Predicting survival of a genetically engineered microorganism, Pseudomonas chlororaphis 3732RN-L11, in soil and wheat rhizosphere across Canada with linear multiple regression models

Predicting survival of a genetically engineeredmicroorganism, Pseudomonas chlororaphis3732RN-L11, in soil and wheat rhizosphere acrossCanada with linear multiple regression models

Thomas A. Edge and R. Campbell Wyndham

Abstract: Pseudomonas chlororaphis 3732RN-L11 survival rates in soil and wheat rhizosphere were measured usingintact soil core microcosms representing 23 sites across Canada. Linear multiple regression (LMR) models were developedto predict the survival rate of this genetically engineered microorganism (GEM) as a function of soil parameters measuredat the time of microcosm inoculation. LMR models were tested by comparing their predicted survival rates with observedsurvival rates from environmental introductions of the GEM by Gagliardi et al. (2001) at five field sites across Canadaover two years. No soil parameter (e.g., % clay) was highly correlated with GEM survival rates in soil or wheatrhizosphere. Total fungal colony-forming units (CFUs), % soil titanium (positive correlations), and % soil magnesium(negative correlation) were found to be the best LMR predictors of GEM survival rates in soil over two years. Totalsoil bacterial CFUs, nitrate, % soil potassium (positive correlations), and exchangeable magnesium (negative correlation)were found to be the best LMR predictors of GEM survival rate in wheat rhizosphere over two years. While LMRmodels were statistically significant, they were unable to reliably predict the survival rate of the GEM in field trial introductions.The results indicate that there can be considerable uncertainty associated with predicting GEM survival for multi-siteenvironmental introductions.

Key words: Pseudomonas chlororaphis 3732RN-L11, survival, soil, Canada.

Résumé : Nous avons mesuré les taux de survie de Pseudomonas chlororaphis 3732RN-L11 dans le sol et les rhizosphèresde blé en utilisant des microcosmes intacts de carottes de sol représentant 23 sites à travers le Canada. Des modèles derégressions linéaires multiples (RLM) ont été conçus afin de prédire le taux de survie de ce micro-organismegénétiquement modifié (MGM) en fonction des paramètres du sol mesurés au moment de l’inoculation du microcosme.Les RLM ont été mis à l’épreuve en comparant les taux de survie prédits à ceux observés lors des introductionsenvironnementales du MGM par Gagliardi et al. (2001) dans cinq sites sur le terrain à travers le Canada. Aucunparamètre de sol (p. ex. % d’argile) n’a pu être fortement corrélé avec les taux de survie du MGM dans le sol ou lesrhizosphères de blé. Les unités formant des colonies (CFU) fongiques totales, le % de titane dans le sol (corrélationspositives) et le % de magnésium dans le sol (corrélation négative) se sont avérés être les meilleurs prédicteurs de RLMpour les taux de survie du MGM dans le sol pendant deux ans. Les CFU bactériennes totales du sol, les nitrates, le % depotassium dans le sol (corrélations positives) et le magnésium échangeable (corrélation négative) se sont avérés être lesmeilleurs prédicteurs de RLM pour les taux de survie du MGM dans la rhizosphère du blé pendant deux ans. Bien queles RLM étaient statistiquement significatives, elles ont été incapables de prédire de façon fiable le taux de survie duMGM dans les introductions pour essais sur le terrain. Les résultats indiquent qu’il peut exister une incertitudeconsidérable associée avec la prédiction de la survie d’un MGM lors d’introductions environnementales à des sitesmultiples.

Mots clés : Pseudomonas chlororaphis 3732RN-L11, survie, sol, Canada.

[Traduit par la Rédaction] Edge and Wyndham 727

Can. J. Microbiol. 48: 717–727 (2002) DOI: 10.1139/W02-066 © 2002 NRC Canada

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Received 3 January 2002. Revision received 14 June 2002. Accepted 5 July 2002. Published on the NRC Research Press Web siteat http://cjm.nrc.ca on 4 September 2002.

T.A. Edge.1 Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada, and National Water Research Institute,Environment Canada, 876 Lakeshore Road, Burlington, ON L7R 4A6, Canada.R.C. Wyndham.2 Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada.

1Corresponding author (e-mail: [email protected]).2Deceased.

I:\cjm\cjm48\4808\W02-066.vp03-Sep-2002 1:24:01 PM

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Introduction

Predicting how well a genetically engineered microorganism(GEM) will survive is an important risk assessment consid-eration pertaining to a proposed environmental introduction.If a GEM does not survive well, it is less likely to have sig-nificant environmental or human health impacts beyond theimmediate time and location of its introduction. A commonapproach to predicting GEM survival prior to an environ-mental introduction has been to study its behavior undersimulated environmental conditions in laboratory micro-cosms. Tiedje et al. (1989) suggested that information on thepotential for establishment of GEMs should be obtainedfrom laboratory tests, such as microcosms, before proceedingwith carefully planned small-scale field trials to evaluate po-tential environmental effects. Krimsky et al. (1995) stressedthat data from microcosm studies should be required for anyregulatory approval of a field release of a GEM. Membercountries of the Organisation for Economic Cooperation andDevelopment (OECD) have recognized the practical value ofassessing the survival of GEMs in simulated natural environ-ments, such as microcosms, for pre-release risk assessmentpurposes (OECD 1992).

One of the limitations of microcosms, however, is thatthey can only represent a few of the potential environmentalintroduction locations for a GEM. While tests can be per-formed in a microcosm simulating one set of environmentalconditions, it may not be appropriate to extrapolate results toanother set of conditions. In such cases, it would be desir-able to have a scientific basis for making predictions aboutthe survival of a GEM at many different introduction siteswithout the need to gather laboratory microcosm data repre-senting every site. At present, such predictions are oftenmade implicitly in risk assessments based on extrapolationfrom available knowledge about the biology and likelycommercial-use pattern of a GEM. Before GEMs are morewidely introduced into the environment, it will be importantto understand the feasibility of supplementing microcosmmethods with inference methods, such as statistical models,for predicting survival trends of GEMs. For example, it wouldbe useful to know if the survival rate of a GEM in soil couldbe accurately predicted based upon knowledge of a few keysoil parameters that are influential for that GEM. The objec-tive of this study was to calculate GEM survival rates in lab-oratory soil microcosms and then use these rates to developpredictive linear multiple regression (LMR) models of sur-vival as a function of soil parameters measured at the timeof GEM inoculation into microcosms.

