P. aquilinumP. strobus, M. canadensis, Aralia nudicaulis,Aster macrophyllum; ss27

Abbreviations: DOCsdissolved organic carbon; TOCstotal organic carbon; ECselectrical conductivity; CyNscarbon to nitro-gen ratio;ssnumber of vascular plant species.

Denotes concentration in 1:5 soilywater extract.a

Denotes amount in air-dry soil sample.b

from bacterial to fungal predominance(Sparlingand Williams, 1986; Raubuch and Beese, 1995).Increased heavy metal concentrations can alsoinfluence soil microbial assemblages. Kandeler etal. (1996) found that heavy metal contaminatedsoils lose some common biochemical propertiesthat are necessary for the functioning of the eco-system. Consequently, the functional diversity ofthe soil microbial assemblage is severelydecreased, and the specific pathways of nutrientcycling impaired.Despite the demonstrated importance of soil

microorganisms, only a few studies including thiscomponent have been conducted on the perturbedSudbury landscape. Maxwell(1991) reported thatthe soils of barren areas surrounding past andexisting smelters in the Sudbury area have anautotrophic microflora that was limited both interms of diversity and biomass; he suggested thatthe continuing erosion of soil from the barren sitesmay be a direct result of this. Indeed, extreme soilerosion has resulted in the formation of an exten-sive area, over 10,000 hectares, of barren landsurrounding the Coniston smelter. On the basis ofa lack of understanding with regard to the soil

microbial component of the terrestrial ecosystem,we examined the many factors that we believedwould most influence soil microbial assemblagestructure in this smelter-degraded landscape. Wefocused on the spatial perturbation gradient report-ed in past research, a gradient of anthropogenicperturbation still profoundly visible on the land-scape today.

2. Study sites and analytical methods

Soil samples were collected from five siteslocated at increasing distances(2, 4, 12, 16 and36 km) from the decommissioned smelter in Con-iston, Ontario, Canada, in the summer(July) of2001. These sites were representative of the his-torical perturbation gradient identified by Anandet al. (2002). Some characteristics of each site aredescribed in Table 1.At each site, 10 individual 300–400-g random

samples were collected and combined from a depthof 1–2 cm(to exclude plant litter) to 10 cm. Thiswas repeated five times and, therefore, each sitewas represented by 50 individual samples, whichcovered an area of several hundred square meters.

250M

.A

nandet

al./

The

Scienceof

theTotal

Environm

ent311

(2003)247–259

Table 2The soil microbial variation in abundance(=10 CFUg of air-dry soil) along the spatial gradient3 y1

Group Soil microbial groups Site number Fitted trend R valueno.

1 2 3 4 5

1 Total number of bacteria 26.8"24.1 27.6"14.0 135.5"54.1 66.5"20.4 132.0"42.8 ys23.128x1.021 0.8152 Oligotrophic bacteria 12.6"10.7 18.5"11.8 114.8"38.1 41.7"18.1 103.6"29.2 ys11.826x1.298 0.8303 Oligotrophic fungi 4.2"5.6 13.2"5.5 39.9"12.9 21.9"7.1 24.5"6.8 ys5.405x1.159 0.8604 Oligotrophic actinomycetes -0.2a -0.2�a 12.5"13.3 3.1"3.2 11.6"7.5 ys0.131x2.727 0.8365 Spore-forming bacteria 5.7"7.2 8.4"6.1 17.7"13.9 18.3"9.8 19.1"11.8 ys5.596x0.833 0.958*

6 Free-living N -fixing2 14.2"15.0 14.0"9.5 35.3"10.4 23.4"10.5 68.7"34.4 ys4.129x y12.931xq24.5002 0.891*

bacteria7 Free-living N -fixing2 1.4"1.3 7.0"9.0 14.2"9.1 6.1"4.8 12.3"8.5 ys2.004x1.207 0.834

actinomycetes8 Cellulolytic bacteria 11.8"8.7 16.0"8.9 63.1"40.5 24.7"12.9 79.7"37.4 ys10.759x1.064 0.8069 Cellulolytic actinomycetes 1.1"1.7 1.7"1.6 27.2"26.2 5.8"3.8 15.5"9.7 ys1.042x1.718 0.79210 Cellulolytic fungi 1.6"2.9 4.1"3.4 3.4"4.9 9.3"9.7 5.0"2.6 ys1.796x0.839 0.83611 Actinomycetes 0.3"0.3 2.5"1.2 2.4"1.9 3.4"1.1 9.7"4.4 ys0.373x1.882 0.947*

12 Filamentous fungi 0.4"0.3 3.0"2.5 21.0"8.1 18.2"8.1 12.9"4.9 ys0.554x2.431 0.924*

sum 80.3 116.2 487.0 242.4 494.6 ys75.840x1.124 0.866PCA 1 y1.704 y1.240 1.527 y0.103 1.520 ysy0.149x q1.650xy3.3152 0.816Shannon Index 1.471 1.745 1.717 1.872 1.677 ysy0.054x q0.377xq1.1582 0.907*

Zero colonies observed.a

Significant levelP-0.05.*


Fig. 1. Principal components analysis of soil microbial variation and environmental factors along the spatial gradient. Percentagesare for the amount of variation captured by the components presented:(a) shows the ordination diagram from PCA including thefirst two components;(a1) soil microbial variation(PCA1&2)88%); (a2) Soil nutrients(PCA1&2)86%); (a3) heavy metals(PCA1&2)85%); (a4) water soluble heavy metals;(A) ordination diagram(PCA1&2)84%); (a5) understory vegetation(PCA1&2)87%); (a6) the total environment(PCA1&2)80%); (b) shows the first(dominant) component variation along thegradient for the same sets of variables;(b1) PCA1)75%; (b2) PCA1)56%; (b3) PCA1)57%; (b4) PCA1)65%; (b5) PCA1)50%; (b6) PCA1)52%.

