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A REVIEW OF 137Cs TRANSFER TO FUNGI AND CONSEQUENCES FOR MODELLING ENVIRONMENTAL TRANSFER
GILLETT*, A.G. and CROUT, N.M.J
Environmental Science Division, School of Biological Sciences, University of Nottingham, LE12 5RD, UK
* To whom all correspondence should be addressed.
Email: [email protected]
ABSTRACT 1
A review of the published literature describing 137Cs transfer to fungi was carried out, 2
summarising the collated data to determine factors controlling transfer and identify an 3
appropriate modelling approach to predict future contamination. 4 137Cs transfer ratios (TR) are derived for fungi species collected within Europe and the CIS. 5
Considerable variability in TRs is demonstrated, with TRs varying between 10 m2 kg-1 across all species and over three orders of magnitude for individual species (e.g. 7
Boletus badius). Generally, meta-information (such as habitat and soil attributes) is poorly 8
reported in the literature so that classification of the TR is limited to the effect of nutritional type 9
(P saprophytic parasitic. Analysis of the literature data set 10
(a heterogeneous source) suggests that there is no statistical evidence to indicate a decrease in 11
TRs for 10 years after the Chernobyl accident. 12
Spatial analysis of a data set for Belgium indicates variability in 137Cs transfer within a 13
sampling location, such that fruitbodies collected over a scale of approximately 5km would show 14
activities as variable as those collected over a much larger scale ( or > 50km). Therefore, it is 15
proposed that the collated data sets for individual species can be used to derive best estimates 16
for the parameters describing the distribution of TRs. These can then be used to estimate an 17
effective TR, which, when combined with local soil deposition level and frequency and effect of 18
culinary practices, can give an estimate of the activity of fungi consumed by the general 19
population. 20
21
22
23
24
INTRODUCTION 25
The importance of the consumption of fungal fruitbodies (i.e. sporocarps) by some animal 26
species, such as roe deer and sheep, as a source of 137Cs intake has been discussed by numerous 27
authors (Hove et al., 1990; Johanson et al., 1994 and Kiefer et al., 1996). The intake of fungi 28
(term used by the authors to indicate fungi sporocarps in the subsequent text) by humans has 29
been shown to be a major factor in autumnal increases of radiocaesium activity of rural 30
populations in Russia (Skuterud et al., 1997a). Urban populations have also been found to have 31
significant radiocaesium intake due to fungi (Mehli and Strand, 1998). Ban-nai et al. (1997) 32
estimated fungi consumption could account for 32% (6 Bq year-1) of the total annual dietary 33
intake of radiocaesium within Japan. Higher potential annual intakes of 137Cs (based on the 34
measured daily dietary activities of potato, vegetable, beef, milk and cranberry collected between 35
September and October 1994) of 4380 Bq per person (adult males) have been calculated for the 36
Chernobyl affected Rovno and Volynsky regions of the Ukraine (Shiraishi et al., 1997). Shutov 37
et al. (1996) estimated fungi could contribute up to 60-70 % of dietary 137Cs intake of those 38
adults collecting fungi and berries from forests within Russia. 39
Although, fungal sporocarps may only account for 0.5% of the overall inventory of radiocaesium 40
(ignoring the fungal mycelium) within a forest ecosystem (Seminat, 1998) their high 41
contamination compared to other plant species (Bakken and Olsen, 1990), long ecological half-42
life (Jacob and Likhtarev, 1996) and dietary importance in some populations, especially within 43
the CIS (Skuterud et al., 1997a), requires their attention in models estimating dose to human 44
populations (Howard and Howard, 1996). 45
Fungi fruitbodies have been known to have high activity concentrations of 137Cs relative to 46
higher agricultural plants (Tsukada et al., 1998; Bakken and Olsen, 1990) since the 1960s and 47
1970s (Kiefer et al., 1965; Haselwandter et al., 1988) and elevated contamination levels have 48
been measured worldwide (e.g. Elstner et al., 1987; Horyna and Randa, 1988; Teherani, 1988; 49
Gaso et al., 1996; Garner and Jenkins, 1991; Sugiyama et al., 1994 and Yoshida et al., 1994). 50
Observed contamination levels of 137Cs, even within the same species, show both high spatial 51
and temporal variability (Fraiture, 1992). Several factors have been implicated : mycelium 52
habitat and depth (Giovani et al., 1990; Guillitte et al., 1994; Rhm et al., 1997); forest type-53
fruitbody location (Andolina and Guillitte, 1990; Fraiture, 1992); sampling strategy (Andolina 54
and Guillitte, 1990); soil clay content (Fraiture et al., 1990); pH (Bakken and Olsen, 1990); soil 55
moisture and/or microclimate (Tsvetnova and Shcheglov, 1994; Jacob and Likhtarev, 1996). 56
It is not presently possible to estimate generic effective ecological half-lives across fungi species 57
because species with superficial mycelium (Collybia and Clitocybe sp.) will attain highest 58
contamination within a few months of fallout whilst other deeper penetrating species (such as 59
Boletus edulis) will achieve contamination peaks several years after deposition (Fraiture et al., 60
1990). Therefore, ecological half-lives can be deduced but may be site-specific and will be 61
closely controlled by forest-type and litterfall (due to the effects on the weathering and recycling 62
of radionuclides), soil properties and seasonal fluctuations in microclimate (Rhm et al., 1998). 63
Amundsen et al. (1996) observed ecological half-lives for transfer factors of between 2 and 6 64
years in Norway for different fungi species (though standard errors were up to 8 years) by 65
sampling soil to a 5 cm depth, whilst Rhm et al. (1998) derived ecological half-lives of between 66
2.8 and 7.7 years for the different horizons within a Bavarian forest utilised by the mycelia of 67
different species. Conversely, using Russian data Jacob and Likhtarev (1996) found no 68
significant time dependency in 137Cs transfer. It is apparent further detailed study is required to 69
clarify any time dependency. 70
Information on the spatial scale over which mushroom contamination varies is generally lacking 71
from the literature with some notable exceptions (Dahlberg et al., 1997). This is a serious gap in 72
knowledge from a modelling perspective because if most of the variation occurs over very small 73
scales (i.e. metres) it will be difficult to predict differences in uptake. The objective of this paper 74
is to review and summarise the data collated for radiocaesium transfer to fungi and to identify an 75
appropriate modelling approach to predict food chain contamination. 76
77
SOURCES OF DATA 78
Two data sets have been used in the analysis : a survey of the published literature and a large 79
scale study carried out in 1986 and 1987 in Belgium (Fraiture et al., 1989). The data and 80
methodology are described below. 81
82
Literature data set 83
A general review of the (primarily) post Chernobyl literature on radiocaesium (137Cs) transfer 84
from soil to fungi fruitbodies has been carried out for the period 1986-1997. Transfer has 85
generally been summarised in the literature as the aggregated Transfer Coefficient commonly 86
referred to as the Tag (Skuterud et al., 1997b) or occasionally as the ATC (Gaso et al., 1996). 87
This is defined as the ratio between fungi activity (at time t) and the initial deposit of 88
radiocaesium (at time t=0, assumed to occur at 1st May 1986). Consequently, the variation in 89
Tags over a period of time (as in this analysis) will include a systematic bias due to the physical 90