The present study used an intact soil core microcosm de-scribed by Angle et al. (1995) to simulate soil and wheatrhizosphere conditions. Gagliardi et al. (2001) found thatthis soil microcosm was useful for predicting survival of aGEM in soil and wheat rhizospheres at five different fieldintroduction sites across Canada over two years. The micro-cosm validation study conducted by Gagliardi et al. (2001)also provided a multi-year data set on the survival of Pseudo-monas chlororaphis 3732RN-L11 in widely different soilsand climatic regions across Canada. This data set, supple-mented by additional laboratory microcosm data collected aspart of the present study, provided a unique opportunity todevelop and test an LMR approach for predicting the sur-

vival of this GEM in experimental field introductions. Toour knowledge, this study provides the first field test of astatistical model for making quantitative predictions of GEMsurvival rate in soil and rhizospheres across a wide range ofclimatic and soil conditions.

Materials and methods

The study was conducted in 1996 (year 1) and 1997 (year 2)using soils collected from six sites at Agriculture and Agri-FoodCanada’s Central Experimental Farm in Ottawa, Ontario. Thesites (A–F) were chosen to represent the diversity of differ-ent agricultural soil types found in the Experimental Farmarea. The study was conducted simultaneously with the soilcore microcosm validation study carried out at six other lo-cations outlined in Gagliardi et al. (2001): the University ofMaryland, College Park, Md. (MD); University of BritishColumbia, Vancouver, B.C. (BC); University of Saskatchewan,Saskatoon, Sask. (SK); Carleton University, Ottawa, Ont.(CL); McGill University, Montreal, Que. (NR) (year 2 only);and at a site separate from A–F at Agriculture and Agri-Food Canada’s Central Experimental Farm in Ottawa, Ont.(AG). Except where stated, materials and methods followGagliardi et al. (2001) for the microcosm validation studyand are only briefly outlined here.

Bacterial strain and mediaThe genetically engineered bacterial strain used in this

study was Pseudomonas chlororaphis 3732RN-L11 (formerlyPseudomonas aureofaciens) to be referred to here as RN-L11.This strain and its parent (strain 3732RN) are resistant torifamycin (100 µg·mL–1) and nalidixic acid (100 µg·mL–1).RN-L11 was developed by inserting a lacZY gene cassetteinto the chromosome using a Tn7-lacZY transposon (Barry1986, 1988). The media and chemicals used are outlined inGagliardi et al. (2001), although an additional antibiotic(nystatin 100 µg·mL–1) was added to the agar media in oneinstance in year 2 to address a contamination problem.

Soil and rhizosphere microcosmsIntact soil microcosm cores were taken at the edge of cul-

tivated fields in early May of each year. The soil micro-cosms consisted of 17.5 cm long hollow cores made from5 cm diameter polyvinyl chloride plumbing pipe that werehammered into the soil surface. Rhizosphere microcosmswere identical to the soil microcosms except that they wereplanted with Canadian prairie spring wheat seeds (hard redspring wheat, Triticum aestivum, cultivar A.C. Karma) justprior to inoculation with RN-L11. Microcosms were placedrandomly in a growth chamber kept at 22°C (approximatelythe 5 year mean growing season temperature for wheat inCanada) and a relative air humidity of 70% during the exper-iment. A photoperiod of 12 h dark and 12 h light was estab-lished. Cores were weighed regularly and watered withsterile, distilled water to maintain about 60% soil moisturecontent.

Microcosm inoculation and samplingMicrocosms were inoculated about one week after soil

collection. They were inoculated with 2.5 mL of RN-L11inoculant (approximately 5 × 108 CFU/mL) that had been

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grown overnight. The inoculum was applied evenly over thesoil surface of the soil microcosms and over the plantedseeds of the rhizosphere microcosms. Soil microcosms weresampled at day 0, day 14, and at a third time depending onthe observed die-off rate (generally either day 42 or day 56).Day 0 for rhizosphere sampling took place 14 days after in-oculation of the seeds to allow for germination and develop-ment of the rhizosphere. Subsequent rhizosphere samplingswere at day 28 and day 84. At each sampling time, four rep-licate cores were used that provided 12 or more data pointsfor regression analyses. Soil and rhizospheres were removedfrom cores, and a Waring blender (Fisher Scientific,Pittsburg, Pa.) was used for extraction of RN-L11. Forrhizosphere microcosms, soil was removed from around therhizosphere, and loosely adhering clumps of soil were gentlycleaned from the roots. The plant portion was cut off at theroot crown, and the entire fresh rhizosphere (roots includingany soil tightly adhering to the roots) was used in theblender extraction step. After three replicate series of dilu-tions, 100 µL was micropipetted onto the surface of selec-tive agar plates. For soils, a detection limit of about 10CFU/g fresh soil could be obtained by plating 1 mL of undi-luted blender sample. For rhizospheres, samples were platedin the same way, except that plating 1 mL of undilutedblender sample provided a detection limit of about 100CFU/g fresh rhizosphere.

Soil analysesThree or four replicate soil cores from each site were

shipped to the University of Maryland, College Park, Md.for analysis of some standard soil parameters (Table 1). Soilwas extracted from a core, sieved through a 4-mm sieve, andanalyzed for total bacteria, total pseudomonads, and totalfungi, as defined by colonies counted on selective media.Similar to the microcosm sampling protocol, 10 g of freshsoil were added to 95 mL of sterile, distilled water in a War-ing blender for extraction of microorganisms. The selectivemedium used for total bacteria was described by Buyer(1995) as a medium that inhibits the growth of Bacillusmycoides and is relatively nonselective for other bacteria.After inoculation, all plates were inverted and incubated inthe dark at 22°C for about 48 h before counting colonies.Pseudomonas Agar F (Difco, Detroit, Mich.) was used as theselective medium for total pseudomonads and was the samemedium used in the microcosm sampling analyses for theGEM, except that it lacked the selective antibiotics. After in-oculation, all pseudomonad plates were inverted and incu-bated in the dark at 22°C for about 24 h before countingcolonies. The selective medium used for total fungi was de-scribed by Buyer et al. (1989), who indicated that a wide va-riety of fungi were found to grow on this medium. Afterinoculation, all fungi plates were inverted and incubated inthe dark at 22°C for about 48 h before counting colonies.