Soil samples with previously removed visible plantroots were sieved through a 2-mm sieve and usedfor the microbiological tests(Zuberer, 1994; Ger-mida, 1993; Zvyagintsev, 1991). The total numberof colony-forming units(CFU) of bacteria weredetermined by Plant Count Agarystandard methodsagar. Abundance of actinomycetes was estimatedusing casein-glycerin medium(0.30 g of casein;10.00 ml of glycerin; 2.00 g of KNO ; 2.00 g of3

K HPO ; 0.05 g of MgSO=7H O; 0.01 g of2 4 4 2

FeSO ; 0.02 g of CaCO ; 2.00 g of NaCl; and4 3

15.00 g of agar dissolved in 1 l of tap water).Microscopic filamentous fungi abundance wasassessed using Martin’s medium with bengal roseand streptomycin sulfate. For the determination ofthe number of oligotrophic microorganisms, thePlant Count Agarystandard methods agar wasdiluted 100 times. Free-living N -fixing microor-2

ganisms were enumerated using an Ashby medium.Cellulolytic microorganisms were evaluated on a


mineral medium with ground filter paper as asource of cellulose(12.00 g of filter paper; 1.00 gof K HPO ; 0.30 g of MgSO=7H O; 2.50 g of2 4 4 2

NaNO ; 0.10 g of CaCl ; 0.10 g of NaCl; 0.01 g3 2

of FeCl ; and 15.00 g of agar dissolved in 1 l of3

distilled water). Tryptic Soy Agar was used forthe enumeration of spore-forming(Bacillus) andcoryneform bacteria.Before plating, soil–water suspensions(10 g of

soil in 95 ml of sterile distilled water) were shakenwith an Eberbach Shaker Bath for 10 min at 120epm in order to separate microorganisms from soilparticles. For the determination of spore-formingbacteria, these suspensions were left standing for10 min at 808C. This effectively destroys vege-tative bacterial cells. For five replicates, appropri-ate dilutions of the soil suspensions, prepared indistilled water, were spread on the surface of asolid nutrient media. The dishes were incubatedfor a period of 3–14 days at 258C. Microorgan-isms that grew on the nutrient media were thenexamined under an optical microscope(OlympusBH-2) at a magnification from 40= to 1000=.The numbers of all microorganisms are expressedas thousands CFUg (colony-forming units per 1y1

g of air-dry soil) in this paper.An X-ray fluorescence spectrum analyser was

used to measure total levels of heavy metals insolid soil samples(arsenic, bromide, chromium,copper, iron, gallium, manganese, nickel, lead,rubidium, selenium, strontium, thorium, titanium,yttrium, zinc, zirconium). Water-soluble trace ele-ments in soil were analysed on a Varian ICP-AESin a 1:5 ratio of water to soil extracts, after 1-hagitation and membrane filtration(cadmium,cobalt, chromium, copper, iron, magnesium, man-ganese, nickel, lead, zinc). Kjeldahl nitrogen(N),total phosphorus(P), potassium (K), calcium(Ca), soil moisture, pH and electrical conductivitywere determined using the generally acceptedmethods described by Carter(1993). The vegeta-tion survey design is presented in detail in Anandet al. (2002).Principal Component Analysis(PCA) was used

extensively to study the biocomplexity of themicrobial assemblages in the environment. PCA isa multivariate statistical technique used to sum-marise and integrate multivariate data and is wide-

ly used in ecology(Gauch, 1982; Pielou, 1984).As such, focus can be moved away from compo-nent parts, towards an integrated characterisationof the community, in line with the definition ofbiocomplexity. The method relies on the identifi-cation and reduction of redundancy in the data.The product of PCA is a set of new componentsthat reflects linear combinations of the originalvariables. Often, most of the variation in thecomplete set of variables can be summarised byone or two components. Therefore, if two variablesare behaving in the same way, they will be com-bined. Biodiversity of microbial assemblages alongthe spatial gradient was measured using the Shan-non Index. We also used Cluster Analysis(single-link nearest neighbour method) as a means topotentially classify ecosystem components alongthe gradient. Cluster Analysis is similar to PCA,but it clusters variables according to their relativedistance or correlation(Podani, 2000). We per-formed cluster analysis using correlations betweenmicrobial taxa. All multivariate statistical analyseswere performed using the software package MUL-TIV (Pillar de Patta, 2001). Univariate responsecurves were fitted using polynomial regression.