decay of 137Cs. To account for this, in this paper, the initial soil deposit has been decay corrected 91
(to the time of fungi sampling) and we shall term the ratio used as the Transfer Ratio or TR 92
(defined as the ratio of fungi activity to soil deposit, both at time t). In practice, the difference 93
between the two transfer terms (TR and Tag) will be relatively small compared to variability that 94
is generally reported within and between species due to other factors. 95
A total of 558 TRs have been found from the 27 literature sources shown in Table 1 (referred to 96
in this paper as the NU97 data set) comprising samples collected from at least 13 countries 97
within Europe and the CIS at 95 different sites. The number of TRs observed for each country 98
was as follows : Ukraine (91); Germany (87); Denmark (54); Italy (47); Finland (45); Sweden 99
(43); Poland (42); Croatia (36); Austria (35); Czech Republic (32); Russia (20); Norway (15); 100
Slovenia (6) and unspecified (5). The largest number of TRs observed at one site (for a number 101
of species) is 54 at Tisvilde Hegn (Denmark), only 15 sites had recorded > 10 TRs. It should be 102
stressed that this review generally uses TRs as summarised by the authors (i.e. arithmetic mean) 103
and, therefore, does not represent the entire population of individual TRs which will consist of 104
many thousands. 105
The TR values have either been directly taken from the published literature source (where soil, 5-106
20cm depth, and fruitbody contamination have been measured directly at the same time) or 107
estimated where a fungi activity has been quoted along with a soil deposition level derived from 108
an aerial gamma survey. If an estimate of initial Chernobyl deposition has been reported by the 109
author this has been used to derive the TR. In this review TR values are presented on a dry 110
weight basis (i.e. m2 kg-1 DW), when reported as fresh weight a conversion has been made 111
assuming a dry matter content percentage of 10%. The average dry matter percentage observed 112
from over 1900 fruit body samples (272 species) by Fraiture et al. (1989) was 7%, with an inter-113
quartile range between 6 and 9%, so that such an assumption is unlikely to introduce a significant 114
source of variation. 115
Due to the rather piecemeal way that TRs have historically been reported in the literature 116
(necessitating the data handling outlined above) TR values reported as being obtained by 117
individual authors in this paper may differ from the transcript from which they were derived. 118
Such an approach is required to allow a proper comparison between authors and across species to 119
be made. 120
121
Belgium data set 122
The activity of 1927 fungi samples were measured by Fraiture et al. (1989) over two fungi 123
seasons, 1986 and 1987, in the Wallone region of Southern Belgium from 120 different sites 124
(nearest settlement names were recorded). The latitude and longitudes were obtained from a 125
suitable Gazetter using the settlement names as geo-references. A 137Cs deposition map for the 126
region was generated from 57 observations of soil deposition (Simon Wright, ITE, personal 127
communication) to allow estimation of the TRs at each sample location. A total of 1811 TRs 128
were generated (116 samples had no geo-reference). The primary use of this data set was to 129
study the spatial variation of fungi 137Cs activity. 130
131
NU97 DATA SET SUMMARY 132
Comparison between NU97 data set values and individual authors 133
The data collated from all of the literature sources (Table 1) are summarised by genus and 134
species in Table 2 and Table 3, respectively. Only those genus or species which have five or 135
more reported values are shown, whilst the statistics presented are derived from the mean TR 136
values reported in the literature. The mean, median and ranges reported by individual authors at 137
specific sites are also indicated for a particular genus or species as a comparison to the statistics 138
derived over the whole literature data set. 139
140
141
Genus 142
A total of 44 different genus have been found with at least one estimated TR value in the 143
literature review, with number of TR values reported within each genus varying between a single 144
entry (e.g. Calocybe) to as many as 132 for the genus Boletus (Table 2), making direct 145
comparisons between genus difficult. The majority of genus show TR distributions which are 146
positively skewed, with the degree of skew increasing significantly with sample size (P
within a factor of 2 to 3). The exception to this observation are the Paxillus and Suillus genera 150
with reported site minimum TR values an order of magnitude higher than that suggested by the 151
NU97 data set. In general, it appears that the variation found by individual authors is similar or 152
consistent to the variation found across a larger range of conditions over the whole of Europe 153
(this is discussed in more detail below). The Boletus genus exhibits the largest variation in TR 154
values with three orders of magnitude difference between the extremes (0.0025-11.6 m2 kg-1 155
DW), whilst the smallest within genus variation is at least one order of magnitude (e.g. Collybia). 156
157
Species 158
Aggregated transfer ratios for a total of 132 different species have been obtained (20 are listed in 159
Table 3), with the number of reported TR values varying between 1 (e.g. Agaricus campestris) 160
and 59 (Boletus edulis). As with the genera a comparison of the statistics between species is 161
difficult due to the different population sizes involved. 162
The range of TRs for individual species observed by individual authors at specific sites is 163
similar to that derived over the NU97 data set (Figure 1 and Table 3), suggesting statistics 164
derived from the latter may be used to estimate the Cs transfer from an individual species with as 165
much uncertainty as using site specific data. Dahlberg et al. (1997) found 60% of the total 166
variation in 137Cs activity of individual fruitbodies of Suillus variegatus was accounted for by the 167
variation within-populations (i.e. found at the same site and/or genetically affiliated) whilst 40% 168
could be attributed to the variation between populations (i.e. between sites). They suggested the 169
large within site variation could be due to a number of contributing factors. The findings of 170
Dahlberg et al. (1997) combined with the NU97 data set findings (Figure 1) supports the 171
hypothesis that the variation in TR for a given species at a specific site is similar to the variation 172
over a range of sites, so that the NU97 data set provides a possible reference data base to describe 173
the 137Cs transfer to specific fungi species across a range of sites. 174
The two main exceptions are provided by Leccinum versipelle and Rozites caperatus with 175
maximum NU97 TR values approximately 10% and 400% those obtained by individual authors. 176
The most variable species is Boletus badius, with TRs between 0.01 and 10 m2 kg-1 DW 177
(NU97), and generally the Boletes exhibit 2-3 orders of magnitude differences between minima 178
and maxima. The Lactariae species show differences of 1-2 orders of magnitude, whilst the least 179
variable genus would appear to be Cantharellus with differences closer to 1 order of magnitude. 180
The effect of land type on TR values is indicated by vadlenkov et al. (1996) in Table 3, with 181
considerably higher Cs accumulation prevalent in the mountain landscape, compared to the 182
lowland agricultural area, for the same species. However, insufficient information has generally 183
