Study area
This study was carried out in the urban community park La Granja in Burjassot, València, Spain during 2019-20. The garden used municipal and rain water for irrigation which is a common practice in many urban community gardens in metropolitan València. A preliminary survey of the area was carried out by computer and visual inspection to decide which points could be most representative, based on their frequent use by man and animals. The action of both is expected to have a major impact on soil characteristics. Sampling was carried out from 25 different points, at a depth of 0-5 cm. Samples were put into a screw-tub and transported to the laboratory for bacterial isolation. Remaining samples were stored at -20\(^{\circ }\)C for further analysis.
Soil characterization
pH measurement
Soil pH was measured by dissolving 1 g of soil in 5 mL distilled water, shaking for 2 min and then waiting 30 min for the soil to settle (assays by triplicate using a pH-meter Consort). A similar procedure was used with pH papers, dissolving 0.5 g of soil in 1 ml distilled water, vortexed for 5 min, and applied in small amounts to pH indicator strips (Universal Test Paper). The pH strip accuracy was tested against standard pH calibration solutions.
Color, texture and carbonate content determination
Soil color was determined by visual inspection and comparison of the samples against a Munsell soil standard chart [19]. Soil texture characteristics were used to classify soils according to the size of the different particles that compose it. The determination of the presence of carbonates in the soil was performed in a reaction that gives rise to effervescence, adding a few drops of 1:1 HCl to the soil samples. The more intense the effervescence, the higher the calcium carbonate content in the soil.
Microbial isolation
A total of 1 g of soil was suspended in 10 mL of sterile water. Afterwards, serial dilutions were prepared and plated on 0.1 mL of the 1:100 to 1:100,000 dilutions in trypticase soy agar (TSA), MacConkey agar (MKA) and malt extract agar (MA) purchased from Conda (Madrid, Spain). After 48-72 h of cultivation, the total number of cultivable microorganisms were counted in the appropriate dilution plate of each culture media. Single colony was selected based on the morphology, color and size of bacteria and further purified three times on TSA broth agar medium by the repeated plate streaking method.
For the lipase assay, a total of 20 colonies from each TSA series were picked onto a new mother plate with the help of a grid. For the analysis of enterobacteriae, isolates from the MKA plates were grouped in different categories according to physiological and morphological criteria.
Determination of Gram
Gram method for staining was used to classify the bacterial isolates from the TSA plates and determinate their microscopical morphology [20]. The preparations were observed in a clear field in a Microscope Eclipse E600 (Nikon) with a digital camera DS-Ri1 (Nikon).
Determination of lipase activity
The 500 isolated bacteria were spread in four replicated Petri plates containing Tween-80 agar using sterile toothpicks. To demonstrate the production of extracellular esterases (lipases) the microorganisms were grown in this medium containing a synthetic lipid that presents ester links between sorbitol and oleic acid (Tween-80). It also contains calcium salts. If the microorganism has esterase activity (lipase), it will hydrolyze the ester link and release the oleic acid of the Tween-80. After 48-h incubation this oleic acid, in the presence of an excess of Ca\(^{2+}\), will precipitate in the form of small crystalline oleate crystals that will form an opaque halo around growth.
Microbial identification
16 S RDNA partial sequence
At least one colony of different morphology was picked and refreshed in TSA plates. Bacteria DNA was extracted using a boiling method [21]. Primers SWI-F (5’ -AGAGTTTGATCCTGGCTCAG-3’) and SWI-R (5’ -GGTTACCTTGTTACGACTT-3’) were used to amplify the 16S rRNA gene. The amplification reaction was performed in a Primus 25 thermocycler (MWG, Ebersberg, Germany) under conditions previously described [22]. Direct sequencing of the PCR products was performed by ABBIPrism BigDye Terminator Cycle Sequence Reaady Reaction Kit (Applied Biosystems, Stafford, TX, USA) in the SCSIE service (Universitat de València (Spain). The sequences were aligned using the BLAST program, with complete sequences of 16S rDNA gene sequences retrieved from the EMBL nucleotide sequence data libraries [23].
MALDI-TOF
The identification of bacterial strains has also been carried out following the protocol recommended by Bruker Daltonics (http://www.bdal.de) by means of the extended direct transfer method. The strains were analyzed from fresh culture. The MALDI-TOF MS technique was performed using a Microflex L20 mass spectrometer (Bruker Daltonics) equipped with an N2 laser. All spectra were acquired in positive linear ion mode. The acceleration voltage was 20 kV [24]. The spectra were acquired as the sum of 240 shots per target. The mass range used for the analysis was 2,000-20,000 Da. Three spectra were obtained per strain by the MALDI Biotyper Realtime Classification (RTC) method. The resulting identification in front of the database MBT 7854 and MBT 7311_RUO (Bruker Daltonics), corresponds to the profile of the highest log score.
Spatial statistical technique (Kriging method)
Spatial statistics allows the analysis of geolocated information by applying different methods, including Inverse Distance Weighting (IDW), spline interpolation, and Kriging. For this work we have used the Kriging approach, which is based on spatial autocorrelation [25]. The determined information from the samples of soil establish a data set in relation to different locations with its GPS coordinates, longitude and latitude, following the Kriging technique [26].
We have used RStudio as a framework for the R statistical programming language [27]. Within this framework, a bunch of libraries can be installed and used for the spatial statistical characterization of the region covered in this study (the script included explains the processing values) [22]. The parameters selected for the spatial statistical study, with Kriging method, were: pH, total amount of microorganisms (Tufc.g), amount of fungi (Fufc.g), amount of Enterobacteria (\(T\_enterobac\)), and finally, different groups of enterobacterial isolates which are related to lactose positive (Group I or GI), lactose negative (GII), slow fermentation (GIII), mucoid colonies (GIV) and other types (GV).
We have previously used the Kriging technique to evaluate some of these parameters in a wider area [22]. In the present work, we have used the “Matérn” model which involves partial-sill (PSill), Nugget, Range and kappa. This variogram is characterised by these parameters. The Sill is the value at which the model first flattens to a constant value representing the total variance where the empirical variogram appears to level off, and the Nugget is the value at which the semi-variogram nearly intercepts the y-value and is related to the amount of short-range variability in the data (here we will work with PSill which stands for “partial sill” and is the sill minus the nugget). The Range is the distance after which data are no longer correlated. Another useful parameter is kappa which measures the smoothness of the Matérn class of the variogram models.
The determined information from the samples of soil establishes a dataset in relation to different locations with their GPS coordinates, longitude and latitude. By denoting the determined value of the number of colonies or number of isolates at a specific location x as Y(x), this data set is defined as \(\{Y(x),x \in D\}\), where D are all the locations of the modelling sets, following the Kriging technique. The objective of the proposed model is the forecast of \(Y(x_o)\) in any location \(x_o\), specifically those in the validation set. The measurement reports contain information about the set of covariables included. Therefore in Eq.1, Y(x) is modelled as an average of each covariable involved in the process in the geographical area considered plus some bounded spatial variability which is explained by the short-term process with spatial dependence, i.e.,
$$\begin{aligned} Y(x)=\mu (x)+\delta (x) \end{aligned}$$
(1)
where \(\mu (x)=E[Y(x)]\) and \(\delta (x)\) is a stationary Gaussian process with zero mean, whose spatial dependence characterization is given by the variogram \(\gamma\) (Eq.2)
$$\begin{aligned} 2\gamma (h) = \textrm{Var}\left[ Y(x+h) - Y(x)\right] = \textrm{Var}\left[ \delta (x+h)-\delta (x)\right] , \end{aligned}$$
(2)
here Var denotes the variance and h is an offset.