3. Results

3.1. Spatial variation of microbial assemblages

Overall, the number of microorganisms variedfrom only 0.4=10 CFUg (filamentous fungi)3 y1

to 136=10 CFUg (total number of bacteria)3 y1

(see Table 2). These low values can be attributedto low soil moisture and shallow organic soilhorizons (-5 cm). The response of microbialgroups along the spatial gradient is presented inTable 2. Four microbial groups showed a signifi-cant trend in spatial dynamics. Three of these(spore-forming bacteria, actinomycetes and fila-mentous fungi) increased with a power law func-tion. One of these(free-living N -fixing bacteria)2

responded in a concave non-monotonic manner(initially decreasing and then increasing). Thevariation in microbial diversity(Shannon Index)along the spatial gradient is also shown in Table2. The best fit to this diversity data was a convexnon-monotonic curve(initially increasing and then


Fig. 2. Cluster analysis on the variation of microorganismsalong the gradient, using single-link nearest neighbour cluster-ing. Numbers on ordinate correspond to microbial groups(please see text). Numbers on abscissa are correlationcoefficients.

decreasing). This reflects highest microbial diver-sity at mid-distance sites.PCA summarised the variation of overall micro-

bial assemblages along the spatial gradient remark-ably well (Fig. 1a1). In fact, over 88% of thevariation was captured in the first two components,with Component 1 accounting for over 75%. Fig.1a1 shows that sites 1 and 2, and sites 3 and 5have similar microbial assemblages, respectively,while site 4 is very different from them. As shownin Fig. 1b1 and Table 2, there is a convex non-monotonic trend in the PCA scores of the dominantcomponent, but it is not statistically significant.Cluster analysis revealed that almost all of themicrobial groups responded to the spatial gradientin the (Fig. 2). Only the cellulolytic fungi(group10) were formed a distinct group, but the spatialdynamics were not found to display any significanttrend.

3.2. Spatial correlation of soil microbial dynamicsand environmental variation

We examined the correlation of numerous envi-ronmental factors potentially relevant to soil micro-

bial variation including: soil properties(pH, moist-ure and nutrients); heavy metal concentration(total and water-soluble levels); and understoryvegetation. In addition, the correlation between theintegration of all these factors(using PCA) andsoil microbial variation has been calculated.Soil pH variation along the spatial gradient

shows a significant convex non-monotonic trend(Table 3). The variation of microbial assemblage-level dynamics and microbial diversity have simi-lar trends as soil pH variation(Table 2); however,they are not statistically correlated(Rsy0.269,Rs0.064, respectively, see Table 3). Soil moisturedisplayed a trend opposite to soil pH(Table 3),yielding a concave non-monotonic trend. Soilmoisture was significantly but negatively correlat-ed to soil microbial diversity(Rsy0.960, Table3). Table 3 also shows that four essential soilnutrients(N, K, P and Ca) have widely varyingdynamics along the gradient. Phosphorus demon-strated a power law increase with sites, calciumincreased exponentially, nitrogen displayed a sig-nificantly convex non-monotonic curve and potas-sium showed a significantly concave non-mono-tonic curve. Only phosphorus was significantlycorrelated to soil microbial assemblage-level(PCA) variation (Rs0.951, Table 3), despite thefact that the microbial assemblage-level dynamicswas logarithmic. PCA summary of the nutrientsshowed that site 4 is an outlier in soil nutrients intwo-dimensional ordination space when comparedwith other sites(Fig. 1a2). However, PCA axis 1scores are still increasing almost monotonically(Fig. 1b2) along the spatial gradient, indicating alinear gradient.Heavy metal concentrations at all sites displayed

a non-monotonic response. Gallium(Ga, Rs0.988), manganese(Mn, Rs0.902), strontium(Sr,Rs0.986), titanium (Ti, Rs0.947), yttrium (Y,Rs0.961) and zinc(Zn, Rs0.981) are significantand concave, and selenium(Se, Rs0.998) ishighly significantly convex, while lead(Pb, Rs0.882) and thorium (Th, Rs0.926) are signifi-cantly convex (Table 3). Lead was the onlyelement correlating with soil microbial Shannondiversity(Rs0.933, Table 3). PCA summarisationrevealed that sites 1, 2 and 3 are similar in heavymetal contamination, while sites 4 and 5 are very

254M

.A

nandet

al./

The

Scienceof

theTotal

Environm

ent311

(2003)247–259

Table 3Variation of soil properties and understory vegetation along the spatial gradient, and their correlation to soil microbial PCA and diversity(Shannon Index)

Soil environment 1 2 3 4 5 Fitted trend R value PCA 1 Shannon Index

Soil propertiespH 4.27 4.32 4.44 4.24 3.94 ysy0.073x q0.363xq3.9542 0.970** y0.269 0.064Moisture(%) 19.13 10.80 7.49 1.97 9.80 ys2.150x y15.650xq33.1342 0.952* y0.530 y0.960**

