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Ecological restoration has the potential to improve and recover soil quality, helping to mitigate soil degradation worldwide. However, soil monitoring is often overlooked in restoration, preventing a comprehensive understanding of how restoration interventions affect soil quality and the time required for recovering different soil properties. Herein, we tested if MAR areas (moderately assisted recovery—tree plantings) in the early restoration stage have better soil quality (and ecosystem services) than UNR areas (unassisted natural recovery—abandoned pastures) and pastures in continuous use (PCU). We collected 72 soil samples in three 5‐year‐old MAR areas and three 5‐year‐old UNR areas, one PCU, and one reference ecosystem (RE) in the Brazilian Atlantic Forest (Mata Atlantica). A total of 45 variables representing macrofauna, fertility, structure, and aggregation were measured and then submitted to multivariate analyses to compute sub‐indicators and a general indicator of soil quality (GISQ). The richness of orders and classes, cationic exchange capacity, total porosity, and biogenic aggregates contributed the most to the sub‐indicators. MAR and UNR areas had similar values to the RE for macrofauna, structure, and aggregation sub‐indicators, but they were far below the RE for fertility. In addition, all areas had lower GISQ values than the RE. Soils were classified as high quality in RE and medium quality in other areas. Our study evidences that some soils' physical and biological properties can be rapidly recovered through forest restoration, but 5 years is not enough time to change the general soil quality and make restoration areas have better conditions than the previous land use. If the restoration goal is to have high soil quality in the short term, corrective actions should be taken to improve soil fertility and help the area along the desired trajectory. [ABSTRACT FROM AUTHOR]

Amazonian rainforests, once thought to be pristine wilderness, are increasingly known to have been widely inhabited, modified, and managed prior to European arrival, by human populations with diverse cultural backgrounds. Amazonian Dark Earths (ADEs) are fertile soils found throughout the Amazon Basin, created by pre‐Columbian societies with sedentary habits. Much is known about the chemistry of these soils, yet their zoology has been neglected. Hence, we characterized soil fertility, macroinvertebrate communities, and their activity at nine archeological sites in three Amazonian regions in ADEs and adjacent reference soils under native forest (young and old) and agricultural systems. We found 673 morphospecies and, despite similar richness in ADEs (385 spp.) and reference soils (399 spp.), we identified a tenacious pre‐Columbian footprint, with 49% of morphospecies found exclusively in ADEs. Termite and total macroinvertebrate abundance were higher in reference soils, while soil fertility and macroinvertebrate activity were higher in the ADEs, and associated with larger earthworm quantities and biomass. We show that ADE habitats have a unique pool of species, but that modern land use of ADEs decreases their populations, diversity, and contributions to soil functioning. These findings support the idea that humans created and sustained high‐fertility ecosystems that persist today, altering biodiversity patterns in Amazonia. [ABSTRACT FROM AUTHOR]

Abstract: We present here an indicator of soil quality that evaluates soil ecosystem services through a set of 5 subindicators, and further combines them into a single general Indicator of Soil Quality (GISQ). We used information derived from 54 properties commonly used to describe the multifaceted aspects of soil quality. The design and calculation of the indicators were based on sequences of multivariate analyses. Subindicators evaluated the physical quality, chemical fertility, organic matter stocks, aggregation and morphology of the upper 5cm of soil and the biodiversity of soil macrofauna. A GISQ combined the different subindicators providing a global assessment of soil quality. Research was conducted in two hillside regions of Colombia and Nicaragua, with similar types of land use and socio-economic context. However, soil and climatic conditions differed significantly. In Nicaragua, soil quality was assessed at 61 points regularly distributed 200m apart on a regular grid across the landscape. In Colombia, 8 plots representing different types of land use were arbitrarily chosen in the landscape and intensively sampled. Indicators that were designed in the Nicaragua site were further applied to the Colombian site to test for their applicability. In Nicaragua, coffee plantations, fallows, pastures and forest had the highest values of GISQ (1.00; 0.80; 0.78 and 0.77, respectively) while maize crops and eroded soils (0.19 and 0.10) had the lowest values. Examination of subindicator values allowed the separate evaluation of different aspects of soil quality: subindicators of organic matter, aggregation and morphology and biodiversity of macrofauna had the maximum values in coffee plantations (0.89; 0.72 and 0.56, respectively on average) while eroded soils had the lowest values for these indicators (0.10; 0.31 and 0.33, respectively). Indicator formulae derived from information gained at the Nicaraguan sites were not applicable to the Colombian situation and site-specific constants were calculated. This indicator allows the evaluation of soil quality and facilitates the identification of problem areas through the individual values of each subindicator. It allows monitoring of change through time and can guide the implementation of soil restoration technologies. Although GISQ formulae computed on a set of data were only valid at a regional scale, the methodology used to create these indices can be applied everywhere. [Copyright &y& Elsevier]

Summary: Macroaggregation is a highly dynamic attribute of soils that is claimed to have a significant impact on their ability to store C and conserve nutrients. A major obstacle to the description and modelling of macroaggregate dynamics, and of the associated processes, is an almost complete ignorance of the real origin of the different types of aggregates found in soils, their turnover times and positions in the soil matrix. We present here a general methodological approach in which the origin of aggregates separated according to visual criteria could be determined by comparing their specific organic matter signatures assessed by Near Infrared Spectrometry (NIRS) to signatures of biogenic structures produced by soil ecosystem engineers (invertebrates and roots) living in the same soil. Macroaggregates and other soil components were separated visually from samples taken at 61 locations regularly distributed across a watershed in Nicaragua and representing crops, pastures, forests, coffee plantations and fallows. Coinertia analyses among soil macroinvertebrate communities and the matrix of soil morphological variables showed highly significant relationships. In Amazonian forest patches and pastures from the state of Pará in Brazil, 75 different types of biogenic structures were collected at the soil surface and on tree trunks, and analysed by the NIRS spectral method. Significant differences among the different types of structures allowed grouping according to their broad phylogenetic origin with large interspecifc differences. In a field experiment conducted at the same site, soils previously under pastures were planted in 16 possible combinations of four plant species, in a fully randomized design replicated three times in different sites. Surface casts of the earthworm species Andiodrilus pachoensis and soil macroaggregates separated by our visual technique had significantly different spectral signatures depending on the location of the plot and the composition of plant cover. However, the comparison of NIRS signatures of soil macroaggregates and casts suggested that Andiodrilus pachoensis was not responsible for the production of the biogenic aggregates that comprised a large proportion on the soil volume in this soil. [Copyright &y& Elsevier]