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Metal resistance in populations of red maple (Acer rubrum L.) and white birch (Betula papyrifera Marsh.) from a metal-contaminated region and neighbouring non-contaminated regions
Environmental Pollution 164 (2012) 53e58
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Metal resistance in populations of red maple (Acer rubrum L.) and white birch (Betula papyrifera Marsh.) from a metal-contaminated region and neighbouring non-contaminated regions Fallon M. Kirkey, Jennifer Matthews, Peter Ryser* Laurentian University, Department of Biology, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6, Canada
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 November 2011 Received in revised form 7 January 2012 Accepted 12 January 2012
Metal resistance in populations of Acer rubrum and Betula papyrifera in the industrially contaminated region of Sudbury, Ontario, was compared with resistance in populations from neighbouring uncontaminated regions. In two one-season experiments, seedlings were grown outdoors on contaminated (mainly Cu, Ni) and uncontaminated substrates. Sudbury populations of both species responded less to contamination than populations from uncontaminated regions. In A. rubrum this difference was small. For both species, Sudbury plants were smaller when grown on uncontaminated substrate. B. papyrifera from Sudbury grew better on contaminated substrate than the other populations. There is indication of variation in metal resistance within the populations from the non-contaminated regions. The data shows that trees may develop adaptive resistance to heavy metals, but the low degree of resistance indicates that the development of such resistances are slower than observed for herbaceous species with shorter generation times. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Heavy metals Metal resistance Cost of resistance Trees
1. Introduction When exposed to elevated levels of heavy metals in the soil, herbaceous plants often develop resistance to these metals (Antonovics et al., 1971; Baker, 1987). The evolution of metal resistance can be rapid, a matter of decades as in examples of zincresistant populations of grasses growing near galvanized fences or electricity pylons (Bradshaw et al., 1965; Al-Hiyaly et al., 1988) or even faster around new smelters (Ernst et al., 1983). On the other hand, woody species with longer life cycles do not seem to acquire a genetically determined metal resistance as easily, their long generation times possibly preventing rapid evolution (Dickinson et al., 1991). Nevertheless, in the genus Betula several species show indications of a genetically determined metal resistance (Brown and Wilkins, 1985; Eltrop et al., 1991; Eränen, 2008). Trees also seem to be able to acquire resistance as a form of phenotypic plasticity, as examples in the genus Acer show. Cell cultures that originate from mature maple trees growing on contaminated soils have been shown to tolerate elevated levels of metals (Watmough and Dickinson, 1996; Watmough and Hutchinson, 1997), while seedlings of the same populations do not show this resistance
* Corresponding author. E-mail address: [email protected] (P. Ryser). 0269-7491/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2012.01.012
(Turner and Dickinson, 1993). Metal resistance has also been found in some clones of Salix and Populus (Dos Santos Utmazian et al., 2007; Migeon et al., 2009; Zacchini et al., 2009), but an adaptive association of this resistance with elevated levels of metals in the original habitats of the clones has not been shown. In Sudbury, Ontario, Canada, a century of mining and smelting led to a barren landscape with severely disturbed vegetation and with acidic, metal-contaminated soils (Winterhalder, 1995). Air quality has markedly improved since the early 1970’s, but soil metal levels remain elevated, especially those of Cu and Ni (SARA, 2008). Several grass species in this region have evolved a resistance to Cu and Ni (Cox and Hutchinson, 1979; Hogan and Rauser, 1979). Trees were largely killed by acid rain and metal deposition, but white birch (Betula papyrifera) and red maple (Acer rubrum) survived the devastation as stunted individuals with multi-stemmed growth and progressive dieback of the stems (Amiro and Courtin, 1981). Some individuals of these trees may originate in the time prior to the industrial contamination (James and Courtin, 1985). Cell cultures from such individuals of red maple have been shown to be resistant to heavy metals (Watmough and Hutchinson, 1997). The genera Acer and Betula are intensively studied with respect to heavy metal resistance. In the literature cited above, Betula has been found to be able to develop a constitutive, genetically determined resistance whereas in Acer only facultative resistance, i.e. phenotypic plasticity has been found. In this project we
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F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
investigated potential metal resistance in seedlings of Acer rubrum and Betula papyrifera, two species that have survived a century of metal pollution in Sudbury and co-occur in the same habitats. Here we describe metal resistance as plant performance, expressed as dry mass, on metal-contaminated substrate compared to growth on control substrate (Wilkins, 1957; Dos Santos Utmazian et al., 2007), using low-level contamination to avoid toxicity-caused mortality. For the plant’s ability to grow on contaminated substrate we use the word resistance, as we are not differentiating whether this ability is a result of avoidance of metal uptake or tolerance of high plant-internal metal concentrations (Baker, 1987). In this study, we also investigated whether a resistance, if found, is associated with reduced performance under non-contaminated conditions, as has been postulated (MacNair, 1993). Metal resistance is often tested in short-term laboratory experiments or in cell cultures, mostly for single metals. But laboratory conditions may not fully mimic the conditions the trees are adapted to, and short-term experiments may not always give a full picture of the plant’s adaptation (Wright, 2007). We assessed the resistance of the seedlings by comparing their growth on contaminated and uncontaminated substrates over one growing season under outdoor conditions. The contamination consisted of a similar mixture of metals as found in the soils of Sudbury. Besides the growth, we investigated the within-population variation in growth on contaminated and uncontaminated substrates, as the underlying genetic variation of the original population is important for a potential development of a resistance (Al-Hiyaly et al., 1993). 2. Materials and methods 2.1. Species studied and origin of the plants used Red maple (Acer rubrum L.) and white birch (Betula papyrifera Marsh.) are deciduous trees widespread in eastern North America and common in the study area. White birch is an early-successional species, whereas red maple is more shadetolerant and thrives on a wide variety of sites (Farrar, 1995). In two experiments resistance to heavy metals in these two species was investigated by testing plants originating from the contaminated region around Sudbury, Ontario, and plants originating from uncontaminated neighbouring regions (Appendix 1). The uncontaminated regions were around Powassan, 130 km east of Sudbury, and around Webbwood, 75 km west of Sudbury. In Powassan red maple was sampled at four locations and white birch at three locations. In Webbwood red maple was sampled at two, and white birch at three locations. The collected seedlings and seeds at a location originated from one to a few trees. The locations within a region were about 4e20 km apart from each other, all within an area of 25 km in diameter. We assume that with such distances gene transfer between the collection sites is low. Subsequently we will refer to the plants originating from these sample sites as populations, while understanding that they are just samples in a continuum. For maple seeds germination rate and seed size were measured, for 15e60 seeds per population. There were no significant differences in either seed size (14.0 mg and 14.6 mg; ANOVA of population averages: p ¼ 0.69, n ¼ 10) or germination rate (29% and 30%; p ¼ 0.91) between the plants with contaminated and non-contaminated
origins, when one population from Sudbury with small seeds and low germination rate had been removed from the dataset (2.5 mg; 2%). From all locations a mixed soil sample was collected, air-dried and analysed for phytoavailable elements using plasma atomic emission spectroscopy (ICP-AES; Thermal Jarrell Ash ICAP 61; Elliott Lake Field Research Laboratory, Sudbury, Ontario, Canada) after extraction with 0.1 M LiNO3 (Abedin and Spiers, 2006). Of the tested elements, Cd, Co, Cu, Ni and P had higher concentrations in Sudbury soils compared to the neighbouring regions (Table 1). There was no significant difference in soil concentrations of Al, As, Ba, Ca, Cr, Fe, K, Mg, Mn, Na, Pb, Sb, Se, Si, Sr, V, or Zn. 2.2. Experimental design For Experiment 1, newly germinated seedlings of red maple and white birch were collected between June 11 and June 27, 2008 and kept in shallow trays filled with sand for about 3 weeks until planted in the experimental substrates. For Experiment 2, red maple seeds were collected in June 2008, stored in paper bags in a laboratory and sown on moist potting soil in October. The pots were kept in an unheated shed over the winter and brought in a heated lab on April 24, 2009. The germinated seedlings were planted outdoors on potting soil in 450 ml pots until being planted in the experimental substrates. For both experiments, roots of the seedlings were gently cleaned of soil and the seedlings were transplanted into 3 L PVC tubes of 36 cm height and 10 cm diameter. The PVC tubes stood outdoors in pools filled with ground water to a depth of 10e14 cm, capillary forces maintaining the substrate moist, except for the top 2 cm. For the early stage of establishment, the seedlings were watered daily from the top. The substrate was glaciofluvial sand (St. Michel Aggregates, Webbwood Ontario, Canada). The control substrate consisted of pure sand, whereas the contaminated substrate contained 2% crushed and 2 mm-sieved slag from a NieCu smelter (Vale; Fisher-Wavy, Sudbury, Ontario, Canada). The bottom third of the pots in the contaminated treatment was filled with 1 L of pure sand to minimize the leaching of metals into the pool, followed by 2 L of 2%-slag/sand mixture. Measurements in previous experiments with a similar arrangement have shown no measurable leaching of metals in the pool water (Santala and Ryser, 2009). Roots of either species did not reach the uncontaminated part of the pot during the experiment. Mycorrhizal symbiosis influences metal resistance of woody species (Jones and Hutchinson, 1986). To facilitate and equalize mycorrhizal inoculation, we added to each pot 100 ml of the supernatant from a suspension of soil collected in a forest with both white birch and red maple in Powassan, Ontario (Experiment 1) and Walford, Ontario (Experiment 2), both uncontaminated regions outside of Sudbury. Due to the shade-tolerant nature of red maple (Loach, 1970), pools with red maple were covered with a canopy of white cotton fabric, which decreased the light intensity by approximately 50%. Each plant received 50 ml of nutrient solution twice weekly. The nutrient solution was made by mixing 3.95 g of Home Gardener All Purpose 20-20-20 fertilizer (Home Hardware, St. Jacobs, Ontario), 0.314 g MgSO4 and 0.573 g CaCl2 into 12 L of water, resulting in an addition of 6.6 mg N, 2.9 mg P, 5.5 mg K, 0.53 mg Mg, 0.70 mg S, 2.5 mg Ca, 7 mg B, 16 mg Cu, 33 mg Fe, 17 mg Mg, 0.17 mg Mo and 16 mg Zn per week and per pot. After the nutrient solution was administered, 100 ml of water was added to ensure nutrient penetration into the substrate. In Experiment 1, the seedlings were transplanted into the PVC tubes on July 12, 2008, in Experiment 2 on May 22, 2009. The substrates for the two experiments were made from sand and slag of the same general origins but different batches, resulting in differences in concentrations of available metals (Table 1). In Experiment 1 the contaminated substrate had elevated levels of Co, Cu and Ni, the same metals as found to be elevated in the Sudbury soils, except for Cd, which in the experimental substrate was below the detection limit. In the substrates of Experiment 2, the availabilities of Cu and Ni were slightly elevated in the slag-containing substrate, whereas Cd and Co could not be detected. The main reason for the
Table 1 Average concentrations of Cd, Co, Cu, Ni and P in soils of the sampling locations in the regions of Powassan, Webbwood and Sudbury, and in the control substrate (sand) and in the contaminated substrate (sand with 2% crushed slag) used in the two experiments. The values are plant-available elements after extraction in 0.01 M LiNO3 (mg g1). Data is presented only for elements with significantly elevated levels in Sudbury soils. The differences were tested with KruskaleWallis non-parametric tests.