PCA 1 of soil nutrients y1.08 y0.24 0.24 0.09 0.99 ys1.124Ln(x)y1.076 0.947* 0.878* 0.532P (mg kg )y1 42.4 79.0 151.2 73.2 159.4 ys46.555x0.689 0.792 0.951** 0.239Ca (%) 0.37 0.28 0.36 0.98 0.48 ys0.260e0.180x 0.590 0.125 0.612N (g kg )y1 0.99 2.41 2.68 2.35 2.85 ysy0.174x q1.412xy0.0622 0.902* 0.791 0.714K (%) 1.16 1.22 1.17 1.35 1.58 ys0.042x y0.154xq1.2982 0.976** 0.525 0.269PCA 1 of heavy metals y1.09 y1.46 y0.82 1.43 1.93 ys0.240x y0.545xy1.0002 0.948* 0.532 0.431As (mg kg )y1 12.82 27.08 17.93 19.99 10.46 ysy2.597x q14.400xq3.0192 0.800 y0.362 0.554Br (mg kg )y1 6.53 7.46 7.50 5.27 5.60 ysy0.247x q1.078xq5.9582 0.768 y0.154 y0.232Cr (mg kg )y1 54.71 59.12 48.14 121.20 61.46 ysy3.160x q26.521xq24.1292 0.450 y0.068 0.673Cu (mg kg )y1 248.78 308.58 226.99 321.39 77.59 ysy30.800x q151.850xq119.9402 0.798 y0.639 0.326Fe (%) 1.93 2.24 2.15 3.42 2.29 ysy0.109x q0.843xq1.0742 0.622 0.100 0.806Ga (mg kg )y1 9.74 11.02 11.29 13.99 16.02 ys0.280x y0.130xq9.7202 0.988** 0.645 0.486Mn (mg kg )y1 167.40 124.30 258.95 513.07 477.08 ys9.549x q43.523xq72.5562 0.902* 0.586 0.526Ni (mg kg )y1 113.77 146.97 149.46 233.70 68.98 ysy22.435x q134.320xy13.6142 0.694 y0.184 0.689Pb (mg kg )y1 23.89 37.77 38.49 56.03 42.63 ysy2.696x q21.752xq4.1702 0.882* 0.436 0.933*

Rb (mg kg )y1 39.75 47.43 40.52 36.46 53.68 ys1.567x y7.710xq49.4642 0.573 0.316 y0.112Se(mg kg )y1 2.22 2.53 2.35 1.82 1.01 ysy0.186x q0.800xq1.6302 0.998*** y0.544 y0.082Sr (mg kg )y1 150.88 127.59 147.16 196.08 240.36 ys11.750x y45.751xq180.4202 0.986** 0.563 0.189Th (mg kg )y1 2.29 5.65 7.35 5.17 4.61 ysy0.836x q5.432xy2.0882 0.926* 0.614 0.682Ti (%) 0.23 0.24 0.27 0.37 0.38 ys0.004x q0.018xq0.1982 0.947* 0.586 0.573Y (mg kg )y1 8.94 8.57 9.53 11.05 11.59 ys0.171x y0.246xq8.7962 0.961** 0.648 0.406Zn (mg kg )y1 35.86 34.03 59.40 67.53 96.69 ys3.195x y3.651xq34.5152 0.981** 0.820 0.319Zr (mg kg )y1 196.18 227.20 226.89 149.62 155.33 ysy9.114x q38.755xq175.0302 0.812 y0.216 y0.241PCA 1 of water-soluble 1.90 0.05 0.22 y0.57 y1.60 ysy1.910Ln(x)q1.829 0.950* y0.688 y0.625heavy metalsCd (mg kg )y1 0.0072 0.0049 0.0065 0.0033 0.0022ysy0.0002x y0.0001xq0.00712 0.886* y0.429 y0.581Co (mg kg )y1 0.063 0.022 0.030 0.033 0.019 ys0.004x y0.029xq0.0822 0.790 y0.598 y0.698Cr (mg kg )y1 0.0099 0.0082 0.0109 0.0314 0.0137ys0.007e0.199x 0.601 0.126 0.659Cu (mg kg )y1 1.78 0.63 0.59 0.35 0.26 ys1.699xy1.142 0.980** y0.692 y0.847Fe (%) 2.75 3.78 3.00 4.12 5.80 ys0.228x y0.727xq3.5562 0.916* 0.494 0.339Mg (mg kg )y1 1.52 2.50 3.10 3.04 4.64 ys1.543x0.615 0.964** 0.846 0.435Mn (mg kg )y1 0.94 0.44 1.68 1.22 2.11 ys0.077x y0.148xq0.8782 0.797 0.898* 0.050Ni (mg kg )y1 1.26 0.58 0.80 0.43 0.37 ysy0.507Ln(x)q1.173 0.894* y0.511 y0.817Pb (mg kg )y1 0.0098 0.0093 0.0105 0.0075 0.0324ys0.003x y0.016xq0.0242 0.891* 0.580 y0.138Zn (mg kg )y1 0.27 0.11 0.15 0.11 0.15 ys0.023xy0.163xq0.3912 0.873 y0.387 y0.935*

Understory vegetationShannon Index 1.47 1.75 1.72 1.87 1.68 ysy0.054x q0.377xq1.1582 0.907* 0.605 0.577PCA 1 for transect 2 y1.70 y1.24 1.53 y0.10 1.52 ysy0.149x q1.650xy3.3152 0.816 0.401 0.650