Remaining fresh soil was air-dried over two days and usedfor additional analyses of physical and chemical parameters.Air-dried soil samples were passed through a grinder tocrush large particles and provided to the University of Mary-land Soil Testing Laboratory (Patterson Hall, College Park,MD 20742) for analyses following their standard test methodsdescribed in University of Maryland (1978). Soil parametersincluded the following: % sand, % silt, and % clay as mea-

sured by a hydrometer; pH as measured in a soil–water solu-tion by a Fisher Scientific Accumet 915 pH meter; ex-changeable calcium, exchangeable magnesium, exchangedpotassium, phosphorus, and nitrate as measured by a flamephotometer (Technicon AutoAnalyzer II System); total cationexchange capacity (mequiv. 100·g–1) as the sum of ex-changeable acidity and cations Mg, K, and Ca (mequiv.100·g–1); soluble salts (ppm) as determined by electricalconductivity; % organic matter as measured by weight losson ignition at 360°C for 2 h; and % organic carbon, hydro-gen, and nitrogen as measured by gas combustion using aLECO CHN600 analyzer (Perkin-Elmer, Norwalk, Conn.).

The elemental composition of oven-dried soils from years1 and 2 was examined using a JEOL JSM-6400 scanningelectron microscope (SEM) at the Carleton University Re-search Facility for Electron Microscopy. A small soil samplefrom a core was spread onto double-sided carbon tape onsmall aluminum posts that were then coated with a thin layerof carbon (10–20 nm). The elemental composition of threereplicate cores from each site was measured by performingthree X-ray analyses for each soil sample from a given soilcore. Each X-ray analysis was for 100 s at a probe current of0.8 nA, 20 kV accelerating voltage, a working distance of39 mm, and a magnification of 50×. These X-ray analysesprovided a crude composite analysis of many different soilparticles characterizing each soil sample rather than an analysis

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Edge and Wyndham 719

Soil parameter

PhysicalClay % claya

Sand % sanda

Silt % silta

BiologicalBact Total soil bacteriab

Pseudo Total soil pseudomonadsb

Fung Total soil fungib

ChemicalpH pHCEC Cation exchange capacityc

Ca Exchangeable calciumd

Mg Exchangeable magnesiumd

K Potassium K2Od

P Phosphorus P2O5d

NO3 Nitrate NO3d

SS Soluble saltse

OM % organic mattera

C % organic carbonf

N % organic nitrogenf

H % organic hydrogenf

SiSEM – CuSEM Scanning electron microscope elementalanalysesf,g

a% dry soil weight.bCFU/g soil.cmequiv. 100�g–1.dkg·ha–1.eppm soil/water suspension.f% molecular weight.g16 elements + summed total elemental reading (TotSEM). The elements

include Si, Al, Ti, Cr, Fe, Mg, Mn, K, Ca, Na, Cl, Zn, S, Ni, P, and Cu.

Table 1. List of soil parameters measured at the time of Pseudo-monas chlororaphis 3732RN-L11 inoculation into microcosms.



of a specific soil particle (e.g., clay particle) within a sample.While quantitative SEM elemental microanalysis has typi-cally used soil samples impregnated within resin and theneither thin sections or polished blocks prepared to satisfysmooth, flat surface requirements, it is possible to obtainuseful semiquantitative data from X-ray analyses of roughsoil surfaces (Sullivan and Bush 1997; Sullivan 1998).Elemental composition of soil (as a % of total molecularweight) was obtained with a Link ZAF-4 program by analy-ses of the area under peaks corresponding to elements de-tected in the energy-dispersive X-ray spectra. To allowcomparison of percent elemental values between samples,the total percent elemental detection was adjusted to 100%for each sample, and percentages for each element were ad-justed accordingly.

GEM experimental field introductionsExperimental field introductions were conducted at five

sites across Canada (BC, SK, CL, NR, and AG) as part ofthe microcosm validation study outlined by Gagliardi et al.(2001). At each site, eight 1-m2 field plots were cleared ofsurface plant and organic material and separated by 1 m be-tween each plot. Four plots were planted with wheat for therhizosphere treatment, and four plots were left fallow. Corru-gated plastic edging 15 cm high was buried 7.5 cm deeparound each plot to prevent RN-L11 runoff and root interac-tions between plots.

Statistical analysesStatistical analyses were conducted using SAS statistical

programs (version 6.08) (SAS Institute Inc., Cary, N.C.) andMicrosoft Excel (Redmond, Wash.). Linear regression equa-tions were calculated to determine RN-L11 survival rates inlaboratory soil and rhizosphere microcosms representing thesix Experimental Farm sites in Ottawa. Equations were alsocalculated to determine RN-L11 survival rates in laboratorysoil and rhizosphere microcosms and field plots representingsix other sites using raw CFU data supplied from the micro-cosm validation study. All data shared from the microcosmvalidation study (microcosm and field plots) were includedin regression analyses, except the data from outside the sam-pling periods used in the present study (e.g., about 60 daysfor soil and about 100 days for rhizospheres). These samplingperiods enabled a focus on GEM survival after inoculationwhen log population declines were expected to be more lin-ear. All CFU data were transformed (log10) to enable calcu-lation of survival rates over time for soil and rhizosphere byleast squares linear regression analysis (PROC REG). Linearregression equations have been regarded as a valuable tool tomodel the population declines of microorganisms introducedinto soils or water (Hurst 1997). LMR approaches have alsobeen used to model these population declines in soil as afunction of environmental variables (Hurst et al. 1980; Hurst1997).