Powassan (n ¼ 4) Webbwood (n ¼ 3) Sudbury (n ¼ 6) KruskaleWallis Experiment 1 Control (n ¼ 6) Slag mix (n ¼ 6) KruskaleWallis Experiment 2 Control (n ¼ 5) Slag mix (n ¼ 5) KruskaleWallis
Cd
Co
Cu
Ni
P
1.6 0.4 3.4 0.2 18.5 6.3 P ¼ 0.009
14.6 6.9 16.4 5.1 146.4 4.0 P ¼ 0.005
22.1 8.4 7.6 6.1 1400 224 P ¼ 0.004
26.2 6.7 23.1 1.7 3237 1170 P ¼ 0.005
62.3 22.2 47.5 25.3 1445 227 P ¼ 0.005
<1.2 <1.2 e
9.2 1.8 37.8 3.4 P ¼ 0.004
27.4 12.3 78.6 8.0 P ¼ 0.010
14.7 8.2 597.3 55.4 P ¼ 0.004
9.6 5.4 130.3 7.5 P ¼ 0.004
<0.7 <0.7 e
<2.3 <2.3 e
6.8 5.6 10.3 7.4 P ¼ 0.055
23.7 6.0 53.8 8.5 P ¼ 0.028
24.3 10.5 25.5 7.9 0 ¼ 0.916
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58 differences in metal availability between the two experiments probably was the higher pH of the sand in Experiment 2 (pH ¼ 7.1), compared to Experiment 1 (pH ¼ 5.9). 2.3. Measurements In Experiment 1, there were 6 replicate plants from each population in each treatment. Due to experimental error two populations of non-Sudbury birches had to be discarded. The above-ground size of the plants was measured nondestructively on August 24, 2008, using image analysis of digital images taken against a white background. Of each birch one picture was taken from the top. Of the taller maple plants two pictures were taken in an angle of 90 to each other, and 45 to the vertical. The images were analysed with ImageJ 1.41o (Public domain software from National Institutes of Health, USA). The plants were harvested after a period of 4 months of growth on November 5, 2008. At this time most leaves had senesced and in order to treat all plants equally the few remaining leaves were not included in the harvest. The roots were carefully washed out from the substrate with water. For each plant diameter at the base of the stem was measured with a caliper and the length of the longest root with a ruler. Dry mass was measured after drying for at least 48 h at 75 C. In Experiment 2, eight replicate plants of each red maple population in each treatment were harvested between July 20 and August 1, 2009. Total leaf area, stem diameter and rooting depth were measured at the harvest. Total plant dry mass was measured after drying for at least 48 h at 75 C. In addition, characteristics of the largest fully developed leaf were measured: fully turgescent fresh mass according to the protocol of Ryser et al. (2008), area with LI-3100 area meter (LICOR Inc., Lincoln, Nebraska, USA) and lamina thickness with Mitutoyo 547-520 IDS-type digimatic thickness gage (Mitutoyo, Kawasaki, Japan). Based on these measurements, leaf dry matter content (LDMC) was calculated as the dry mass to turgescent mass ratio, and the specific leaf area (SLA) as the leaf area to dry mass ratio. 2.4. Statistical analyses Statistical analyses were conducted with SyStat 5.2.1 for Macintosh using General Linear Models. The measured or calculated variables for each species were used as dependent variables, and slag treatment (Control vs. 2% slag), and metal contamination at origin (Sudbury vs. non-Sudbury origin) as independent factorial variables. Population (location of collection) was used as a factor nested in the exposure at origin factor. Our conclusions are based on the comparison of contaminated and non-contaminated origins of the trees, each described by the replicate populations within each category of origin. To attain a normal distribution of the data in Experiment 1 the projected area and rooting depth were logtransformed and plant dry mass Box-Cox transformed prior to analyses. In Experiment 2, dry mass and total leaf area were square root transformed, LDMC and lamina thickness were log-transformed. As the leaf traits LDMC, SLA and lamina thickness are known to show allometric variation (Vernescu and Ryser, 2009), leaf dry mass was used as a covariable in the analyses of these variables.
Table 2 Average values of the size-related variables for A. rubrum and B. papyrifera seedlings originating from a metal-contaminated region (Sudbury) and non-contaminated regions (Powassan and Webbwood) on metal-contaminated substrate and on control substrate in the two experiments. The presented regional values are mean values of 6 population means with 6 (Exp. 1) or 8 (Exp. 2) replicate plants each (1 SE). Origin
Sudbury
Treatment
Control
Experiment 1 Acer rubrum Total dry mass (g) Projected area (cm2) Stem diameter (mm) Root depth (cm) Betula papyrifera Total dry mass (g) Projected area (cm2) Stem diameter (mm) Root depth (cm) Experiment 2 Acer rubrum Total dry mass (g) Leaf area (cm2) Stem diameter (mm) Root depth (cm)
Slag
Control
To characterize the intra-population variation, coefficient of variation (CV) was calculated for each of the measured size-related variables for each population in each treatment. Differences in intra-population CVs were analysed using General Linear Models with the treatment and origin as independent factorial variables.
3. Results 3.1. Experiment 1 The addition of 2% slag to the substrate reduced growth of all red maple populations (Table 2). The projected above-ground area in August and the total dry mass without leaves in November were reduced by 67e76%, stem diameter by 30e44% and rooting depth by 76% (Tables 2, 3). The relative growth-reducing effects of slag on the measured variables were only slightly smaller for the Sudbury populations, but the origin treatment interaction was significant or close to significant (p < 0.06) for all the variables (Table 3). However, the significance of this interaction was mainly a result of the larger differences between the plants of different origins in the control treatment (Table 2). Sudbury plants were generally smaller, expressed as projected area, dry mass, diameter or rooting depth (Tables 2, 3). These differences were more pronounced on control substrate than on contaminated substrate, indicating a reduced ability of the Sudbury plants to increase their growth in the absence of metal contamination. Post-hoc tests showed significant differences (p < 0.01; Bonferroni) for dry mass, projected area and stem diameter between Sudbury and non-Sudbury populations in the control treatment, but not in the metal-contaminated treatment. Growth of white birch was reduced by the addition of slag, but less than growth of red maple. The difference in the response between plants of Sudbury and non-Sudbury origin was larger in case of white birch than in case of red maple (Tables 2, 3). Dry mass of white birch from non-Sudbury origins was reduced by the slag addition by 67%, the projected above-ground area by 64%, stem diameter by 32% and root length by 57%. For Sudbury populations the reductions were less: 36%, 26%, 14% and 51% for the four variables, respectively. Actually, in two of the six Sudbury populations, the negative effect of slag on leaf area, total dry mass, and stem diameter was less than 10%.
