Spatial Structure Variability Research Articles

Autoencoding Topographic Factors.

Topographic factor models separate overlapping signals into latent spatial functions to identify correlation structure across observations. These methods require the underlying structure to be held fixed and are not robust to deviations commonly found across images. We present autoencoding topographic factors, a novel variational inference scheme, to decompose irregular observations on a lattice into a superposition of low-rank sources. By exploiting recent developments in variational autoencoders, we replace fixed sources with a nonlinear mapping that parameterizes an unnormalized distribution on the lattice. In doing so, we permit sources to drift dynamically, filtering residual differences in location across comparable areas of interest. This gives an implicit mapping to a unique latent representation while simultaneously forcing the posterior to model group variability in spatial structure. Simulation results and applications to functional imaging demonstrate the effectiveness of our method and its ability to outperform existing spatial factor models.

The dependence of synchrony on timescale and geography in freshwater plankton

AbstractSpatial synchrony is defined by related fluctuations through time in population abundances measured at different locations. The degree of relatedness typically declines with increasing distance between sampling locations. Standard approaches for assessing synchrony assume isotropy in space and uniformity across timescales of analysis, but it is now known that spatial variability and timescale structure in population dynamics are common features. We tested for spatial and timescale structure in the patterns of synchrony of freshwater plankton in Kentucky Lake, U.S.A. We also evaluated whether different mechanisms may drive synchrony and its spatial structure on different timescales. Using wavelet techniques and matrix regression, we analyzed phytoplankton biomass and abundances of seven zooplankton taxa at 16 locations sampled from 1990 to 2015. We found that zooplankton abundances and phytoplankton biomass exhibited synchrony at multiple timescales. Timescale structure in the potential mechanisms of synchrony was revealed primarily through networks of relationships among zooplankton taxa, which differed by timescale. We found substantial interspecific variability in geographic structures of synchrony and their causes: all mechanisms we considered strongly explained geographic structure in synchrony for at least one species, while Euclidean distance between sampling locations was generally less well supported than more mechanistic explanations. Geographic structure in synchrony and its underlying mechanisms also depended on timescale for a minority of the taxa tested. Overall, our results show substantial and complex but interpretable variation in structures of synchrony across three variables: space, timescale, and taxon. It seems likely these aspects of synchrony are important general features of freshwater systems.

Variation in the Current Shear Front and Its Potential Effect on Sediment Transport Over the Inner Shelf of the East China Sea in Winter

AbstractThe current shear fronts (CSFs) in the East China Sea have been observed during winter in previous studies. In this research, we systematically study characteristics and variation mechanism of the CSFs and its potential effects on the transport of water and suspended sediment and on the inner shelf mud area (ISMA), using the methods of observation data analysis and numerical simulation. The spatial structures, shear intensities, and sea‐land locations of the CSFs are obviously affected by wind, temperature and salinity. The CSFs curve seaward due to the nonuniformity of topography, and the tide causes its periodic and sea‐land shift. The landward U component near the CSFs indicate little cross‐CSF transport (i.e., the barrier effect). The CSFs are important hydrodynamic factors that affect the formation of the ISMA. The periodic shift of the CSFs in the longitudinal direction and its variations in spatial structure are the main mechanisms affecting the spatial distribution and thickness of the ISMA. However, the barrier effect of the CSFs can be interrupted, and both the strong wind in the northeast direction and baroclinic conditions are beneficial for cross‐CSF transport. Furthermore, cross‐CSF transports occur in the upper layers under northwest wind condition, in areas with nonuniformity of topography and during the spring tide period. Three cross‐shelf channels are identified in areas where the CSFs curve seaward resulting from nonuniformity of topography.

