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There is growing urgency for integration and coordination of global environmental and ecological data and indicators required to respond to the 'grand challenges' the planet is facing, including climate change and biodiversity decline. A consistent stratification of land into relatively homogenous strata provides a valuable spatial framework for comparison and analysis of ecological and environmental data across large heterogeneous areas. We discuss how statistical stratification can be used to design national, European and global biodiversity observation networks. The value of strategic ecological survey based on stratified samples is first illustrated using the United Kingdom (UK) Countryside Survey, a national monitoring programme that has measured ecological change in the UK countryside for the last 35 years. We then present a design for a European-wide sampling design for monitoring common habitats, and discuss ways of extending these approaches globally, supported by the recently developed Global Environmental Stratification. The latter provides a robust spatial analytical framework for the identification of gaps in current monitoring efforts, and systematic design of new complementary monitoring and research. Examples from Portugal and the transboundary Kailash Sacred Landscape in the Himalayas illustrate the potential use of this stratification, which has been identified as a focal geospatial dataset within the Group on Earth Observation Biodiversity Observation Network (GEO BON).

The probability of exceeding critical thresholds of Cd concentrations in the soil was mapped at a national scale. The critical thresholds in soil were based on food quality criteria for Cd in crops or in organs of cattle (Bos taurus), and were calculated by inverting a regression model for the Cd concentration in the crop, with the Cd concentration in soil, soil organic matter (SOM) content, clay content, and pH as predictors. The probability of exceeding the critical threshold for Cd in soil per node of a 500- x 500-m grid was approximated by Monte Carlo simulation, using the estimated cumulative distribution functions (cdf) of SOM, clay, pH, and Cd as input. The cdfs were estimated by simple indicator kriging with local prior means. For SOM, clay, and pH, detailed maps of soil type and land use were used to define subregions with assumed constant local means of the indicators (a priori distributions). The cdfs were sampled by Latin hypercube sampling. We accounted for correlation between the actual and critical Cd concentrations in soil by drawing Cd values from cdfs conditional on SOM and clay. The estimated probability for grassland is negligible, even in areas with high Cd concentrations in soil, and for maize (Zea mays L.) land the probability is almost everywhere smaller than 5%. For arable soils, however, these probabilities commonly are larger than 5% when sugar beet (Beta vulgaris L.) or wheat (Triticum aestivum L.) is taken as a reference crop, and locally exceed 50%.

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Heavy metals seriously threaten the health of human beings when they enter the food chain. Therefore, policymakers require precise predictions of heavy metal concentrations in agricultural crops. In this paper we quantify the uncertainty of regression predictions of Cd and Pb in wheat (Triticum aestivum L.) and the contributions to the uncertainties in these predictions associated with inputs to the regression model. For each node of the 500- x 500-m grid covering the arable soils in The Netherlands, a latin hypercube sample size of 1000 is constructed from the uncertainty distributions of the explanatory variables (pH, soil organic matter [SOM], and heavy metal concentration in soil), the regression coefficients, and the random term of the regression model. This sample is used as input for the regression model to obtain 1000 values from the uncertainty distributions of the log(Cd) and log(Pb) concentration in wheat. There were no nodes where the recent EU quality standards for Cd and Pb (0.2 mg kg(-1) fresh wt.) in wheat were almost certain to be exceeded. For most nodes with clay soils, the quality standard for Cd in wheat almost certainly will not be exceeded; for Pb this is much less certain. The uncertainty in the Cd concentration in soil contributes most to the uncertainty in the predicted Cd concentrations in wheat (36% on the average), followed by the random term of the regression model (23%). For Pb the contribution of the random term is by far the largest (52%).

The analytical determination of microbial biomass carbon is time‐consuming, which limits its use as a reference biochemical property for characterizing soil fertility and soil biodiversity of soil mapping units (SMUs). This paper explores whether the efficiency of sampling strategies for estimating the means of microbial biomass C (MBC) of SMUs can be increased with dsDNA as an ancillary variable in a regression estimator, leading to a model‐assisted sampling strategy. The map unit means of dsDNA are unknown; therefore, to implement the regression estimator a two‐phase sampling strategy is required. The two‐phase sampling design was tested in three soil units of the Brtonigla area (Istria, Croatia) that are widely involved in wine production. In the first phase, 20 locations per SMU were selected at which the ancillary variable dsDNA was determined in field‐moist and air‐dried soil. In the second phase, a subsample of ten locations per map unit were selected at which MBC was determined. The estimated sampling variances of the regression estimator were compared with the sampling variances of the design‐based estimator with a cost‐equivalent number of sampling points. The model‐assisted strategy was more accurate in two of the soil units in which the correlation between MBC and dsDNA was strong (adjusted R2 larger than 0.55). Measurements of dsDNA on field‐moist samples gave more precise estimated means of MBC than those on air‐dried samples. Highlights Use of dsDNA to estimate the means of microbial biomass C (MBC) within soil mapping units (SMUs). Comparison of model‐assisted and design‐based sampling strategies to estimate mean MBC of SMUs. Model‐assisted strategy was more efficient in two SMUs; field‐moist dsDNA was better than air‐dried dsDNA. dsDNA increased the efficiency of sampling strategies to estimate mean MBC of SMUs with homogeneous land use.

A method for designing efficient sampling schemes for reconnaissance surveys of contaminated bed sediments in water courses is presented. The method can be used in networks of water courses, for instance to estimate the total volume of bed sediment of a defined quality class. The water courses must be digitised as arcs in a Geographical Information System.The method comprises six steps: (1) stratifying the water courses;(2) choosing a variogram; (3) calculating the parameters of the variance model; (4) choosing a compositing scheme; (5) choosing the values for the cost-model parameters; and (6) optimising the sampling scheme. The method is demonstrated with a survey of the main water courses in the reclaimed areas of Oostelijk Flevoland and Zuidelijk Flevoland.