Table 3 Results of the General Linear Models to analyze the effects of treatment (control vs. 2% slag) and origin (Sudbury vs. non-Sudbury) on dry mass, projected leaf area (Experiment 1) or total leaf area (Experiment 2), stem diameter, and rooting depth of A. rubrum (Experiments 1 and 2) and B. papyrifera (Experiment 1). Population (location of collection; for maple 12 sites, for birch 10 sites) was used as a factorial variable nested in the origin factor. In Experiment 1 the number of replicate plants was 6, in Experiment 2 it was 8. R2
Non-Sudbury Slag
0.98 26.1 3.73 29.8
0.07 1.8 0.10 1.6
0.23 6.5 2.09 7.1
0.03 0.5 0.11 0.7
0.68 17.2 3.10 25.4
0.06 1.7 0.11 0.8
0.17 5.7 2.16 6.2
0.02 0.4 0.13 0.2
1.23 12.4 2.63 35.3
0.12 1.5 0.19 1.6
0.40 4.5 1.78 15.1
0.07 0.9 0.19 0.5
0.67 8.9 2.39 32.0
0.12 2.4 0.16 2.0
0.43 6.6 2.06 15.6
0.04 0.9 0.16 0.5
0.44 62.5 1.90 25.3
0.02 3.0 0.05 1.2
0.35 49.4 1.82 22.8
0.03 5.1 0.05 1.8
0.35 54.5 1.79 22.7
0.04 6.2 0.07 2.0
0.40 60.8 1.96 24.3
0.04 7.1 0.12 0.49
55
Experiment 1 Acer rubrum Dry mass 0.618 Proj. area 0.673 Diameter 0.488 Root depth 0.718 Betula papyrifera Dry mass 0.358 Proj. area 0.271 Diameter 0.272 Root depth 0.578 Experiment 2 Acer rubrum Dry mass 0.128 Leaf area 0.110 Diameter 0.096 Root depth 0.085
Tr Orig
Treatment
Origin
F
P
F
P
Pop F
P
F
P
161.9 223.3 100.5 295.2
<0.001 <0.001 <0.001 <0.001
11.1 9.2 4.5 1.0
0.002 0.003 0.037 0.309
1.7 1.9 0.9 2.2
0.094 0.052 0.513 0.025
3.7 7.7 7.3 3.7
0.056 0.006 0.008 0.056
23.2 8.2 12.4 91.6
<0.001 0.005 <0.001 <0.001
3.5 0 0.3 0
0.066 0.931 0.617 0.970
2.0 2.4 1.9 1.2
0.065 0.025 0.078 0.311
8.2 5.7 4.4 2.9
0.005 0.019 0.038 0.094
0.1 0.2 0.5 0.1
0.767 0.634 0.493 0.788
0.3 0.2 0.1 0.2
0.597 0.674 0.823 0.671
2.0 1.6 1.5 1.4
0.056 0.117 0.172 0.218
5.3 4.2 3.8 2.3
0.023 0.043 0.053 0.132
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F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
Table 4 Average Coefficients of Variation for 12 populations of A. rubrum and 10 populations of B. papyrifera in the two experiments with origin in contaminated (Sudbury) and non-contaminated (Powassan, Webbwood) regions in metal-contaminated and control treatments (Mean values 1 SE). Origin
Non-Sudbury Control
Experiment 1 Acer rubrum Total dry mass Projected area Stem diameter Root depth Betula papyrifera, Total dry mass Projected area Stem diameter Root depth Experiment 2 Acer rubrum Total dry mass Leaf area Stem diameter Root depth
Sudbury Slag
Control
Slag
0.43 0.34 0.18 0.17
0.05 0.07 0.03 0.06
0.59 0.66 0.35 0.41
0.08 0.08 0.05 0.05
0.49 0.39 0.31 0.36
0.07 0.05 0.04 0.06
0.38 0.34 0.26 0.25
0.03 0.04 0.05 0.04
0.43 0.81 0.35 0.26
0.12 0.13 0.05 0.07
0.67 0.88 0.32 0.30
0.12 0.13 0.05 0.07
0.69 0.83 0.32 0.28
0.10 0.11 0.04 0.05
0.63 0.67 0.24 0.15
0.10 0.11 0.04 0.05
0.54 0.55 0.24 0.34
0.07 0.08 0.03 0.04
0.42 0.05 0.44 0.05 0.25 0.01 0.34 0.07
0.45 0.13 0.48 0.13 0.19 0.04 0.37 0.09
0.39 0.06 0.40 0.06 0.16 0.04 0.33 0.05
On control substrate, Sudbury birches had lower total dry mass, lower projected area, thinner stems and shorter roots compared to non-Sudbury birches, but on the contaminated substrate all these variables were larger for the Sudbury birches. The treatment origin interaction was significant for all variables but rooting depth, for which the interaction was only a trend (p < 0.10; Table 3). However, in post-hoc tests for the two substrates separately the difference between birches of different origins was significant only for dry mass on control substrate (p < 0.05, Bonferroni). In Experiment 1 the patterns of intra-population variation (CV) of the measured variables with respect to population origin and metal treatment were similar for both species. Slag treatment decreased the CVs of all the measured variables in the Sudbury populations and increased those of the non-Sudbury populations, with the exception of birch stem diameter in the non-Sudbury populations. However, treatment origin interactions were significant for maple only (Tables 4, 5).
Table 5 Results of a General Linear model to test the effects of treatment and origin on the population Coefficient of Variation of the measured variables for A. rubrum and B. papyrifera in the two experiments. R2
Experiment 1 Acer rubrum Dry mass Projected area Diameter Root depth Betula papyrifera Dry mass Projected area Diameter Root depth Experiment 2 Acer rubrum Dry mass Leaf area Diameter Root depth
Tr Orig
Treatment
Origin
F
P
F
P
F
P
0.257 0.450 0.298 0.370
0.2 4.2 1.8 1.4
0.699 0.054 0.200 0.253
1.6 4.2 0.4 0.1
0.226 0.053 0.560 0.755
5.2 7.9 6.4 10.3
0.034 0.011 0.020 0.004
0.163 0.095 0.165 0.206
0.7 0.1 1.1 0.5
0.410 0.791 0.303 0.496
1.0 0.9 1.5 1.1
0.342 0.364 0.240 0.300
1.8 0.6 0.3 2.0
0.198 0.445 0.618 0.176
0.129 0.107 0.282 0.014
1.3 1.3 0.2 0.1
0.266 0.263 0.685 0.773
0.6 0.4 5.7 0.1
0.436 0.531 0.029 0.821
0.2 0.0 0.5 0.1
0.700 0.881 0.511 0.726
3.2. Experiment 2 In Experiment 2 with red maple, the effect of slag in the substrate was less pronounced than in Experiment 1 due to lower metal availability in soil solution (Table 1). In the non-Sudbury populations total dry mass, total leaf area, stem diameter and rooting depth decreased due to slag addition, whereas for the populations originating in Sudbury the slag addition resulted in slightly larger values in these variables (Table 2). The main effect of the treatment was not significant, but the origin treatment interaction was significant for all variables but rooting depth (Table 3). In contrast to Experiment 1, there was no significant origin treatment interaction for the CVs of the populations. On average, the populations from Sudbury had thicker leaf lamina but lower leaf dry matter content. The contrasting differences in these two variables resulted in no significant difference in SLA between the plants of different origins (Tables 6, 7). 4. Discussion The data indicate that a century of mining and smelting with associated soil contamination in the Sudbury area has resulted in genetically determined changes in B. papyrifera and A. rubrum with respect to their response to heavy metals. The negative effects of metal contamination were less pronounced in plants originating in Sudbury area than in plants originating in non-contaminated areas, both in seedlings collected in the field, or when seedlings derived from seeds were examined. Although significant, in case of A. rubrum the differences in response were small. In case of B. papyrifera the differences were more pronounced, some Sudbury populations barely responding to the contamination. Evidence of genetically determined adaptive resistance in trees has previously been found within the genus Betula (Brown and Wilkins, 1985; Eltrop et al., 1991; Eränen, 2008), but not in the genus Acer. Acer pseudoplatanus seedlings from trees growing on Zn and Pb contaminated soils were not found to be resistant to these elements (Turner and Dickinson, 1993) although a phenotypically acquired resistance could be observed in cell cultures derived from mature trees growing on these sites (Watmough and Dickinson, 1996). Metal resistance in cell cultures derived from mature trees has also been shown for red maple in the Sudbury area (Watmough and Hutchinson, 1997). Long generation times have been considered to prevent a rapid development of genetically determined adaptive heavy metal resistance in trees in general (Dickinson et al., 1991; Pulford and Watson, 2003). Our data confirms a genetically determined adaptive resistance within the genus Betula, and shows that there is also a genetic component in heavy metal response in A. rubrum. Metal resistance is a sought-after trait in trees, as this would enable the species to be used for remediation of metal-contaminated sites (Merkle, 2006; Pulford and Watson, 2003). In B. papyrifera, some of the Sudbury
Table 6 Average values for the Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) and lamina thickness for the youngest fully developed leaf of red maple seedlings in Experiment 2 originating from the contaminated (Sudbury) and non-contaminated regions (Powassan and Webbwood). The presented regional values are mean values of 6 population means with 8 replicate plants each (1 SE). Origin
Non-Sudbury
Treatment
Control
Sudbury Slag
Control
Slag
32.5 1.3 33.6 0.7 34.4 1.1 33.8 1.7 SLA (m2 kg1) 0.287 0.007 0.282 0.007 0.270 0.006 0.268 0.011 LDMC (g1 g1) 147 3 142 3 149 3 152 2 Lamina thickness (mm)
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
57
Table 7 Results of the General Linear Models to analyze the effects of slag treatment and origin on specific leaf area (SLA), leaf dry matter content (LDMC) and lamina thickness of A. rubrum populations. Population was used as a factorial variable nested in the Origin factor, and leaf dry mass as a covariable. R2
SLA LDMC Thickness
0.133 0.180 0.132
Treatment
Origin
Tr Orig
Pop
Leaf DM
F
P
F
P
F
P
F
P
0.1 1.1 0.0
0.743 0.291 0.881
1.3 8.2 6.4
