Recreation and terrain effect on the spatial variation of the apparent soil electrical conductivity in an urban park

Keywords: recreation; soil electrical conductivity; variogram; Matern model: digital elevation model.


Recreation is an important cultural ecosystem service and is able to significantly affect soil heterogeneity and vegetation functioning. This study investigated the role of the relief and tree stand density in the apparent soil electrical conductivity variation within an urban park. The most suitable variogram models were assessed to evaluate the autocorrelation of the regression models. The map of the spatial variability of apparent soil electrical conductivity was built on the basis of the most suitable variogram. The experimental polygon was located in the Botanical Garden of Oles Honchar Dnipro National University (Dnipro City, Ukraine). The experimental polygon was formed by a quasi-regular grid of measurement locations located about 30 m apart. The measurements of the apparent electrical conductivity of the soil in situ were made in May 2018 at 163 points. On average, the value of soil apparent electric conductivity within the investigated polygon was 0.55 dSm/m and varied within 0.17–1.10 dSm/m. Such environment predictors as tree stand density, relief altitude, topographic wetness index, and potential of relief to erosion were able to explain 48% of the observed variability of soil electrical conductivity. The relief altitude had the greatest influence on the variation of soil electrical conductivity, which was indicated with the highest values of beta regression coefficients. The most important trend of the electric conductivity variation was due to the influence of relief altitude and this dependence was nonlinear. The smallest values of the soil electrical conductivity were recorded in the highest and in lowest relief positions, and the largest values were detected in the relief slope. Recreational load can also be explained by the geomorphology predictors and tree stand density data. These predictors can explain 32% of the variation of recreational load. The variogram was built both for the soil apparent electrical conductivity dataset and for the residuals of the regression model. As a result of the procedure of the models’ selection on the basis of the AIC we obtained the best estimation of the variogram models parameters for the electrical conductivity and for the regression residuals of the electrical conductivity. The level of recreation was correlated statistically significantly with the apparent soil electrical conductivity. The residuals of regression models in which geomorphological indicators and tree stand density were used as predictors have a higher correlation level than the original variables. The soil electrical conductivity may be a sensitive indicator of the recreation load.


Abramowitz, M., & Stegun, I. (1972). Handbook of mathematical functions with formulas, graphs, and mathematical tables. 10th Printing. U.S. Department of Commerce, National Bureau of Standards, Washington.

Amrein, D., Rusterholz, H.-P., & Baur, B. (2005). Disturbance of suburban fagus forests by recreational activities: Effects on soil characteristics, above-ground vegetation and seed bank. Applied Vegetation Science, 8(2), 175–182.

Balzan, M. V., & Debono, I. (2018). Assessing urban recreation ecosystem services through the use of geocache visitation and preference data: A case-study from an urbanised island environment. One Ecosystem, 3, e24490.

Beven, K., & Kirkby, N. (1979). A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24, 43–69.

Brouwer, R., Brander, L., Kuik, O., Papyrakis, E., & Bateman, I. (2013). A synthesis of approaches to assess and value ecosystem services in the EU in the context of TEEB. University Amsterdam Institute for Environmental Studies.

Brygadyrenko, V. V. (2015). Influence of moisture conditions and mineralization of soil solution on structure of litter macrofauna of the deciduous forests of Ukraine steppe zone. Visnyk of Dnipropetrovsk University. Biology, Ecology, 23(1), 50–65.

Cambardella, C. A., Moorman, T. B., & Novak, J. M. (1994). Field scale variability of soil properties in central iowa soils. Soil Science Society of America Journal, 58(5), 1501–1511.

Chiesura, A. (2004). The role of urban parks for the sustainable city. Landscape and Urban Planning, 68(1), 129–138.

Corwin, D. L. (2005). Geospatial measurements of apparent soil electrical conductivity for characterizing soil spatial variability. In: Alvarez–Benedi, J. (Ed.). Soil–Water–Solute Process Characterization. An Integrated Approach. CRC Press, Boca Raton. Pp. 639–672.