255M

.A

nandet

al./

The

Scienceof

theTotal

Environm

ent311

(2003)247–259

Table 3(Continued)

Soil environment 1 2 3 4 5 Fitted trend R value PCA 1 Shannon Index

Acer rubrum 0.00 0.00 0.97 4.83 0.95 ysy0.348x q2.760xy3.1042 0.622 0.201 0.728Agrostis scabra 0.65 0.28 0.11 0.00 0.00 ys0.057x y0.501xq1.0822 0.998*** y0.783 y0.824Betula papyifera 2.36 1.58 20.22 11.40 5.05 ysy2.757x q18.063xy15.7382 0.731 0.669 0.411Comptonia peregrina 0.00 0.00 0.00 1.44 0.36 ysy0.051x q0.525xy0.6482 0.569 0.106 0.677Cornus canadensis 0.00 0.00 0.00 0.43 0.00 ysy0.031x q0.227xy0.2582 0.463 y0.038 0.674Deschampsia flexuosa 0.00 0.00 7.47 2.58 1.02 ysy1.106x q7.096xy6.9122 0.703 0.674 0.323Diervilla lonicera 0.00 0.00 0.00 0.00 0.09 ys0.013x y0.059xq0.0542 0.926* 0.563 y0.073Diphasiastrum digitatum 0.00 0.00 0.00 6.24 2.43 ysy0.099x q1.701xy2.2862 0.646 0.185 0.660Gaultheria procumbens 0.00 0.00 0.00 6.15 3.65 ys0.082x q0.852xy1.5002 0.755 0.288 0.614Maianthemum canadensis 0.00 0.00 0.00 0.15 8.40 ys1.189x y5.441xq4.9502 0.931* 0.565 y0.061Pinus resinosa 0.00 0.00 0.00 0.00 1.06 ys0.151x y0.700xq0.6362 0.926* 0.563 y0.073Pinus strobus 0.00 0.00 0.00 0.00 1.43 ys0.204x y0.940xq0.8582 0.926* 0.563 y0.073Populus grandidentata 0.00 0.00 0.00 0.00 1.51 ys0.216x y0.992xq0.9062 0.926* 0.563 y0.073Picea abies 0.00 0.00 0.00 9.58 11.41 ys0.946x y2.434xq1.0982 0.937* 0.469 0.434Quercus rubra 0.18 0.00 1.87 10.62 3.83 ysy0.453x q4.5091xy5.2462 0.676 0.276 0.702Salix spp. 0.00 0.00 0.00 0.14 0.03 ysy0.006x q0.054xy0.0662 0.550 0.087 0.679Vaccinium angustifolium 0.62 0.66 5.18 14.89 49.92 ys5.369x y20.933xq17.9902 0.986** 0.642 0.137Vaccinium myrtilloides 0.08 0.00 0.00 1.40 0.00 ysy0.089x q0.656xy0.6962 0.415 y0.075 0.632Total environmentPCA 1 of all the y2.66 y1.76 y1.12 2.34 3.20 ys0.197x q0.398xy3.3642 0.971** 0.612 0.553environmental factors

P-0.05.*

P-0.01.**

P-0.001.***


different, as shown in Fig. 1a3. The overall trendof heavy metals based on PCA scores of compo-nent one are significantly concave non-monotonic(Rs0.948, Fig. 1b3, Table 3). However, this trendis not correlated to soil microbial assemblage-levelvariation or diversity(Rs0.532,Rs0.431, respec-tively, Table 3).Most responses for water-soluble heavy metals

were non-monotonic. However, some showed otherresponse types. Of the significant trends, Table 3shows that: cadmium(Cd, Rs0.886) was convexnon-monotonic; copper decreased linearly(Cu,Rs0.980); iron (Fe,Rs0.916) and lead(Pb,Rs0.891) were concave non-monotonic; magnesium(Mg, Rs0.964) followed a power law; and nickel(Ni, Rs0.894) decreased logarithmically. Manga-nese was significantly correlated with soil micro-bial assemblage-level variation(PCA, Rs0.898)while zinc is significantly correlated with microbialdiversity (Rsy0.935). A PCA performed on thewater-soluble heavy metal concentrations revealedthat three sites(2, 3 and 4) are similar in chemicalcomposition, while sites 1 and 5 were considerablydifferent. The PCA axis 1 value of the water-soluble heavy metal concentrations was a decreas-ing logarithmic function(Fig. 1b4, Table 3), butit is not correlated to soil microbial assemblage-level variation (Rsy0.688) or diversity (Rsy0.625, Table 3).All plant species showed non-monotonic

responses.Agrostic scabra, Diervillla lonicera,Maianthemum canadensis, Pinus resinosa, Pinusstrobus, Populus grandidentata, Picea abies, andVaccinium angustifolium were all significantlyconcave(R)0.920, Table 3). Shannon diversityof vegetation responds non-monotonically, but con-trary to individual species responses, shows aconvex trend. PCA summary of vegetation isshown in Fig. 1a5, but PCA scores did not showa significant trend(Fig. 1b5). No correlation wasfound between single plant species, plant diversity,or assemblage-level plant variation and microbialvariation.In order to examine the correlation of soil

microbial assemblage with overall environmentalfactors (i.e. the ‘total’ environment), PCA wasemployed again. PCA successfully integrates thesoil environmental factors and heavy metals; this

is shown clearly in ordination space where site 1,2 and 3 are similar, and sites 4 and 5 are verydifferent (Fig. 1a6). The PCA scores of the totalenvironment is increasing in a concave non-mon-otonic fashion(Fig. 1b6, Table 3), however, theyare not correlated with soil microbial assemblagevariation (Rs0.612) or Shannon diversity(Rs0.553, Table 3).