0.264 0.005 0.013
2.5 2.2 1.3
0.014 0.034 0.266
1.1 0.4 1.0
0.301 0.523 0.307
populations showed a growth reduction of less than 10% in response to the experimental contamination, which may make them useful for restoration purposes. However, the actual white birch trees growing in the most severely contaminated areas in Sudbury do not appear healthy. Other stresses than metals may be contributing to this, such as thin nutrient-poor soils and seasonal droughts. Also A. rubrum showed significant differences in the response to metals between the populations originating in contaminated and non-contaminated sites, but our data do not indicate that any of the populations would actually perform well at high levels of heavy metals. In Experiment 1 all populations suffered from slag addition, and in Experiment 2 metal concentrations were well below those found on contaminated sites. Nevertheless, the small but significant differences in the degree of resistance point to gradual acquisition of metal resistance in these tree species. The slow development of resistance in these trees may be a result of long generation times, the more pronounced resistance in birch possibly reflecting shorter generation times due to its faster growth and early-successional status. In Sudbury grasses such as Agrostis scabra with much shorter generation times than trees have been found to have resistance indices around 100% (Archambault and Winterhalder, 1995). Costs of metal resistance, i.e., reduced growth of resistant plants under control conditions, have been frequently postulated (MacNair, 1993). In the case of B. pubescens subsp. czerepanovii, plants of the resistant populations were smaller than those of the non-resistant ones when grown under non-contaminated conditions (Eränen, 2008). In our experiments the smaller size of white birch and red maple of Sudbury origin when grown on noncontaminated substrate could indicate a lower growth rate as a cost of metal resistance, but which also could be an adaptation to another regional stress such a drought. Differences in leaf dry matter content and lamina thickness indicate genetic differences in growth-related traits between the populations of contaminated and non-contaminated regions. In an interspecific comparison of 10 grass species, Sabreen and Sugiyama (2008) found these traits contributing to a negative relationship between metal resistance and growth rate under uncontaminated conditions. The presence of resistant genotypes in the original population is essential for a rapid evolution of metal resistance when exposed to elevated levels of metals (Bradshaw, 1991; MacNair, 1993). AlHiyaly et al. (1993) demonstrated that variation in development of zinc resistance in populations in Agrostis capillaris around galvanized pylons was related to initial presence of resistant genotypes in a population. In the present experiment, variation patterns of the CVs among the populations of white birch and red maple indicate a more uniform presence of resistance within the Sudbury populations compared to other populations, but also that variation in resistance exists in the populations of noncontaminated areas. In Experiment 1 the variation within nonSudbury populations increased in response to metal contamination, both in maple and in birch, indicating intra-population variation in the ability to grow in the presence of heavy metals. On the other hand, size variation within the Sudbury populations was larger when grown on non-contaminated substrate. This can be
e 5.8 4.4
e 0.017 0.037
explained as a result of changes in selective forces. In a contaminated environment the primary target of selection is the ability to tolerate metals, not the growth rate. Although growth rate and resistance may to a certain degree negatively correlate with each other, variation in growth rate is determined by multiple traits and these traits are likely to show more genetic variation when not directly selected for. This will result in an increased variation in plant growth when the main selective force, metal contamination, is removed. The lack of any differences in intra-population variation in Experiment 2 can be explained by the low -level of metal contamination. We conclude that metal resistance can also evolve in trees with relatively long generation times, but it is a slow and gradual process. Although the average degree of resistance may not be high, variation in this resistance can provide material for a search for resistant genotypes for purposes of restoration of metalcontaminated environments. Acknowledgements This research was supported by a Collaborative Research and Development Grant of the Natural Sciences and Engineering Research Council of Canada (NSERC) with Vale and the Centre for Excellence in Mining Innovation (CRDPJ 372568-08) Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.envpol.2012.01.012. References Abedin, J., Spiers, G., 2006. Metal bioavailability in smelter-impacted land systems. In: Proceedings of the 31st Annual Meeting and Conference of the Canadian Land Reclamation Association, Ottawa. 1e17 pp. Al-Hiyaly, S.A., McNeilly, T., Bradshaw, A.D., 1988. The effects of zinc contamination from electricity pylons e evolution in a replicated situation. New Phytologist 110, 571e580. Al-Hiyaly, S.A.K., McNeilly, T., Bradshaw, A.D., Mortimer, A.M., 1993. The effect of zinc contamination from electricity pylons. Genetic constraints on selection for zinc tolerance. Heredity 70, 22e32. Amiro, B.D., Courtin, G.M., 1981. Patterns of vegetation in the vicinity of an industrially disturbed ecosystem, Sudbury, Ontario. Canadian Journal of Botany 59, 1623e1639. Antonovics, J., Bradshaw, A.D., Turner, R.G., 1971. Heavy metal tolerance in plants. Advances in Ecological Research 7, 1e85. Archambault, D.J., Winterhalder, E.K., 1995. Metal tolerance in Agrostic scabra from the Sudbury, Ontario, area. Canadian Journal of Botany 73, 766e775. Baker, A.J., 1987. Metal tolerance. New Phytologist 106, 93e111. Bradshaw, A.D., McNeilly, T.S., Gregory, R.P.G., 1965. Industrialization, evolution and the development of heavy metal tolerance in plants. In: Goodman, G.T., Edwards, R.W., Lambert, J.M. (Eds.), Ecology and the Industrial Society. The British Ecological Society Symposium No. 5. Blackwell Scientific Publications, Oxford, pp. 327e343. Bradshaw, A.D., 1991. Genostasis and the limits to evolution. Philosophical Transactions of the Royal Society B: Biological Sciences 333, 289e305. Brown, M.T., Wilkins, D.A., 1985. Zinc tolerance in Betula. New Phytologist 99, 91e100. Cox, R.M., Hutchinson, T.C., 1979. Metal co-tolerances in the grass Deschampsia caespitosa. Nature 279, 231e233. Dickinson, N.M., Turner, A.P., Lepp, N.W., 1991. How do trees and other long-lived plants survive in polluted environments? Functional Ecology 5, 5e11.
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Dos Santos Utmazian, M.N., Wieshammer, G., Vega, R., Wenzel, W.W., 2007. Hydroponic screening for metal resistance and accumulation of cadmium and zinc in twenty clones of willows and poplars. Environmental Pollution 148, 155e165. Eltrop, L., Brown, G., Joachim, O., Brinkmann, K., 1991. Lead tolerance of Betula and Salix in the mining area of Mechernich/Germany. Plant and Soil 131, 275e285. Eränen, J.K., 2008. Rapid evolution towards heavy metal resistance by mountain birch around two subarctic copper-nickel smelters. Journal of Evolutionary Biology 21, 492e501. Ernst, W.H.O., Verkleij, J.A.C., Vooijs, R., 1983. Bioindication of a surplus of heavy metals in terrestrial ecosystems. Environmental Monitoring and Assessment 3, 297e305. Farrar, J.L., 1995. Trees in Canada. Fitzhenry and Whiteside Ltd., Markham, and Canadian Forest Service, Ottawa, Canada. Hogan, G.D., Rauser, W.E., 1979. Tolerance and toxicity of cobalt, copper, nickel and zinc in clones of Agrostis gigantea. New Phytologist 83, 665e670. James, G.I., Courtin, G.M., 1985. Stand structure and growth form of the birch transition community in an industrially damaged ecosystem, Sudbury, Ontario. Canadian Journal of Forest Research 15, 809e817. Jones, M.D., Hutchinson, T.C., 1986. The effect of mycorrhizal infection on the response of Betula papyrifera to nickel and copper. New Phytologist 102, 429e442. Loach, K., 1970. Shade tolerance in tree seedling II: growth analysis of plants raised under artificial shade. New Phytologist 69, 273e286. MacNair, M.R., 1993. The genetics of metal tolerance in vascular plants. New Phytologist 124, 541e559. Merkle, S.A., 2006. Engineering forest trees with heavy metal resistance genes. Silvae Genetica 55, 263e268. Migeon, A., Richaud, P., Guinet, F., Chalot, M., Blaudez, D., 2009. Metal accumulation by woody species on contaminated sites in the north of France. Water, Air, and Soil Pollution 204, 89e101. Pulford, I.D., Watson, C., 2003. Phytoremediation of heavy metal-contaminated land by treesda review. Environment International 29, 529e540.
