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We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %–95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil–landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor.

The global challenge of feeding two billion more people by 2050, using more sustainable agricultural practices whilst dealing with uncertainties associated with environmental change, requires a transformation of food systems. We present a new perspective for how advances in network science can provide novel ways to better understand, harness, and restore multiple ecological processes in agricultural environments. We describe: (i) a network-focused framework for managing agro-ecosystems that accounts for the multiple interactions between biodiversity and associated ecosystem services; (ii) guidance for incorporating socio-economic factors into ecological networks; and (iii) the potential to upscale network methods to inform efforts to build resilience, including global food-supply chains. In doing so we aim to facilitate the application of network science as a systems-based way to tackle the challenges of securing an equitable distribution of food.

SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.

In this work, an innovative methodology to generate the automatic ground motion areas mapping is presented. The methodology is based on the analysis of the Synthetic Aperture Radar (SAR)-based displacement time series. The procedure includes two modules developed using the ModelBuilder tool (ArcGis). These modules allow to identify the ground motion areas (GMA) using only one dataset and the persistent GMA (PGMA) considering the different monitored periods and datasets. These areas represent clusters of targets characterized by the same displacement time series trend. The procedure was tested using different sensors such as ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 covering the periods, 1992–2000, 2003–2010, 2012–2016 and 2014–2017, respectively, over an area of about 500 km2 in the Venetian-Friulian coastal Plain (NE Italy). The resulting mapping allows to detect priority areas where to address further in situ investigations such as to verify the presence of localized buried landforms.

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protec-tion. As a consequence, soil is becoming one of the top priorities for the global environmental policy agenda. Conventional soil maps suffer from large limita-tions, i.e. most of them are static and often obsolete, are often generated at coarse scale, and can be uneasy to handle. Digital Soil Mapping has been developed as a solution to generate high-resolution maps of soil properties over large areas. Two projects, GlobalSoilMap and SoilGrids, presently aim at delivering the first generation of global, high-resolution soil property fine grids. In this paper, we briefly describe the GlobalSoilMap history, its present status and present achievements, and illustrate some of these with (mainly) French examples. At given moment there is still an enormous potential for forthcoming research and for delivering products more helpful for end users. Key here is the continuous progress in available covariates, in their spatial, spectral and temporal coverage and resolution through remote sensing products. All over the world, there is still a very large amount of point soil data still to be rescued and this effort should be pursued and encouraged. Statistically advances are expected by exploring and implementing new models. Especially relevant are spatial-temporal models and contemporary Artificial Intelligence for handling the complex big data. Advances should be made and research efforts are needed on estimating the uncertainties, and even on estimating uncertainties on uncertainties. Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and models) should be made in order to get the most useful information from each of these predictions, and to identify how controlling factors may change depending on scales.

Purpose: Prior studies have identified a racial disparity in incidence and survival of squamous cell carcinoma of the anus (SCCA) in the young African American male population. We aim to determine whether racial disparities are independent of income and urban location. Methods: The National Cancer Institute's Surveillance of Epidemiology and End Results database was queried for data on patients with SCCA for the years of 2000?2013. Cox regression was used to determine the effect of race, county median family income, rural?urban continuum, and stage of disease on overall survival. Results: The incidence rate of SCCA was significantly higher in black men

Abstract The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.

Los bordes de caminos rurales (BCR) son elementos clave para conservar la biodiversidad y los servicios ecosistémicos en los paisajes agrícolas. Su restauración y manejo requieren de protocolos sencillos que valoren (diagnostiquen) su estado de conservación y funcionalidad ecológica. En este trabajo elaboramos un índice del valor de conservación (IVC) de los BCR por medio de la construcción de un árbol de decisiones realizado a partir de la consulta a expertos. El índice, que puede asumir valores entre 4 y 30, evalúa el estado de conservación y la funcionalidad ecológica de los BCR según 11 atributos relacionados principalmente con la estructura de la vegetación. El IVC fue aplicado a 30 BCR del sudeste bonaerense (Argentina). Sus valores oscilaron entre 4 y 29. Los BCR cultivados fueron los de menor valor. Los BCR con IVC más alto (>21) se caracterizaron por la mayor riqueza de especies nativas, entomófilas y de valor cultural, la mayor cobertura de especies nativas y pastos perennes, la menor cobertura de malezas y especies invasoras, y la presencia de al menos una especie vulnerable y/o fitotelmata. Este índice representa una primera contribución aplicable al diagnóstico del estado de conservación de los BCR a través de la identificación de atributos claves de la estructura de la vegetación sugeridos por expertos. Además, el índice provee información relativamente sintética sobre el estado de los BCR, los que, pese a sus múltiples funciones ecológicas, rara vez son tenidos en cuenta para su conservación dentro de la planificación territorial. DOI: https://doi.org/10.25260/EA.17.27.3.0.541