been reported by individual authors to make a statistical assessment of land-use and type, forest-184
type or similar factors impossible. 185
All species listed in Table 3 are edible according to Dickinson and Lucas (1979) and Kaltchenko 186
(1997). Of the 26 commonly eaten species within the Ukraine (Kaltchenko, 1997) 10 are listed 187
in Table 3, with coverage generally lacking for the Russula species. This data provides a useful 188
guide to the likely contamination for a given soil deposition (see Discussion). 189
190
EFFECT OF FUNGI NUTRITIONAL TYPE 191
Reporting of site specific conditions such as fungi fruitbody habitat, forest type and soil 192
properties is generally poor within the literature so that it was not possible to derive any 193
relationships or classification using such variables within this study. However, it was possible to 194
classify the fungi into three nutritional types based on the type of substrate from which the 195
mycelium derives its nutrients (Guillitte et al., 1990; Juliet Frankland, ITE personal 196
communication): mycorrhizal or symbionts (M); saprophytic (S) and parasitic (P). The 197
mycelium of mycorrhizal species are associated with fine roots of higher plants which supply 198
them with hydrocarbons whilst they aid roots to extract mineral salts from the soil. Saprophytic 199
species derive nutrients by decomposing the litter layer through enzyme excretion, whilst 200
parasitic species derive resources directly from higher plants. 201
The NU97 data set provided 709 values of fungi activity (534 M, 156 S and 19 P) and 530 TRs 202
(440 M, 78 S and 12 P). These are unbalanced and consequently the use of ANOVA was not 203
appropriate (Robinson, 1987). Therefore, the method of residual maximum likelihood (REML) 204
was used to estimate the effect of nutritional type (fixed model) and variance components in a 205
linear model with no random effects assumed, only random error (Genstat 5 Committee, 1993). 206
The REML analysis for the nutritional types is summarised in Table 4, with effects presented 207
relative to the mycorrhizal species. For both the fungi activity and TR there are significant 208
(P saprophytic > or parasitic. Therefore a classification based on nutritional type is 210
justified, over this wide range of conditions and initial deposition levels. 211
A number of authors have also attempted to classify levels of 137Cs or transfer factors based on 212
an ecological nutritional approach (Giovani et al., 1990; Guillitte, 1990; Belli and Tikhomirov, 213
1996 and Yoshida and Muramatsu, 1994). It is generally accepted that radiocaesium 214
discrimination (compared to potassium) occurs during transfer from the fungi mycelium into root 215
cells (Byrne, 1988; Kammerer et al., 1994; Wirth et al., 1994) so accumulation of 137Cs is higher 216
for mycorrhizal (or symbiotic) species (e.g. Guillitte et al., 1994). However, transfer may also be 217
affected by infection or co-existence of species with differing levels of Cs-affinity (Aumann et 218
al., 1989). 219
The effect of vegetation cover or habitat in which the fungi fruitbodies were collected was 220
investigated for the calculated TRs of the Belgium data set, in which a total of 14 different 221
habitats were recorded by (Fraiture et al., 1989). The most commonly collected mycorrhizal 222
species, Russula ochroleuca, across the largest number of sites (62) was used with the fixed 223
model defined as habitat type and the random model as the site. The habitat type effect was 224
significant (P oak (habitat codes as 225
reported by the authors). This demonstrates for a particular species 137Cs transfer will vary 226
according to the type of tree beneath which it is found, at least within the first few years after a 227
deposition event (samples were collected in 1987 and 1988). This may reflect differences in tree 228
architecture, litter fall, and leaf decay. Fraiture (1992) summarises the major mechanisms by 229
which initial deposition patterns may vary spatially beneath canopies of different tree species. 230
The effect may be less marked after a period of time when needle shedding and continued 231
weathering within coniferous trees will re-distribute the initial deposit onto the forest floor, 232
although needles are normally retained for between 3 to 6 years reducing the total contamination 233
due to radioactive decay (Fraiture, 1992). However, the importance of spatial variability in soil 234
deposition (due to initial canopy interception) within a forest stand may be reduced as the 235
mycelium of individual fungi species have been observed to spread over many square metres 236
(Dahlberg, 1997). 237
238
FREQUENCY DISTRIBUTION FUNCTIONS TO DESCRIBE 137CS TRANSFER 239
Two types of standard probability distribution function were fitted to the two TR data sets (NU97 240
and Belgium) : Normal and Log-normal. The TRs were classed into genus, species and 241
nutritional type and the distributions were fitted, using Genstat version 3.22 (Genstat 5 242
Committee, 1993), with the number of class intervals equal to the square root of the number of 243
observations. This inevitably resulted in unequal class intervals between different species, genus 244
and nutritional type. Distributions were not fitted to data sets that had less than 18 TR values. In 245
each case it was possible to determine the most appropriate distribution for the underlying data 246
set, based on the deviance between the expected and observed frequency of TR values in each 247
TR class interval. 248
For the best-fitting distributions for individual and pooled species (see below), the distribution 249
parameters ( and with standard errors) are listed in Table 5. The distribution pattern of 137Cs 250
transfer factors and activities has generally been found to be log-normal (e.g. Mietelski et al., 251
1994; Yoshida et al., 1994), which is consistent with the findings in this review (the TRs for 252
only three of the 24 species listed in Table 5 are normally distributed). Jacob and Likhtarev 253
(1996) presented (as graphs) species specific distributions for mushroom 137Cs transfer factors 254
for eight common species found in Belarus. Four of the five species common to both this review 255
and their data set showed similar TR distribution patterns (Boletus badius, Boletus edulis, 256
Russula ochroleuca and Cantharellus cibarius) with TRs for Armillaria mellea indicated as 257
being normally distributed in their data set. 258
The Belgium data set provides the majority of species listed in Table 5 and it is assumed that 259
these will be representative on a larger scale. Generally, the differences between the observed 260
and fitted distributions are not significant (P>0.05). The fitted parameters for individual species 261
were ranked and the differences between them for species of similar rank were tested for 262
significance (t-test). If the differences were not significant (P>0.05) the data sets were pooled 263
and the distribution function re-fitted to the pooled observations. The result of pooling data sets 264
is shown in Table 5 by grouping species with the same fitted distribution parameters. In some 265
cases (e.g. Paxillus involutus and Boletus badius) it was necessary to re-normalise the frequency 266
distribution to allow for that part of the function below zero. The effective transfer ratios were 267
generated using the modelling approach outlined below (see Modelling Approach). 268
The fitting of frequency distribution patterns were also investigated for the data grouped by 269
genus and nutritional type but the results were generally poor (data not shown). If only the genus 270
of a particular fruitbody collected could be determined then derived genus parameters could be 271