Ryser, P., Bernardi, J., Merla, A., 2008. Determination of leaf fresh mass after storage between moist paper towels: constraints and reliability of the method. Journal of Experimental Botany 59, 2461e2467. Sabreen, S., Sugiyama, S., 2008. Trade-off between cadmium tolerance and relative growth rate in 10 grass species. Environmental and Experimental Botany 63, 327e332. Santala, K.R., Ryser, P., 2009. Influence of heavy-metal contamination on plant response to water availability in white birch, Betula papyrifera. Experimental and Environmental Botany 66, 334e340. SARA-Group, 2008. Sudbury soils study. Final report, vol. 1. Sudbury area risk assessment, Sudbury, Ontario, Canada. Turner, A.P., Dickinson, N.M., 1993. Survival of Acer pseudoplatanus L. (Sycamore) seedlings on metalliferous soils. New Phytologist 123, 509e521. Vernescu, C., Ryser, P., 2009. Constraints on leaf structural traits in wetland plants. American Journal of Botany 96, 1068e1074. Watmough, S.A., Dickinson, N.M., 1996. Variability of metal resistance in Acer pseudoplatanus L. (sycamore) callus tissue of different origins. Environmental and Experimental Botany 36, 293e302. Watmough, S.A., Hutchinson, T.C., 1997. Metal resistance in red maple (Acer rubrum) callus cultures from mine and smelter sites in Canada. Canadian Journal of Forest Research 27, 693e700. Wilkins, D.A., 1957. A technique for the measurement of lead tolerance in plants. Nature 180, 37e38. Winterhalder, K., 1995. Early history of human activities in the Sudbury area and ecological damage to the landscape. In: Gunn, J.M. (Ed.), Restoration and Recovery of an Industrial Region: Progress in Restoring the Smelter-damaged Landscape Near Sudbury, Canada. Springer, New York, pp. 17e31. Wright, J.W., 2007. Local adaptation to serpentine soils in Pinus ponderosa. Plant and Soil 293, 209e217. Zacchini, M., Pietrini, F., Mugnozza, G.S., Iori, V., Pietrosanti, L., Massacci, A., 2009. Metal tolerance, accumulation and translocation in poplar and willow clones treated with cadmium in hydroponics. Water, Air and Soil Pollution 197, 23e34.
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Metal resistance in populations of red maple (Acer rubrum L.) and white birch (Betula papyrifera Marsh.) from a metal-contaminated region and neighbouring non-contaminated regions Fallon M. Kirkey, Jennifer Matthews, Peter Ryser* Laurentian University, Department of Biology, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6, Canada
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 November 2011 Received in revised form 7 January 2012 Accepted 12 January 2012
Metal resistance in populations of Acer rubrum and Betula papyrifera in the industrially contaminated region of Sudbury, Ontario, was compared with resistance in populations from neighbouring uncontaminated regions. In two one-season experiments, seedlings were grown outdoors on contaminated (mainly Cu, Ni) and uncontaminated substrates. Sudbury populations of both species responded less to contamination than populations from uncontaminated regions. In A. rubrum this difference was small. For both species, Sudbury plants were smaller when grown on uncontaminated substrate. B. papyrifera from Sudbury grew better on contaminated substrate than the other populations. There is indication of variation in metal resistance within the populations from the non-contaminated regions. The data shows that trees may develop adaptive resistance to heavy metals, but the low degree of resistance indicates that the development of such resistances are slower than observed for herbaceous species with shorter generation times. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Heavy metals Metal resistance Cost of resistance Trees
1. Introduction When exposed to elevated levels of heavy metals in the soil, herbaceous plants often develop resistance to these metals (Antonovics et al., 1971; Baker, 1987). The evolution of metal resistance can be rapid, a matter of decades as in examples of zincresistant populations of grasses growing near galvanized fences or electricity pylons (Bradshaw et al., 1965; Al-Hiyaly et al., 1988) or even faster around new smelters (Ernst et al., 1983). On the other hand, woody species with longer life cycles do not seem to acquire a genetically determined metal resistance as easily, their long generation times possibly preventing rapid evolution (Dickinson et al., 1991). Nevertheless, in the genus Betula several species show indications of a genetically determined metal resistance (Brown and Wilkins, 1985; Eltrop et al., 1991; Eränen, 2008). Trees also seem to be able to acquire resistance as a form of phenotypic plasticity, as examples in the genus Acer show. Cell cultures that originate from mature maple trees growing on contaminated soils have been shown to tolerate elevated levels of metals (Watmough and Dickinson, 1996; Watmough and Hutchinson, 1997), while seedlings of the same populations do not show this resistance
* Corresponding author. E-mail address: [email protected] (P. Ryser). 0269-7491/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2012.01.012
(Turner and Dickinson, 1993). Metal resistance has also been found in some clones of Salix and Populus (Dos Santos Utmazian et al., 2007; Migeon et al., 2009; Zacchini et al., 2009), but an adaptive association of this resistance with elevated levels of metals in the original habitats of the clones has not been shown. In Sudbury, Ontario, Canada, a century of mining and smelting led to a barren landscape with severely disturbed vegetation and with acidic, metal-contaminated soils (Winterhalder, 1995). Air quality has markedly improved since the early 1970’s, but soil metal levels remain elevated, especially those of Cu and Ni (SARA, 2008). Several grass species in this region have evolved a resistance to Cu and Ni (Cox and Hutchinson, 1979; Hogan and Rauser, 1979). Trees were largely killed by acid rain and metal deposition, but white birch (Betula papyrifera) and red maple (Acer rubrum) survived the devastation as stunted individuals with multi-stemmed growth and progressive dieback of the stems (Amiro and Courtin, 1981). Some individuals of these trees may originate in the time prior to the industrial contamination (James and Courtin, 1985). Cell cultures from such individuals of red maple have been shown to be resistant to heavy metals (Watmough and Hutchinson, 1997). The genera Acer and Betula are intensively studied with respect to heavy metal resistance. In the literature cited above, Betula has been found to be able to develop a constitutive, genetically determined resistance whereas in Acer only facultative resistance, i.e. phenotypic plasticity has been found. In this project we
54
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
investigated potential metal resistance in seedlings of Acer rubrum and Betula papyrifera, two species that have survived a century of metal pollution in Sudbury and co-occur in the same habitats. Here we describe metal resistance as plant performance, expressed as dry mass, on metal-contaminated substrate compared to growth on control substrate (Wilkins, 1957; Dos Santos Utmazian et al., 2007), using low-level contamination to avoid toxicity-caused mortality. For the plant’s ability to grow on contaminated substrate we use the word resistance, as we are not differentiating whether this ability is a result of avoidance of metal uptake or tolerance of high plant-internal metal concentrations (Baker, 1987). In this study, we also investigated whether a resistance, if found, is associated with reduced performance under non-contaminated conditions, as has been postulated (MacNair, 1993). Metal resistance is often tested in short-term laboratory experiments or in cell cultures, mostly for single metals. But laboratory conditions may not fully mimic the conditions the trees are adapted to, and short-term experiments may not always give a full picture of the plant’s adaptation (Wright, 2007). We assessed the resistance of the seedlings by comparing their growth on contaminated and uncontaminated substrates over one growing season under outdoor conditions. The contamination consisted of a similar mixture of metals as found in the soils of Sudbury. Besides the growth, we investigated the within-population variation in growth on contaminated and uncontaminated substrates, as the underlying genetic variation of the original population is important for a potential development of a resistance (Al-Hiyaly et al., 1993). 2. Materials and methods 2.1. Species studied and origin of the plants used Red maple (Acer rubrum L.) and white birch (Betula papyrifera Marsh.) are deciduous trees widespread in eastern North America and common in the study area. White birch is an early-successional species, whereas red maple is more shadetolerant and thrives on a wide variety of sites (Farrar, 1995). In two experiments resistance to heavy metals in these two species was investigated by testing plants originating from the contaminated region around Sudbury, Ontario, and plants originating from uncontaminated neighbouring regions (Appendix 1). The uncontaminated regions were around Powassan, 130 km east of Sudbury, and around Webbwood, 75 km west of Sudbury. In Powassan red maple was sampled at four locations and white birch at three locations. In Webbwood red maple was sampled at two, and white birch at three locations. The collected seedlings and seeds at a location originated from one to a few trees. The locations within a region were about 4e20 km apart from each other, all within an area of 25 km in diameter. We assume that with such distances gene transfer between the collection sites is low. Subsequently we will refer to the plants originating from these sample sites as populations, while understanding that they are just samples in a continuum. For maple seeds germination rate and seed size were measured, for 15e60 seeds per population. There were no significant differences in either seed size (14.0 mg and 14.6 mg; ANOVA of population averages: p ¼ 0.69, n ¼ 10) or germination rate (29% and 30%; p ¼ 0.91) between the plants with contaminated and non-contaminated