Spatial variability and size structure of particles and plankton in the Fram Strait

Changes within marine pelagic fields in the Arctic affect global biogeochemical cycles. The size structure and distribution of particles and plankton (P&P) were studied in the main northward passage of Atlantic water to the Arctic region, which is affected in various ways by climate change. The wide size spectrum of P&P (3 µm–5 mm) was assessed by combining measurements from two complementary optical counters, a Laser In-Situ Scattering and Transmissometry (LISST) instrument and a Laser Optical Plankton Counter (LOPC). The measurements were performed in the upper 100 m layer at 55 stations in summer in 2013. The P&P were attributed to various functional groups – particles, phytoplankton, marine snow, and zooplankton – to test their spatial coupling with hydrodynamic forcing. Although our study confirms that the Fram Strait is a region of strong biophysical contrasts, the expected coastal-offshore gradient has not been distinguished clearly. Three mechanisms could explain P&P patches: particle flocculation due to contact with fresh waters from ice and fjords, enhanced production due to coastal upwelling as well as widening the range of warm surface water advection northwards. Particularly rich biological hot spots were recognised in the northern, shallow parts of the west Spitsbergen shelf, and near the marginal ice edges, confirming that these areas are highly vulnerable to marine predators. Moreover, combining optical measurements from two size-specific optical counters contributes greatly to reducing the substantial knowledge gap regarding wide size structure and distribution of thus far neglected seawater components, such as non-living particles and marine snow aggregates, occurring in this vulnerable Arctic region.

The spatial variability structure of soil attributes using a detailed sampling grid in a typical periglacial area of Maritime Antarctica

Large spatial variations on soil attributes and vegetation distribution in terrestrial ecosystems occurs in maritime Antarctica. Guano deposition, organic matter accumulation and vegetation coverage, associated with soil drainage conditions, are the main drivers of soil development, varying considerably in time and space. Yet, the spatial variability of soil attributes remains little documented in this region, being constantly altered due to the intense periglacial processes. The objective of this study was to determine spatial variability structure of a representative ice-free area, to understand how changes on landscape, vegetation coverage and biological activity affect soil attribute distribution, using a detailed grid guaranteeing the recognition/representation of the variability range at Keller Peninsula, King George Island, Maritime Antarctica. A representative heterogeneous area was selected in February 2015, where a large variation on the spatial distribution of vegetation and guano occur, in a typical Antarctic periglacial landscape. A 133-point 50 × 50 m grid with a minimum separation distance of 0.5 m was installed, in which sectors were delimited according to guano influence and vegetation cover. Soil was sampled (0–10 cm) at each point of the grid, to map and model the spatial variability of general soil attributes. Principal component analysis (PCA) was used to select the most important soil attributes in this periglacial landscape. Further, experimental semivariograms were calculated and thematic kriging maps obtained, allowing the visual interpretation of the phenomena. Soil attributes significantly (p < 0.05) varied across the delimited sectors. Soil bases (Ca, Mg, K, Na) and physical soil attributes (sand and silt) were the most important variables selected through PCA. We observed that the spatial variability structure of soil attributes is highly influenced by the heterogeneity of ice-free areas in Maritime Antarctica. In periglacial zones where guano is deposited, soil acidification occurs, resulting in high exchangeable Al contents and potential acidity, associated with elevated amounts of organic matter and phosphorus. In this area, the spatial dependence is stronger. The strength of the spatial dependence rapidly decreases out of the influence of guano deposition, where a more heterogeneous environment is observed. Vegetation coverage also altered the spatial variability structure by stabilizing landscape, avoiding nival erosion and enhancing soil nutrient status. Bare soils reveal a more pronounced skeletal character, with low soil nutrient assembly due to the weak nutrient cycling with a great variability across the landscape. Results reported in this study are expected to represent other similar periglacial landscapes in maritime Antarctica.