La agricultura ofrece situaciones interesantes para estudiar la sucesión ecológica en comunidades de malezas. Existe evidencia empírica que indica que la composición florística de las comunidades de malezas se modifica ante cambios ambientales y tecnológicos, lo que ha sido interpretado en el marco de la teoría de sucesión. A su vez, las reglas de ensamblaje proveen un marco conceptual útil para describir y predecir los patrones de cambio en comunidades de malezas. El objetivo de este artículo es presentar la aplicación de un enfoque basado en las reglas de ensamblaje de comunidades para estudiar los cambios florísticos en comunidades de malezas. Las reglas de ensamblaje se asocian con factores específicos que explican los patrones observados a nivel de comunidad. Estas reglas operan a modo de filtros que restringen el número de especies del conjunto regional que forman las comunidades locales. El conjunto de especies es definido aquí en tres dominios espaciales: el geográfico, el paisaje y el tipo de hábitat. En escalas espaciales extensas (1000-10000 km2), el conjunto de especies es determinado por los factores que regulan las tasas de especiación y extinción y la migración de plantas entre regiones distantes. Los efectos de la complejidad del paisaje son mayores a nivel regional, la dispersión aumenta su influencia en los mosaicos de parches (100 m2-10 ha), mientras que la heterogeneidad de hábitat es más importante a nivel de parche (1-1000 m2-1 ha). En parcelas pequeñas (

Abstract  In this study we have analysed and compared the genomic composition, meiotic behaviour, and meiotic affinities of Zea perennis and Zea mays ssp. mays. To do so we studied the parental taxa and the interspecific hybrid Zea perennis × Zea mays ssp. mays, using classical cytogenetic methods, as well as GISH and FISH. GISH enabled us to recognize the genomic source of each chromosome involved in the meiotic configurations of this hybrid, and established the genomic affinities between their parental species. The results obtained here reinforce the hypothesis of the amphiploid origin of Zea perennis and, together with previous research, indicate that the chromosomes with divergent repetitive sequences in maize and Zea luxurians could be the remnants of a relict parental genome not shared with Zea perennis. [ABSTRACT FROM AUTHOR]

Variation in genome size of 24 populations belonging to 11 NW Patagonian species of Berberis was analysed as a function of the environment and geographical location. The variation showed three levels of discontinuity, two of which corresponded to diploid species (2 n = 28) while the third corresponded to polyploid species (2 n = 56). Diploids with DNA content ranging from 1.463 pg to 1.857 pg includedBerberis cabrerae , B. chillanensis, B. montana, B. serrato-dentata and B. bidentata. Diploids with DNA content ranging from 2.875 pg to 3.806 pg included B. linearifolia, B. darwinii, B. parodii and B. empetrifolia. The genome size of the polyploid species B. buxifolia and B. heterophylla ranged from 5.809 pg to 6.844 pg. Principal component analysis (PCA) was applied to represent the variability of environmental conditions. The eigenvectors of the principal component axes showed that PC1 discriminates the populations according to rainfall, types of vegetation and geomorphology; altitude and latitude, on the other hand, contribute to PC2 and PC3, respectively. From these results it is concluded: (1) that diploids with lower DNA content grow in high-elevation sites having greater rainfall but lower water availability; (2) diploids with higher DNA content are associated with half-elevation forests where the vegetative period is longer, the water availability is greater and the temperatures are higher; and (3) the distribution pattern of polyploids is considerably wider than that of diploids, which are geographically and ecologically restricted to forest areas. These results suggest that the C-value plays an important role in the ability of the species to adapt to different growing conditions. Copyright 2000 Annals of Botany Company [ABSTRACT FROM AUTHOR]