origins, when one population from Sudbury with small seeds and low germination rate had been removed from the dataset (2.5 mg; 2%). From all locations a mixed soil sample was collected, air-dried and analysed for phytoavailable elements using plasma atomic emission spectroscopy (ICP-AES; Thermal Jarrell Ash ICAP 61; Elliott Lake Field Research Laboratory, Sudbury, Ontario, Canada) after extraction with 0.1 M LiNO3 (Abedin and Spiers, 2006). Of the tested elements, Cd, Co, Cu, Ni and P had higher concentrations in Sudbury soils compared to the neighbouring regions (Table 1). There was no significant difference in soil concentrations of Al, As, Ba, Ca, Cr, Fe, K, Mg, Mn, Na, Pb, Sb, Se, Si, Sr, V, or Zn. 2.2. Experimental design For Experiment 1, newly germinated seedlings of red maple and white birch were collected between June 11 and June 27, 2008 and kept in shallow trays filled with sand for about 3 weeks until planted in the experimental substrates. For Experiment 2, red maple seeds were collected in June 2008, stored in paper bags in a laboratory and sown on moist potting soil in October. The pots were kept in an unheated shed over the winter and brought in a heated lab on April 24, 2009. The germinated seedlings were planted outdoors on potting soil in 450 ml pots until being planted in the experimental substrates. For both experiments, roots of the seedlings were gently cleaned of soil and the seedlings were transplanted into 3 L PVC tubes of 36 cm height and 10 cm diameter. The PVC tubes stood outdoors in pools filled with ground water to a depth of 10e14 cm, capillary forces maintaining the substrate moist, except for the top 2 cm. For the early stage of establishment, the seedlings were watered daily from the top. The substrate was glaciofluvial sand (St. Michel Aggregates, Webbwood Ontario, Canada). The control substrate consisted of pure sand, whereas the contaminated substrate contained 2% crushed and 2 mm-sieved slag from a NieCu smelter (Vale; Fisher-Wavy, Sudbury, Ontario, Canada). The bottom third of the pots in the contaminated treatment was filled with 1 L of pure sand to minimize the leaching of metals into the pool, followed by 2 L of 2%-slag/sand mixture. Measurements in previous experiments with a similar arrangement have shown no measurable leaching of metals in the pool water (Santala and Ryser, 2009). Roots of either species did not reach the uncontaminated part of the pot during the experiment. Mycorrhizal symbiosis influences metal resistance of woody species (Jones and Hutchinson, 1986). To facilitate and equalize mycorrhizal inoculation, we added to each pot 100 ml of the supernatant from a suspension of soil collected in a forest with both white birch and red maple in Powassan, Ontario (Experiment 1) and Walford, Ontario (Experiment 2), both uncontaminated regions outside of Sudbury. Due to the shade-tolerant nature of red maple (Loach, 1970), pools with red maple were covered with a canopy of white cotton fabric, which decreased the light intensity by approximately 50%. Each plant received 50 ml of nutrient solution twice weekly. The nutrient solution was made by mixing 3.95 g of Home Gardener All Purpose 20-20-20 fertilizer (Home Hardware, St. Jacobs, Ontario), 0.314 g MgSO4 and 0.573 g CaCl2 into 12 L of water, resulting in an addition of 6.6 mg N, 2.9 mg P, 5.5 mg K, 0.53 mg Mg, 0.70 mg S, 2.5 mg Ca, 7 mg B, 16 mg Cu, 33 mg Fe, 17 mg Mg, 0.17 mg Mo and 16 mg Zn per week and per pot. After the nutrient solution was administered, 100 ml of water was added to ensure nutrient penetration into the substrate. In Experiment 1, the seedlings were transplanted into the PVC tubes on July 12, 2008, in Experiment 2 on May 22, 2009. The substrates for the two experiments were made from sand and slag of the same general origins but different batches, resulting in differences in concentrations of available metals (Table 1). In Experiment 1 the contaminated substrate had elevated levels of Co, Cu and Ni, the same metals as found to be elevated in the Sudbury soils, except for Cd, which in the experimental substrate was below the detection limit. In the substrates of Experiment 2, the availabilities of Cu and Ni were slightly elevated in the slag-containing substrate, whereas Cd and Co could not be detected. The main reason for the
Table 1 Average concentrations of Cd, Co, Cu, Ni and P in soils of the sampling locations in the regions of Powassan, Webbwood and Sudbury, and in the control substrate (sand) and in the contaminated substrate (sand with 2% crushed slag) used in the two experiments. The values are plant-available elements after extraction in 0.01 M LiNO3 (mg g1). Data is presented only for elements with significantly elevated levels in Sudbury soils. The differences were tested with KruskaleWallis non-parametric tests.
Powassan (n ¼ 4) Webbwood (n ¼ 3) Sudbury (n ¼ 6) KruskaleWallis Experiment 1 Control (n ¼ 6) Slag mix (n ¼ 6) KruskaleWallis Experiment 2 Control (n ¼ 5) Slag mix (n ¼ 5) KruskaleWallis
Cd
Co
Cu
Ni
P
1.6 0.4 3.4 0.2 18.5 6.3 P ¼ 0.009
14.6 6.9 16.4 5.1 146.4 4.0 P ¼ 0.005
22.1 8.4 7.6 6.1 1400 224 P ¼ 0.004
26.2 6.7 23.1 1.7 3237 1170 P ¼ 0.005
62.3 22.2 47.5 25.3 1445 227 P ¼ 0.005
<1.2 <1.2 e
9.2 1.8 37.8 3.4 P ¼ 0.004
27.4 12.3 78.6 8.0 P ¼ 0.010
14.7 8.2 597.3 55.4 P ¼ 0.004
9.6 5.4 130.3 7.5 P ¼ 0.004
<0.7 <0.7 e
<2.3 <2.3 e
6.8 5.6 10.3 7.4 P ¼ 0.055
23.7 6.0 53.8 8.5 P ¼ 0.028
24.3 10.5 25.5 7.9 0 ¼ 0.916
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58 differences in metal availability between the two experiments probably was the higher pH of the sand in Experiment 2 (pH ¼ 7.1), compared to Experiment 1 (pH ¼ 5.9). 2.3. Measurements In Experiment 1, there were 6 replicate plants from each population in each treatment. Due to experimental error two populations of non-Sudbury birches had to be discarded. The above-ground size of the plants was measured nondestructively on August 24, 2008, using image analysis of digital images taken against a white background. Of each birch one picture was taken from the top. Of the taller maple plants two pictures were taken in an angle of 90 to each other, and 45 to the vertical. The images were analysed with ImageJ 1.41o (Public domain software from National Institutes of Health, USA). The plants were harvested after a period of 4 months of growth on November 5, 2008. At this time most leaves had senesced and in order to treat all plants equally the few remaining leaves were not included in the harvest. The roots were carefully washed out from the substrate with water. For each plant diameter at the base of the stem was measured with a caliper and the length of the longest root with a ruler. Dry mass was measured after drying for at least 48 h at 75 C. In Experiment 2, eight replicate plants of each red maple population in each treatment were harvested between July 20 and August 1, 2009. Total leaf area, stem diameter and rooting depth were measured at the harvest. Total plant dry mass was measured after drying for at least 48 h at 75 C. In addition, characteristics of the largest fully developed leaf were measured: fully turgescent fresh mass according to the protocol of Ryser et al. (2008), area with LI-3100 area meter (LICOR Inc., Lincoln, Nebraska, USA) and lamina thickness with Mitutoyo 547-520 IDS-type digimatic thickness gage (Mitutoyo, Kawasaki, Japan). Based on these measurements, leaf dry matter content (LDMC) was calculated as the dry mass to turgescent mass ratio, and the specific leaf area (SLA) as the leaf area to dry mass ratio. 2.4. Statistical analyses Statistical analyses were conducted with SyStat 5.2.1 for Macintosh using General Linear Models. The measured or calculated variables for each species were used as dependent variables, and slag treatment (Control vs. 2% slag), and metal contamination at origin (Sudbury vs. non-Sudbury origin) as independent factorial variables. Population (location of collection) was used as a factor nested in the exposure at origin factor. Our conclusions are based on the comparison of contaminated and non-contaminated origins of the trees, each described by the replicate populations within each category of origin. To attain a normal distribution of the data in Experiment 1 the projected area and rooting depth were logtransformed and plant dry mass Box-Cox transformed prior to analyses. In Experiment 2, dry mass and total leaf area were square root transformed, LDMC and lamina thickness were log-transformed. As the leaf traits LDMC, SLA and lamina thickness are known to show allometric variation (Vernescu and Ryser, 2009), leaf dry mass was used as a covariable in the analyses of these variables.
Table 2 Average values of the size-related variables for A. rubrum and B. papyrifera seedlings originating from a metal-contaminated region (Sudbury) and non-contaminated regions (Powassan and Webbwood) on metal-contaminated substrate and on control substrate in the two experiments. The presented regional values are mean values of 6 population means with 6 (Exp. 1) or 8 (Exp. 2) replicate plants each (1 SE). Origin
Sudbury
Treatment
Control
Experiment 1 Acer rubrum Total dry mass (g) Projected area (cm2) Stem diameter (mm) Root depth (cm) Betula papyrifera Total dry mass (g) Projected area (cm2) Stem diameter (mm) Root depth (cm) Experiment 2 Acer rubrum Total dry mass (g) Leaf area (cm2) Stem diameter (mm) Root depth (cm)
Slag
Control
To characterize the intra-population variation, coefficient of variation (CV) was calculated for each of the measured size-related variables for each population in each treatment. Differences in intra-population CVs were analysed using General Linear Models with the treatment and origin as independent factorial variables.
