Rev Bras Cienc Solo 2017;41:e0170021 Article Division - Soil in Space and Time | Commission - Pedometry Environmental Correlation and Spatial Autocorrelation of Soil Properties in Keller Peninsula, Maritime Antarctica André Geraldo de Lima Moraes(1), Marcio Rocha Francelino(2), Waldir de Carvalho Junior(3), Marcos Gervasio Pereira(4), André Thomazini(5)* and Carlos Ernesto Gonçalves Reynaud Schaefer(6) (1) Universidade Federal Rural do Rio de Janeiro, Departamento de Solos, Programa de Pós-Graduação em Agronomia - Ciência do Solo, Seropédica, Rio de Janeiro, Brasil. (2) Universidade Federal Rural do Rio de Janeiro, Departamento de Solos, Seropédica, Rio de Janeiro, Brasil. (3) Empresa Brasileira de Pesquisa Agropecuária, Embrapa Solos, Rio de Janeiro, Rio de Janeiro, Brasil. (4) Universidade Federal Rural do Rio de Janeiro, Departamento de Solos, Seropédica, Rio de Janeiro, Brasil. (5) Universidade Federal de Viçosa, Departamento de Solos, Programa de Pós-Graduação em Solos e Nutrição de Plantas, Viçosa, Minas Gerais, Brasil. (6) Universidade Federal de Viçosa, Departamento de Solos, Viçosa, Minas Gerais, Brasil. ABSTRACT: The pattern of variation in soil and landform properties in relation to environmental covariates are closely related to soil type distribution. The aim of this study was to apply digital soil mapping techniques to analysis of the pattern of soil property variation in relation to environmental covariates under periglacial conditions * Corresponding author: at Keller Peninsula, Maritime Antarctica. We considered the hypothesis that covariates E-mail: [email protected] normally used for environmental correlation elsewhere can be adequately employed in Received: January 21, 2017 periglacial areas in Maritime Antarctica. For that purpose, 138 soil samples from 47 soil Approved: August 28, 2017 sites were collected for analysis of soil chemical and physical properties. We tested the correlation between soil properties (clay, potassium, sand, organic carbon, and pH) and How to cite: Moraes AGL, Francelino MR, Carvalho Júnior environmental covariates. The environmental covariates selected were correlated with W, Pereira MG, Thomazini A, soil properties according to the terrain attributes of the digital elevation model (DEM). Schaefer CEGR. Environmental correlation and spatial The models evaluated were linear regression, ordinary kriging, and regression kriging. autocorrelation of soil properties The best performance was obtained using normalized height as a covariate, with an R2 of in Keller Peninsula, Maritime 2 Antarctica. Rev Bras Cienc Solo. 0.59 for sand. In contrast, the lowest R of 0.15 was obtained for organic carbon, also using 2017;41:e0170021. the regression kriging method. Overall, results indicate that, despite the predominant https://doi.org/10.1590/18069657rbcs20170021 periglacial conditions, the environmental covariates normally used for digital terrain Copyright: This is an open-access mapping of soil properties worldwide can be successfully employed for understanding article distributed under the terms of the Creative Commons the main variations in soil properties and soil-forming factors in this region. Attribution License, which permits unrestricted use, distribution, Keywords: kriging, geostatistical methods, soil variability. and reproduction in any medium, provided that the original author and source are credited. https://doi.org/10.1590/18069657rbcs20170021 1 Moraes et al. Environmental correlation and spatial autocorrelation of soil properties in… INTRODUCTION Antarctica, which is the coldest continent, has unique climatic and weathering conditions that lead to unique soil formation in ice-free areas at very slow rates (Simas et al., 2008). Pedogenesis in Antarctica is generally less advanced, due to the combination of freezing conditions, low liquid water availability, weak biological activity, and chemical and physical processes occurring only during the summer. In comparison with continental Antarctica, the Maritime region has higher temperature and precipitation rates, contributing to greater colonization of plant species and soil microorganisms, which favors a higher degree of weathering (Pereira and Putzke, 2013). In addition, these factors are frequently associated with terrestrial input of nutrients by marine birds (guano), which are highly important for soil-forming processes in Antarctica, enhancing pedogenesis through the phosphatization process and forming widespread ornithogenic soils (Simas et al., 2008). Ornithogenic soils are characterized by low pH, deep soil development, and very high phosphorus contents (Mehlich-1), and total organic carbon contents (Simas et al., 2008). In addition, soil types and properties vary significantly across the landscape, mainly