Observations and Modeling of Traveling Ionospheric Disturbance Signatures From an Australian Network of Oblique Angle‐of‐Arrival Sounders

AbstractA network of oblique angle‐of‐arrival (AoA) ionosondes was installed as part of the Elevation‐scanned Oblique Incidence Sounder Experiment (ELOISE) in September 2015. The ELOISE experimental campaign was designed to study the spatial and temporal structure of ionospheric variability at midlatitudes, of which traveling ionospheric disturbances are a key component. The new AoA sounder makes use of Defence Science and Technology Group's direct‐digital high‐frequency transmitter and receiver technology, to enable multichannel collection of both ionograms and channel scattering functions (Doppler spectra) on a common 2‐D array. In this paper, the array design and onboard signal processing for the AoA sounder is described, along with a sample of results showing typical disturbance signatures across the delay, Doppler frequency, bearing, and elevation measurements. Realistic parameterized models of electron density perturbations, along with geometric ray tracing, were used to synthesize the effects of medium‐ to large‐scale traveling ionospheric disturbances on the sounder observables and aid in interpreting the measured signatures.

Spatial variability of soil properties in the floodplain of a river oasis in the Mongolian Altay Mountains

In Mongolia, recent national policy pushed for the expansion and intensification of land use for crop and forage production, which may adversely affect soil quality of river oases. A better understanding of the small-scale variation of soil properties and the driving factors of soil quality are important for developing and refining site-specific agricultural management practices and for maintaining soil fertility. The current study hypothesized that spatial variability structure of ancient sedimentary processes override the actual variation of soil properties caused by land-use and environmental effects. The objectives were (i) to determine the small-scale variability of major topsoil properties within an exemplary river oasis in Western Mongolia, (ii) to assess the spatial structure of this variability (heterogeneity), and (iii) to identify factors driving the heterogeneity of soil biological properties. Within a four ha plot, 130 topsoil samples were collected in a constant 20 m grid and analyzed for major physico-chemical and microbial properties. The soil variability was assessed by descriptive statistics, the heterogeneity of properties by semivariogram model parameters, and heterogeneity driving factors by multiple linear regression analysis. Soil parameters showed a relatively high variability, particularly of CaCO3-C and EC (86% and 163%, respectively). In contrast to that, both parameters showed a relatively low heterogeneity (nugget to sill ratio of ≥0.67) whereas most soil parameter were characterized by a moderate or strong spatial dependency (nugget to sill ratio down to 0.2 as for microbial biomass C) and by relatively low range values (down to 37 m as for ergosterol and microbial biomass C), respectively. This small-scale heterogeneity with relatively many small patches reflected inherent soil factors of the floodplain, which had a strong influence on soil properties as the extensive land use in the river oasis Bulgan Sum Center did not lead to a homogenization of the fluvial depositions. Besides the minor salinization, which had a positive effect on ergosterol and basal respiration, SOC determined biological soil properties. This underlines the significance of SOC to preserve the scarce and susceptible agro-ecological resources of the river oasis in Western Mongolia and likely many similar ones across Central Asia.

Hierarchically nested river landform sequences. Part 1: Theory

AbstractPast river classifications use incommensurate typologies at each spatial scale and do not capture the pivotal role of topographic variability at each scale in driving the morphodynamics responsible for evolving hierarchically nested fluvial landforms. This study developed a new way to create geomorphic classifications using metrics diagnostic of individual processes the same way at every spatial scale and spanning a wide range of scales. We tested the approach on flow convergence routing, a geomorphically and ecologically important process with different morphodynamic states of erosion, routing, and deposition depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality represent this process at any flow; they are nozzle, wide bar, normal channel, constricted pool, and oversized. These landforms are then nested within themselves by considering their longitudinal sequencing at key flows representing geomorphically important stages. A data analysis framework was developed to answer questions about the stage‐dependent spatial structure of topographic variability. Nesting permutations constrain and reveal how flow convergence routing morphodynamics functions in any river the framework is applied to. The methodology may also be used with other physical and biological datasets to evaluate the extent to which the patterning in that data is influenced by flow convergence routing. Copyright © 2018 John Wiley &amp; Sons, Ltd.