4. Discussion

The effect of industrial pollution on ecosystemsis not simple. Pollution does not necessarily affectall components of an ecosystem in the same way,and the effects may interact producing surprisinglocal behaviour. These effects may also not followpredictable or linear gradients of ecosystemresponse. The historic point source pollution in theSudbury landscape from nickel mining and smel-tering (metallurgical) activities is an example ofsuch biocomplexity. Past studies have shown thata clear pollution gradient exists in the lichencommunities and forest vegetation in the Sudburylandscape(Freedman and Hutchinson, 1980; Ami-ro and Courtin, 1981; Anand et al., 2002). Thishas also been seen to be the case in analogoussystems, for example, the Harjaavala landscape(Southwestern Finland), which has been impactedfrom similar air and heavy metal pollution fromnickel mining and smeltering activities(Saleemaet al., 2001). Few studies, however, have attemptedto examine the effects of point source pollution onthe soil environment(biological, physical andchemical components) and its relationship to otherecosystem components(e.g. forest understoryvegetation).Focus on the soil microbial assemblage revealed

that most taxa showed monotonic, non-linearincrease in abundance along the spatial gradient,as we might have expected based on the assump-tion that the spatial gradient represents sites withdecreasing pollution effects. The only exceptionwas the free-living N -fixing bacteria, which2

showed a significant concave non-monotonicresponse. Interestingly, the two assemblage-levelmeasures, namely, PCA and Shannon diversity,showed a convex non-monotonic response, withthe latter being statistically significant. This sug-


gests that at the assemblage level, a linear gradientin microbial response does not exist. On the otherhand, if mid-distance sites indeed represent mid-perturbation levels(or disturbance), then the non-monotonic response of diversity is to be expectedunder the intermediate disturbance hypothesis(highest diversity occurs at intermediate distur-bance levels), well known in ecology(Connell,1978). Indeed, this would also correspond to theplant diversity response(convex, non-monotonic).The vegetation along the spatial gradient variesfrom severely damaged barrens(site 1) to unnot-iceably damaged forest(site 5) (Table 1). Thealteration of vegetation should affect the soil envi-ronment in many ways, including changing lightintensity, wind, temperature, and soil moisture andnutrients; these should lead to remarkable changesin soil microbial diversity and structure(Greips-son, 1995; Greipsson and Crowder, 1992; Greips-son and El-Mayas, 1999). However, we found nosignificant correlations directly between soilmicrobial variation and vegetation. The biologicalevidence(microbial diversity and vascular plantdiversity) on its own may suggest that there existsa linear perturbation gradient. However, theseresults also might suggest that the spatial gradientitself is a concave(initially decreasing and thenincreasing) perturbation gradient.Further insight into both characterisation of the

spatial gradient and into the patterns observed inthe soil microbial dynamics was expected throughconsideration of the soil properties, pollution levelsand specific vegetation correlates. We begin withthe discussion of soil properties(nutrients, mois-ture and pH). When all nutrients were summarisedusing PCA, they reflected a significant non-linearbut monotonic response along the spatial gradient.Nutrient levels, in general, are increasing butlevelling off. Interestingly, this corresponded to(and significantly correlated with) the summarisedmicrobial dynamics(by PCA, but not ShannonIndex), despite the fact that the latter was foundto be non-monotonic. However, this pattern wasnot necessarily seen when soil properties wereconsidered independently. For example, phospho-rus and calcium appear to be increasing exponen-tially with distance from the smelter, butphosphorous level was the only soil property that

significantly correlated to microbial assemblage-level variation(PCA). Unsurprisingly, our analysisreveals that nutrients are important for microbialdynamics. They were limiting in sites close to thesmelter, but perhaps not at distant sites. Phospho-rous was the most important and limiting nutrient.Soil moisture was the only soil property that

correlated significantly to microbial diversity; thisis a well-known relationship. It happens to showa significant concave response; however, we can-not generalise very much about this trend due tothe high variability of soil moisture within thesites and in time. Soil pH showed a highly signif-icant convex response, but it was not significantlycorrelated with either microbial assemblage-levelvariation or diversity.Heavy metal contamination of soils was expect-

ed, in general, to decrease with increasing distancefrom the smelter, but this was not the case. Weexplain this by the fact that since the decommis-sioning of the Coniston smelter, a 381-m super-stack was constructed which has the ability totransport heavy metals over larger distances. Nick-el and copper did not show significant trends alongthe gradient; they seem to remain at high levels atall sites. Selenium showed a highly significantconvex trend, while zinc showed a significantconcave trend. Among all metals analysed, onlylead was significantly correlated to microbialdiversity. Summarisation of total heavy metal lev-els by PCA revealed a concave trend that wassimilar to soil moisture, but opposite to pH.Water-soluble heavy metals are thought to be