used albeit with less accuracy than if the species parameters were applied. 272
between 2 and 5 years. This contrasting finding may be a consequence of using heterogeneous 298
data taken over a large spatial scale in this study (Europe and the CIS) and probably greater 299
between site variability in terms of experimental procedures, climate, soil type and land use. 300
However, other authors have (over shorter time periods of 4-5 years) found both an increase 301
(Borio et al., 1991) and no significant decrease in activity levels (Kammerer et al., 1994) of 302
various fungi species. Analysis of the NU97 data set (using a variety of sources from Europe and 303
CIS) indicates that generally there is no statistical evidence to indicate any significant decrease in 304
TRs close to 10 years after the Chernobyl accident. This may suggest that contamination levels 305
of common mushroom species are approximately constant, though data heterogeneity makes a 306
definitive conclusion difficult. 307
However, some site specific data sets (within the NU97 data set) could be analysed for time 308
dependency using the data reported by Belli and Tikhomirov (1996) in Russia and Ukraine, with 309
time series (n 4) per site for a particular species. Three species were considered : Lactarius 310
necator; Lactarius rufus and Paxillus involutus. As an example, the time series for the former 311
are presented for 4 different sites in Figure 4. These time series were analysed to determine the 312
lines of best fit (assuming a straight line model with either negative or positive slope) which are 313
plotted as the solid lines. The effect of site was also tested to determine if the slopes (and 314
intercepts) were significantly different between sites. 315
Significantly different intercepts (P 0.01) were found for each site (for a particular species), 316
suggesting initial site properties such as soil, forest/land type and form of deposition (distance 317
from source) will be important in determining the initial post deposit transfer factor. For two of 318
the species, Lactarius rufus and Paxillus involutus, no significant differences (P > 0.05) in the 319
rate at which the TR values are increasing or decreasing across sites (respectively) were found. 320
Three out of the four sites for Lactarius necator indicated a negative intercept which may suggest 321
a linear function may not be the most appropriate model, whilst at the remaining site (Dityatky, 322
28.5km S of Chernobyl) the TR was apparently decreasing with time (Figure 4). In some 323
instances a better fitting exponential model could be applied, although this sort of model 324
(implying increasing or decreasing rates of 137Cs uptake) was not considered as meaningful. It is 325
clear that the fitted trend line for site D1 (Figure 4) is dependent on the recorded TR 7 years after 326
the initial deposition, so that it could be argued that a better description would be achieved with a 327
slope of 0 followed by a year in which transfer was exceptionally high. 328
Rhm et al. (1998) grouped 14 mushroom species collected within a coniferous forest near 329
Hochstadt (Bavaria) into four groups depending on the location of their mycelium within the 330
organic-mineral soil layers, derived from observations of the 137Cs : 134Cs ratio. By representing 331
soil horizons as a five compartment model they deduced ecological half-lives ranging from 2.8 332
(litter horizon) to 7.7 years (upper mineral horizon), with different mushroom species exhibiting 333
decreasing (mycelium in litter and organic layers), constant (mycelium solely in organic layer) 334
and increasing (mycelium in organic and mineral layer) contamination dependent on which 335
combination of horizons their mycelium exploited. It is expected that the ecological half-lives 336
(and TRs) they obtained are site-specific (Rhm et al., 1998) due to local soil attributes (e.g. 337
clay content), forest type and vary between seasons due to climatic influences on soil humidity 338
and fruitbody age. The latter two factors may help explain the non-significant coefficients of 339
determination they obtained. 340
Although evidence exists to suggest that transfer of 137Cs to fungi does show time dependency, 341
this study of the available data indicates no clear effect of time can be deduced. Amundsen et al. 342
(1996) suggest that a period of observation of an individual species (15-50 samples) at the same 343
site for 6-7 years may not be long enough to determine a precise ecological half-life of 137Cs in 344
fungi beyond concluding that it may approach the physical half-life. 345
346
347
348
349
SPATIAL VARIATION 350
The Belgium data set provides good spatial coverage of both activities and TRs for fruitbodies, 351
with observations at 120 sites within the Wallonne Region in Southern Belgium (approximately 352
200km by 150km). It was proposed that analysis of the spatial correlation of the variation 353
between sites would indicate over what scale it would be important to predict or estimate 354
accurately the mushroom fruitbody activity or TR. The hypothesis was that the variation in 355
radiocaesium transfer within a site was as large as that between sites (as suggested by the 356
findings of Dahlberg et al., 1997). This could be tested by describing the spatial continuity in 357
terms of an experimental variogram whereby the difference between sites is a function of the 358
distance between them (Burrough, 1997). 359
The Belgium data set provided a 2 sets of data which enables this hypothesis to be tested: 360
1. fungi activity and TR across all sites and species (n=120); 361
2. fungi activity and TR for one species, Russula ochroleuca (n=62). 362
The pattern of the experimental variogram obtained by using the activity and TR was very similar 363
and therefore only the activity data is described in this analysis (i.e. no assumptions for soil 364
deposition were required). Data set 1 provided the most complete data set (in terms of spatial 365
coverage) but had the disadvantage of incorporating the variation in 137Cs uptake due to different 366
species, whereas data set 2 provides the most complete data set for a particular species not 367
incorporating any species variation. At least 50-100 geo-referenced data points are required to 368
achieve a stable variogram (Burrough, 1997) limiting the analysis to one particular species 369
(Russula ochroleuca). 370
In both cases, the fungi activity are positively skewed and it was necessary to transform the data 371
(natural logarithm) as suggested by Burrough et al. (1996). The experimental variograms fitted 372
in this study were omnidirectional, i.e. the data was sufficiently isotropic in all directions to 373
negate the requirement for an analysis in specific directions. Standard rules of thumb were 374
used to determine suitable values for the cut-off width or maximum lag distance and the lag 375
increment (for example: EPA, 1991; Isaaks and Srivastava, 1989; and PCRaster, 1996). The 376
GEOEAS version 1.2.1 software (EPA, 1991) was used. The cut-off width was taken as 377
50,000m whilst the lag increment was varied between 4000 and 10,000m. 378
The experimental semi-variograms for the transformed fungi activity across all species for the 379
120 sample sites are shown in Figure 5 for a range of lag increments. A relatively stable 380
structure is indicated for lag increments greater than 5000 m at distances of more than 5000m. 381
The sampling strategy of the Belgium study limits the interpretation of the variation to be 382
expected at such small distances (average nearest neighbour distance for the 120 sites is 8400m). 383