Univariate analyses of survival rates and soil parameterswere conducted using the SAS PROC UNIVARIATE proce-dure. Soil parameters were first examined to see if they werenormally distributed by examining frequency histograms andresults from Wilks–Shapiro tests. They were also examinedafter log10, square root, inverse (1/X), exponential (X2), andtrigonomic sine function transformations to determine if their

distribution appeared more normal. If a significant improve-ment was detected from examining frequency histogramsand results from the Wilks–Shapiro tests, the transformedparameter was used in subsequent analyses. Soil parametersused in correlation and multivariate analyses were the meanvalue of three or four replicate cores from each site. Correla-tions between soil parameters and survival rates were exam-ined by Pearson’s correlation coefficient and nonparametricSpearman’s coefficient of rank correlation tests using SASPROC CORR.

Development of LMR modelsAs a result of small sample sizes for each year (year 1 n = 11;

year 2 n = 12), sites were pooled between years to calculatesoil and rhizosphere LMR models for years 1&2 (n = 23). Aweighted multiple regression approach (one per survival ratestandard error) was taken to calculate all LMR models togive less weight in calculations to those sites that wereknown to have survival rate values with a higher variance.Several multiple regression procedures were performed toidentify a reduced number of soil parameters to include inthe final LMR models. Potentially useful soil parameterswere first identified using SAS PROC REG MAXR andSTEPWISE procedures and then selected for more thoroughanalysis by the PROC REG ADJRSQ procedure. TheADJRSQ procedure examines all possible combinations of pa-rameters and identifies sets of parameters with the highest ad-justed R2 values. Since the ADJRSQ requires extensivecomputation time, only 10 parameters could be practicallyanalyzed by this procedure. These 10 parameters were se-lected to be % clay and pH, and the best eight parametersidentified by the MAXR and STEPWISE procedures. Per-cent clay and pH were included because of their relativeease of measurement and a theoretical basis for assumingtheir importance in influencing bacterial survival (Alexander1986; England et al. 1993; Stotzky 1997; van Veen et al.1997).

The best LMR models were those with the highest adjustedR2 and where parameters were not highly correlated amongthemselves (e.g., Spearman coefficients < 0.7), such that theremight be concerns about multicollinearity. Final LMR mod-els were those that maximized the adjusted R2 with the few-est number of soil parameters. To reduce concern aboutstatistical overfitting, final LMR models were sought withfour or fewer soil parameters. Residual values were plottedto determine whether they met assumptions of normality andhomoscedasticity and whether there was evidence ofcurvilinear relationships. Residuals were tested for normality(Wilks–Shapiro test), and studentized residuals and Cook’sdistance values were examined to detect outlier and influen-tial observations. Variance inflation factors were also exam-ined to assess concerns for multicollinearity.

LMR verification analyses compared the Years 1&2 LMRpredicted survival rates with those observed from laboratorymicrocosm measurements. A less-biased verification stepwas also performed using a jackknife “leave one out” test(SAS-REG – macro jackknife). This jackknife procedure re-calculated the Years 1&2 soil and rhizosphere LMR models,leaving one site out each time, and applied each LMR topredict the survival rate of the site left out. Predicted and ob-served survival rates were then compared. Another jackknife

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procedure was performed to examine the stability of soil pa-rameters included in Years 1&2 LMR models. Twenty-threeYears 1&2 LMR models were calculated, leaving one siteout for each calculation. Each of these jackknife Years 1&2LMR models was developed to consist of the best four soilparameters that were selected by the previously describedLMR building steps. The frequency of soil parameters ap-pearing in these 23 different LMR models was used to as-sess Years 1&2 LMR parameter stability.

LMR validation analyses were conducted to enable aquantitative and qualitative evaluation of how well the Years1&2 LMR models predicted survival rates of RN-L11 in theoutdoor field plots. Validation of Years 1&2 LMR modelswas done on a quantitative basis by plotting the LMR-predictedsurvival rates against observed survival rates from the envi-ronmental introductions of the GEM in experimental fieldplots. The comparison was also done on a qualitative basisby dividing observed field plot survival rate values into highand low survival classes based on the median survival ratevalue. The percentage error rate for classification of survivalrates using the LMR models was then determined. Quantita-tive and qualitative cross-validation studies were also con-ducted to determine whether an LMR calculated for one yearcould reliably predict survival rates for the other year.

Results

A summary of soil analysis data for several key parameters

is provided in Table 2. There was a considerable range acrosssites for the following soil parameters: % clay (2–37%), pH(5.8–7.9), and % organic matter (2.4–6.2%). Soil parametersalso showed considerable variability between years at somesites despite the close proximity of core sampling locations.The measurement of soluble salts was considered unreliable,and the data were clearly not normally distributed, so thisparameter was not used in subsequent LMR analyses. Al-most all soils had undetectable levels of CrSEM (scanningelectron microscope elemental analysis (% molecularweight)), NiSEM, and PSEM, and these parameters were notincluded in LMR analyses.

All linear regression analyses of RN-L11 survival in soiland rhizosphere microcosms and field plots were significant(P < 0.05). In some instances, regression models accountedfor relatively low amounts of variation in the data, althoughanalysis of regression residuals indicated linear regressionsgenerally provided good models for RN-L11 survival. Popu-lation levels of RN-L11 declined over all observation peri-ods. The range of microcosm survival rates was greater forsoils (–0.0102 to –0.1561 log CFU·(g soil)–1·day–1) than forrhizospheres (–0.0120 to –0.0608 log CFU·(g root)–1·day–1).RN-L11 generally declined more slowly in the rhizospherethan in bulk soil at each site. The mean survival rate in soilmicrocosms was –0.0657 log CFU·(g soil)–1·day–1 (n = 23)compared with –0.0314 log CFU·(g root)–1·day–1) (n = 23) inrhizosphere microcosms. RN-L11 also generally had a fasterdecline in field plots than in microcosms. The mean field