A Variational Merging Approach to the Spatial Description of Environmental Variables

AbstractHigh‐resolution images of environmental variables are highly valuable sources of information in research and environmental management. Obtaining spatially continuous information from ground observations is challenging due to the wide variety of factors that affect classical interpolation methods. While geostatistical methods have produced noteworthy results, they generally rely on important assumptions and strongly depend on the availability of observed data. In turn, satellite‐based or model‐based gridded images generally represent the global spatial structure of environmental variables but are subject to bias. With the objective of exploiting the benefits of both sources of information, we propose a new mathematical formulation to merge observed data with gridded images of environmental variables using partial differential equations in a variational setting. With a convenient functional, formed as the sum of two competing terms, two simultaneous goals are achieved: to improve the description of the spatial structure in maps generated by simple deterministic interpolation methods and to increase the reliability of satellite‐based or model‐based images. Either satellite‐based or model‐based information is included in a regularity term to provide the global spatial structure, while in situ data are included in a fidelity term. The resulting maps can be considered a merging of interpolated in situ data with satellite or model imagery. The method is first evaluated using simulated images generated by geostatistical simulation and then applied to actual temperature and precipitation data in selected regions in the Tropical Andes. The results indicate that the method is capable of generating realistic maps while performing well in terms of validation.

Evaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA

Detailed measures of growth pattern and structural heterogeneity applied in this study helped to quantify the immediate effects of various thinning regimes on forest structure and the resulting alterations in tree size as well as observed longer term stand dynamics. Forest management, stand structure, and tree growth are highly inter-correlated. Prior analyses, however, have resulted in mixed outcomes with limited success in revealing ecological mechanisms. The study aimed at evaluating the relationship between forest structure and stand dynamics by applying several sophisticated measures of growth pattern and structural heterogeneity. Data from a controlled and fully stem-mapped commercial thinning experiment with seven contrasting treatments including a non-thinned control at six locations across the Acadian Forest of Maine, USA, was used. Stand-level attributes examined included tree size and growth heterogeneity, spatial tree distribution, and growth dominance. Thinning generally reduced stand structural heterogeneity compared to the non-thinned control. In addition, the spatial arrangement of trees changed from fully random (non-thinned control) to a more clustered (removal of dominant and co-dominant individuals) or regular distribution (removal of intermediate and suppressed individuals). Overall, stand growth exhibited increasing (non-thinned control, removal of intermediate and suppressed individuals) or decreasing growth dominance of large trees (removal of co-dominant competitors). Forwarder trails increased basal area growth of individual trees up to a distance from the trail of approximately 5 m. Findings of this study validate an earlier insight according to which interactions between management practices, forest structure, and tree growth form a permanent feedback loop.

The spatial heterogeneity of land surface conditions and its influence on surface fluxes over a typical underlying surface in the Tibetan Plateau

Accurately estimating the surface fluxes of over the heterogeneous land surface in Tibetan Plateau will be helpful to advance the understanding of its influence on regional climate and hydrology. This paper presents a study on the spatial heterogeneity of land surface parameters in terms of the spatial variability and spatial structure of land surface parameters and the influence on surface fluxes over a typical land surface in Tibetan Plateau. The results suggest that the sensible heat fluxes (H) and latent heat fluxes (LE) in the study area in the rain and dry seasons show apparent spatial variabilities due to the spatial heterogeneity in the leaf area index (LAI) and land surface undulations. The relative frequency distribution of H and LE at the spatial resolution of 30 m suggests that the spatial variability of surface fluxes has a close relationship with the spatial heterogeneity of land surface temperature (LST) and LAI. The variogram analyses of LST, LAI, H, and LE in the study area in rain season indicate that the spatial structures of LST and LAI are different and the spatial structures of H and LE are strongly influenced by the spatial structures of LST and LAI in both rain and dry seasons. The optimal pixel sizes for LST, LAI, H, and LE in the study area are 506, 156, 500, and 225 m in the rain season. The optimal pixel sizes for LST, H, and LE in the study area are 165, 165, and 162 m in the dry season. An analysis of the relative frequency distributions (RFDs) of the LST, LAI, H, and LE at different spatial resolutions in the rain and dry seasons reveals that their values at the maximum relative frequency keep stable although their spatial variabilities become weak as the spatial resolution decreases. The averages of LST, LAI, H, and LE of different spatial resolutions of the study area in rain and dry seasons vary within small ranges, suggesting that the influence of spatial resolution on the averaged land surface parameters and surface fluxes in the study area is small. This work will be helpful for the accurate estimation of the surface fluxes over a large heterogeneous land surface for regional climate modeling in Tibetan Plateau.

Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consistent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.

Enhancing image resolution of soils by stochastic multiscale image fusion

Soil structure defines major physical properties and biophysical functions of soils. Imaging soil structure using different 2/3D techniques is a routine methodology used by soil scientists. Still, for structured soils their spatial variability and hierarchical structure imposes a significant challenge for all imaging methods in terms of field-of-view and resolution trade-off. While creating a truly multiscale 3D digital model of soil is without question of utmost importance, there is currently no single imaging method that could potentially encompass all necessary relevant soil scales within a single image. In this paper, we tested for the first time an image fusion technique to produce a multiscale soil image based on separate images obtained with different spatial resolutions. The method is based on universal soil structure descriptors, i.e. spatial correlation functions, which were shown to be very useful in soil applications. Using a relatively simple 2D test case based on X-ray tomography (XCT) images at three different scales, we show the applicability of image fusion for soil images and solve a long standing problem of imaging resolution. In total we fused seven images into a single image: one 114μm resolution macroscale XCT image (porosity <0.01), four 15μm resolution microscale XCT images (with porosities 0.039–0.049), and two 3.3μm resolution microscale XCT images (with porosities of 0.24 and 0.76). The resulting single, 15μm resolution image represented 6×6cm2 of soil structure. Its porosity increased from <0.01 to 0.073 due to representation of all pore sizes visible on the images prior to fusion. Current drawbacks of the approach are discussed and an outline is provided of its future usage to address important soil structure issues.

Change detection methods based on low-rank sparse representation for multi-temporal remote sensing imagery

With the development of remote sensing image applications, remote sensing imagery is an important technology to make a dynamic detection for changes of lands or coastal zones. Though high resolution remote sensing imagery provides very good performance and large information for the spatial structure variation, the limitation of its spatial structure makes change detection difficult. In this paper, we propose two change detection methods for multi-temporal remote sensing images which are based on low-rank sparse decomposition and based on low-rank matrix representation. An observation matrix is constructed by ordering each band of remote sensing images into a vector. We utilize bilateral random projection method to make low rank decomposition to get a sparse matrix. We then obtain the change map by using nearest neighbor to cluster the change parts from the sparse matrix. On the other hand, by dividing the difference set of multi-temporal remote sensing images into non-overlapping squares with equal size and tiling these squares, an observation matrix is built up. We make up a feature space matrix by low rank matrix representation to build a sparse representation model, and combine nearest neighbor method to make change detection for multi-temporal remote sensing dataset. This change detection method is addressed by iterating between kernel norm minimization and sparsity minimization. The experimental results show that our proposed methods perform better in detecting changes than the other change detection methods.

Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach

In reservoir engineering, it is attractive to characterize the difference between reservoir models in metrics that relate to the economic performance of the reservoir as well as to the underlying geological structure. In this paper, we develop a dissimilarity measure that is based on reservoir flow patterns under a particular operational strategy. To this end, a spatial-temporal tensor representation of the reservoir flow patterns is used, while retaining the spatial structure of the flow variables. This allows reduced-order tensor representations of the dominating patterns and simple computation of a flow-induced dissimilarity measure between models. The developed tensor techniques are applied to cluster model realizations in an ensemble, based on similarity of flow characteristics.