This result suggests there is no distance-fungi activity variation relationship, with most of the 384
difference between 137Cs uptake occurring over a relatively small scale (< 5 km). In other words, 385
within the limitation of the data set it is possible to conclude that the variation within a forest site 386
may be as large as the variation between sites. 387
Dahlberg et al. (1997) studied the variation of 137Cs activity in individual fruitbodies of Suillus 388
variegatus at 7 sites over areas less than 50m by 50m. Their findings indicate that most of the 389
variation occurred over very small distances, at scales that would be difficult to predict without 390
very detailed soil and habitat information. Analysis of data at such a detailed scale could be 391
useful in clarifying further the spatial continuity of mushroom contamination by 137Cs. 392
When just one particular species (Russula ochroleuca) observed at 62 sites is analysed a 393
markedly different pattern of variation is found (Figure 6), especially over the first lag increment. 394
Although, the variation for one species is only slightly lower than that obtained over a large 395
number of species. The more limited spatial coverage results in only 10 pairs in the first lag at an 396
increment of 7km, rising to 38 pairs for a 10km increment. At the latter increment no discernible 397
spatial pattern is evident, whilst at the lower lag increment sizes it would appear the variation 398
actually increase at locations closer together. The data set suggests at distances up to 5-10km the 399
variation in fungi activity for one species is as large at that experienced over greater distances. 400
401
MODELLING APPROACH 402
The spatial analysis of the Belgium data set indicates variability of 137Cs transfer within a 403
sampling location such that fruitbodies collected over an area on a scale of approximately 5km 404
would show activities as variable as those collected over a much larger scale. Therefore, a 405
typical mushroom gatherer who, over the course of a mushroom season, may gather this 406
particular product from a relatively large area (perhaps a number of forest locations or forests) 407
would be expected to collect fruitbodies with highly variable activities. Thus, the majority of the 408
variation in 137Cs uptake may occur over a scale smaller than the mushroom gatherer collects 409
mushrooms. In this case using an effective TR for a particular species, derived from the 410
distribution parameters for individual species (Belgium and NU97 data sets, Table 5), would give 411
a more reliable estimate of the 137Cs transfer ratio. The effective species TR (TRi effective) can 412
be calculated using the fitted distribution parameters (Table 5) as described by Equation 1. 413
TR pdf TR dTRi effective i i=
0
Equation 1 414
where : 415
TRi effective = effective transfer ratio for species i (m2 kg-1 DW); 416
pdf = the probability density or frequency distribution function. 417
The effective TR represents the TR that should be applied if it is assumed that a sample is drawn 418
from the p.d.f.s given. The effective TRs estimated using Equation 1 and confidence intervals 419
(68%) are also given in Table 5. 420
An estimate of the total 137Cs activity a mushroom gatherer would collect is given by Equation 2. 421
Activity B D TRi i effective= Equation 2 422
where : 423
Activityi = total activity in mushrooms gathered for species i (Bq); 424
Bi = total biomass of fungi species i collected (kg DW); 425
D = soil deposition level (decay corrected) in area of mushroom picking (Bq m-2). 426
The total weight of fungi collected could be easily determined (e.g. Voigt et al., 1998), and a dry 427
weight fraction of 10% could be assumed to convert to dry matter. This total activity combined 428
with daily consumption patterns and the appropriate dose conversion factors could be used to 429
estimate the daily intake due to this particular semi-natural product for each species collected. 430
Due to the incorporation of the spatial variation within the distribution parameters of Table 5 this 431
approach assumes the gatherer would sample from the entire population of TRs, which would 432
be valid if fungi were collected over an area on a scale of at least 5km. Consequently this 433
method may only be appropriate for estimating the collective dose to a population, rather than the 434
individual dose. Uncertainties in the distribution parameter estimates have been used to generate 435
confidence intervals for the effective TRs, providing estimates for the uncertainty in 436
corresponding dose estimates. 437
The frequency and effect of culinary practices (processing factors) on the modification of the 438
potential activity (derived from Equation 2) have been studied and quantified (Jacob and 439
Likhtarev, 1996; Beresford et al., 1998), and these could be combined with the proposed model 440
to estimate the effective activity within mushrooms consumed by the general population. 441
442
DISCUSSION 443
Methodological differences between authors may account for some of the variation in transfer 444
ratios reported within this paper. For example, use of soil deposition levels estimated from field 445
or aerial gamma surveys and rainfall measurements (e.g. vadlenkov et al., 1996; Elstner, 1989) 446
will be less representative of the localised soil contamination (e.g. Guillitte et al., 1994) which 447
varies markedly within forest ecosystems (Fraiture, 1992; Wirth et al., 1994). Some authors 448
report soil activity concentrations (e.g. Heinrich, G., 1992) and TFs (Yoshida et al., 1994) so 449
TRs cannot be derived from the data sets, whilst some of the literature data sets have not been 450
used because measured soil depositions have not been reported (Yoshida et al., 1994) or 451
definition of units used are not clear (Tsvetnova et al., 1994). Andolina and Guillitte (1990) 452
presented a methodology for soil sampling within forest ecosystems and suggest a 453
standardisation of methods to enable comparisons between studies. Consideration, of the 454
potentially large scale (many square metres) over which fungi mycelium can take up 137Cs 455
indicates an appropriate scale for soil sampling. Smith et al. (1993) found that between 10 and 456
20 individual fungi fruitbodies were required (based on the measured activity concentrations) in 457
order to lie within a factor of two of the log-mean activities. It is reasonable to assume that a 458
similar soil sample size would be required to achieve the same level of accuracy within 459
heterogeneous forest sites. 460
Analysis of time series TRs at a number of sites in Russia and the Ukraine (Belli and 461
Tikhomirov, 1996) indicates the rate of increase (Lactarius rufus) or decrease (Paxillus 462
involutus) of TR is similar across sites, though for some species (Lactarius necator) variation 463
does occur (Figure 4). Considering TR time series data set averaged over a large spatial scale 464
(Figure 3), it is apparent that there is no strong evidence to suggest TRs have decreased since the 465
Chernobyl accident. Although, some authors (Rhm et al., 1998) have provided evidence for 466
and quantified the decrease in fungi TRs at particular sites, with ecological half-lives between 3 467
and 8 years dependent on nutritional source. 468
Generally, some or all of the detailed meta-information required for a study of this nature (such 469
as habitat, sample location relative to trees, localised climate, soil nutrient status and attributes) 470
is not reported so that classification schemes are often limited to generic nutritional types. 471
Problems also arise in analysing population means and individual population data together. It is 472