Site % sand % silt % clay pH % OM Pseudo Fung Bact

Year 1A 49.3 17.3 33.3 7.07 2.70 5.84 4.79 7.06B 62.7 25.3 12.0 7.47 4.13 5.62 5.22 7.38C 36.0 26.7 37.3 6.60 5.43 5.81 5.57 7.40D 89.7 7.3 3.0 5.80 2.80 6.09 5.27 7.24E 46.7 22.0 31.3 6.63 4.60 6.21 5.78 7.57F 55.0 26.3 18.7 7.70 3.93 6.07 5.69 7.32BC 86.3 9.0 4.7 6.13 5.57 5.98 4.35 6.71SK 34.3 44.0 21.7 7.53 5.93 6.26 5.81 7.62CL 59.3 28.7 12.0 7.53 3.57 6.00 5.15 7.18AG 81.3 16.0 2.7 6.23 3.57 6.21 5.56 7.50MD 43.3 42.0 14.7 5.80 2.63 6.09 5.16 7.23Year 2A 51.0 17.7 31.3 7.47 3.48 6.64 5.98 7.28B 66.0 22.3 11.7 7.60 3.20 6.52 5.99 7.35C 48.0 16.0 36.0 7.27 4.07 6.60 6.06 7.17D 92.7 4.3 3.0 5.87 2.53 6.39 5.44 6.92E 44.0 26.3 29.7 6.77 4.20 6.60 5.75 7.09F 61.0 24.3 14.7 7.93 3.21 6.66 6.05 7.23BC 88.7 8.0 3.3 5.83 4.39 6.28 5.31 6.92SK 35.0 45.3 19.7 7.37 6.24 6.59 6.26 7.37CL 78.7 14.3 7.0 7.40 3.41 6.42 6.07 7.09AG 84.3 13.7 2.0 6.20 2.87 6.58 6.25 7.12MD 46.0 40.0 14.0 5.90 2.41 6.65 5.88 7.19NR 60.0 21.7 19.0 5.87 3.69 6.51 5.38 7.08

Note: Values are the mean of three or four replicates soil cores. Site abbreviations are as follows: A–F, Agri-Food Canada’s Central ExperimentalFarm; BC, University of British Columbia; SK, University of Saskatchewan; CL, Carleton University; AG, a site separate from A–F at Agri-FoodCanada’s Central Experimental Farm; MD, University of Maryland. Soil parameter abbreviations and units are shown in Table 1.

Table 2. Some key physical, chemical, and biological soil parameters measured at the time of Pseudomonas chlororaphis 3732RN-L11inoculation into microcosms.



plot survival rate was –0.0807 log CFU·(g soil)–1·day–1 insoil (n = 9) and –0.0383 log CFU·(g root)–1·day–1) for rhizo-spheres (n = 9).

No single soil parameter proved very useful for predictingRN-L11 survival over the two-year study period. Total pseudo-monads and % soil sodium (NaSEM) were the only parame-ters that were significantly correlated (P < 0.05) with soilmicrocosm survival rates over two years (years 1&2), al-though these correlations were weak (Table 3). There wereno significant correlations (P < 0.05) between any of the soilparameters and rhizosphere microcosm survival rates overtwo years (years 1&2). It was notable that any soil parameterthat was significantly correlated with survival rate in oneyear (e.g., CaSEM) was not significantly correlated with thesurvival rate in the other year.

Soil and rhizosphere Years 1&2 LMR models are shownin Table 4 along with the other exploratory LMR analysesthat calculated LMR models based only on year 1 or year 2data. All LMR models for soil and rhizosphere were statisti-cally significant (P < 0.05). The partial regression coeffi-cients for all soil parameters in Years 1&2 LMR modelswere significant (P < 0.05) except for log10 (NO3) in theYears 1&2 rhizosphere LMR. This parameter was maintainedin the model because it significantly improved the model’sadjusted R2, and its importance was observed in jackknife

analyses of LMR parameter stability. Consideration was givento including a fourth parameter (NaSEM)2 in the Years 1&2LMR for soil. However, it was not included because it didnot significantly improve the model’s adjusted R2, and itsimportance was not observed in jackknife analyses of LMRparameter stability. Scatterplots of LMR soil parametersagainst survival rates did not indicate the presence of obvi-ous nonlinear relationships. Analysis of Years 1&2 LMR re-siduals and studentized residuals did not indicate curvilinearrelationships or the need for any further data transforma-tions. Variance inflation factors were close to 1, suggestinglittle concern about multicollinearity in either the Years 1&2soil or rhizosphere LMR models. There were no observa-tions with undue influence on LMR models as judged byCook’s D values.

No soil parameters were found in common between Years1&2 soil and rhizosphere LMR models. In the Years 1&2soil LMR, total soil fungi and soil % titanium (TiSEM) werepositively correlated with RN-L11 survival rates, while soil %magnesium (MgSEM) was negatively correlated with RN-L11soil survival rates. In the Years 1&2 rhizosphere LMR, totalsoil bacteria, nitrate (NO3), and soil % potassium (KSEM)were positively correlated with RN-L11 survival rates, whileexchangeable magnesium (Mg) was negatively correlatedwith RN-L11 rhizosphere survival rates. Despite their antici-

© 2002 NRC Canada


Year 1 (n = 11) Year 2 (n = 11) Years 1&2 (n = 23)

Parameter Coeff. Parameter Coeff. Parameter Coeff.

Soil survival rate correlationsNaSEM –0.55 CaSEM –0.91a Pseudo 0.45a

P –0.51 TiSEM 0.68a NaSEM –0.42a

H –0.49 Ca –0.67a CaSEM –0.41Pseudo 0.43 pH –0.66a pH –0.36Mg –0.43 CEC –0.55 NO3 –0.34MgSEM –0.4 Fung –0.5 TiSEM 0.32Clay –0.4 NaSEM –0.44 H –0.31SiSEM –0.35 N –0.4 P –0.3pH –0.31 KSEM –0.39 CEC –0.28CaSEM –0.3 NO3 –0.36 OM –0.27OM –0.3 Clay 0.06 Clay –0.12Rhizosphere survival rate correlationsNO3 0.37 OM –0.74a Bact 0.4Fung 0.36 NO3 –0.6 Mg –0.32P –0.3 C –0.57 NaSEM –0.26H –0.29 H –0.56 P –0.23Mg –0.28 N –0.54 pH –0.22N 0.27 Pseudo 0.43 Hydr –0.21pH –0.25 Mg –0.35 SSEM –0.2AlSEM 0.25 Bact 0.33 Pseudo –0.2Bact 0.23 CuSEM –0.33 OM –0.18FeSEM 0.21 K –0.32 TiSEM 0.16Clay –0.12 Clay –0.19 Clay –0.11

Note: The ten highest parameter correlations and % clay are shown. Soil parameter abbreviations areshown in Table 1.

aP < 0.05.