related to landscape characteristics, especially drainage (Pereira and Putzke, 2013). Antarctic soils have been the subject of several studies (Beyer et al., 2000; Simas et al., 2008; Francelino et al., 2011; Moura et al., 2012) that have promoted an increasing understanding of soil-forming processes and soil distribution and classification. The foundations of the Digital Soil Mapping (DSM) technique were laid by McBratney et al. (2003). Lagacherie and McBratney (2006) defined DSM as “the creation of spatial information systems of soils by numerical models in order to infer the spatial and temporal variations of soil properties and classes, from observations, knowledge and covariate-related environmental data”. Recently, DSM has improved soil studies and has been tested and used in soil surveys around the world (Carvalho Junior et al., 2014; Hartemink and Minasny, 2014; Vaysse and Lagacherie, 2015; Chagas et al., 2016; Pahlavan-Rad et al., 2016; Malone et al., 2017). However, little is known about the use of digital soil and environmental mapping techniques in the Antarctic environment. The DSM has emerged as an alternative to resolve uncertainties and subjectivities that the traditional approach presents. In this context, new methods for quantitative modeling of soil distribution have been proposed to describe, classify, and study the patterns of spatial variation of soils across the landscape. From this, it has been possible to increase knowledge of spatial variability of soils across the landscape, generating accurate information about this natural resource. This has been accomplished through a set of quantitative techniques called Pedometrics (Hartemink and Minasny, 2014). This technique becomes important and useful in regions where detailed soil sampling and description is highly complex and difficult due to the local characteristics, such as Antarctica. The aim of this study was to apply digital soil mapping techniques to analyze the pattern of variation of soil properties in relation to environmental covariates under periglacial conditions at Keller Peninsula, Maritime Antarctica. We tested the hypothesis that covariates normally used for environmental correlation worldwide (Hartemink and Minasny, 2014) can be adequately employed in periglacial areas of Maritime Antarctica to understand spatial distribution of soil processes/properties. These are possibly some of the most complex and anisotropic areas for soil mapping purposes, due to their natural heterogeneity on short-range scales. MATERIALS AND METHODS Study area This study was carried out in Keller Peninsula, covering an area of 428 ha, located in Admiralty Bay, King George Island, Maritime Antarctica (between 61° 54’ E - 62° 16’ S and 57° 35’ N – 59° 02’ W), where the Comandante Ferraz Brazilian Antarctic Station is located Rev Bras Cienc Solo 2017;41:e0170021 2 Moraes et al. Environmental correlation and spatial autocorrelation of soil properties in… (Figure 1). The monthly mean air temperature ranges from -6.4 °C in July to +2.3 °C in February, and the mean annual precipitation is 367 mm (INPE, 2009). The altitude ranges from 0 to 340 m a.s.l. (Francelino et al., 2011). The geology is predominantly basalt- andesites and pyritized andesite rocks. Overall, soils are usually shallow and skeletal, mainly composed of Leptosols and Cryosols (Francelino et al., 2011). Soil sampling Soil samples were collected in a grid arrangement in February 2012, covering most of the ice-free area of the peninsula, from a total of 47 points at the depths of 0.00-0.05, 0.05-0.10, and 0.10-0.30 m. We used a semi-regular grid with a minimum separation distance of 180 m and maximum separation distance of 500 m between neighboring points. All collection points were marked with a geodetic GPS device (high accuracy ± 10 mm error), CS Leica 1200 + model (Figure 1). Soil analyses The soil samples were air dried, ground, and passed through a 2-mm sieve to remove larger pieces of root material, pebbles, and gravels. Particle size analysis was performed by the pipette method (Claessen, 1997). The pH was determined in a 1:5 soil:deionized water ratio. Exchangeable potassium was extracted with Melich-1 and determined by flame emission (Claessen, 1997). -1 Total soil organic carbon was quantified by wet oxidation with 0.167 mol L K2Cr2O7 in the presence of sulfuric acid with external heating (Yeomans and Bremner, 1988). Geostatistical model Environmental covariates were derived from a digital elevation model (DEM) with a spatial resolution of 5 m (adapted from Mendes Junior, 2012). These covariates were calculated using the System for Automated Geoscientific Analyses (SAGA GIS) and ArcGIS 10.0 software. The terrain characteristics used were aspect, curvature, plan curvature, 100000