suggested that a greater understanding of the mechanisms governing 137Cs transfer to mushroom 473
fruitbodies would be achieved through a thorough re-evaluation of existing raw data sets and the 474
reporting of meta-information in greater detail and a consistent manner. This may prove a cost-475
effective and beneficial approach providing a data base for semi-natural products equivalent to 476
that presently constructed for agricultural products by the International Union of Radioecologists 477
(Sheppard and Evenden, 1997). This study also highlights a need for further clarification of the 478
role of soil nutrient status upon the long-term uptake of radiocaesium within the fungi mycelium 479
and the dynamics and transport to fruiting bodies within field settings to determine the relative 480
importance of the local microclimate (temperature and humidity). Such studies should 481
concentrate on single species which are suited to study both in the field and laboratory studies 482
(for example Dahlberg et al., 1997). It may then be possible to develop a generic modelling 483
approach (not based on transfer factors) for the transfer of radiocaesium to fungal sporocarps 484
similar to that developed for vascular plants by Absalom et al. (1999). 485
The proposed modelling approach, using species specific characteristics (parameters for the 486
mean, variance and assumed distribution pattern), is consistent with the hypothesis of Tsukada et 487
al. (1998) that physiological differences between species of mushrooms causes the large 488
fluctuations in radiocaesium activity concentrations between species, as reported by many 489
authors for both 137Cs (e.g. Kammerer et al., 1994; Yoshida et al., 1994) and stable Cs (Seeger 490
and Schweinshaut, 1981). The approach also agrees with evidence provided from the spatial 491
analysis of the Belgium data set, which indicates that on the scale of mushroom gathering (over a 492
season) the variability in fungi fruitbody 137Cs transfer will be high. 493
494
CONCLUSIONS 495
This review of transfer ratios has shown : 496
variation in species TRs measured across Europe and the CIS is similar to that observed by 497
authors at individual sites; 498
confirmation of significant differences (P S P); 500
in general no significant time dependency in TRs can be deduced across such a 501
heterogeneous data set; 502
the variation in TRs to be relatively constant when collected over distances greater than about 503
5km; 504
species specific frequency distribution parameters can be used to estimate an effective TR to 505
be used for predicting doses to populations from this semi-natural product. 506
507
Acknowledgements 508
The authors are grateful to Cath Barnett, Simon Wright and Brenda Howard (ITE) for their 509
contribution and helpful comments in developing the data base. This study was supported 510
financially by the European Commission (Contract F14P-CT95-0015 and Contract F14P-CT95-511
0021c) and this support is gratefully acknowledged. 512
513
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726
Figure Captions 726
727
Figure 1 Range of aggregated transfer factors, TR (m2 kg-1 DW), observed by individual authors 728 at specific sites compared to that found across the whole literature search (NU97) 729
Figure 2 Variation of TR values obtained and number recorded from published sources for each 730 year since the Chernobyl nuclear power plant accident 731 732
Figure 3 TR time-series data for the most commonly recorded species, median values and inter-733 quartile range (n>20) obtained across a number of sites (NU97 data set). Note the last data point 734 in the Lactarius rufus time-series is excluded from the regression line 735 736
Figure 4 Time series TR data for Lactarius necator for individual sites (taken from Belli and 737 Tikhomirov, 1996) (D1=Dityatky, Kiev province, 26km S of Chernobyl (Ukraine); D2=Dityatky, Kiev province, 738 28.5km S of Chernobyl (Ukraine); K1=Klintsy, Bryansk province, 210km NE of Chernobyl (Russia); 739 S1=Shepelitchy, Kiev province, 7km W of Chernobyl (Ukraine)) 740 741
Figure 5 Experimental semi-variogram of fungi activity over all sites and species for the log 742 transformed Belgium data set (see text for details) 743 744
Figure 6 Experimental semi-variogram of fungi activity over 62 sites for one species, Russula 745 ochroleuca, for the log transformed Belgium data set (see text for details) 746 747
748
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NU97
Rozites caperatus
0.01.02.03.04.05.06.07.08.09.0
10.011.0
FRA9
2
HOW
94b
HOW
94b
KAM
94
NU97
ELS87 = Slovenia, 1987 FRA92 = Slovenia, 1986 KAM94 = unknown site, 1988-91 HOW94a = Austria, 1987-91 HOW94b = Finland, 1984-91 HOW94c = Germany, 1987 HOW94d = Czechoslovakia, 1988 HOW94e = Austria / Germany, 1988-91 HOW94f = Norway, 1988
RAN90a = Finland, 1986 RAN90b = Finland, 1987 RAN90c = Finland, 1988 SMI93 = Finland, 1993 SVA96a = Czech Republic, (mountain landscape) SVA96b = Czech Republic, (agricultural landscape) TEH88 = Czech Republic, unknown date
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Fig. 2
0.0001
0.001
0.01
0.1
1
10
100
0 1 2 3 4 5 6 7 8 9 10Years since the Chernobyl Nuclear Power Plant Accident
TR (m
2 kg
-1 )
0
20
40
60
80
100
120
140
160
Num
ber
of T
R's
re
port
ed
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Fig. 3
Boletus edulis (n=59), 22 sites
y = 0.0053x + 0.0793R2 = 0.0188
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 1 2 3 4 5 6
Boletus badius (n=52), 19 sitesy = -0.0294x + 1.7094
R2 = 0.015
0.00
1.00
2.00
3.00
4.00
5.00
0 1 2 3 4 5 6 7 8 9
Paxillus involutus (n=39), 12 sitesy = 0.1081x + 0.8902
R2 = 0.2761
0.00
1.00
2.00
3.00
4.00
5.00
6.00
0 1 2 3 4 5 6 7 8 9
Lactarius rufus (n=27), 8 sites
y = 0.4137x + 0.0763R2 = 0.861, P < 0.05
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
0 1 2 3 4 5 6 7
Cantharellus cibarius (n=25), 19 sites
y = 0.029x + 0.1415R2 = 0.3788
0.000.100.200.300.400.500.600.700.800.901.00
0 1 2 3 4 5 6
Lactarius necator (n=24),6 sites
y = 0.0839x - 0.077R2 = 0.6702, P < 0.05
0.000.200.400.600.801.001.201.401.601.802.00
0 1 2 3 4 5 6 7 8
years since Chernobyl years since Chernobyl
TR (m
kg
-1 D
W)
TR (m
kg
-1 D
W)
TR (m
kg
-1 D
W)
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Fig. 4
Site D 1
y = 0 .2569x - 0.7666R 2 = 0 .6417
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0 1 2 3 4 5 6 7 8
Site D 2
y = -0.0116x + 0 .1149R 2 = 0 .436
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0 1 2 3 4 5 6 7 8
Site S1
y = 0 .088x - 0 .0693R 2 = 0 .8195
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 1 2 3 4 5 6 7
Site K 1
y = 0 .2053x - 0.2251R 2 = 0 .8214
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0 1 2 3 4 5 6
TR (m
kg
-1 D
W)
years since C hernobyl years since C hernobyl
TR (m
kg
-1 D
W)
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Fig. 5
0
0.5
1
1.5
2
2.5
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000
Distance (m)
Fungi
ac
tivity
(tr
ansf
orm
ed) s
emiv
aria
nce
6000
7000
8000
lag increment (m)
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Fig. 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000
Distance (m)
Fungi
ac
tivity
(tr
ansf
orm
ed) s
emiv
aria
nce
70008000900010000
lag increment (m)
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Table 1 Summary of data as reported by author in the literature data base (NU97 data set)
REFERENCE FUNGI SOIL OTHER
A
UTH
OR
S
N
um
ber
of s
peci
es st
udi
ed A
ctiv
ity (B
q/kg
) SD
TR
(m
/kg)
SD
R
ange
(m
in-m
ax)
TF
(B
q/kg
fu
ngi
/ B
q/kg
so
il) SD
Cs
13
7:13
4 ra
tio
D
epo
sit (B
q/m
) O
bser
ved
/ E
stim
ated
Co
nce
ntr
atio
n (B
q/kg
) O
bser
ved
/ E
stim
ated
Cs
13
7:13
4 ra
tio
So
il Ty
pe So
il A
ttrib
ute
s (e.