Table 3. Spearman’s correlation coefficients between soil parameters and Pseudomonaschlororaphis 3732RN-L11 survival rates in soil and rhizosphere microcosms over two yearsof lab experiments.



pated importance, the soil parameters % clay and pH werenot selected as important predictors in the Years 1&2 LMRmodels, even though attempts were made to retain themthrough preliminary statistical steps to reduce soil parameternumbers. None of the soil parameters included in the Year 1soil LMR or the Year 1 rhizosphere LMR were also found intheir corresponding Year 2 LMR models. The results of thejackknife LMR analysis to examine parameter stability ofthe Years 1&2 LMR models confirmed the importance ofthe originally selected soil parameters, as they were consis-

tently selected more frequently than other parameters inLMR models. The most frequently selected soil parameterfor predicting RN-L11 survival in soil was soil % titanium.The most frequently selected soil parameter for predictingRN-L11 survival in rhizospheres was exchangeable magne-sium.

Validation of the Years 1&2 LMR models involved com-paring their predicted survival rates with observed survivalrates in field plots in the environment (Fig. 1). The Years1&2 LMR models did not provide very accurate predictionsof the GEM survival rates in either soil or rhizosphere fieldplots. Years 1&2 LMR predictions were calibrated to ob-served field plot survival rates by the regression equationsdisplayed in Fig. 1. Validation of the Years 1&2 LMR mod-els from a qualitative perspective involved rating how reli-able they were at classifying soil or rhizosphere field plotsurvival rates as either high or low (Table 5). The Years 1&2rhizosphere LMR incorrectly classified three of nine fieldplot survival rates (error rate = 33%), while the Years 1&2soil LMR incorrectly classified five of nine field plot sur-vival rates (error rate = 56%). In model verification analyses,both Years 1&2 LMR models were more reliable at classify-ing the 23 microcosm survival rates than the LMR modelsdeveloped only from either Year 1 or year 2 data. The cross--validation analysis of Year 1 and Year 2 LMR models indi-cated that the LMR models developed from data in one yearcould not be used to reliably predict survival rates in theother year. While Year 1 and Year 2 LMR models could cor-rectly classify the microcosm survival rates from their per-spective years reasonably well, they usually had error ratesof more than 50% for classifying microcosm survival ratesfrom the other year.

A comparison of the accuracy of LMR and microcosm ap-proaches for predicting survival rates in soil and rhizospherefield plots, respectively, is shown in Fig. 2. In many casesboth the Years 1&2 LMR and microcosm-derived survivalrate predictions underestimated the survival rate observed infield plots. The microcosm approach appeared to be the moreconservative approach, since it only overestimated RN-L11declines in the field for one of nine soil sites and one of ninerhizosphere sites. The Years 1&2 LMR overestimatedRN-L11 declines in the field for three of nine soil sites andfour of nine rhizosphere sites. These overestimations werealso larger than those from microcosms. Both Years 1&2

© 2002 NRC Canada


LMR LMR modelFvalue P > F R2

AdjustedR2

Soil

Years 1&2 (n = 23) Y = 0.02108(MgSEM) – 0.03634(fung) – 0.07347(TiSEM) + 0.17801 11.54 0.0002 0.65 0.59

Year 1 (n = 11) Y = 0.01779(NaSEM)2 – 0.03443(MgSEM) + 0.00008(Mg) + 0.00181(P) – 0.10862 7.71 0.0151 0.84 0.73

Year 2 (n = 11) Y = 0.01697(sin(Ca)) – 0.09092(TiSEM) – 0.02854(sin(CaSEM)) + 0.01008(FeSEM) – 0.07022 30.72 0.0004 0.95 0.92

Rhizosphere

Years 1&2 (n = 23) Y = 0.00136(Mg) – 0.02658(bact) – 0.01302(log10NO3) – 0.00149(KSEM)2 + 0.15976 7.44 0.001 0.62 0.54

Year 1 (n = 11) Y = 0.07294(TiSEM) + 0.00007(Mg) + 0.08520(log10SSEM) + 0.00006(TotSEM)2 – 0.21088 9.05 0.0103 0.86 0.76

Year 2 (n = 11) Y = 0.00689(log10clay) – 0.72357(log10bact) + 0.01059(log10NO3) + 0.02270(log10K) +0.51392

21.74 0.001 0.94 0.89

Note: Soil parameter abbreviations are in Table 1.

Table 4. Linear multiple regression (LMR) models calculated to predict survival rates for Pseudomonas chlororaphis 3732RN-L11 insoil and wheat rhizospheres.

Fig. 1. Comparison of Years 1&2 LMR predicted survival rateswith observed survival rates for Pseudomonas chlororaphis3732RN-L11 in (a) soil field plots and (b) wheat rhizospherefield plots. Survival rate values were converted into positive val-ues for this figure to more easily visualize the observed–pre-dicted relationship.



LMR models and microcosms had relatively poor ability tocorrectly classify field plot survival rates into high and lowclasses. The soil microcosms had a classification error rateof 44%, while the rhizosphere microcosms had a classifica-tion error rate of 56%.

Discussion

The current study provided a broad, multisite field validationtest for LMR models developed to predict survival of a GEMin soils and rhizospheres. While there have been studies tovalidate soil microcosm results for predicting GEM survivalin field plots (Bolton et al. 1991; Angle et al. 1995; Gagliardiet al. 2001), we are unaware of studies testing statisticalmodels to predict GEM survival across such a wide range ofsoils and climatic conditions. While Bashan et al. (1995)studied bacterial survival in microcosms in relation to a vari-ety of soil parameters, they did not develop statistical mod-els for predicting survival in experimental field studies.