Population Genomic Analysis of the Blue CrabCallinectes sapidusUsing Genotyping-By-Sequencing

Previous genetic studies of the blue crab Callinectes sapidus along the U.S. Atlantic and Gulf coasts have reported weak or temporally variable spatial structure, suggesting high gene flow among distant populations possibly facilitated by long-distance larval dispersal or other features of blue crab life history. The use of relatively few genetic markers, however, may have limited power to detect subtle but significant structure that could inform fisheries management. In this study, the potential for genome-scale datasets to uncover subtle patterns of population structure in the blue crab was examined using a high-throughput genotyping approach (genotyping-by-sequencing) that generated data for more than 9,600 single nucleotide polymorphisms (SNPs) in crabs from three populations: Panama City Beach, FL, Agawam River, MA, and Porto Alegre, Brazil. Principle components analyses among the three populations revealed very distinct clustering of the Brazilian samples from U.S. populations, likely reflecting restricted gene flow across the equator. Detailed analysis of population structure between the two U.S. populations revealed low but significant genetic differentiation (FST = 0.0103), with FST values ranging from -0.05 to 0.48. Previous studies have failed to detect significant genetic structure on a similar geographic scale. FST outlier analysis identified 242 loci (2.45% of total) with statistically extreme values at the false discovery rate α = 0.05 level, only 16 of which showed significant sequence homology to annotated proteins via BLASTx alignment. Top BLASTx hits were to crustacean or arthropod sequences and 8 of the 16 had high sequence similarity to transposable elements or related machinery. Finally, results of population assignment tests for the two U.S. populations showed that the full marker dataset provided good power to assign individuals back to their population of origin (∼83% and 92% success for Panama City Beach and Agawam River, respectively), which dropped significantly when using only 500 randomly selected SNPs (∼61% and 72% success). Overall, this study demonstrates the great utility of high-throughput sequencing technologies for characterizing fine-scale patterns of genetic structure in blue crabs, and this approach should substantially improve the delineation of stock structure and further advance our understanding of blue crab population connectivity and ecology.

Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta

Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different spatial resolutions, as determined by the grid sizes used. Landsat satellite images of the Pearl River Delta (PRD) in China from the years 2000, 2005, 2010 and 2015 were hierarchically aggregated using different grid sizes. Statistical analyses of urbanization velocity derived using different spatial resolutions (or grid sizes) were used to investigate the relationships between socio-economic indicators and the velocity of urbanization for 27 large cities in PRD. The results revealed that those cities with above-average urbanization velocities remain unaffected by the spatial resolution (or grid-size), and the relationships between urbanization velocities and socio-economic indicators are independent of spatial resolution (or grid sizes) used. Moreover, empirical variogram models, the local variance model, and the geographical variance model all indicated that coarse resolution version (480 m) of Landsat images based on aggregated pixel yielded more appropriate results than the original fine resolution version (30 m), when identifying the characteristics of spatial autocorrelation and spatial structure variability of urbanization patterns and processes. The results conclude that the most appropriate spatial resolution for investigations into urbanization velocities is not always the highest resolution. The resulting patterns of urbanization velocities at different spatial resolutions can be used as a basis for studying the spatial heterogeneity of other datasets with variable spatial resolutions, especially for evaluating the capability of a multi-resolution dataset in reflecting spatial structure and spatial autocorrelation features in an urban environment.