g. %
cl
ay)
La
nd-
cov
er (ar
able
o
r fo
rest
ty
pe)
Lo
catio
n
Amundsen et al. (1996) 10 O OBattiston et al. (1989) 15 O OBelli and Tikhomirov (1996) 14 O OBem et al. (1990) 8 E OByrne et al. (1988) 6 EElstner et al. (1987) 17 E OElstner et al. (1989) 32 O OFranic et al. (1992) 20 O OGiovani et al. (1990) * OGuillitte et al. (1994) 38 O OHeinrich et al. (1989) 9 O OHeinrich, E (1992) 8 E OHeinrich, G (1992) 113 O OHoryna and Rand (1988) 21 O OIAEA (1994) 9 O OKammerer et al. (1994) 28 OLux et al. (1995) 11 O OMascanzoni (1990) 5Mietelski et al. (1994) 6Pietrzak-Fils et al. (1996) 2 O ORantavaara et al. (1990) 8Smith et al. (1993) 16Strandberg (1992) 33 O OSvadlenkova et al. (1996) 8 OTeherani (1988) 5 EWasser and Grodzinskaya (1993) 55 E OZagrodzki et al . (1994) 1 E O
Note : all weights refer to dry weight fungi or soillocation refers to a site name that can be geo-referenced
data reported for majority of speciesdata reported for some species
* miscellaneous macromycetes
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Table 2 Data summary for NU97 TR database by GENUS (n 5) Genus TR (m2 kg-1 DW) Literature TR values
(m2 kg-1 DW) Name n Geometric
mean
Median LQ (25%)
UQ (75%)
ArithmeticMean
Range Skew Range
Amanita 15 0.2578 0.2736 0.0529 1.0078 1.0954 0.0076-5.2485 1.94 - Armillaria 10 0.0347 0.0362 0.0127 0.0962 0.0626 0.0064-0.177 1.05 0.0218 - 0.0502 Boletus 132 0.2198 0.2444 0.0637 0.9766 0.7944 0.0012-9.9758 3.81 0.0025 - 11.6 Cantharellus 50 0.2800 0.2855 0.1632 0.6206 0.4356 0.0108-1.5091 1.23 0.01 - 2.47 Clitocybe 7 0.2366 0.4643 0.2286 0.6027 0.4901 0.0103-1.2933 0.95 - Collybia 9 0.1444 0.2050 0.1337 0.2603 0.1874 0.03-0.303 -0.61 - Cortinarius 18 1.2940 1.8656 0.9785 3.2476 2.5538 0.0212-10.2381 1.83 0.0144 - 3.84 Hygrophorus 5 1.2964 1.8300 0.5472 2.0565 2.4092 0.2412-7.3712 1.83 - Laccaria 5 1.9949 2.0800 0.6053 5.4000 3.8604 0.4309-10.7857 1.29 0.48 - 4.68 Lactarius 83 0.5194 0.7237 0.2434 1.4593 1.0526 0.0058-4.3808 1.45 0.015 - 6.31 Leccinum 23 0.1431 0.1100 0.0609 0.3364 0.2437 0.0155-0.885 1.30 0.005 - 0.74 Lycoperdon 11 0.0431 0.0300 0.0224 0.0756 0.0945 0.0088-0.514 2.61 - Macrolepiota 12 0.0135 0.0130 0.0086 0.0279 0.0254 0.0007-0.1106 2.26 - Paxillus 42 0.8598 1.3668 0.5703 2.5558 1.6498 0.0116-5.41 0.74 0.627 - 8.97 Ramaria 5 0.1507 0.1981 0.0696 0.2003 0.2184 0.0488-0.5753 1.64 - Rozites 13 2.2206 2.2500 1.2228 8.2789 4.1137 0.08-10.9123 0.80 0.01 - 2.7 Russula 34 0.4132 0.3616 0.2163 0.8887 0.7617 0.0445-5.2704 2.98 - Suillus 19 0.4902 0.6964 0.2937 0.8652 0.7301 0.0169-2.1019 1.04 0.175 - 4.800 Note : 1. statistics are calculated from all available TR values reported by individual authors and groups and so represent the best estimates over a range of sites and conditions; 2. the TR Literature ranges listed represent the minimum to maximum values observed/reported by individual authors within a Genus (not necessarily the same author(s), site or species); 3. the Skew statistic characterises the degree of asymmetry of a distribution around its mean.
Table 3 Data summary for NU97 TR data for edible SPECIES (n > 5)
Gill
ett a
nd
Crout
A re
view
of 13
7 Cs
tran
sfer
to fun
gi :
impo
rta
nce
of s
peci
es, sp
atia
l sca
le and
time
Spec
ies
NU
97 TR
val
ues
(m
2 kg
-1
DW
) R
eport
ed lit
eratu
re TR
val
ues
(m
2 kg
-1
DW
) N
ame
n
Geo
met
ric
mea
n
Med
ian
LQ
U
Q A
rithm
etic
Mea
n
Ran
ge
Skew
M
ean
Med
ian
R
ange
A
uth
or(s
)
Arm
illar
ia m
elle
a 9
0.03
29
0.02
55
0.01
00
0.10
95
0.06
33
0.00
64-0.
177
0.98
0.
0359
0.
0349
0.
0218
-0.
0502
v
adle
nko
v
et al.
(1996
)d B
ole
tus
badi
us
53
0.95
07
1.17
08
0.64
67
2.14
93
1.62
85
0.00
66-9.
9758
2.
82
0.76
3.22
2.19
1.48
76
0.37
2 2.
30
-
0.83
29
0.04
8-2.
64
0.44
-7.
1 0.
43-6.
1 0.
5292
-3.
0183
Elst
ner
et
a
l. (19
87)
IAEA
(19
94)
Kam
mer
er et
a
l. (19
94)
vad
len
kov
et al.
(1996
)d B
ole
tus
chry
sen
tero
n
8 0.
4790
0.
6188
0.
2200
1.
6725
0.
9722
0.
03-2.
6667
0.
78
0.79
1.84
0.43
94
0.60
2.00
0.47
57
0.01
-2.
07
0.16
5-4.
11
0.32
-0.
5379
IAEA
(19
94)
vad
len
kov
et al.
(1996
)c v
adle
nko
v
et al.
(1996
)d B
ole
tus
edulis
59
0.
0643
0.
1087
0.
0191
0.
2369
0.
1572
0.
0012
-0.
7994
1.
59
0.28
0.06
0.25
0.04
5 0.
96
0.33
0.05
0.01
63
0.02
88
0.96
0.04
-0.
42
0.03
-0.
13
0.00
25-1.
6875
0.00
5-0.
175
0.17
98-1.
7470
IAEA
(19
94)
Kam
mer
er et
a
l. (19
94)
Ran
tavaa
ra et
al.
(1990
) Sm
ith et
a
l. (19
93)
Tehe
rani (
1988
) Ca
nth
arel
lus
ciba
rius
26
0.17
61
0.20
21
0.12
79
0.32
88
0.23
74
0.01
08-0.
7187
1.
18
0.29
0.35
0.16
25
0.66
5 0.
0489
0.20
0.28
0.10
50
0.66
5 0.
0489
0.01
-0.
96
0.02
-1.
00
0.01
37-0.
4375
0.27
3-1.
06
0.02
91-0.
0686
IAEA
(19
94)
Kam
mer
er et
a
l. (19
94)
Ran
tavaa
ra et
al.
(1990
) v
adle
nko
v
et al.
(1996
)c v
adle
nko
v
et al.
(1996
)d Ca
nth
arel
lus
lute
scen
s 9
0.39
27
0.26
85
0.16
63
1.32
26
0.62
92
0.09
99-1.
5091
0.
73
Canth
arel
lus
tuba
eform
is 11
0.
8013
0.
8250
0.
6800
0.
9200
0.
8243
0.
55-1.
18
0.50
0.
98
1.13
0.82
5
1.08
1.12
0.81
25
0.44
-1.
61
0.92
-1.
37
0.38
75-1.
425
IAEA
(19
94)
Kam
mer
er et
a
l. (19
94)
Ran
tavaa
ra et
al.