GEM survivalPopulation levels of RN-L11 declined over time after

introduction into all microcosms. The survival rates forRN-L11 observed in this study were similar to those ob-served for RN-L11 by Angle et al. (1995) and Gagliardi etal. (2001). As also reported by Gagliardi et al. (2001), therewas rarely a case where viable RN-L11 cells could not berecovered, and there was no evidence of RN-L11 entering aviable but nonculturable state in these agricultural soils. Thesurvival rates were also comparable to Pseudomonas survivalrates reported in the scientific literature by van Elsas et al.(1990). These authors reported that Pseudomonas survivalrates in soil ranged from about –0.02 to –0.1 log CFU·(gsoil)–1·day–1. The decline of RN-L11 in soils was consistentwith many other studies that have shown that population lev-els of microorganisms introduced into nonsterile soils de-cline over time (van Veen et al. 1997).

There were many environmental factors that could haveinfluenced the survival of RN-L11. Population levels of RN-L11almost always declined more slowly in wheat rhizospheresthan bulk soil. This was predictable, since the parent of RN-L11was originally isolated from a rhizosphere, and it wouldlikely have been more naturally adapted to survival in arhizosphere environment than bulk soil. However, none ofthe soil parameters measured in this study were strongly cor-related with RN-L11 survival rate in soil or rhizospheresover two years. While Gagliardi et al. (2001) significantlycorrelated RN-L11 survival rate with pH and carbon:nitrogen

ratios, these correlations were not significant in the largerdata set used in the present study. It had been anticipatedthat % clay would have been an important soil parameter forpredicting RN-L11 survival rates. The presence of clays hasbeen suggested as an important factor influencing bacteriasurvival in soil (England et al. 1993; Stotzky 1997; vanVeen et al. 1997). Many studies have found an increasedclay content in soil (e.g., soil texture or % clay) was associ-ated with enhanced bacteria survival (Marshall and Roberts

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Soil LMR model Rhizosphere LMR model

Experiment Years 1&2 Year 1 Year 2 Years 1&2 Year 1 Year 2

Years 1&2 field plots (n = 9) 56 44 22 33 44 56Years 1&2 microcosms (n = 23) 30 35 35 22 43 30Year 1 microcosms (n = 11) 27 18 55 9 27 45Year 2 microcosms (n = 11) 27 55 18 36 64 9

Note: High or low survival rate classes were distinguished by median survival rate values for 23 microcosms(soil = –0.0602; rhizosphere = –0.0303) or 9 field plots (soil = –0.0798; rhizosphere = –0.0381).

Table 5. Error rates (%) for linear multiple regression (LMR) models in predicting correct high or lowsurvival rate classes for Pseudomonas chlororaphis 3732RN-L11 in field plot or microcosm experiments.

Fig. 2. Comparison of Years 1&2 LMR and microcosm predictedsurvival rates with observed survival rates for Pseudomonaschlororaphis 3732RN-L11 in (a) soil field plots and (b) wheatrhizosphere field plots. Survival rate values were converted intopositive values for this figure to more easily visualize the com-parisons. Site abbreviations are in the text.



1963; van Elsas et al. 1986, 1989; Heijnen and van Veen1991). In the current study, however, it is possible that %clay was too coarse a measurement for predicting survivalrates and that specific types of clay minerals rather than theentire clay fraction may have been more important in influ-encing RN-L11 survival.

Another study that tried to predict bacterial survival in mi-crocosms based on soil factors found that percentages ofclay, nitrogen, organic matter, and water-holding capacity insoils were positively associated with survival of Azospirillumbrasilense, while percentages of CaCO3, fine sand, andrough sand were negatively associated with survival (Bashanet al. 1995). However, they found only 1 of 15 soil parameters(CaCO3) to be significantly correlated (negatively) with sur-vival rate of A. brasilense.

LMR evaluationWhile no single soil parameter proved very valuable for

predicting RN-L11 survival rates, it was possible to developLMR models that explained a significant amount of the soiland rhizosphere survival rate variation in microcosms. In ad-dition, some of the soil parameters selected for the Years1&2 LMR models could have had some biological relevancefor understanding the survival of RN-L11 in soil, althoughno evidence is provided here of causal relationships. BothYears 1&2 LMR models for soil and rhizosphere found (i)soil magnesium (MgSEM or Mg) to be negatively correlatedwith GEM survival rates and (ii) a biological factor (totalfungi or total bacteria) to be positively correlated with GEMsurvival rates.

Indigenous microorganisms competing for available re-sources in soil have been considered a biological factor thatcan adversely affect the survival of introduced microorgan-isms (Recorbet et al. 1992; van Veen et al. 1997). Acea et al.(1988) suggested populations of bacteria introduced into soilcould be reduced because of their susceptibility to starva-tion, protozoa predation, and possibly antibiotic-producingor lytic microbes. It has been shown that inhibiting the in-digenous microflora using antibiotics has resulted in the es-tablishment of inoculant microorganisms in rhizospheres athigher levels than those obtained in controls without antibi-otics added (Bashan 1986). Fungi are often the dominantcomponent of soil biomass, and they are known to producebacteriolytic enzymes and antibiotics and to attack livingbacteria (Thorn 1997). It is possible that measurements oftotal fungi and total soil bacteria could have represented ameasure of the population size of the natural microbial floracompeting with and influencing survival of RN-L11 in soiland wheat rhizospheres.