Spatial variation in the solute transport attributes of adjacent Typic Haplusteps, Mollic Ustifluvents, and Lithic Ustipsamments

Spatial variation in water flow and solute transport are important considerations when assessing field-scale remediation options, and for managing water and chemicals during agricultural production. Here, we evaluated the relationship between laboratory-measured solute transport variables pore-water velocity (v), dispersivity (λ), and retardation coefficient (R) and land use, soil type, and other independently measured soil properties in adjacent Typic Haplusteps, Mollic Ustifluvents, and Lithic Ustipsamments. We also characterized the spatial variation in v, R, coefficient of hydrodynamic dispersion (D), and λ. We obtained 100 geo-referenced, undisturbed soil columns (8.4cm diameter and of varying length) from the topsoil at sites with varying land uses, topographies, and soil types, and synchronized disturbed soil samples to analyze the basic soil properties. We conducted miscible displacement tests on all 100 columns, and predicted the retardation coefficient (R) and D using the computer program CXTFIT, and measured v. All four variables, except R, had strongly right-skewed and kurtotic distributions, and were highly variable. Log λ (logarithm to base 10) was significantly correlated with land use, with the mean of λ being significantly greater in grasslands, followed by orchards and croplands. By contrast, v was significantly correlated with soil type, with the mean being significantly greater in Lithic Ustipsamments. Log v (logarithm to base 10) was also significantly correlated with the soil CaCO3 content, log organic matter content, pH, cation exchange capacity, and log electrical conductivity, whereas neither v nor λ was correlated with soil texture measures. An analysis of the spatial structure of solute transport variables using semivariograms showed that R and λ had considerably high nugget effect. Log-transformed semivariograms performed well using ordinary point kriging for v and D. However, λ and R exhibited a very weak spatial dependency that could not be interpolated using ordinary kriging at the current sampling resolution. Therefore, this should be considered when designing future sampling programs to analyze spatial structure of R and λ.

Structure and biodiversity of coralligenous assemblages dominated by the precious red coral Corallium rubrum over broad spatial scales.

Data on species diversity and structure in coralligenous outcrops dominated by Corallium rubrum are lacking. A hierarchical sampling including 3 localities and 9 sites covering more than 400 km of rocky coasts in NW Mediterranean, was designed to characterize the spatial variability of structure, composition and diversity of perennial species inhabiting coralligenous outcrops. We estimated species/taxa composition and abundance. Eight morpho-functional groups were defined according to their life span and growth to characterize the structural complexity of the outcrops. The species composition and structural complexity differed consistently across all spatial scales considered. The lowest and the highest variability were found among localities (separated by >200 km) and within sites (separated by 1–5 km), respectively supporting differences in diversity indices. The morpho-functional groups displayed a consistent spatial arrangement in terms of the number, size and shape of patches across study sites. These results contribute to filling the gap on the understanding of assemblage composition and structure and to build baselines to assess the response of this of this highly threatened habitat to anthropogenic disturbances.

Quantifying spatio-temporal variation of leaf chlorophyll and nitrogen contents in vineyards

Precision viticulture requires the characterisation of the spatio-temporal variability of the vineyard status to design the appropriate management for each area. The goal of this work was to characterise the spatio-temporal variability of leaf chlorophyll (Chl) and nitrogen (N) content and their relationship with the vegetative growth in a three ha commercial vineyard (Vitis vinifera L.) using a geostatistical approach. Leaf Chl and N contents were assessed by two fluorescence indices provided by a hand-held fluorescence sensor. Fluorescence measurements were taken along five dates, from veraison to harvest, on 72 sampling points delineated on a regular grid across the vineyard. Shoot pruning weight (SPW) was measured for each sampling point as indicator of the grapevine vegetative growth. Geostatistical analysis was applied to model the spatial variability of leaf Chl and N content and SPW. The spread showed an increase of the variability of leaf Chl and N content during the ripening period, reaching maximum values prior to harvest. The variograms illustrated a similarity of the spatial variability structure of leaf Chl at all timings, unlike N which showed changing spatial variability structures along the ripening period. The Kappa index evidenced a slight intra-season stability for both Chl and N and showed that N could not be used alone as an indicator to delineate vigour management areas. The existence of spatio-temporal variability of key vegetative components was proved and its knowledge is crucial to implement precision viticulture approach such as variable rate application of fertilizers or water as needed.