more available to organisms, and thus may beconsidered to be better indicators for microbialdynamics. Manganese was the only water-solubleheavy metal that was correlated to microbialassemblage, and zinc was the only one to microbialdiversity; however, neither of them showed signif-icant trends along the gradient. Despite this, thesummary of these based on PCA showed that therewas a gradient(logarithmic).In general, the impact of heavy metals on soil

microbes is not remarkable. Although the totalconcentration of heavy metals in soils of all siteswas rather high compared uncontaminated sites,the concentrations of water-soluble heavy metalswere relatively low. The impact of such low


concentrations of heavy metals on soil microorgan-isms can be identified in manipulative experiments.However, it is usually very difficult or even impos-sible to find an influence of such low concentra-tions on the number of soil microorganisms infield conditions. The soil pH variation was nothigh enough to cause significant changes in thestructure of soil microbial communities. We aretempted to agree with Gundermann and Hutchin-son(1995) that the main reason for relatively lowconcentrations of heavy metals and slightly higherpH in soils of sites nearest to the smelter wasrelated to erosion processes. The authors reportedthat over the past 20 years since the closure of theConiston facility in 1972, concentrations of nickel,copper and hydrogen ions of soils at sites locatedup to 7 km from the smelter have decreaseddramatically. Unfortunately, the low percentage oforganic matter in these soils has also decreasedover the same time. Both these results suggestleaching and erosion processes at work, removingmost of the metals from the top 5-cm layer of thesoil profile.It appears that what strongly influence soil

microbial variation were soil nutrients and mois-ture, not soil pH and heavy metal contamination,the two main influences from the smelter pollutionthat we anticipated. This suggests that the impactof smelter on soil microbial community is disap-pearing and that soil erosion is probably one ofthe main negative impacts of mining activity atpresent. It seems that soil erosion is the mainfactor limiting the growth and development of soilmicroorganisms. The erosion leads to dramaticdecrease of thickness of soil organic horizon, aswell as to losses of macro-nutrients necessary formany groups of soil microorganisms.The results of this study highlight important

aspects about biocomplexity, particularly in thecontext of pollution impacts. First, components ofbiocomplexity may not respond to perturbation insimilar ways. Second, the components may interactto produce unanticipated effects. Comprehensiveunderstanding about the effect of humankind onecosystems must include a biocomplexityapproach, where diversity as a whole, the interac-tions between its components, and finally interac-

tions of these with the environment are allconsidered(NSF, 2002).

Acknowledgments

Funding from the Elliot Lake Research FieldStation of Laurentian University(DM), LaurentianUniversity Research Fund(MA) and NSERC ofCanada(MA and DM) is gratefully acknowledgedas well as support from the Canadian ShieldEnvironmental Research Network(CSERN).Thanks also to S. Kaufman for editing of themanuscript.

References

Amiro BD, Courtin GM. Patterns of vegetation in the vicinityof an industrially disturbed ecosystem, Sudbury, Ontario.Can J Botany 1981;59:1623–1639.

Anand M, Tucker BC, Desrochers R. Ecological monitoringof terrestrial ecosystem recovery from man-made perturba-tion: assessing community complexity. In: Brebbia CA,Martin-Duque JF, editors. Air Pollution X. UK: WIT Press,2002. p. 341–350.

Bever JD. Feedback between plants and their soil communitiesin an old field community. Ecology 1994;75:1965–1977.

Brady NC, Weil RR. The Nature and Property of Soils. 12thedition. Upper Saddle Hall, NJ: Prentice Hall, 1999. (881pp)

Carter MR. Soil Sampling and Methods of Analysis. USA:Lewis Publishers, 1993. (823 pp).

Connell JH. Diversity in tropical rainforests and coral reefs.Science 1978;199:1302–1310.

Couteaux M, Raubuch M, Berg M. Response of protozoanˆand microbial communities in various coniferous forest soilsafter transfer to forests with different levels of atmosphericpollution. Biol Fertil Soils 1998;27:179–188.

Curl EA, Truelove B. The Rhizosphere. Berlin: Springer–Verlag, 1986. (288 pp).

Dudka S, Ponce-Hernandez R, Hutchinson TC. Current levelof total element concentrations in the surface layer ofSudbury’s soils. Sci Total Environ 1995;162:161–171.

Dybas CL. From biodiversity to biocomplexity: a multidisci-plinary step towards understanding our environment. Bio-science 2001;51:426–430.

Freedman B, Hutchinson TC. Effects of smelter pollutants onforest leaf litter decomposition near a nickel–copper smelterat Sudbury, Ontario. Can J Bot 1980;58:1722–1736.

Gauch HG. Multivariate Analysis in Community Ecology.New York: Cambridge University Press, 1982. (298 pp).

Germida JJ. Cultural methods for soil microorganisms. In:Carter MR, editor. Soil Sampling and Methods ofAnalysisLewis Publishers, USA, 1993. p. 263–276.


Greipsson S. Effect of iron plaque on roots of rice on growthof plants in excess zinc and accumulation of phosphorus inplants in excess copper and nickel. J Plant Nutr1995;18:1659–1665.