(1990
) La
ctar
ius
nec
ato
r 24
0.
1671
0.
2152
0.
0820
0.
4126
0.
3144
0.
0058
-1.
388
1.91
2.42
1.
43-6.
31
vad
len
kov
et al.
(1996
)c La
ctar
ius
rufu
sb
29
1.15
19
1.25
01
0.72
37
2.36
74
1.65
61
0.02
71-4.
3808
1.
02
1.37
5 2.
61
0.66
16
0.76
25
2.58
0.68
58
0.16
25-6.
0875
1.05
-4.
52
0.44
89-0.
8510
Ran
tavaa
ra et
al.
(1990
) v
adle
nko
v
et al.
(1996
)c v
adle
nko
v
et al.
(1996
)d La
ctar
ius
torm
ino
susa
6
0.80
30
0.86
25
0.47
78
1.28
44
1.07
92
0.25
44-2.
7214
1.
43
0.93
75
0.92
5 0.
4-1.
5 R
anta
vaa
ra et
al.
(1990
) La
ctar
ius
triv
ialis
b 9
1.32
94
1.92
00
1.37
00
2.15
00
1.72
61
0.1-
2.66
-1.
05
1.37
0.88
75
1.02
1.02
5 0.
07-3.
47
0.01
87-3.
0 IA
EA (19
94)
Ran
tavaa
ra et
al.
(1990
) Le
ccin
um
sc
abru
m
9 0.
2718
0.
3500
0.
1823
0.
5310
0.
3907
0.
0322
-0.
885
0.50
Lecc
inum
ver
sipel
le
11
0.07
06
0.06
19
0.05
38
0.10
76
0.08
99
0.01
55-0.
2868
2.
23
0.11
0.12
13
0.05
0.05
0.02
-0.
74
0.01
25-0.
5625
IAEA
(19
94)
Ran
tavaa
ra et
al.
(1990
) Ly
cope
rdon
pe
rlatu
m
9 0.
0390
0.
0300
0.
0249
0.
0686
0.
0574
0.
01-0.
2087
2.
24
Mac
role
pio
ta pr
oce
ra
9 0.
0141
0.
0229
0.
0054
0.
0288
0.
0299
0.
0007
-0.
1106
1.
84
Paxill
us
invo
lutu
sa
40
0.86
27
1.57
31
0.49
56
2.58
78
1.69
07
0.01
16-5.
41
0.66
1.
0843
1.
0843
0.
6777
-1.
4956
v
adle
nko
v
et al.
(1996
)d R
ozi
tes
cape
ratu
s 10
1.
4496
1.
4868
0.
9837
2.
8707
2.
5663
0.
08-10
.15
97
2.26
0.
904
1.54
2.25
0.88
0 -
2.15
0.08
4-2.
48
0.78
-2.
30
1.89
-2.
70
Fran
ic et
a
l. (19
92)
IAEA
(19
94)
Kam
mer
er et
a
l. (19
94)
Russ
ula
o
chro
leuca
9
0.30
62
0.30
00
0.23
02
0.36
31
0.35
64
0.12
5-0.
9048
1.
92
Suill
us
lute
us
7 0.
4225
0.
2766
0.
1810
0.
9843
0.
6936
0.
1462
-2.
1019
1.
41
Suill
us
var
iega
tus
8 0.
4161
0.
6528
0.
5047
0.
7676
0.
5940
0.
0169
-0.
8804
-1.
32
0.71
25
0.61
25
0.17
5-1.
700
Ran
tavaa
ra et
al.
(1990
) Fo
otn
ote
s :
a ed
ible
if
coo
ked;
b in
edib
le; c
m
oun
tain
la
nds
cape
(S
um
ava,
Cz
ech
repu
blic
); d
agric
ultu
ral l
ands
cape
(T
emel
n, Cz
ech
repu
blic
).
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Table 4 Fungi nutritional type 137Cs activities and TRs, means and effects as predicted by REML analysis for the NU97 data set
Data type/units Nutritional type Observations Mean Effect Activity Mycorrhizal 534 25562 0 (Bq kg-1 DW) Parasitic 19 1671 -23891 (NU97 data set) Saprophytic 156 11103 -14459 Average S.E.D 12332 Maximum S.E.D 15841 Minimum S.E.D 5933 Wald statistic (D.F.) 7.8 (2) P
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
Table 5 Statistics describing the distribution parameters for TRs of individual fungi species and proposed effective TRs (NU97 and Belgium data sets) Dataset Species Best-ftting
distribution n se se TReffective
(m2 kg-1) CI (68%)
BE Laccaria laccata log(X) 31 -0.0938 0.4096 2.28 0.2897 7.022 2.839 BE Cortinarius armillatus log(X) 25 1.9016 0.0641 0.3202 0.0453 7.076 11.840 BE Dermocybe cinnamomea log(X-a) 31 2.2134 0.4489 0.4351 0.2005 6.228 5.700 BE Tylopilus felleus,Laccaria amethystina,Boletus chrysenteron log(X-a) 164 0.6779 0.1119 0.9865 0.0969 3.072 0.608 BE & NU Cortinarius delibutus,Boletus badius log(X-a) 200 0.6778 0.1067 0.728 0.0772 2.242 0.630 BE & NU Lactarius rufus,Cantharellus tubaeformis log(X-a) 61 0.3928 0.1961 0.636 0.1272 1.492 0.529 BE Russula ochroleuca log(X)1 127 -0.6127 0.1132 1.2759 0.0801 1.226 0.231 BE Hydnum repandum log(X) 22 -2.0204 0.3483 1.6331 0.2464 0.504 0.268 BE & NU Kuehneromyces mutabilis,Lactarius necator log(X) 42 -1.7287 0.1902 1.2323 0.1345 0.380 0.100 BE Collybia butyracea log(X) 18 -1.3542 0.1298 0.5506 0.0918 0.302 0.042 BE Boletus subtomentosus log(X) 36 -1.8564 0.1768 1.0605 0.125 0.275 0.061 NU Boletus edulis log(X)2 59 -2.7436 0.2122 1.6295 0.1501 0.243 0.080 NU Cantharellus cibarius log(X-a) 26 -1.1215 0.4364 0.4395 0.1977 0.242 0.162 BE Lepista nebularis log(X) 33 -2.117 0.1989 1.1426 0.1407 0.232 0.060 BE Lepista nuda log(X) 25 -2.246 0.1997 0.9986 0.1413 0.175 0.043 BE Armillaria mellea log(X) 29 -3.1491 0.1856 0.9994 0.1313 0.071 0.016 BE Cortinarius anomalus normal 30 10.633 1.2614 6.9073 0.8922 11.513 1.264 BE Clitocybe clavipes & Cortinarius brunneus normal 60 4.9863 0.4543 3.5182 0.3213 5.530 0.456 NU Paxillus involutus normal 40 1.6907 0.2187 1.3827 0.1547 1.963 1.370 Note difference between fitted and observed distribution patterns significant at : 1 P
Gillett and Crout A review of 137Cs transfer to fungi : importance of species, spatial scale and time
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