Corwin, D. L., & Lesch, S. M. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46(1–2), 11–43.

Corwin, D. L., Kaffka, S. R., Hopmans, J. W., Mori, Y., Lesch, S. M., Oster, J. D. (2003). Assessment and field-scale mapping of soil quality properties of a saline-sodic soil. Geoderma, 114(3–4), 231–259.

de Wijs, H. J. (1951). Statistics of ore distribution. Part I. Frequency distribution of assay values. Journal of the Royal Netherlands Geological and Mining Society, New Series, 13, 365–375.

de Wijs, H. J. (1953). Statistics of ore distribution. Part II. Theory of binomial distribution applied to sampling and engineering problems. Journal of the Royal Netherlands. Geological and Mining Society, New Series, 15, 12–24.

Dowman, I. J. (1999). Encoding and validating data from maps and images. Geographical Information Systems. Volume 1. Second Edition. John Wiley & Sons, New York. Pp. 437–450.

Faly, L. I., & Brygadyrenko, V. V. (2018). Influence of the herbaceous layer and litter depth on the spatial distribution of litter macrofauna in a forest plantation. Biosystems Diversity, 26(1), 46–51.

Faly, L. I., Kolombar, T. M., Prokopenko, E. V., Pakhomov, O. Y., & Brygadyrenko, V. V. (2017). Structure of litter macrofauna communities in poplar plantations in an urban ecosystem in Ukraine. Biosystems Diversity, 25(1), 29–38.

Göl, C., Bulut, S., & Bolat, F. (2017). Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey. Journal of African Earth Sciences, 134, 85–91.

Gritsan, Y. I., Kunakh, O. M., Dubinina, J. J., Kotsun, V. I., & Tkalich, Y. I. (2019). The catena aspect of the landscape diversity of the “Dnipro-Orilsky” Natural Reserve. Journal of Geology, Geography and Geoecology, 28(3), 417–431.

Guilland, C., Maron, P. A., Damas, O., & Ranjard, L. (2018). Biodiversity of urban soils for sustainable cities. Environmental Chemistry Letters, 16(4), 1267–1282.

Handcock, M. S., & Stein, M. L. (1993). A Bayesian analysis of kriging. Technometrics, 35, 403–410.

Hengl, T., Heuvelink, G. B. M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120, 75–93.

Hojati, M., & Mokarram, M. (2016). Determination of a topographic wetness index using high resolution digital elevation models. European Journal of Geography, 7(4), 41–52.

Keskin, H., & Grunwald, S. (2018). Regression kriging as a workhorse in the digital soil mapper's toolbox. Geoderma, 326, 22–41.

Kumar, S., Lal, R., & Liu, D. (2012). A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 189–190, 627–634.

Kunah, O. M., & Papka, O. S. (2016). Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images. Visnyk of Dnipropetrovsk University, Biology, Ecology, 24(1), 78–86.

Kunah, O. M., & Papka, O. S. (2016). Geomorphological ecogeographical variables defining features of ecological niche of common milkweed (Asclepias syriaca L.). Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 1, 243–275.

Kunah, O. M., Zelenko, Y. V., Fedushko, M. P., Babchenko, A. V., Sirovatko, V. O., & Zhukov, O. V. (2019). The temporal dynamics of readily available soil moisture for plants in the technosols of the Nikopol manganese ore basin. Biosystems Diversity, 27(2), 156–162.

Kutiel, P., Zhevelev, H., & Harrison, R. (1999). The effect of recreational impacts on soil and vegetation of stabilised coastal dunes in the Sharon park, Israel. Ocean and Coastal Management, 42, 12, 1041–1060.

Lepczyk, C. A., Aronson, M. F. J., Evans, K. L., Goddard, M. A., Lerman, S. B., & MacIvor, J. S. (2017). Biodiversity in the city: Fundamental questions for understanding the ecology of urban green spaces for biodiversity conservation. BioScience, 67(9), 799–807.