Greipsson S, Crowder AA. Amelioration of copper and nickeltoxicity by iron plaque on roots of rice(Oryza sativa). CanJ Botany 1992;70:824–830.

Greipsson S, El-Mayas H. Large-scale reclamation of barrenlands in Iceland by aerial seeding. Land Degradation Devel-op 1999;10:185–193.

Gundermann DE, Hutchinson TC. Changes in soil chemistry20 years after the closure of a nickel–copper smelter nearSudbury, Ontario, Canada. J Geochem Explor 1995;52:231–236.

Gunn JM. Restoration and recovery of an industrial region.New York, NY: Springer-Verlag, 1995. 358 pp.

Hutchinson TC, Symington MS. Persistence of metal stress ina forested ecosystem near Sudbury, 66 years after closureof the O’Donnell roast bed. J Geochem Explor1997;58:323–330.

Insam H. Are the soil microbial biomass and basal respirationgoverned by the climate regime? Soil Biol Biochem1990;22:525–532.

Kandeler E, Kampichler C, Horak O. Influence of heavymetals on the functional diversity of soil microbial com-munities. Biol Fertil Soils 1996;23:299–306.

Landmark A. Soil degradation and the use of agricultural andorganic industrial by products as soil amendments 1999;Nhttp:yywww.hort.agri.umn.eduyh5015y99fpapersylandmark.htmM.

Lees D. Green rebirth. Can Geographic 2000;3:60–70.Maxwell CD. Floristic changes in soil algae and cyanobacteriain reclaimed metal contaminated land at Sudbury, Canada.Water Air Soil Pollut 1991;60:381–393.

National Science Foundation. Biocomplexity in the Environ-ment(BE) for: Integrated Research and Education in Envi-ronmental Systems 2002;Nhttp:yywww.nsf.govypubsy2002ynsf02010ynsf02010.htmlM.

Orloci L. The complexities and scenarios of ecosystem anal-´ysis. In: Patil GP, Rao CR, editors. Multivariate Environ-mental Statistics. New York: North HollandyElsevier, 1993.p. 421–430.

Pillar de Patta V. MULTIV, Universidade Federal Rio Grandedo Sul, Porto Alegre, Brazil. 2001.

Pennanen T, Frostegard A, Fitze H, Baath E. Phospholipidfatty acid composition and heavy metal tolerance of soilmicrobial communities along two heavy metal-polluted gra-

dients in coniferous forests. Appl Environ Microbiol1996;62:420–428.

Perry D, Amaranthus M. The plant–soil bootstrap: microor-ganisms and reclamation of degraded ecosystems. In: BergerJ, editor. Environmental Restoration: Science and Strategiesfor Restoring the Earth. Washington, DC: Island Press,1990. p. 94–102.

Pielou EC. The Interpretation of Ecological Data. New York:John Wiley & Sons, 1984. (263 pp).

Podani J. Introduction to the Exploration of MultivariateBiological Data. Leidan: Backhuys Publishers, 2000. (200pp).

Raubuch M, Beese F. Pattern of microbial indicators in forestsoils along a European transect. Biol Fertil Soils1995;19:362–368.

Sadovnikova LK. Heavy metals. In: Orlov DS, Vasil’evskayaVD, editors. The Soil and Ecological Monitoring and SoilProtection. Moscow Univ. Press, 1994. pp. 362–368.(inRussian).

Saleema M, Vanha-Majamaa I, Derome J. Understory vegeta-tion along a heavy-metal pollution gradient in SW Finland.Environ Pollut 2001;112:339–350.

Scheu S, Parkinson D. Changes in bacterial and fungal bio-mass C, bacterial and fungal biovolume and ergosterolcontent after drying, remoisting and incubation of differentlayers of cool temperate forest soils. Soil Biol Biochem1994;26:1515–1525.

Sparling GP, Williams BL. Microbial biomass in organic soils:estimation on biomass C, and effect of glucose or celluloseamendments on the amounts of N and P released byfumigation. Soil Biol Biochem 1986;18:507–513.

Turkington R, Holl FB, Chanway CP, Thompson JD. Theinfluence of microorganisms, particularly Rhizobium, onplant competition in grass-legume communities. In: DavyAJ, Hutchings MJ, Watkinson AR, editors. Plant PopulationEcology. Oxford, UK: Blackwell Scientific Publications,1988. p. 346–366.

Zak JC, Fresquez PR, Visser S. Soil microbial processes anddynamics: their importance to effective reclamation. In:Wade GL, Chambers JC, editors. Evaluating ReclamationSuccess: The Ecological Considerations. General TechnicalReport NE-164USDA Forest Service, 1992. p. 3–16.

Zuberer DA. Recovery and enumeration of viable bacteria. In:Weaver RW, editor. Soil Science. Methods of Soil Analysis.Part 2, Microbiological and Biochemical PropertiesyEdito-rial Committee. Soil Science Society of America BookSeries, vol. No. 5 1994. p. 119–144.

Zvyagintsev DG. Methods of Soil Microbiology and Biochem-istry. Moscow, Russia: Moscow Univ. Press, 1991. 303 pp.

characterising biocomplexity and soil microbial dynamics along a smelter-damaged landscape gradient

Documents