There may also have been a link between higher soil mag-nesium and the presence of certain clay minerals influentialfor the survival of RN-L11. Magnesium has been consideredmore commonly associated with certain 2:1 lattice clay mineralslike vermiculite (Bohn et al. 1985; Troeh and Thompson1993) and montmorillonite (Bohn et al. 1985; Foth 1990).Mokwunye and Melsted (1972) and Camberato and Pan (2000)suggested that soils with the highest 2:1 lattice clay contentgenerally have the highest magnesium content. X-ray dif-fraction analyses of a few of the soils used in the presentstudy indicated the presence of clay minerals like vermicu-lites and chlorites at the SK and CL sites, which showed en-

hanced RN-L11 survival (Gagliardi et al. 2001). These clayminerals were not detected at the BC and AG sites whereRN-L11 survival was relatively poor. Clay minerals likemontmorillonite have been associated with enhanced survivalof bacteria species in soil (Marshall and Roberts 1963;Marshall 1975; Osa-Afiana and Alexander 1982). The 2:1lattice clays are also typically part of the smaller particlesize fraction of clay minerals (<0.0002 mm diameter), whichmay provide for smaller pore spaces and protection of intro-duced microorganisms from predators like protozoa. Inter-estingly, Evans et al. (1993) found that survival of aRhizobium leguminosarum strain in soil was most stronglycorrelated with exchangeable magnesium (e.g., more thanother parameters like % clay). These authors could not eluci-date a clear role for Mg in influencing survival, althoughthey suggested that it might have been related to clay miner-alogy and higher magnesium concentrations associated withthose clay minerals favourable to protecting the bacteria fromsoil dessication and high temperature.

Despite the statistical significance of Years 1&2 LMR mod-els and the potential biological relevance of some of theLMR parameters, the Years 1&2 LMR models did not providereliable predictions of RN-L11 survival rates in the experi-mental field plots. While data scatterplots and analysis of re-siduals did not indicate obvious nonlinear relationshipsbetween survival rates and the selected soil parameters, it ispossible that the simple linear relationships assumed in thisstudy inadequately characterized survival rate and soil pa-rameter relationships. It is also possible that none of the soilparameters measured were of critical importance to the sur-vival of the RN-L11 or that the range of their values may nothave presented a significant enough environmental challengeto clearly affect RN-L11 survival. With the complexity ofsoil environments, perhaps no single parameter would belikely to have a predominant influence on survival for therange of agricultural soils used in this study. These resultsreflect views of Sayler and Ripp (2000) that the vast array ofenvironmental factors influencing GEMs will make the deri-vation of a reliable modeling approach for GEM survival adaunting task.

Additional challenges were inherent in developing LMRmodels from laboratory microcosms that were held at con-stant temperature, soil moisture, and air humidity. As a re-sult, weather events likely influenced GEM survival in fieldplots but not in laboratory microcosms. Gagliardi et al. (2001)suggested that cooler mean monthly temperature and 30- to40-mm rainfall events were correlated with enhanced andlower GEM persistence in soil field plots, respectively. Theynoted sudden drops in GEM population levels at CL in 1996and at NR in 1997 after heavy rainfall events. In these in-stances, the microcosm may not have provided a sufficientlyreliable simulation of environmental conditions in the field.An additional experimental constraint in the current studywas the relatively small sample size of sites for deriving andtesting LMR models. Such constraints necessitate caution ininterpreting LMR predictions, most notably because in thisstudy, statistically significant LMR models developed in oneyear were unable to reliably predict survival in the otheryear. Small sample sizes will likely be an inherent constraintfor such survival modeling approaches because of the costand complexity of conducting multisite studies.

© 2002 NRC Canada




© 2002 NRC Canada


Uncertainty challengesIt should be recognized that the survival predictions in

this study were only for soil and wheat rhizospheres. Wide-spread commercial applications of a GEM could also requireconsideration of survival in other environmental compart-ments, ranging from aerosols to surface waters. In addition,survival is only the first step in determining the fate of aGEM introduced into the environment. In developing mathe-matical models for predicting the potential spread of a GEMin an agricultural setting, Lewis et al. (1996) considered thata GEM might end up in at least eight distinct compartments(aerosols, dust, plant surfaces, surface soil, subsurface soil,surface water, groundwater, and rhizosphere) and that transfersbetween sedentary and more mobile compartments couldsubstantially increase the invasive speed of a GEM. Consid-eration of additional aspects such as GEM dispersal andgene transfer events could raise large cumulative uncertain-ties for predicting the environmental fate of a GEM.

The establishment of new national and international bio-technology regulatory regimes has raised public expectationsfor reliable assessments of the potential risks posed by envi-ronmental introductions of GEMs. However, at the sametime, concerns have been raised about the ability to assessthe invasiveness of genetically engineered organisms and thelimitations of making “invasion ecology” a predictive (as op-posed to after the fact) science (Kareiva et al. 1996). It willbe increasingly important for regulatory agencies to monitorenvironmental introductions of GEMs to gain an understandingof the level of uncertainty associated with pre-introductionpredictions of GEM survival. While the specific LMR mod-els developed in the current study were unable to reliablypredict GEM survival rates in field plots across Canada, it ispossible that the use of other soil parameters, a greater rangeof soil types, or other modeling approaches focused on smallergeographic scales might be useful for GEMs in the future.Additional research is required to better understand the limi-tations of using statistical models for predicting GEM sur-vival in multisite environmental introductions.

Acknowledgements

This paper is dedicated to the memory of Dr. R. CampbellWyndham, who passed away on March 20, 2002. Manythanks are due to the contributions of Dr. Scott Findlay, Uni-versity of Ottawa; Dr. Michael Smith, Carleton University;and Sarah Rosolen, Carleton University. We would also liketo thank Dr. Terry McIntyre, Environment Canada, and HansYu, Health Canada, for the opportunity to participate in themicrocosm validation project funded under the National Bio-technology Strategy (NBS). We thank Dr. Scott Angle, Uni-versity of Maryland; Dr. Chris Chanway, University ofBritish Columbia; Dr. Joel Gagliardi, University of Maryland;Dr. Jim Germida, University of Saskatchewan; Dr. CharlesGreer, McGill University; Dr. Morris Levin, University ofMaryland; and Dr. Robert Watson, Agriculture and Agri-Food Canada, for kindly sharing their data from the NBSmicrocosm project. Particular thanks are due to Dr. ScottAngle and Dr. Joel Gagliardi for providing much supportand financial assistance for soil analyses at the University ofMaryland. We thank Dr. Cheng Huang, Carleton University,for assistance with scanning electron microscope analyses.

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Documents

Predicting survival of a genetically engineered microorganism, Pseudomonas chlororaphis 3732RN-L11, in soil and wheat rhizosphere across Canada with linear multiple regression models