Levin, M. J., Kim, K. H. J., Morel, J. L., Burghardt, W., Charzynski, P., & Shaw, R. K. (2017). Soils within cities. Schweizerbart Science Publishers, Stuttgart.

Maltsev, Y. I., Maltseva, I. A., Solonenko, A. N., & Bren, A. G. (2017). Use of soil biota in the assessment of the ecological potential of urban soils. Biosystems Diversity, 25(4), 257–262.

Matern, B. (1986). Spatial variation. Lecture Notes in Statistics. Springer, New York.

McBratney, A. B., & Pringle, M. J. (1999). Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture, 1, 125–152.

McKinney, M. L. (2006). Urbanization as a major cause of biotic homogenization. Biological Conservation, 127(3), 247–260.

Minasny, B., & McBratney, A. B. (2005). The Matern function as a general model for soil variograms. Geoderma, 128, 192–207.

Mondal, A., Khare, D., Kundu, S., Mondal, S., Mukherjee, S., & Mukhopadhyay, A. (2017). Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. The Egyptian Journal of Remote Sensing and Space Science, 20, 61–70.

Morel, J. L., Chenu, C., & Lorenz, K. (2014). Ecosystem services provided by soils of urban, industrial, traffic, mining, and military areas (SUITMAs). Journal of Soil and Sediments, 15(8), 1659–1666.

Olaya, V., & Conrad, O. (2008). Geomorphometry in SAGA. In: Hengl, T., & Reuter, H. I. (Eds.). Geomorphometry: Concepts, software, applications. Elsevier.

Oral, D., Özcan, M., Gökbulak, F., Efe, A., & Hizal, A. (2013). Response of understory vegetation to exclosure in a heavily compacted forest recreational site. Journal of Environmental Biology, 34, 811–817.

Özcan, M., Ökbulak, F. G., & Hizal, A. (2013). Exclosure effects on recovery of selected soil properties in a mixed broadleaf forest recreation site. Land Degradation and Development, 24(3), 266–276.

Panagos, P., Borrelli, P., & Meusburger, K. (2015). A new European slope length and steepness factor (ls-factor) for modeling soil erosion by water. Geosciences, 5, 117–126.

Peng, G., Bing, W., Guangpo, G., & Guangcan, Z. (2013). Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of Northern China. PLoS One, 8, e83592.

Pickett, S. T. A., Cadenasso, M. L., Grove, J. M., Groffman, P. M., Band, L. E., Boone, C. G., & Wilson, M. A. (2008). Beyond urban legends: An emerging framework of urban ecology, as illustrated by the Baltimore Ecosystem Study. BioScience, 58(2), 139–150.

Pregitzer, C. C., Sonti, N. F., & Hallett, R. A. (2016). Variability in urban soils influences the health and growth of native tree seedlings. Ecological Restoration, 34(2), 106–116.

Raciti, S., Groffman, P., Jenkins, J., Pouyat, R., Fahey, T., Pickett, S., & Cadenasso, M. (2011). Accumulation of carbon and nitrogen in residential soils with different land-use histories. Ecosystems, 14(2), 287–297.

Ribeiro Jr., P. J., & Diggle, P. J. (2016). GeoR: Analysis of geostatistical data. R package version 1.7-5.2.

Ribeiro, P. J., Christensen, O. F., & Diggle, P. J. (2003). Geostatistical software – geoR and geoRglm. DSC 2003 Working Papers.

Sarah, P., Zhevelev, M. H., & Oz, A. (2015). Urban park soil and vegetation: Effects of natural and anthropogenic factors. Pedosphere, 25(3), 392–404.

Scalenghe, R., & Marsan, F. A. (2009). The anthropogenic sealing of soils in urban areas. Landscape and Urban Planning, 90, 1–10.

Shit, P. K., Bhunia, G. S., & Maiti, R. (2016). Spatial analysis of soil properties using GIS based geostatistics models. Modeling Earth Systems and Environment, 2, 495.