3. Results 3.1. Experiment 1 The addition of 2% slag to the substrate reduced growth of all red maple populations (Table 2). The projected above-ground area in August and the total dry mass without leaves in November were reduced by 67e76%, stem diameter by 30e44% and rooting depth by 76% (Tables 2, 3). The relative growth-reducing effects of slag on the measured variables were only slightly smaller for the Sudbury populations, but the origin treatment interaction was significant or close to significant (p < 0.06) for all the variables (Table 3). However, the significance of this interaction was mainly a result of the larger differences between the plants of different origins in the control treatment (Table 2). Sudbury plants were generally smaller, expressed as projected area, dry mass, diameter or rooting depth (Tables 2, 3). These differences were more pronounced on control substrate than on contaminated substrate, indicating a reduced ability of the Sudbury plants to increase their growth in the absence of metal contamination. Post-hoc tests showed significant differences (p < 0.01; Bonferroni) for dry mass, projected area and stem diameter between Sudbury and non-Sudbury populations in the control treatment, but not in the metal-contaminated treatment. Growth of white birch was reduced by the addition of slag, but less than growth of red maple. The difference in the response between plants of Sudbury and non-Sudbury origin was larger in case of white birch than in case of red maple (Tables 2, 3). Dry mass of white birch from non-Sudbury origins was reduced by the slag addition by 67%, the projected above-ground area by 64%, stem diameter by 32% and root length by 57%. For Sudbury populations the reductions were less: 36%, 26%, 14% and 51% for the four variables, respectively. Actually, in two of the six Sudbury populations, the negative effect of slag on leaf area, total dry mass, and stem diameter was less than 10%.
Table 3 Results of the General Linear Models to analyze the effects of treatment (control vs. 2% slag) and origin (Sudbury vs. non-Sudbury) on dry mass, projected leaf area (Experiment 1) or total leaf area (Experiment 2), stem diameter, and rooting depth of A. rubrum (Experiments 1 and 2) and B. papyrifera (Experiment 1). Population (location of collection; for maple 12 sites, for birch 10 sites) was used as a factorial variable nested in the origin factor. In Experiment 1 the number of replicate plants was 6, in Experiment 2 it was 8. R2
Non-Sudbury Slag
0.98 26.1 3.73 29.8
0.07 1.8 0.10 1.6
0.23 6.5 2.09 7.1
0.03 0.5 0.11 0.7
0.68 17.2 3.10 25.4
0.06 1.7 0.11 0.8
0.17 5.7 2.16 6.2
0.02 0.4 0.13 0.2
1.23 12.4 2.63 35.3
0.12 1.5 0.19 1.6
0.40 4.5 1.78 15.1
0.07 0.9 0.19 0.5
0.67 8.9 2.39 32.0
0.12 2.4 0.16 2.0
0.43 6.6 2.06 15.6
0.04 0.9 0.16 0.5
0.44 62.5 1.90 25.3
0.02 3.0 0.05 1.2
0.35 49.4 1.82 22.8
0.03 5.1 0.05 1.8
0.35 54.5 1.79 22.7
0.04 6.2 0.07 2.0
0.40 60.8 1.96 24.3
0.04 7.1 0.12 0.49
55
Experiment 1 Acer rubrum Dry mass 0.618 Proj. area 0.673 Diameter 0.488 Root depth 0.718 Betula papyrifera Dry mass 0.358 Proj. area 0.271 Diameter 0.272 Root depth 0.578 Experiment 2 Acer rubrum Dry mass 0.128 Leaf area 0.110 Diameter 0.096 Root depth 0.085
Tr Orig
Treatment
Origin
F
P
F
P
Pop F
P
F
P
161.9 223.3 100.5 295.2
<0.001 <0.001 <0.001 <0.001
11.1 9.2 4.5 1.0
0.002 0.003 0.037 0.309
1.7 1.9 0.9 2.2
0.094 0.052 0.513 0.025
3.7 7.7 7.3 3.7
0.056 0.006 0.008 0.056
23.2 8.2 12.4 91.6
<0.001 0.005 <0.001 <0.001
3.5 0 0.3 0
0.066 0.931 0.617 0.970
2.0 2.4 1.9 1.2
0.065 0.025 0.078 0.311
8.2 5.7 4.4 2.9
0.005 0.019 0.038 0.094
0.1 0.2 0.5 0.1
0.767 0.634 0.493 0.788
0.3 0.2 0.1 0.2
0.597 0.674 0.823 0.671
2.0 1.6 1.5 1.4
0.056 0.117 0.172 0.218
5.3 4.2 3.8 2.3
0.023 0.043 0.053 0.132
56
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
Table 4 Average Coefficients of Variation for 12 populations of A. rubrum and 10 populations of B. papyrifera in the two experiments with origin in contaminated (Sudbury) and non-contaminated (Powassan, Webbwood) regions in metal-contaminated and control treatments (Mean values 1 SE). Origin
Non-Sudbury Control
Experiment 1 Acer rubrum Total dry mass Projected area Stem diameter Root depth Betula papyrifera, Total dry mass Projected area Stem diameter Root depth Experiment 2 Acer rubrum Total dry mass Leaf area Stem diameter Root depth
Sudbury Slag
Control
Slag
0.43 0.34 0.18 0.17
0.05 0.07 0.03 0.06
0.59 0.66 0.35 0.41
0.08 0.08 0.05 0.05
0.49 0.39 0.31 0.36
0.07 0.05 0.04 0.06
0.38 0.34 0.26 0.25
0.03 0.04 0.05 0.04
0.43 0.81 0.35 0.26
0.12 0.13 0.05 0.07
0.67 0.88 0.32 0.30
0.12 0.13 0.05 0.07
0.69 0.83 0.32 0.28
0.10 0.11 0.04 0.05
0.63 0.67 0.24 0.15
0.10 0.11 0.04 0.05
0.54 0.55 0.24 0.34
0.07 0.08 0.03 0.04
0.42 0.05 0.44 0.05 0.25 0.01 0.34 0.07
0.45 0.13 0.48 0.13 0.19 0.04 0.37 0.09
0.39 0.06 0.40 0.06 0.16 0.04 0.33 0.05
On control substrate, Sudbury birches had lower total dry mass, lower projected area, thinner stems and shorter roots compared to non-Sudbury birches, but on the contaminated substrate all these variables were larger for the Sudbury birches. The treatment origin interaction was significant for all variables but rooting depth, for which the interaction was only a trend (p < 0.10; Table 3). However, in post-hoc tests for the two substrates separately the difference between birches of different origins was significant only for dry mass on control substrate (p < 0.05, Bonferroni). In Experiment 1 the patterns of intra-population variation (CV) of the measured variables with respect to population origin and metal treatment were similar for both species. Slag treatment decreased the CVs of all the measured variables in the Sudbury populations and increased those of the non-Sudbury populations, with the exception of birch stem diameter in the non-Sudbury populations. However, treatment origin interactions were significant for maple only (Tables 4, 5).
Table 5 Results of a General Linear model to test the effects of treatment and origin on the population Coefficient of Variation of the measured variables for A. rubrum and B. papyrifera in the two experiments. R2
Experiment 1 Acer rubrum Dry mass Projected area Diameter Root depth Betula papyrifera Dry mass Projected area Diameter Root depth Experiment 2 Acer rubrum Dry mass Leaf area Diameter Root depth
Tr Orig
Treatment
Origin
F
P
F
P
F
P
0.257 0.450 0.298 0.370
0.2 4.2 1.8 1.4
0.699 0.054 0.200 0.253
1.6 4.2 0.4 0.1
0.226 0.053 0.560 0.755
5.2 7.9 6.4 10.3
0.034 0.011 0.020 0.004
0.163 0.095 0.165 0.206
0.7 0.1 1.1 0.5
0.410 0.791 0.303 0.496
1.0 0.9 1.5 1.1
0.342 0.364 0.240 0.300
1.8 0.6 0.3 2.0
0.198 0.445 0.618 0.176
0.129 0.107 0.282 0.014
1.3 1.3 0.2 0.1
0.266 0.263 0.685 0.773
0.6 0.4 5.7 0.1
0.436 0.531 0.029 0.821
0.2 0.0 0.5 0.1
0.700 0.881 0.511 0.726
3.2. Experiment 2 In Experiment 2 with red maple, the effect of slag in the substrate was less pronounced than in Experiment 1 due to lower metal availability in soil solution (Table 1). In the non-Sudbury populations total dry mass, total leaf area, stem diameter and rooting depth decreased due to slag addition, whereas for the populations originating in Sudbury the slag addition resulted in slightly larger values in these variables (Table 2). The main effect of the treatment was not significant, but the origin treatment interaction was significant for all variables but rooting depth (Table 3). In contrast to Experiment 1, there was no significant origin treatment interaction for the CVs of the populations. On average, the populations from Sudbury had thicker leaf lamina but lower leaf dry matter content. The contrasting differences in these two variables resulted in no significant difference in SLA between the plants of different origins (Tables 6, 7). 4. Discussion The data indicate that a century of mining and smelting with associated soil contamination in the Sudbury area has resulted in genetically determined changes in B. papyrifera and A. rubrum with respect to their response to heavy metals. The negative effects of metal contamination were less pronounced in plants originating in Sudbury area than in plants originating in non-contaminated areas, both in seedlings collected in the field, or when seedlings derived from seeds were examined. Although significant, in case of A. rubrum the differences in response were small. In case of B. papyrifera the differences were more pronounced, some Sudbury populations barely responding to the contamination. Evidence of genetically determined adaptive resistance in trees has previously been found within the genus Betula (Brown and Wilkins, 1985; Eltrop et al., 1991; Eränen, 2008), but not in the genus Acer. Acer pseudoplatanus seedlings from trees growing on Zn and Pb contaminated soils were not found to be resistant to these elements (Turner and Dickinson, 1993) although a phenotypically acquired resistance could be observed in cell cultures derived from mature trees growing on these sites (Watmough and Dickinson, 1996). Metal resistance in cell cultures derived from mature trees has also been shown for red maple in the Sudbury area (Watmough and Hutchinson, 1997). Long generation times have been considered to prevent a rapid development of genetically determined adaptive heavy metal resistance in trees in general (Dickinson et al., 1991; Pulford and Watson, 2003). Our data confirms a genetically determined adaptive resistance within the genus Betula, and shows that there is also a genetic component in heavy metal response in A. rubrum. Metal resistance is a sought-after trait in trees, as this would enable the species to be used for remediation of metal-contaminated sites (Merkle, 2006; Pulford and Watson, 2003). In B. papyrifera, some of the Sudbury
Table 6 Average values for the Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) and lamina thickness for the youngest fully developed leaf of red maple seedlings in Experiment 2 originating from the contaminated (Sudbury) and non-contaminated regions (Powassan and Webbwood). The presented regional values are mean values of 6 population means with 8 replicate plants each (1 SE). Origin
Non-Sudbury
Treatment
Control
Sudbury Slag
Control
Slag
32.5 1.3 33.6 0.7 34.4 1.1 33.8 1.7 SLA (m2 kg1) 0.287 0.007 0.282 0.007 0.270 0.006 0.268 0.011 LDMC (g1 g1) 147 3 142 3 149 3 152 2 Lamina thickness (mm)
F.M. Kirkey et al. / Environmental Pollution 164 (2012) 53e58
57
Table 7 Results of the General Linear Models to analyze the effects of slag treatment and origin on specific leaf area (SLA), leaf dry matter content (LDMC) and lamina thickness of A. rubrum populations. Population was used as a factorial variable nested in the Origin factor, and leaf dry mass as a covariable. R2
SLA LDMC Thickness
0.133 0.180 0.132
Treatment
Origin
Tr Orig
Pop
Leaf DM
F
P
F
P
F
P
F
P
0.1 1.1 0.0
0.743 0.291 0.881
1.3 8.2 6.4
0.264 0.005 0.013
2.5 2.2 1.3
0.014 0.034 0.266
1.1 0.4 1.0
0.301 0.523 0.307
populations showed a growth reduction of less than 10% in response to the experimental contamination, which may make them useful for restoration purposes. However, the actual white birch trees growing in the most severely contaminated areas in Sudbury do not appear healthy. Other stresses than metals may be contributing to this, such as thin nutrient-poor soils and seasonal droughts. Also A. rubrum showed significant differences in the response to metals between the populations originating in contaminated and non-contaminated sites, but our data do not indicate that any of the populations would actually perform well at high levels of heavy metals. In Experiment 1 all populations suffered from slag addition, and in Experiment 2 metal concentrations were well below those found on contaminated sites. Nevertheless, the small but significant differences in the degree of resistance point to gradual acquisition of metal resistance in these tree species. The slow development of resistance in these trees may be a result of long generation times, the more pronounced resistance in birch possibly reflecting shorter generation times due to its faster growth and early-successional status. In Sudbury grasses such as Agrostis scabra with much shorter generation times than trees have been found to have resistance indices around 100% (Archambault and Winterhalder, 1995). Costs of metal resistance, i.e., reduced growth of resistant plants under control conditions, have been frequently postulated (MacNair, 1993). In the case of B. pubescens subsp. czerepanovii, plants of the resistant populations were smaller than those of the non-resistant ones when grown under non-contaminated conditions (Eränen, 2008). In our experiments the smaller size of white birch and red maple of Sudbury origin when grown on noncontaminated substrate could indicate a lower growth rate as a cost of metal resistance, but which also could be an adaptation to another regional stress such a drought. Differences in leaf dry matter content and lamina thickness indicate genetic differences in growth-related traits between the populations of contaminated and non-contaminated regions. In an interspecific comparison of 10 grass species, Sabreen and Sugiyama (2008) found these traits contributing to a negative relationship between metal resistance and growth rate under uncontaminated conditions. The presence of resistant genotypes in the original population is essential for a rapid evolution of metal resistance when exposed to elevated levels of metals (Bradshaw, 1991; MacNair, 1993). AlHiyaly et al. (1993) demonstrated that variation in development of zinc resistance in populations in Agrostis capillaris around galvanized pylons was related to initial presence of resistant genotypes in a population. In the present experiment, variation patterns of the CVs among the populations of white birch and red maple indicate a more uniform presence of resistance within the Sudbury populations compared to other populations, but also that variation in resistance exists in the populations of noncontaminated areas. In Experiment 1 the variation within nonSudbury populations increased in response to metal contamination, both in maple and in birch, indicating intra-population variation in the ability to grow in the presence of heavy metals. On the other hand, size variation within the Sudbury populations was larger when grown on non-contaminated substrate. This can be
e 5.8 4.4
e 0.017 0.037
explained as a result of changes in selective forces. In a contaminated environment the primary target of selection is the ability to tolerate metals, not the growth rate. Although growth rate and resistance may to a certain degree negatively correlate with each other, variation in growth rate is determined by multiple traits and these traits are likely to show more genetic variation when not directly selected for. This will result in an increased variation in plant growth when the main selective force, metal contamination, is removed. The lack of any differences in intra-population variation in Experiment 2 can be explained by the low -level of metal contamination. We conclude that metal resistance can also evolve in trees with relatively long generation times, but it is a slow and gradual process. Although the average degree of resistance may not be high, variation in this resistance can provide material for a search for resistant genotypes for purposes of restoration of metalcontaminated environments. Acknowledgements This research was supported by a Collaborative Research and Development Grant of the Natural Sciences and Engineering Research Council of Canada (NSERC) with Vale and the Centre for Excellence in Mining Innovation (CRDPJ 372568-08) Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.envpol.2012.01.012. References Abedin, J., Spiers, G., 2006. Metal bioavailability in smelter-impacted land systems. In: Proceedings of the 31st Annual Meeting and Conference of the Canadian Land Reclamation Association, Ottawa. 1e17 pp. Al-Hiyaly, S.A., McNeilly, T., Bradshaw, A.D., 1988. The effects of zinc contamination from electricity pylons e evolution in a replicated situation. New Phytologist 110, 571e580. Al-Hiyaly, S.A.K., McNeilly, T., Bradshaw, A.D., Mortimer, A.M., 1993. The effect of zinc contamination from electricity pylons. Genetic constraints on selection for zinc tolerance. Heredity 70, 22e32. Amiro, B.D., Courtin, G.M., 1981. Patterns of vegetation in the vicinity of an industrially disturbed ecosystem, Sudbury, Ontario. Canadian Journal of Botany 59, 1623e1639. Antonovics, J., Bradshaw, A.D., Turner, R.G., 1971. Heavy metal tolerance in plants. Advances in Ecological Research 7, 1e85. Archambault, D.J., Winterhalder, E.K., 1995. Metal tolerance in Agrostic scabra from the Sudbury, Ontario, area. Canadian Journal of Botany 73, 766e775. Baker, A.J., 1987. Metal tolerance. New Phytologist 106, 93e111. Bradshaw, A.D., McNeilly, T.S., Gregory, R.P.G., 1965. Industrialization, evolution and the development of heavy metal tolerance in plants. In: Goodman, G.T., Edwards, R.W., Lambert, J.M. (Eds.), Ecology and the Industrial Society. The British Ecological Society Symposium No. 5. Blackwell Scientific Publications, Oxford, pp. 327e343. Bradshaw, A.D., 1991. Genostasis and the limits to evolution. Philosophical Transactions of the Royal Society B: Biological Sciences 333, 289e305. Brown, M.T., Wilkins, D.A., 1985. Zinc tolerance in Betula. New Phytologist 99, 91e100. Cox, R.M., Hutchinson, T.C., 1979. Metal co-tolerances in the grass Deschampsia caespitosa. Nature 279, 231e233. Dickinson, N.M., Turner, A.P., Lepp, N.W., 1991. How do trees and other long-lived plants survive in polluted environments? Functional Ecology 5, 5e11.
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