Sikorski, P., Szumacher, I., Sikorska, D., Kozak, M., & Wierzba, M. (2013). Effects of visitor pressure on understory vegetation in Warsaw Forested Parks (Poland). Environmental Monitoring and Assessment, 185(7), 5823–5836.

Siqueira, G. M., Dafonte, J. D., González, A. P., Vázquez, E. V., Armesto, M. V., & Filho, O. G. (2016). Spatial soil sampling design using apparent soil electrical conductivity measurements. Bragantia, 74(5), 459–473.

Stein, M. L. (1999). Interpolation of spatial data: Some theory for kriging. New York, Springer.

Sujetovienė, G., & Baranauskienė, T. (2016). Impact of visitors on soil and vegetation characteristics in urban parks of central lithuania. Journal of Environmental Research, Engineering and Management, 72(3), 51–58.

Sun, Y., & Mobasheri, A. (2017). Utilizing crowdsourced data for studies of cycling and air pollution exposure: A case study using strava data. International Journal of Environmental Research and Public Health, 14(3), 274.

Svirejeva-Hopkins, A., Schellnhuber, J. H., & Pomaz, V. L. (2004). Urbanized territories as a specific component of the global carbon cycle. Ecological Modelling, 173(2–3), 295–312.

Tang, X., Xia, M., Pérez-Cruzado, C., Guan, F., & Fan, S. (2017). Spatial distribution of soil organic carbon stock in Moso Bamboo forests in subtropical China. Scientific Reports, 7, 81.

Uygur, V., Irvem, A., Karanlik, S., & Akis, R. (2010). Mapping of total nitrogen, available phosphorous and potassium in Amik Plain, Turkey. Environmental Earth Sciences, 59(5), 1129–1138.

Vašát, R., Pavlů, L., Borůvka, L., Drábek, O., & Nikodem, A. (2013). Mapping the topsoil pH and humus quality of forest soils in the North Bohemian Jizerské Hory Mts. region with ordinary, universal, and regression kriging: Cross-validation comparison. Soil and Water Research, 8, 97–104.

Vasenev, V. I., Stoorvogel, J. I., & Vasenev, I. I. (2013). Urban soil organic carbon and its spatial heterogeneity in comparison with natural and agricultural areas in the Moscow region. Catena, 107, 96–102.

Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists. Chichester, John Wiley & Sons.

Whittle, P. (1954). On stationary processes in the plane. Biometrika, 41, 434–449.

Yorkina, N., Maslikova, K., Kunah, O., & Zhukov, O. (2018). Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol Manganese Ore Basin, Ukraine). Ecologica Montenegrina, 17, 29–45.

Yorkina, N., Zhukov, O., & Chromysheva, O. (2019). Potential possibilities of soil mesofauna usage for biodiagnostics of soil contamination by heavy metals. Ekológia (Bratislava), 38(1), 1–10.

Zhukov, O. V., Kunah, O. M., Dubinina, Y. Y., Fedushko, M. P., Kotsun, V. I., Zhukova, Y. O., & Potapenko, O. V. (2019). Tree canopy affects soil macrofauna spatial patterns on broad- and meso-scale levels in an Eastern European poplar-willow forest in the floodplain of the River Dnipro. Folia Oecologica, 46(2), 123–136.

Zhukov, O. V., Kunah, O. M., Taran, V. O., & Lebedinska, M. M. (2016). Spatial variability of soils electrical conductivity within arena of the river Dnepr valley (territory of the Natural Reserve “Dniprovsko-Orilsky”). Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University, 6(2), 129–157.

Zhukov, O., Kunah, O., Dubinina, Y., Zhukova, Y., & Ganga, D. (2019). The effect of soil on spatial variation of the herbaceous layer modulated by overstorey in an Eastern European poplar-willow forest. Ekológia (Bratislava), 38(3), 353–372.

Zuazo, V. H. D., & Pleguezuelo, C. R. R. (2008). Soil-erosion and runoff prevention by plant covers. A review. Agronomy for Sustainable Development, 28(1), 65–86.